Background:Clostridium difficile infection (CDI) is prevalent in healthcare settings. The emergence of hypervirulent and antibiotic resistant strains has led to an increase in CDI incidence and frequent outbreaks. While the main virulence factors are the TcdA and TcdB toxins, antibiotic resistance is thought to play a key role in the infection by and dissemination of C. difficile.Methods: A CDI outbreak involving 12 patients was detected in a tertiary care hospital, in Lisbon, which extended from January to July, with a peak in February, in 2016. The C. difficile isolates, obtained from anaerobic culture of stool samples, were subjected to antimicrobial susceptibility testing with Etest®strips against 11 antibiotics, determination of toxin genes profile, PCR-ribotyping, multilocus variable-number tandem-repeat analysis (MLVA) and whole genome sequencing (WGS).Results: Of the 12 CDI cases detected, 11 isolates from 11 patients were characterized. All isolates were tcdA-/tcdB+ and belonged to ribotype 017, and showed high level resistance to clindamycin, erythromycin, gentamicin, imipenem, moxifloxacin, rifampicin and tetracycline. The isolates belonged to four genetically related MLVA types, with six isolates forming a clonal cluster. Three outbreak isolates, each from a different MLVA type, were selected for WGS. Bioinformatics analysis showed the presence of several antibiotic resistance determinants, including the Thr82Ile substitution in gyrA, conferring moxifloxacin resistance, the substitutions His502Asn and Arg505Lys in rpoB for rifampicin resistance, the tetM gene, associated with tetracycline resistance, and two genes encoding putative aminoglycoside-modifying enzymes, aadE and aac(6′)-aph(2″). Furthermore, a not previously described 61.3 kb putative mobile element was identified, presenting a mosaic structure and containing the genes ermG, mefA/msrD and vat, associated with macrolide, lincosamide and streptogramins resistance. A substitution found in a class B penicillin-binding protein, Cys721Ser, is thought to contribute to imipenem resistance.Conclusion: We describe an epidemic, tcdA-/tcdB+, multidrug resistant clone of C. difficile from ribotype 017 associated with a hospital outbreak, providing further evidence that the lack of TcdA does not impair the infectious potential of these strains. We identified several determinants of antimicrobial resistance, including new ones located in mobile elements, highlighting the importance of horizontal gene transfer in the pathogenicity and epidemiological success of C. difficile. Clostridium difficile, recently renamed as Clostridioides difficile ; some strains additionally produce a binary toxin, CDT, while others produce only TcdB  was used to determine the genetic relatedness of the strains and whole-genome sequencing (WGS) to identify determinants of resistance.Here we describe a multidrug resistant clone from PCR ribotype 017 ®kit, were collected between January and July 2016, during an outbreak in a hospital from the Lisbon Metropolitan Area, and sent to the National Reference Laboratory for Gastrointestinal Infections, hosted in the Portuguese National Institute of Health, for laboratory-based epidemiological surveillance of CDI. As described previously, stool samples were inoculated onto ChromID C. difficile agar  after ethanol shock and incubated under anaerobic conditions for 48 h at 37°C  breakpoints were used (Table Minimum inhibitory concentrations (MICs) of chloramphenicol, clindamycin, erythromycin, gentamicin, imipenem, metronidazole, moxifloxacin, rifampicin, tetracycline, tigecycline and vancomycin were determined with Etest strips (bioMérieux), according to the manufacturer’s instructions. Plates were incubated under anaerobic conditions for 48 h at 37°C. The European Committee on Antimicrobial Susceptibility Testing (EUCAST) breakpoints established for ed Table .Multilocus variable-number tandem-repeat analysis was carried out following the method developed by van den Berg et al. to amplify the loci A6, B7, C6, E7, G8, and CDR60 , with an1) and improvement, (Trimmomatic v0.36), draft genome sequences were de novo assembled using SPAdes (version 3.10.1) (2 (3 was used for in silico Multilocus Sequence Typing (MLST) and allele determination. Core-genome single nucleotide polymorphism (SNP)-based analysis was performed using Snippy v3.14. Only variant sites with minimum mapping quality of 60, minimum of > 10 reads covering the variant position and > 90% reads differing from the reference genome were considered. Putative antimicrobial resistance (AMR) genes were identified using both CARD5 and ResFinder6 (7 (8 against the non-redundant (nr) and wgs databases were performed to identify the presence (and similarity level) of determinants of resistance in other available genomes. The genome of strain M68 from ribotype 017 (Acc. No. NC_017175) was used as reference. Raw sequence reads of the three C. difficile isolates subjected to WGS were deposited in Sequence Read Archive under the Bioproject accession number PRJNA478136.Three strains   followed10.1)  was PCR amplified using primers ermG850D (5′ GGATTCGGAGAGGTTATAATGAACAAAG 3′) and ermG1660R (5′ ATAGTTTAGCGGCCGCATTTTAACTTATGCTACCCTACC 3′) and genomic DNA from strain A  and the resulting strains used to transfer the plasmids, by conjugation, into C. difficile 630Δerm with selection for thiamphenicol resistance (15 μg/ml) as described before , erythromycin (>256 mg/L), gentamicin (>256 mg/L), imipenem (>32 mg/L), moxifloxacin (>32 mg/L), rifampicin (>32 mg/L), and tetracycline (16 mg/L), being susceptible to metronidazole, vancomycin, chloramphenicol and tigecycline , and a disrupted tcdA with a 1.8 kb deletion at the 3′ end and an early stop codon at amino acid 47, which is typical of ribotype 017. Regarding the accessory genes of the PaLoc, no mutations were found in tcdE, coding a holin-like protein necessary for toxin secretion, or in the putative negative regulator of toxin production tcdC . The transcriptional regulator tcdR, which has a frameshift mutation in the reference strain M68 (locus CDM68_RS03600) due to a deletion at nucleotide 165 that leads to an early stop codon, is in frame, and predicted as functional, in our strains.The 11 isolates shared a high genetic proximity, as determined by MLVA, and therefore only three, representing the outbreak period and belonging to different MLVA types, isolates A (from January), B  and K (from July), were selected for WGS , of which 33 distinguished the strain M68 from the outbreak strains, being that isolates A and B had no differences between each other and isolate K had 2 SNPs distinguishing it from isolates A and B, which is consistent with nosocomial transmission.aadE (aminoglycoside 6-adenylyltransferase) and aac(6′)-Ie-aph(2″)-Ia -Ie/aminoglycoside O-phosphotransferase APH(2″)-Ia), were found in the sequenced isolates. BLASTn search against the nr database showed that aadE and aac(6′)-Ie-aph(2″)-Ia, which are homologous to the loci CDM68_RS08230 and CDM68_RS08245, respectively, in the reference strain M68, are not frequent in C. difficile genomes. On the other hand, they are common in other bacterial genera. The gene aadE is found with 100% coverage and identity in several Campylobacter coli genomes, as well as in a few genomes of Campylobacter jejuni, Streptococcus agalactiae and Enterococcus faecalis, among others. The gene aac(6′)-Ie-aph(2″)-Ia found in our isolates is present with 100% coverage and identity in many Staphylococcus spp. genomes, but also Enterococcus spp. and Campylobacter spp, among others.WGS data revealed the presence of several determinants of resistance Table . Two gentetM , homologous to the locus CDM68_RS01945 in strain M68, was also present in our isolates and was identified in the conjugative transposon Tn916 (Acc. No. KC414929).The tetracycline resistance determinant rpoB, leading to the amino acid substitutions His502Asn and Arg505Lys , both known to be associated with rifampicin resistance, were present in the three sequenced isolates.The substitution Thr82Ile in GyrA , which is responsible for fluoroquinolones resistance, and two mutations in Furthermore, we found the mutation 2162G > C in the homolog of locus CDM68_RS05670, which codes for a penicillin-binding protein (PBP), PBP3 . This muermG gene was identified in a cluster of genes associated with macrolide, lincosamide and streptogramins (MLS) resistance that also included the genes mefA and msrD, both associated with macrolide efflux resistance, and vat, coding for a Streptogramin A acetyltransferase  and strains 7499-CF/ST37 and VL_0008, both belonging to ST37 . Another strain, VL_0387 (Acc. No. FALC01000010), also from ST37, contains a highly similar element  but in which the region containing the ermG and the transposase is inverted, when comparing to the isolates from this study. Seven other C. difficile draft genomes  harbor a similar element (86% coverage and 98.4% sequence identity) that does not contain the MLS resistance portion, which points to the mosaic origin of this element. Likewise, the genome of C. difficile strain M120 (ribotype 078) exhibits a ∼40 kb region  with 62.8% coverage and 90.6% sequence identity with the element present in our strains, while not containing the flanking RM system nor the MLS resistance cluster.An e Figure . This cle Figure . This ree Figure . Three oC. difficile genomes. For instance, the genomic region spanning the RM system and the prophage has a high homology with two genomes of Thermoanaerobacter sp., covering 70% of the element with 88% sequence identity (Acc. Nos. NC_014538 and NC_010320). The proteins coded by the RM system are common in the class Clostridia and are also found in Enterococcus cecorum. The prophage region is found with 89% sequence identity, covering 62% of the element, in the genome of Clostridium bornimense strain M2/40T (Acc. No. HG917868) and the cluster of MLS resistance genes is found in three genomes of Enterococcus cecorum with 98.5% sequence identity, covering 9% of the element .The 61.3 kb putative mobile element has homology with other non-mefA and msrD present in this element are found with >99% coverage and >95% sequence identity in many bacterial species, most of which are Streptococcus spp., mainly S. pneumoniae and S. pyogenes, but also in E. cecorum, Neisseria gonorrhoeae and Acinetobacter junii, among other species. The vat gene is present in a few C. difficile genomes and is also found with >96% coverage and >91% sequence identity in several E. cecorum, E. faecium and Streptococcus suis genomes.The genes ermG gene present in this element is found in multiple species with a sequence coverage and identity ≥99%, including Lysinibacillus sphaericus (Acc. Nos. NG_047827 and M15332), E. cecorum (mentioned above), E. faecium (Acc. No. CP003351), Bacteroides spp.  and nine C. difficile genomes , among which is the non-toxinogenic strain C. difficile Z31.The ermG-containing region is absent in reference strain M68 , is present in strain M68, while being absent in all the isolates from this study.The 61.3 kb ermG and confirmed its presence in the remaining outbreak isolates.The primer pair ermG-F (5′ TCACATAGAAAAAATAATGAATTGCATAAG 3′) and ermG-R (5′ CGATACAAATTGTTCGAAACTAATATTGT 3′) was used to amplify a 652 bp amplicon of ermG is located in a region showing evidence of other HGT events . This 72 kb region covers 90% of CTn5 with 99% sequence identity but in the isolates of this study it is interrupted by two genetic insertions of 8 and 22 kb , while ∼10 kb of the 22 kb insertion has 99.9% sequence identity with regions of three genomes, namely Flavonifractor sp., Enterococcus faecium and C. difficile .The element containing the s Figure , such asermG-inducible C. difficile 630Δerm strain was subjected to antimicrobial susceptibility testing by diffusion gradient with Etest strips against erythromycin and clindamycin. Confirming that the expression of ermG confers resistance to MLS antibiotics, the MICs of erythromycin and clindamycin were both of >256 mg/L in the C. difficile 630Δerm conjugant expressing the ermG, when comparing with the MICs observed for C. difficile 630Δerm ermG- strain .The C. difficile clone from ribotype 017 implicated in a CDI outbreak and identified several determinants of resistance through WGS data analysis. Two novel mechanisms of resistance were described here, namely, the ermG gene, which mediates the resistance to MLS antibiotics and is carried by a putative mobile element exhibiting a mosaic structure, and a mutation in a PBP that is likely associated with imipenem resistance.In the present work, we studied a multidrug resistant TcdA-negative C. difficile strain and has been considered a recently emerging type, being associated with outbreaks in some European countries (C. difficile main pathogenicity factors (TcdA) does not seem to affect the spreading or infectious potential of these strains.Ribotype 017 is the most prevalent TcdA-negative ountries . In a feountries . As suchC. difficile. According to a pan-European study, most clinical isolates in Europe are susceptible to imipenem, although ribotype 027 showed elevated MICs compared to other ribotypes -Ie-aph(2″)-Ia, that seem to have a low prevalence in C. difficile but are widespread in Enterococcus spp., Campylobacter spp., Staphylococcus spp. or Streptococcus spp. Anaerobes, such as C. difficile, however, are naturally resistant to aminoglycosides    and hencuman gut . This paC. difficile multidrug resistant clone implicated in a hospital outbreak presenting new resistant determinants that seemingly promoted the spreading success of this clone. Our data show that C. difficile is continually evolving through HGT and indicate that antibiotic selective pressure continues to be a major driving force in the development and emergence of new epidemic strains.In summary, in this study we described a All authors contributed to the work described in the paper, as well to the writing and revision of the document.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


OBJECTIVES: Oxidative stress results from an imbalance between the generation and elimination of oxidant species. This condition may result in DNA, RNA and protein damage, leading to the accumulation of genetic alterations that can favor malignant transformation. Persistent infection with high-risk human papillomavirus types is associated with inflammatory responses and reactive oxygen species production. In this context, oxidative stress, chronic inflammation and high-risk human papillomavirus can act in a synergistic manner. To counteract the harmful effects of oxidant species, protective molecules, known as antioxidant defenses, are produced by cells to maintain redox homeostasis. In recent years, the use of natural antioxidants as therapeutic strategies for cancer treatment has attracted the attention of the scientific community. This review discusses specific molecules and mechanisms that can act against or together with oxidative stress, presenting alternatives for cervical cancer prevention and treatment. Oxidative stress (OS) results from an imbalance in the formation and elimination of oxidant species. Accumulation of these molecules may lead to cell dysfunction as a consequence of accumulated oxidative modifications in several biomolecules To maintain redox homeostasis, protective molecules called “antioxidant defenses” act to preserve the balance between formation and removal of ROS and RNS. Cellular antioxidant systems are currently divided into enzymatic and non-enzymatic groups. The enzymatic group comprises catalase, superoxide dismutase (SOD), glutathione peroxidase (Gpx) and glutathione-S-transferase (GST). The non-enzymatic group is composed of molecules such as vitamins C and E, lipoic acid, carotenoids, flavonoids and others Excess levels of unneutralized free radicals and cellular active intermediates are the major cause of OS Persistent infection with high-risk HPV types (HR-HPV) is the main etiological cause for the development of several epithelial tumors at different anatomic locations. The most strongly HPV-associated malignancy is cervical carcinoma, where almost all tumors are positive for HR-HPV DNA In addition to HPV infection, several cofactors contribute to cervical cancer development. These include low socioeconomic status, early sexual initiation, multiple sexual partners, smoking, multiparity, immunosuppression and use of oral contraceptives E6 and E7 viral oncoproteins have a central role in tumor development. In high-grade lesions and tumors, these proteins are upregulated mainly due to the loss or disruption of the viral E2 gene (see below), which normally restricts the expression of viral oncogenes Alterations in expression and activity of some antioxidant proteins, including peroxiredoxins, catalase, quinone oxidoreductase-1 and superoxide dismutase (SOD) family proteins, can be detected in pre-neoplastic and neoplastic tissues associated with HPV infections. For example, expression of SOD2, a crucial antioxidant enzyme responsible for controlling the redox status of normal and tumor cells, is upregulated in several HPV-associated tumors, including penile and cervical carcinomas Inflammasomes are cytosolic multiprotein complexes that assemble after exposure to pathogens or danger-associated molecular patterns, leading to caspase activation and, consequently, secretion of inflammatory cytokines and cell death. Although different inflammasome complexes have been described, with unique activation triggers, they typically consist of a cytosolic pattern recognition receptor (PRR) such as RIG-I-like receptor (RLR), AIM2-like receptor (ALR) or nucleotide-binding domain and leucine-rich repeat-containing (NLR) protein, an adaptor protein (ASC) and pro-caspase-1. The OS generated during HPV infections can be modulated by the infected keratinocytes as well as by activated neutrophils and macrophages Furthermore, protein absent in melanoma-2 (AIM2) activates caspase-1 and inflammasomes by binding to foreign cytoplasmic double stranded DNA (dsDNA) and its adaptor ACS Nuclear factor-kappa B (NF-κB) is a pleiotropic transcription factor composed of dimers of five different members of the Rel transcription factor family that include p105/p50, p100/p52, RelA, RelB and cRel NF-κB plays a central role in inflammation and immune responses but also acts in different cellular processes, including development, cell growth, survival and proliferation. Furthermore, this transcription factor has a role in many pathological conditions, including cancer, where it may be involved in tumor growth and metastasis ROS levels can be affected by NF-κB activity through its influence on the expression of antioxidant proteins, including SOD1, SOD2, Ferritin Heavy Chain (FHC), Glutathione S-transferase pi (GST-pi), Glutathione peroxidase-1 (Gpx1) and Dihydrodiol dehydrogenase (DDH1). However, ROS may activate or inhibit NF-κB signaling depending on the signaling pathway step (upstream or downstream) and cell type analyzed NF-κB activation by free radicals or enzymes is a complex event and can cause various biological effects. For example, its activation by SOD2 has been associated with lung adenocarcinoma progression and poor prognosis Interestingly, curcumin, a natural antioxidant that is discussed below, could prevent tumor-induced thymic atrophy, restoring NF-κB activity, and thus acted as an immunorestorative compound A crucial event in HPV-mediated malignant transformation is the integration of HR-HPV DNA into the cell genome. This event results in E6 and E7 oncogene overexpression, which leads to the destabilization of p53 and pRb tumor suppressor proteins, respectively Genome oxidative damage was previously shown to be strongly involved in epigenetic alterations DNA repair pathways can be affected by HPV oncoproteins, resulting in the inactivation of important groups of genes, such as base excision repair (BER), nucleotide excision repair (NER), DNA mismatch repair (MMR), microhomology-mediated end joining (MMEJ), Fanconi anemia (FA), ataxia-telangiectasia mutated (ATM), and the ATM and Rad3-related (ATR) genes During HR-HPV infection, apoptosis is hindered by disruption of many regulatory pathways, which results in altered cell proliferation associated with accumulation of mutations and alterations in gene expression Recently, natural compounds with chemopreventive and chemotherapeutic properties, which have antioxidant features, have received increased attention Curcuma longa - Zingiberaceae family) rhizome Curcumin, also known as diferuloylmethane , is one in vivo, inhibiting the activity of proangiogenic molecules, such as fibroblast growth factor 2 (FGF-2), matrix metalloproteases and COX2, as noted previously Moreover, curcumin downregulates other crucial transcription factors responsible for controlling cell growth and survival pathways, including signal transducer and activator of transcription 3 (STAT3), cyclooxygenase 2 (COX2), Akt, antiapoptotic proteins and activator protein 1 (AP1) Due to its biological properties and low toxicity, curcumin is an interesting alternative to therapeutic agents used for the treatment of several types of cancer In addition, curcumin was shown to downregulate HPV18 transcription in HeLa cells, inhibiting the AP1 pathway and reversing the expression patterns of the c-fos and fra-1 transcription factors Another important polyphenol with anticancer properties is epigallocatechin-3-gallate (ECGC). This compound  it is thEGCG is a potent inhibitor of the Akt/NF-κB and mammalian target of rapamycin (mTOR) signaling pathways inducing apoptosis and cell survival mechanisms. Moreover, EGCG can cause NRF2-mediated antioxidant induction and reduce inflammation A recent study showed that EGCG exhibits free radical scavenging properties in cells isolated from cervical cancer biopsies, decreasing cell proliferation and increasing the activity of antioxidant enzymes, such as SOD and Gpx Interestingly, EGCG can act synergistically with cisplatin, inhibiting the growth of cervical cancer-derived cell lines. This finding suggests that cisplatin treatment is potentiated with EGCG in HeLa cells, regulating pathways involved in cell survival and apoptosis. This combination may improve cervical cancer treatment, especially in the context of chemoresistance to cisplatin A clinical trial was carried out to verify the efficacy of two green tea extract-derived compounds, EGCG and polyphenon E, in a study involving ninety women with HPV-related cervical lesions. These patients were divided in four different groups, according to the following regimens: local application of a polyphenon E ointment, daily oral dose (200 mg) of polyphenon E, both local and oral polyphenon E and 200 mg daily oral dose of EGCG. Patients treated with EGCG or polyphenon E responded positively to the treatment, with a reduction in HPV DNA copies and regression of virus-associated lesions, while the majority of the untreated group showed no improvement Resveratrol or 3,5,4-trihydroxystilbene , a phytoResveratrol exhibits antiproliferative effects on cervical cancer-derived cell lines. This effect was characterized by accumulation of cells in the S-phase of the cell cycle The chemopreventive efficacy of resveratrol has already been demonstrated in hepatocellular, skin, prostate and lung cancers, through several regulatory pathways Pyridoxal-5′-phosphate (PLP) is the bioactive form of vitamin B6 . There aVitamin B6 is required as a co-enzyme for many biochemical reactions, including amino acid synthesis and catabolism, bioactive amine synthesis , hemoglobin synthesis, and glycogenolysis Vitamin B6 could sensitize several cancer cell lines to apoptosis after cisplatin-mediated DNA damage by depleting intracellular glutathione. Furthermore, low PDXK expression was associated with poor prognosis in two independent cohorts of non-small cell lung cancer patients In a cohort of thirty-three hepatocellular carcinoma patients that randomly received placebo or vitamin B6 (50 mg/d), the results indicated that vitamin B6 improved the antioxidant capacity by reducing homocysteine levels in patient plasma Recent data have shown that vitamin C, also known as ascorbic acid , is anotp53-mediated pathway. Therefore, low concentrations of cisplatin were required to induce cancer cell death. Hence, it is tempting to speculate that, in combination with vitamin C, low amounts of cisplatin could be used in cancer patients, reducing its side effects In SiHa cells, the association of vitamin C and cisplatin enhanced cisplatin-mediated apoptosis induction through a Interestingly, ascorbyl stearate (ASC-S), a fatty acid ester derivative from ascorbic acid, exhibits a potent proapoptotic activity. Recently, Mane et al. Finally, the direct impact of ascorbic acid on cervical carcinogenesis has been suggested. A cross-sectional study published by Hwang et al. in vivo. In addition, the maintenance and regeneration of α-tocopheryl quinone (a non-oxidized form of α-tocopherol) may also have an important role in CIN and in cervical cancer. However, further investigations of α-tocopheryl quinone as a potential marker of OS in precancerous lesions and cervical cancer are strongly needed Results from previous studies suggest the existence of an association between plasma levels of vitamin E, also known as α-tocopherol or 5,7,8-trimethyltocol , and HPVDespite the positive results with the use of the antioxidant compounds described above, traditional cancer chemotherapy still relies on the induction of OS triggered by ROS levels higher the than tumor elimination capacity, which contributes to tumor cell death One of the most important systems involved in cell protection against elevated levels of free radicals, including those induced by chemotherapy and radiotherapy, is composed of glutathione (GSH) and GSH-related enzymes An alternative treatment for cervical cancer involves molecules capable of inhibiting antioxidant pathways. For example, exposure of HeLa cells to pinostrobin, a dietary bioflavonoid, was associated to reduced cell viability and downregulated GSH and NO2- levels. Pinostrobin-associated cell death involves upregulation of apoptotic extrinsic and intrinsic pathways components, as well as DNA and mitochondrial damage, probably as a consequence of ROS accumulation Another compound, auranofin, an inhibitor of thioredoxin reductase, can also trigger apoptosis while depleting GSH and increasing ROS intracellular levels. In addition, its effect on cervical cancer cell lines can be intensified in combination with a GSH synthesis inhibitor, L-buthionine sulfoximine alfa and beta, and c-Kit The use of enzyme inhibitors such as tyrosine kinase inhibitors (TKIs) to modulate ROS effects constitutes an important alternative to cancer therapies currently under investigation. Sunitinib, for instance, is the most common TKI administered in clinical medicine, and it effectively blocks VEGF receptors According to some published data, sunitinib could have antioxidant activity due to the improved lipid peroxidation and increased GSH levels observed after cisplatin treatment, reducing OS-triggered side effects and improving chemotherapeutic efficacy Another approach involving enzyme inhibitors was applied in a study that developed a new radiosensitization technique using a hydrogen peroxide solution (Oxydol) named KORTUC (Kochi Oxydol-Radiation Therapy for Unresectable Carcinomas). This approach aimed to treat patients with local advanced unresectable neoplasms, through tumor antioxidative enzyme blockage, leading to tumor sensitization to radiotherapy Oxidative stress is a consequence of an imbalance between pro- and antioxidant factors. The antioxidant system maintains the oxidative process within physiological limits, preventing injuries that can culminate in irreparable systemic damage and diseases.Signaling pathways related to redox control in HPV-mediated carcinogenesis represent promising targets for cancer treatment. Some antioxidant molecules, including natural compounds, may be useful preventive or therapeutic alternatives. Despite the positive results of antioxidant compounds, OS induction is still an effective therapeutic approach used in traditional chemotherapy. This approach aims to enhance reactive species production to levels that overcome the tumor elimination capacity, leading to tumor cell death. However, evidence has shown that an increase in antioxidant enzymes by cancer cells has an important role in OS control and possibly in drug resistance.in vitro or animal model studies, relatively few clinical trials have been carried out. These trials are essential for the establishment of new therapies targeting oxidative stress, including those using natural compounds.Current knowledge indicates that several molecules and enzyme inhibitors act to modulate ROS and that they are some of the most significant therapeutic targets for anticancer compound development and should be extensively studied. Despite several lines of evidence based on Silva GA contributed to the abstract, introduction, figures, therapeutic approaches and conclusion and revised the text and references. Nunes RA contributed to preparation and revision of the text. Morale MG contributed to the introduction and revised the text and references. Aguayo F and Boccardo E revised the text. Termini L prepared and revised the text.


The current outbreak of viral pneumonia, caused by novel coronavirus SARS-CoV-2, is the focus of worldwide attention. The WHO declared the COVID-19 outbreak a pandemic event on Mar 12, 2020, and the number of confirmed cases is still on the rise worldwide. While most infected individuals only experience mild symptoms or may even be asymptomatic, some patients rapidly progress to severe acute respiratory failure with substantial mortality, making it imperative to develop an efficient treatment for severe SARS-CoV-2 pneumonia alongside supportive care. So far, the optimal treatment strategy for severe COVID-19 remains unknown. Intravenous immunoglobulin (IVIg) is a blood product pooled from healthy donors with high concentrations of immunoglobulin G (IgG) and has been used in patients with autoimmune and inflammatory diseases for more than 30 years. In this review, we aim to highlight the known mechanisms of immunomodulatory effects of high-dose IVIg therapy, the immunopathological hypothesis of viral pneumonia, and the clinical evidence of IVIg therapy in viral pneumonia. We then make cautious therapeutic inferences about high-dose IVIg therapy in treating severe COVID-19. These inferences may provide relevant and useful insights in order to aid treatment for COVID-19. Recently, the outbreak of a febrile respiratory disease caused by a novel beta-coronavirus, designated SARS-CoV-2, has become the focus of worldwide attention. The event was declared a Pandemic by the WHO on Mar 12, 2020, and the number of confirmed cases is still climbing worldwide. Typical clinical features of COVID-19 include fever, respiratory symptoms such as dry cough and shortness of breath, and fatigue , 4. HoweThe severity of respiratory viral infections depends on a fine balance between the virulence of the pathogen and the inflammatory response of the host's immune system . AlthougBased on the previous clinical experience in China, it was proposed that early initiation of high-dose intravenous immunoglobulins (IVIg) and low-molecular-weight heparin might be effective in improving the prognosis of severe and critically ill COVID-19 patients , 17. TheIVIg is a blood preparation isolated and concentrated from healthy donors consisting of over 95% of IgG and trace amounts of IgA or IgM. IVIg has been used in clinical practice for many years. Different doses of IVIg serve diverse medical indications, and the clinical efficacy of IVIg differs with dosage . At low 2 fragment (the dimeric antigen-binding fragment), which is responsible for specific antigen binding, and the Fc fragment , responsible for Fc receptor (FcR) and complement binding. Several hypotheses have been proposed to explain the immunomodulatory mechanisms of high-dose IVIg, including the F(ab)′2-mediated and the Fc-mediated mechanisms ′chanisms .2 fragment provide the basis for passive immunity that allows for immediate protection against microbes in immunodeficient patients. In vitro and preclinical experiments confirmed that IVIg contains multivalent pathogen-specific neutralizing IgG antibodies against common opportunistic pathogens and toxins  ′ toxins) –26. Vari toxins) –29.2-mediated neutralization of inflammatory cytokines, chemokines, and complement fragments, along with regulation of immune cell apoptosis might contribute to the conversion of pro-inflammatory to anti-inflammatory conditions. Meanwhile, a possible explanation for the fact that only high-dose IVIg can exert anti-inflammatory effects is that a sufficient amount of antigen-specific IgG is required to achieve therapeutic activity.In addition to microbial binding, a wide range of endogenous antigen-specific IgG are also present in IVIg preparations. These endogenous antigens may include: inflammatory cytokines , chemokines , apoptosis-related molecules , complement fragments (C3a and C5a), and anti-idiotypic antibodies  –41. F′ therapy –44. TherThe Fc fragment of IgG is responsible for binding to Fc receptors (FcRs) and complement . IgG-specific Fc receptors (FcγRs) belong to type I FcRs and are classified into activating FcγRs  and an inhibitory FcγR (FcγRIIB), depending on their intracellular motif and the function they mediate. Type II FcRs, including C-type lectins , type I lectins (CD22), and non-classical FcRs, such as FcRn and FCRL, also act as effector targets of IVIg . The Fc-Given the central role of activating FcγRs in mediating excessive antibody-dependent effector functions, a possible explanation of the potential anti-inflammatory function of high-dose IVIg lies in that it significantly raises the concentration of IgG above the normal plasma levels and contributes to a functional blockade of FcγRs, limiting access of immune complexes to these activating receptors. Moreover, IgG from IVIg preparations has been proven to be active through inhibitory ITAMi signaling in immune regulation .The role of dimeric IgG in IVIg preparations is emphasized by an enhanced understanding of the optimal IgG form to interact with activating FcγRs. In physiological conditions, monomeric IgG1 is more likely to bind FcγRI with high affinity rather than to other FcγRs with low affinity . AlthougEndogenous pathogenic autoantibodies, mainly IgG, play a predominant role in the immunopathogenesis of many autoimmune and infectious diseases. A hallmark characteristic of long serum half-life of endogenous pathogenic IgG is mainly due to FcRn. FcRn is a non-classical FcR that can interact with IgG in a pH-dependent manner, which is sufficient in rescuing IgG from lysosomal degradation and recycling it to the cell surface , 53. HigA balance between activating and inhibitory FcγRs is critical for a well-regulated immune response, and a disbalance markedly influences immunopathology in autoimmune and infectious diseases. FcγRIIB is the only ITIM-containing FcγR and negatively regulates many aspects of immune and inflammatory responses. After efficient high-dose IVIg therapy, an upregulation of FcγRIIB on immune cells is considered to contribute to the anti-inflammatory process , 60, 61.via direct interaction with myeloid regulatory cells expressing SIGN-R1 (mice) or DC-SIGN (human)  , 62, 63.or cells , 65. Howor cells , 67. Moror cells –72. It s2-mediated neutralization of complement C3a and C5a, the interaction between the Fc fragment and complement C1q, C3b, and C4b in a dose-dependent manner, contributes to immunomodulatory effects of IVIg on the classical complement pathway ′ pathway –75. The  pathway , 77. The pathway .2-mediated mechanisms and unspecific Fc-mediated mechanisms are not mutually exclusive, but is more likely to regulate the immune system in synergy, giving rise to the immunomodulatory effects of high-dose IVIg in specific clinical settings.As an extremely complex preparation, IVIg contains a large number of bioactive moieties, and the entirety of effects from IVIg is therefore not fully understood yet. The proposed antigen-specific F(ab)′Although an active immune response is essential for pathogen elimination in acute respiratory viral infections, excessive defensive reactions might wreak havoc on healthy cells and tissues. Complications or mortality of respiratory viral infections are often associated with excessive production of pro-inflammatory cytokines and ensuing multiple organ dysfunction. Although the immunopathogenesis of SARS-CoV-2 has not yet been fully described, the histopathological evidence strongly suggests a critical role of an excessive immune response in mediating extensive damage of the lung and other organs, similar to previous observations in SARS, MERS, influenza, and RSV disease, where hyper-inflammatory responses have been shown to be involved in the lung pathology. In this section, we review how a dysfunctional immune response may cause immunopathology in severe viral pneumonia, resulting in the current understanding of IVIg therapy in modulating the hyper-inflammatory conditions.The cytokine storm syndrome is a form of systemic inflammatory response common to severe acute viral pneumonias, and its presence has also been suggested in severe cases of COVID-19. There is a correlation between severity of the cytokine storm and prognosis of severe illnesses . At the Endothelial cells can secrete inflammatory mediators, including cytokines, chemokines, histamine, matrix metalloproteinases, and adhesion molecules, which can lead to inflammatory cell infiltration and tissue damage, and plays an essential role in inflammation, hemostasis, and angiogenesis.SARS-CoV-2 infects host cells using the ACE2 receptor, which is widely expressed on endothelial cells. Endothelial cell infection and endotheliosis have been observed in some COVID-19 patients . Direct Although vasculitis and autoantibodies against endothelial cells have been reported and detected in SARS patients, direct endothelial cell infection hasn't been demonstrated yet –84. Thesin vitro observations. High-dose IgG has been shown to specifically and completely inhibit TNF-α-induced secretion of pro-inflammatory cytokines  and production of E-selectin in cultured human coronary artery endothelial cells  are antigen-presenting cells of the innate immune system. Plasmacytoid dendritic cells (pDCs) are unique sentinel cells that can recognize a virus through PRRs, and enhance the secretion of IFN , 92.Data from COVID-19 patients and a ferret model reveal low levels of IFN with contrastingly high levels of IL-6 and a strong chemokine response, suggesting a similar hyper-inflammatory response pattern to SARS and MERS . SeveralThe immunomodulatory effects of IVIg on DCs differ depending on dosage. IVIg accelerates maturation at physiological doses, but inhibits the maturation, activation, and function at therapeutic doses . High-doWhether infiltrating inflammatory monocyte-macrophages (IMM) are beneficial or deleterious after a viral infection is mainly dependent on their ability to secrete inflammatory cytokines and chemokines. A dysregulated cytokine response can also promote excessive activation of IMM, leading to immunopathological effects on healthy tissues.Macrophage activation syndrome (MAS) was reported in several COVID-19 patients with severe respiratory failure, and the production of associated pro-inflammatory cytokines (IL-6 and TNFα) might contribute to hyper-inflammatory conditions . It was Increased pathogenic IMM influx has been consistently observed in severe or lethal SARS patients, along with mice and Chinese rhesus macaque models , 110. ThIn vitro, IVIg inhibits the production of pro-inflammatory cytokines by M1 macrophages and triggers macrophage polarization via FcγRIII-mediated mechanisms , and neutrophil extracellular traps (NET) by neutrophils can help to neutralize pathogens. Paradoxically, however, neutrophil reactivity can also enhance tissue damage in hyper-inflammatory conditions such as severe virus infection.Excessive neutrophils and NET production are considered a potential predictor of prognosis in influenza , 123. Si2 fragments account for the anti-inflammatory activity on neutrophils. In patients with Kawasaki disease, IVIg therapy reduces the activation and nitric oxide production of neutrophils ′2-mediated mechanisms (in vitro as well as in rat models (Both Fc and F(ab)′trophils . In vivochanisms . Furtherchanisms , 129. Int models .Natural killer (NK) cells are innate lymphocytes that play an essential role in the control of respiratory viral infections. Within days after a respiratory viral infection, NK cells are activated, recruited to the lung, and act as effector cells in a classical FcγR-mediated function, namely antibody-dependent cytotoxicity (ADCC). However, a dysfunction of NK cells may also be responsible for immunopathogenesis during infection.Increased inhibitory receptor NKG2A expression on NK cells and reduced production of CD107a, IFN-γ, IL-2, and TNF-α, indicate a functional exhaustion of NK cells during SARS-CoV-2 infection, similar to that observed in SARS . In patiAfter high-dose IVIg therapy, a reduction in number and cytotoxic activity of NK cells is observed in patients with autoimmune diseases –137. TheCytokines secreted by different subclasses of CD4+ T cells trigger the immune response to pathogens and maintain immune homeostasis. However, an imbalance of Th1, Th2, Th17, and Treg subclasses is associated with dysfunctional cytokine responses during severe viral infections.The profiles of serum cytokines revealed an increased concentration of Th17 cells in COVID-19 patients , 140. Inin vitro experiments ′2 fragments and inhibits the differentiation and expansion of Th17 cells  and cytotoxic granules in CD8+ T cells are commonly observed in COVID-19 patients , 168. AnIn vitro and animal model studies report similar inhibitory effects (via their F(ab)′2 fragment ′2-mediated mechanisms  and Fc-mediated mechanisms acting on inhibitory FcRs , as well as the TLR-9 signaling cascade.Effects of IVIg on B cells vary with the dosage: at low doses, IVIg induces proliferation of B cells and the production of antibodies , 184. OnFcγRIIB is the only classical FcγR expressed on B cells, and its elevated expression levels after effective high-dose IVIg therapy may be involved in inhibitory effects of IVIg on B cells , 186. Hode novo B cell activation. This is consistent with the immunomodulatory functions of IVIg at low doses . ADE has been implicated widely in flavivirus infections, such as WNV and DENV, and high-dose IVIg is commonly used in treating WNV encephalitis with satisfactory effect. However, little is known about ADE in respiratory virus infection –199.ADE is currently being considered as a potential contributor to the immunopathology of SARS. People who succumbed to SARS had significantly higher S glycoprotein-specific neutralizing antibodies in serum during the early stage of infection, indicating the possible presence of ADE . Similarin vivo evidence of ADE in MERS is lacking, it has been shown that MERS-CoV infections depended on monoclonal antibody (MAb) concentration, binding affinity of MAb for the viral receptor DPP4, and tissue expressions of DPP4 and FcγR, providing a molecular basis of ADE in MERS-CoV  were empirically used . AlthougIn a randomized controlled trial, IVIg therapy efficiently improved the serum IgG concentration of severe SARS patients compared to the control group . HoweverApart from hypogammaglobulinemia, two further common features of severe SARS are leukopenia and thrombocytopenia, and these features seemed to improve in SARS patients upon IVIg therapy , 234. ThInterestingly, an IgM-enriched IVIg preparation also showed significant beneficial effects in deteriorating SARS patients who failed corticosteroid and ribavirin treatment . A signiMERS is a highly fatal respiratory disease caused by MERS-CoV, with two major historic outbreaks in 2012 and 2015. The rapid deterioration of health in a large number of patients made it unrealistic and unethical to perform randomized controlled treatment trials. Therefore, the current availability of strong evidence is minimal, and only a few case series reported administration of IVIg late in the course of MERS. These studies discussed the possible efficacy of IVIg in reversing severe thrombocytopenia through immunomodulatory mechanisms , 236.Although influenza has been around for centuries, severe influenza remains a health challenge for humans. The complications or deaths are usually associated with the extensive induction of pro-inflammatory cytokines in severely affected influenza patients . ImmunomIn a previous randomized controlled trial, patients infected with the severe 2009 pandemic influenza A (H1N1) were randomized to receive 0.4 g/kg for one dose of Flu-IVIg (anti-influenza hyperimmune intravenous immunoglobulin with high HAI antibodies level) or IVIg therapy . PatientIn contrast, other RCTs using IVIg treatment at high doses have shown improved survival. In two similar studies, pediatric patients with the severe 2009 pandemic influenza A (H1N1) were randomized to receive standard care or standard care plus 1 g/kg/d high-dose IVIg for 2 days , 229. SiRSV is a major respiratory pathogen that causes extensive respiratory symptoms, including both upper and severe lower respiratory tract infection (URTI/LRTI) in infants, the elderly, and immunocompromised individuals . AvailabA study showed that hypogammaglobulinemia was a significant risk factor for fatal outcomes of RSV infections in a hematology and transplant unit, and treatment with IVIg at regular doses was unable to reverse the poor prognosis . By contSeveral published studies suggest that a combined therapy of RBV and IVIg improves the outcome of HSCT and leukemia patients with RSV-URTI, and additional recommendations are given for high-risk patients with RSV-LRTI –242. HowIn summary, IVIg therapy exhibits different levels of potential clinical benefits in SARS, MERS, influenza, and RSV infections, though currently there is no high-level evidence to support IVIg use in these infections. Of note, the presented results are partly limited to pediatric and immunocompromised patients, and the clinical benefits of IVIg therapy may vary in different patient populations . It is also noteworthy that most studies for these viral infections show possible benefits from IVIg therapy at higher doses, which indicates that a high dosage might be key for clinical efficacy. Considering the immunomodulatory effects of high-dose IVIg on the excessive immune cascade, high-dose IVIg therapy, as stated above, may be considered a potential adjunctive treatment for severe COVID-19 patients.The national diagnosis and treatment protocol for COVID-19  and recommendations from the Peking Union Medical College Hospital have suggested the application of IVIg therapy in severe and critically ill COVID-19 patients , 18. Sevn = 58) in China included severe and critical ill COVID-19 patients that were administered IVIg therapy after admission  and corticosteroid (160 mg/d) succeeded in reversing the deteriorating condition of severe COVID-19 patients who had previously failed low-dose IVIg (10 g/d) and corticosteroid treatment . HoweverThe administration of IVIg therapy (25 g/d for 5 days) at the time of initiation of respiratory distress of severe COVID-19 patients may improve the prognosis . It is nBased on these clinical observations, the administration of high-dose IVIg therapy at the appropriate point may be able to prevent disease progression and improve the prognosis of patients with severe COVID-19. Currently ongoing randomized controlled trials of high-dose IVIg therapy in severe COVID-19 patients are evaluating the benefit of IVIg compared to standard care (NCT04261426 and NCT04350580) and will be able to further provide more information about the clinical effects of IVIg therapy in COVID-19.Although a large number of clinical trials have demonstrated that IVIg therapy is well-tolerated, various side effects have been reported . The maj2 and Fc mediated mechanisms and the known clinical effects in treating severe virus pneumonia such as SARS, MERS, influenza, and RSV disease, the early application of high-dose IVIg therapy may be considered in the management of severe COVID-19 patients. Currently, limited clinical practice of high-dose IVIg in treating SARS-CoV-2 infection has been reported to show potential clinical benefits. Still, more research is needed but these inferences may provide relevant and useful insights and help in confronting the COVID-19 epidemic.The IVIg preparation is a widely used pooled human blood product that can provide passive immunity and modulate the immune functions. Although, for COIVD-19 the pathogen-specific effects of IVIg are not relevant yet, since available preparations were collected from healthy donors without pre-existing immunity early before the pandemic began. The anti-inflammatory and immunomodulatory effects on the various immune cells of high-dose IVIg may account for its clinical benefits. Based on these potential supportive F(ab)′XL conceived and wrote the manuscript and prepared figures. WC and TL contributed to the modification and revision of the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Using the convex structure of positive operator value measurements and several quantities used in quantum metrology, such as quantum Fisher information or the quantum Van Trees information, we present an efficient numerical method to find the best strategy allowed by quantum mechanics to estimate a parameter. This method explores extremal measurements thus providing a significant advantage over previously used methods. We exemplify the method for different cost functions in a qubit and in a harmonic oscillator and find a strong numerical advantage when the desired target error is sufficiently small. Motivated by the fact that quantum systems can offer an important advantage over classical systems in precision when estimating a parameter6, there has been intense theoretical and experimental advances in the area in the last years8. Moreover, precision in the estimation of parameters has several applications in the development of quantum technologies10 and quantum state manipulation11.One of the goals of metrology is to provide an optimal strategy to measure the value of a parameter under certain fixed conditions. For completeness, both an approximate value of the parameter and an estimation of the error must be given. If the physical system from which the parameter is to be estimated is analyzed within the framework of quantum mechanics, we shall speak about To estimate a parameter of a physical system one acquires data through measurements; the estimation of the parameter is obtained by applying a function, known as the estimator, to the data. The probability distribution of measurement outcomes can be modelled using a statistical model of the experiment: the probability distribution of outcomes conditioned to the value of the parameter. This statistical model might describe, say, a noisy measurement apparatus. In this article we specialize to the case of quantum mechanics, where the probability distribution is given by the Born rule and the statistical model is obtained once it is decided which operator is going to be measured. In addition to the random component of measurement, we also consider classical noise, which we include through the density matrix formalism.12.The cost function that quantifies the error of the parameter estimation of a quantum system (for example the mean square error) depends on both the measurement to be performed and the estimator; the optimal measurement strategy consists of the quantum measurement and the estimator that extremizes it. However, finding the extreme of a cost function over all possible quantum measurements and estimators is not simple. Alternatively, when a Cramér-Rao type inequality exists, the problem can be reduced to find the extreme of another cost function (for example the Fisher information) over all the quantum measurements. This simplifies the problem because it is not longer necessary to maximize over the space of estimators. One still has to deal with extremizing a cost function over all quantum measurements, which in general is difficult. However, under special circumstances, such as symmetries, the problem can be simplifiedpositive operator value measures (POVMs), evaluate the cost function on this sample, and keep the maximum value obtained. This method, that we call the random sampling method (RSM), is very inefficient since the POVM space is large.It is possible, though very costly, to numerically find the maximum over all quantum measurements of cost functions. The straightforward way to solve the problem is to randomly sample the space of all 13, in a way that is orders of magnitude faster than using the RSM. We rely on the following: (i) the cost functions of interest in quantum metrology are convex with respect to the POVMs and (ii) the set of POVMs is convex14. Therefore, following the maximum principle16, the maximum over the POVMs must be on an extremal point of the POVM set. Our approach is simply to sample randomly extremal POVMs and get the highest value of the appropriate cost function. It is easy to produce efficiently a general POVM from a random unitary matrix, however, it is not trivial to produce randomly extremal POVMs. For this we used the algorithm proposed by Sentís et al.17.In this paper, we show how to numerically find the maximum over all quantum measurements of the Fisher information and the Bayesian version of this bound, the Van Trees boundrandom extreme sampling method (RESM). The techniques presented here can be used, for example, to find numerically the maximum value of the quantum Van Trees information18, the quantum Fisher information (even if the input state is not pure), or any convex cost function. Together with the maximum, the corresponding quantum measurement is obtained. The method can also be applied to the case of multivariable convex cost functions, and thus used to find the optimal measurement in the estimation of several parameters. For example: Given a multivariable statistical model we can construct the Fisher matrix, its diagonal elements gives a bound on the variance of each parameter. We can use our method to find the quantum measurement that maximizes any convex cost function of the diagonal elements.We call our method the 19. The problem is the following: we have a set of possible decisions, from which one is chosen depending on the outcome of a quantum system measurement. The distribution probability for the decisions is given by a quantum measurement. We look for the best decision. How good is the decision is rated by a cost function defined in the space of quantum measurements. The problem of finding the best decision is mapped to the problem of maximizing this cost function over all the quantum measurements. Note that quantum parameter estimation can be cast as a problem of statistical decision theory, where the decision to be chosen is an estimation of the parameter.Our approach can also be used in other areas different from quantum metrology, where a maximization over POVMs is required. An example is statistical decision theoryIn this section we first introduce some convex cost functions that give bounds to the error of parameter estimation. Then we present our main result: a numerical algorithm that allow us to find the maximum over all quantum measurement of any convex cost. We finish with examples of interest where we apply the algorithm.We discuss how to get bounds for the error made in an estimation process. We use two error measures: the mean squared error and the Bayesian mean squared error, which is used when some information is known about the parameter. This discussion is general and just assumes that a statistical model is given. We also discuss how to apply these ideas for a quantum system.In this section, we introduce some basic quantities needed to develop further the discussion. LetThe uncertainty of the estimator is given by the mean squared error, defined asWe say that the measurement strategy is optimal if the estimator minimizes the mean squared error. Finally, let us define the Fisher information21:If the estimator is unbiased (i.e. if Eq.  holds), 22.Note that fixed time. The dynamics of the system depends on the parameters of the Hamiltonian; after the evolution, the state of the system depends on the parameter we want to estimate: We want to find the best measurement strategy to estimate a parameter, General measurements in quantum mechanics are described by the positive operator valued measure (POVM) formalism, which we briefly recall in order to fix the notation. If In order for Eq.  to be a Notice that 23Fixing the POVM and thinking of  as the d1; we call this POVM the optimal POVM. If the quantum state is pure there are analytical formulas for finding The quantity Equation . Since EEquation  is validWe consider the case where we have some partial knowledge of the parameter to be estimated. An example: We want to estimate the velocity of one particle in a dilute gas at temperature The Bayesian Cramér-Rao inequality can be used to decide what is the best estimator in the situation where we have partial knowledge of the parameter. We model the parameter as the random variable 13,It can be shown that the error is bound from below by the Cramér-Rao type inequalityThe first term of the sum is the expectation value of the Fisher information; the second term is the Fisher information of the probability distribution of the possible values of the parameter. The last term codifies what we already know about the parameter. As can be seen from the previous equation, the generalized Fisher information is larger than the Fisher information due to the knowledge we already have of the parameter. This has a simple interpretation: we can use mentclasspt{minimaIf we want to estimate the outcomes of a random variable, the problem of finding the best strategy is exactly the same as discussed in this section. In this case the experiment is repeated several times with the values of the parameter satisfying the probability distribution of the random variable. An example: measure the velocity of several particles in thermal equilibrium in a dilute gas. The velocities for the classical particles obey a Maxwell-Boltzmann distribution.24, a review of bayesian inference in physics.In this context, we found usefulWe want to estimate the parameter ough Eq. , and theThe POVM, ormation , togetheWe call 18.If we want to minimize the error in the parameter estimation, and we codify what we know about the parameter in the probability distribution The calculation of cost functions such as 25 and the Fisher information are convex. Fisher information can be rewritten as F. From these two observations, we infer that the Van Trees information is also convex, as the integral of convex functions is also convex.The quantum Van Trees information is convex; this follows directly noticing that the set of POVMsSince the maximum of the convex cost functions lies on the extremal points of all POVMs, we only need to search in this subset simplifying greatly the optimization task. A way to sample randomly such a set is presented in the following sub-section.26.The outline of the algorithm is as follows: We produce a random POVM, and decompose it in extremals. We then evaluate the cost function using extremal POVMs and choose the one which yields the highest value. We repeat the procedure several times and keep the optimal POVM. We provide an implementation in an online repository27 backwards, which transforms a general POVM into a usual projective measurement in an enlarged space.To produce a random POVM, we use the purification algorithm14, nothing is gained if the dimension of the ancilla space is larger than The first step is to produce a random unitary matrix that acts on both the original space and an ancilla Hilbert space. The dimension of this ancilla space is the number of outputs of the initial POVM. Because the extremal POVMs have at most circular unitary ensemble (CUE)28. The easiest way to construct a representative member of the CUE is to construct a member of the Gaussian unitary ensemble (GUE)28, the ensemble of hermitian matrices invariant under unitary conjugation subject to the condition that the ensemble average of the trace of the square of the matrices is fixed. Generating a member 26.The aforementioned unitary matrix is chosen with a measure invariant with respect to multiplication by unitary operators, i.e., with the Haar measure. The ensemble induced by the aforementioned measure is called the 27. Equation \documentNumerical algorithm to estimate the quantum Fisher information and the quantum Van Trees information. In the examples we observe a computational speedup when using the algorithm presented here compared with methods that randomly sample the whole POVMs space.In this section we apply the method described in the section We use the algorithm to calculate the quantum Fisher information for estimating a parameter encoded in a pure qubit state. We use known analytical results to benchmark our numerical method.30.We consider a spin 1/2 particle in the state,Given a set of states parametrized by Each of these POVMs has two elements that corresponds to the outcomes Using Eq.  we find 23. In this example the quantum Fisher information is the maximum of The Fisher information for this probability distribution isFRSM and FRESM are the Fisher information numerically calculated using the RSM and RESM respectively. In Fig. In order to evaluate the performance of the proposed algorithm, we apply the RSM and RESM methods and compare their performance with the exact result . We defiNow we consider that we have some information about equality . We assuπ in Eq. . First w by Eqs.  and 25)\documentBecause we are using a subset of all the POVMS  see Eq. . This imNow we apply RESM to calculate We observed that surprisingly, almost any extremal POVM is useful for finding the maximum ween Eq.  and the We calculate the Van Trees information for the phase estimation problem, one of the workhorses of quantum metrology. Since for this case no analytical solutions are known, this is an interesting testbed for our method.18 for a similar calculation. We probe the system with a coherent state, such that one path yields the state α a complex number) and the otherWe want to estimate the phase difference π/4 and trimmed at the edges (0 and 2π). Using RESM, we calculate the quantum Van Trees information for different values of Assume that we know, with some error, the size and the refractive index of the object that creates the phase difference. We can make an initial estimation of the phase difference between the two paths because it is proportional to the length travelled inside the object. We can model this situation assuming that θ) is encoded. For the Fig. π/4.As a final example, we consider estimating a parameter, chosen from a given distribution, encoded in a non-pure state. Again, there are no analytical expressions for the quantum Fisher information for this case. We calculate he state . Let32\dIn Fig. ome POVM  with \do see Eq. . We see deration  is a mixThe random extreme sampling method (RESM) can be used to find efficiently the maximum of a cost function over all possible quantum measurements. Particularly it is useful for finding limits in the precision of parameter estimation, through the cost function known as the quantum Fisher information, when the state to be measured is a mixed state. It can also be used to find the optimal measurement strategy by a given convex cost function by finding the POVM that maximizes it, at a considerable lower computational cost.26. To reproduce the results presented in 2.3.1, set the flag -o to Qubit and vary the flag --EtaAngle from 0 to π. For the results in sections 2.3.2 and 2.3.2, set the flag -o to CohPlusTherGaussian and to DispTherGaussian respectively. We also set the temperature with -T 0.001, the number of times to sample the space with -s 150 , the dimension of the Hilbert space to describe the system with --HilbertDim 7 and the number of outcomes of the POVM with --Outcomedim 10. For the pure state, as in 2.3.2, set -o CohPlusTherGaussian --MixConstant 1. The squared norm of α is set with the option --MeanPhotonNumb, which can be varied to reproduce the plots. The whole data set can be obtained with the command make all.The code implementation can be obtained in the repository


Background: O6-methylguanine-DNA methyltransferase (MGMT) methylation status affects tumor chemo-resistance and the prognosis of glioblastoma (GBM) patients. We aimed to investigate the role of MGMT methylation in the regulation of GBM immunophenotype and discover an effective biomarker to improve prognosis prediction of GBM patients.Methods: A total of 769 GBM patients with clinical information from five independent cohorts were enrolled in the present study. Samples from the Cancer Genome Atlas (TCGA) dataset were used as the training set, whereas transcriptome data from the Chinese Glioma Genome Atlas (CGGA) RNA-seq, CGGA microarray, GSE16011, and the Repository for Molecular Brain Neoplasia (REMBRANDT) cohort were used for validation. A series of bioinformatics approaches were carried out to construct a prognostic signature based on immune-related genes, which were tightly related to the MGMT methylation status. In silico analyses were performed to investigate the influence of the signature on immunosuppression and remodeling of the tumor microenvironment. Then, the utility of this immune gene signature was analyzed by the development and evaluation of a nomogram. In vitro experiments were further used to verify the immunologic function of the genes in the signature.Results: We found that MGMT unmethylation was closely associated with immune-related biological processes in GBM. Sixty-five immune genes were more highly expressed in the MGMT unmethylated than the MGMT-methylated group. An immune gene-based risk model was further established to divide patients into high and low-risk groups, and the prognostic value of this signature was validated in several GBM cohorts. Functional analyses manifested a universal up-regulation of immune-related pathways in the high-risk group. Furthermore, the risk score was highly correlated to the immune cell infiltration, immunosuppression, inflammatory activities, as well as the expression levels of immune checkpoints. A nomogram was developed for clinical application. Knockdown of the five genes in the signature remodeled the immunosuppressive microenvironment by restraining M2 macrophage polarization and suppressing immunosuppressive cytokines production.Conclusions:MGMT methylation is strongly related to the immune responses in GBM. The immune gene-based signature we identified may have potential implications in predicting the prognosis of GBM patients and mechanisms underlying the role of MGMT methylation. Glioma is a type of central nervous system (CNS) neoplasms and accounts for the majority of intracranial malignant tumors in adults. Glioblastoma (GBM), which is defined as the World Health Organization (WHO) grade IV glioma, shows a highly aggressive, heterogeneous, and lethal phenotype. Currently, the etiology of GBM is still largely unknown. Although surgical resection combined with chemotherapy or radiotherapy has been widely used as a routine clinical treatment, the prognosis of patients has even not improved significantly, with a median overall survival fewer than 15 months  catalyzes the DNA repair process by removing the alkylation of the O6 position of guanine  in recurrent GBM (NCT02017717) was launched in 2014. A recent clinical trial indicated that the neoadjuvant PD-1 blockade could upregulate the amount of T cells, promote the expression of interferon-γ-related genes, and further offer a promising survival benefit in recurrent GBM  mutation status was closely associated with immune response in GBM, and a prognostic model based on IDH mutation was further developed to predict the clinical outcomes of patients . The microarray gene expression profile matrix files of the GSE16011 project (including 159 GBM samples) and the Repository for Molecular Brain Neoplasia (REMBRANDT) database (including 220 GBM samples) were downloaded from the Gene Expression Omnibus (GEO) database (www.ncbi.nlm.nih.gov/geo/). The corresponding profiles comprising detailed clinicopathological characteristics were also generated from each data source. The TCGA RNA-seq dataset was used as the training set and the CGGA RNA-seq dataset as the validation set. Furthermore, the CGGA microarray profile, as well as the GSE16011 and REMBRANDT datasets, were used to test the prognostic value of the risk model developed in the TCGA cohort.We retrospectively collected a large-scale profile composed of five independent datasets involving 769 GBM patients . The RNAwww.immport.org), and 1,100 immune-related genes were extracted from the “IMMUNE_RESPONSE” gene set . Finally, after combining these two independent gene sets, 1,763 immune-related genes were selected for the next analyses  between different MGMT methylation status in the CGGA RNA-seq cohort were identified using the “limma” R package  were screened for further analyses.Hundred and thirty-seven GBM patients with data on gene expression and MGMT methylation status was selected in the TCGA cohort, and the differentially expressed genes (DEGs) between MGMT unmethylated and methylated samples were identified using the “DESeq2” R package  regression model with 10-fold cross-validation was adopted to select the most useful prognostic factors for GBM patients. The optimal lambda value was estimated based on the minimum criteria. Then, the risk score for each sample was calculated using the expression levels of genes and corresponding regression coefficients obtained from the multivariable Cox regression analysis. The formula of the risk score model was as follows:N is the number of genes in the signature, βgenei is the regression coefficient, and expgenei represents the expression value of a specific gene in a single sample. The cutoff for classifying GBM patients into high and low-risk groups was determined using X-title 3.6.1 software , immature DCs (iDCs), activated DCs (aDCs), neutrophils, mast cells, eosinophils, and macrophages, as well as those associated with cells of the adaptive immune system, including B, T central memory (Tcm), CD8+ T, T effector memory (Tem), T follicular helper (Tfh), Tγδ, Th1, Th2, Th17, and Treg cells, were included in the gene list. A numeric matrix consists of the enrichment score of each immune cell type in a single sample was obtained via ssGSEA. For the specific macrophage subtype (M2 macrophage), the xCell algorithm, a novel gene signature-based method, was performed to infer the contents .We used the computational method of Tumor Immune Dysfunction and Exclusion (TIDE)  score, IDH mutation status, and risk score. A combined nomogram was generated as a quantitative tool for predicting the likelihood to die of each patient using the “regplot” R package. The concordance index (C-index) was calculated to assess the consistency between model prediction and actual clinical outcomes of patients. The calibration plot was generated to evaluate the accuracy of the prediction for 1-, 2-, and 3-year overall survival using this nomogram by the “rms” R package.Multivariable Cox proportional hazards regression analysis was applied with the following clinical-relevant covariates: gender, age, Archival paraffin-embedded GBM tissues were collected from 15 patients who underwent surgery at the First Affiliated Hospital of Nanjing Medical University. The diagnosis of GBM was confirmed by two experienced pathologists. This study was approved by the ethics committee of the First Affiliated Hospital of Nanjing Medical University. All samples were collected under protocols approved by the institutional review boards of Nanjing Medical University, and all donors provided informed consent.MGMT promoter of these samples was assessed using the pyrosequencing (PSQ) assay. Briefly, genomic DNA was extracted from the tumor tissues using the QIAamp DNA Mini Kit according to the manufacturer's instructions , and then DNA was treated with the EpiTect bisulfite kit to convert cytosine to uracil . Bisulfite-treated DNA was amplified using the specific PSQ primer 5′-YGTTTTGYGTTTYGAYGTTYGTAGGTTTTYGYGGTGYGTA-3′ and then treated with the PyroMark Q24 MGMT kit . The methylation status was determined by the mean value of the methylated alleles percentage, and the value over 10% was regarded as MGMT methylation . U87 and U251 cells were grown in Dulbecco's modified Eagle's medium  supplemented with 10% fetal bovine serum , 2 mM L-glutamine, and 1% penicillin–streptomycin. THP-1 cells were maintained in RPMI-1640  supplemented with 10% FBS. All cells were cultured with 5% COTHP-1 cells were treated with 100 ng/mL phorbol 12-myristate 13-acetate  for 24 h to simulate a macrophage-like (M0) phenotype. Following treatment, the M0 macrophages were polarized into M2 macrophages by incubating with 20 ng/mL IL-4  for 24 h.GAPDH primers, forward: 5′-AGAACATCATCCCTGCCTCTACTG-3′, reverse: 5′-ACGCCTGCTTCACCACCTTC-3′; CD68 primers, forward: 5′-GGAAATGCCACGGTTCATCCA-3′, reverse: 5′-TGGGGTTCAGTACAGAGATGC-3′; CD14 primers, forward: 5′-ACGCCAGAACCTTGTGAGC-3′, reverse: 5′-GCATGGATCTCCACCTCTACTG-3′; CD206 primers, forward: 5′-TCCGGGTGCTGTTCTCCTA-3′, reverse: 5′-CCAGTCTGTTTTTGATGGCACT-3′; CD163 primers, forward: 5′-TTTGTCAACTTGAGTCCCTTCAC-3′, reverse: 5′-TCCCGCTACACTTGTTTTCAC-3′; IL-10 primers, forward: 5′-GACTTTAAGGGTTACCTGGGTTG-3′, reverse: 5′-TCACATGCGCCTTGATGTCTG-3′; VDR primers, forward: 5′-GTGGACATCGGCATGATGAAG-3′, reverse: 5′-GGTCGTAGGTCTTATGGTGGG-3′; GATA3 primers, forward: 5′-GCGGGCTCTATCACAAAATGA-3′, reverse: 5′-GCCTTCGCTTGGGCTTAAT-3′; TNFSF9 primers, forward: 5′-GGCTGGAGTCTACTATGTCTTCT-3′, reverse: 5′-ACCTCGGTGAAGGGAGTCC-3′; TNFRSF9 primers, forward: 5′-AGCTGTTACAACATAGTAGCCAC-3′, reverse: 5′-GGACAGGGACTGCAAATCTGAT-3′; LILRA5 primers, forward: 5′-TCACGGCTGAGATTCGACAG-3′, reverse: 5′-CCTGCGAGAGCCATAGCATC-3′.Total RNA from cells was extracted using TRIzol reagent , and complementary DNA (cDNA) was synthesized using a reverse transcription-PCR Kit . RT-PCR was performed using SYBR qPCR Master Mix  and operated with LightCycler 480 System . The primer sequences used in this study were listed as follows: VDR, GATA3, TNFSF9, TNFRSF9 siRNA reagents, and a pool of three LILRA5-targeting siRNAs , were transfected into U87, U251 cells, and polarized M2 macrophage at the concentration of 50 nM with Lipofectamine 3000 reagent . The target sequences were as follows: GATA3, 5′-GGGCUCUACUACAAGCUUCTT-3′; VDR, 5′-CCCACCUGGCUGAUCUUGUCAGUUA-3′; TNFSF9, 5′-UAUUCCGACCUCGGUGAAGGG-3′; TNFRSF9, 5′-AAGCAGTTACTACAAGGATCC-3′; LILRA5-siRNA1, 5′-GUCCUUGGGAUUCUGAUAUTT-3′; LILRA5-siRNA2, 5′-GGAAUACCGUCUGGUUAAATT-3′; LILRA5-siRNA3, 5′-GUGACAGGAUUCUACAACATT-3′. Cells were harvested for the next assay after incubated for 48 h.The VDR, GATA3, TNFSF9, TNFRSF9, and LILRA5 siRNA for 48 h, blocked with 5% BSA and 0.01% Triton-X 100 before incubation with primary antibodies. The cells were treated with primary antibodies at 4°C overnight and then incubated with fluorescence-conjugated secondary antibodies. DAPI (Invitrogen) was applied to stain the nuclei before capturing by a fluorescence confocal microscope . The primary antibodies used in the IF assay were listed as follows: CD68 , CD163 , CD206 . For IHC staining, the assay was conducted by following the manufacturer's protocol as previously published , VDR , TNFSF9 , TNFRSF9 , LILRA5 . IHC staining of GBM tissues for individual target protein was assessed by measuring the ratio of the integrated optical density (IOD) using Image-pro plus software (version 6.0). Images were captured from six random fields, and the final score was set as an average per field.The polarized M2 macrophages were treated with GATA3, VDR, TNFSF9, TNFRSF9, and LILRA5 for 48 h. The culture supernatants were collected, and then the protein expression levels of VEGFA, TGF-β, and CCL2 were detected using the ELISA kits. All the ELISA kits were purchased from Proteintech. The procedures were performed according to the supplier's protocol.U87 and U251 cells were seeded in six-well plates and treated with siRNAs-targeting www.r-project.org) was used for all statistical analyses.The LASSO Cox regression analysis was carried out using the “glmnet” R package. Restricted mean survival (RMS) represents the loss in average life expectancy for patients, and the RMS, as well as time ratio for the risk model in each dataset, were calculated using the “survRM” R package. Tumor purity, which represents the heterogeneity of each tumor sample, was estimated by the “ESTIMATE” R package  and MGMTM (MGMT methylated) groups in these two cohorts, respectively. GSEA analysis showed that immune-related biological processes, such as humoral immune response , T cell activation involved in immune response , adaptive immune response , cytokine production involved in immune response , and regulation of innate immune response , were significantly enriched in the MGMTU group in the TCGA cohort , such as CXC motif chemokine ligand , immunoglobulin kappa variable family , and immunoglobulin heavy constant gamma cluster .To further investigate the potential immune pathways and key immune-related genes driving this phenomenon, differentially expressed genes between the entified . Sixty-fTNFSF9), TNF receptor superfamily member 9 (TNFRSF9), interleukin 1 receptor type 1 (IL1R1), vitamin D receptor (VDR), CD70, leukocyte immunoglobulin-like receptor A5 (LILRA5), GATA-binding protein 3 (GATA3), and CXC motif chemokine ligand 13 (CXCL13) were selected as independent factors associated with the overall survival of GBM patients : 1.75–3.87, P < 0.001] . Time-dependent ROC curves were generated to evaluate the efficiency of this risk signature in predicting 1-, 2-, and 3-year survival of GBM patients, and the risk model showed considerable predictive potential in different cohorts . The HRs for the signature in the univariate and multivariate analyses were 3.107  and 2.476 , respectively. The risk model was further validated in the CGGA RNA-seq cohort, with HRs of 1.896  and 1.644 , respectively  > 0.4, P < 0.05] in the TCGA and CGGA RNA-seq cohorts (P < 0.05) (P < 0.05)  . Similar < 0.05) .R = −0.6, P < 0.0001 in TCGA, and R = −0.4, P < 0.0001 in CGGA RNA-seq)  .P < 0.05) (P < 0.05). Similarly, high-risk patients in the CGGA RNA-seq cohort had significantly higher quantities of pro-tumor immune cells, except for pDCs and mast cells (P < 0.05) , immature dendritic cells (iDCs), CD56dim natural killer cells, macrophages, neutrophils, regulatory T cells (Tregs), and mast cells ( < 0.05) . At the  < 0.05) , were hi < 0.05) . Therefovia changes in the expression of immunosuppressors.To investigate the potential mechanisms underlying the correlation between the genes related to the immune response and the risk score, we assessed the relationship between the risk score and glioma-associated immunosuppressors. The gene list of glioma-related immunosuppressors, including immunosuppressive cytokines and checkpoints, tumor-supportive macrophage chemotactic and skewing molecules, immunosuppressive signaling pathways, and immunosuppressors were obtained from the published literature   were found to be positively associated with the risk score in both the TCGA and CGGA RNA-seq cohorts ( < 0.05) . These rTIM-3, TIGIT (except in the CGGA RNA-seq cohort), LAG-3, PD-1, CTLA-4, CD80, PD-L1, PD-L2, B7-H3, and IDO1  was significantly more likely to benefit from treatment with immune checkpoint inhibitors than the low-risk group  (P < 0.001) (P = 0.024)  . SubMap < 0.001) . Here, S= 0.024) .MGMT methylation status on the chemosensitivity to TMZ in GBM patients. Since the immune-gene-based signature was built based on the MGMT methylation status, we investigated whether this risk model can be used as an effective indicator for stratifying patients into different subgroups which may show distinct sensitivities to TMZ treatment. In both the TCGA and CGGA RNA-seq cohorts, whole GBM samples were divided into two groups according to their TMZ-based chemotherapy history, and then survival analyses for patients with high and low risk were carried out. In the TCGA cohort, patients who received TMZ treatment showed significantly better prognosis than those without chemotherapy in the high-risk group  (P > 0.05)  (P > 0.05) (P < 0.001) and CGGA RNA-seq  cohorts (There is a large body of work implicating the influence of = 0.003) , while n > 0.05) . As we m= 0.027) , while n > 0.05) . As for  cohorts , while n cohorts . These fTo develop a quantitative tool for predicting the prognosis of GBM patients in the TCGA cohort, we established a nomogram by integrating clinicopathological risk factors and immune gene signature based on the multivariable Cox proportional hazards model . The poiIn silico analysis revealed that all these five key genes in the risk signature were more highly expressed in the MGMTU GBM samples. Here, we further validated the protein expression levels of these genes in our clinical specimens. Fifteen GBM samples were divided into MGMTM (n = 8) and MGMTU (n = 7) groups according to the MGMT promoter methylation status. The detailed PSQ results of the samples were illustrated in MGMTU groups compared with the MGMTM samples   . These re scores .CD68 and CD14  than those of the control group  and Th1 cells by binding to its native ligand TNFSF9  signaling and further suppresses the development of pancreatic ductal adenocarcinoma  was carried out to test the efficacy and safety of nivolumab alone vs. bevacizumab , CGGA (http://www.cgga.org.cn/), and GEO database (https://www.ncbi.nlm.nih.gov/geo/).Publicly available datasets were analyzed in this study. These data can be found here: TCGA (The studies involving human participants were reviewed and approved by the ethics committee of the First Affiliated Hospital of Nanjing Medical University. The patients/participants provided their written informed consent to participate in this study.PZ, LZ, and JZ: conception and design. PZ: administrative support. LZ, SX, and YW: provision of study materials or patients. LZ, SX, JZ, and ZL: collection and assembly of data. LZ and JZ: data analysis and interpretation. All authors: contributed manuscript writing and final approval of manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Pancreatic cancer is refractory to most current treatment options. Immunotherapy emerges as an effective and novel therapeutic strategy for several solid tumors. However, most of the clinical trials on immunotherapy have failed in pancreatic cancer. Understanding the underlying mechanism that drives immune evasion of pancreatic cancer is critical for overcoming resistance to therapy. Recently, Dr. He Ren and colleagues proposed a novel concept that a subset of epithelial cells in pancreatic cancer mimics the phenotype and function of regulatory T cells, named as “quasi-regulatory T cells.” These cells contribute to enhanced immune evasion, angiogenesis, and metastasis of pancreatic cancer, thus providing potential therapeutic targets to improve the sensitivity of immunotherapy for this devastating disease. This ground-breaking concept will advance our understanding on the immune evasion of pancreatic cancer and chart novel paths towards the development of personalized treatment for pancreatic cancer. Pancreatic adenocarcinoma (PDAC) remains the most lethal cancer with an overall 5-year survival rate of 10%. Current standard therapies for PDAC have rarely achieved long-term remission even in patients at early stages. Moreover, PDAC appears to be refractory to the immunotherapy, partially due to the tumor immunosuppressive microenvironment (TIM) [+Tregs to mediate immune evasion. This study uncovered the interaction between different types of FOXP3+ cells and indicated that the tumor-intrinsic factors could serve as potential biomarkers to predict the response of treatments that target Tregs [+ T cells. Thus, c-FOXP3 represents a key factor in reprogramming the tumor immune microenvironment [Dr. He Ren and colleagues have recently published a series of papers focusing on understanding the role of immune regulatory factors derived from malignant epithelial cells –7. They et Tregs . Immune-Interleukin 35 (IL-35) is a potent immune inhibitory cytokine primarily expressed by Tregs. Ren and collaborators found that IL-35 was highly expressed in the malignant epithelial cells of PDAC and associated with poor prognosis. Meanwhile, IL-35 stimulated ICAM1 expression via STAT-1 and mediated PDAC extravasation and metastasis . This fiTGF-β is a classic immune inhibitory cytokine, expressed by multiple cell types including pancreatic cancer cells. Ren has recently found that EHF, one of the epithelium-specific ETS (ESE) transcription factors, negatively regulated the transcription of TGF-β . In pancTaken together, Ren and colleagues described the characteristics of cell-intrinsic factors that mimic the phenotype and function of Tregs. They firstly showed that a subtype of “quasi-regulatory T cells” exist during pancreatic tumorigenesis. These cells increased the secretion of several critical immune regulatory factors by the upregulation of FOXP3 and downregulation of EHF. In addition, overexpression of PD-L1 and loss of E-Cadherin could be used as the surface marker of “quasi-regulatory T cells.” Previous studies of TIM mainly focused on the infiltrating immune cells and stromal cells. This ground-breaking concept will advance our understanding on the immune evasion of pancreatic cancer and chart novel paths towards the development of personalized treatment for pancreatic cancer (Fig. In future studies, several key questions need to be answered to better understand the underlying mechanisms of “quasi-regulatory T cells” driven immune evasion of PDAC. First of all, during the development of pancreatic cancer, what is the master regulator that controls the formation of “quasi-regulatory T cells”? Secondly, how to identify the biological effect of “quasi-regulatory T cells” during pancreatic tumorigenesis? Last but not the least, what is the difference between the transcriptomic networks of quasi-regulatory T cells and regulatory T cells in the same PDAC tissue? Through further investigation of those questions, we should be able to better understand the biology of PDAC, especially on immune evasion, and develop novel therapeutic targets to improve the effectiveness of immunotherapy in PDAC.


Ptyodactylus guttatus) ranging in body mass between 4.2–11.5 g. We then  used this calibrated  DXA measurements to determine the best linear measurement calculated CI for this species. Condition indices (CIs) are used in ecological studies as a way of measuring an individual animal’s health and fitness. Noninvasive CIs are estimations of a relative score of fat content or rely on a ratio of body mass compared to some measure of size, usually a linear dimension such as tarsus or snout-vent length. CIs are generally validated invasively by lethal fat extraction as in a seasonal sample of individuals in a population. Many alternatives to lethal fat extraction are costly or time consuming. As an alternative, dual-energy X-ray absorptiometry (DXA) allows for non-destructive analysis of body composition and enables multiple measurements during an animal’s life time. DXA has never been used for ecological studies in a small, free-ranging lizard before, therefore we calibrated this method against a chemical extraction of fat from a sample of 6 geckos . Fat percentage regressed with body mass significantly predicted the DXA fat percentage . Live wet mass was significantly correlated with predicted fat mass  for specimens more than 4.8 g. Among the five calculated non-invasive CIs that we tested, the best was mass/SVL.We found that fat mass measured with DXA was significantly correlated with the mass of chemically extracted fat for specimens more than 4.8 g  for both sexes. Branta bernicla bernicla, [Neochmia phaeton, [Odocoileus virginianus, [Suricata suricatta, [Callospermophilus lateralis, [Chelonia mydas, [Zootoca vivipara, [Body condition is a term used by ecologists to rank the ‘quality’ of an individual animal, usually in relation to the amount of energy reserves  an animal has available . Many anernicla, ), reprodphaeton, ), foragiinianus, ; meerkatricatta, ), survivteralis, ), diseasa mydas, ) and disivipara, ). It is ivipara, . In popuivipara, , 34.Body fat, due to its high energy content, is the best measure of body condition of an animal . Body faBioelectrical Impedance Analysis (BIA) appears to be a better predictor of body fat than body condition estimates calculated from mass and SVL . HoweverMacaca mulatta ; measuridation, ), identiidation, ), determviduals, ) and chaviduals, ). In repviduals, , 37 whicviduals, , 28). Inviduals, ). The usPtyodactylus guttatus inhabited the premises [The study was conducted at Midreshet Ben-Gurion in the northern Negev desert, Israel (30°51′8.27″N 34°47′0.24″E) from summer 2003 until autumn 2004. The study site was a complex of guest rooms surrounded by a two-meter high wall over an area 13x150m. A dense population of the Israeli fan-toed gecko premises . The Isrpremises , 45 Zlotpremises  in the fpremises , 47, 201premises , 46).Ohaus digital scale to 0.1 g precision, snout-vent length (SVL), using digital calipers, and the width at the base of the tail. Six additional individuals were captured and euthanized for the calibration necessary for this study  The smallest gecko (55.6 mm) with body mass 4.2 g was excluded to improve accuracy from 55 to 8.5% error. The snout vent length (SVL) of these geckos was 61.5–91.7 mm and their body mass 4.8–11.5 g. We killed only six geckos in order to minimize destructive sampling as much as possible.Fifty-five geckos were hand captured, measured (morphometrics) and scanned (DXA) and released at the site of capture. Of these, 30 gecko’s scan data were included in the comparison, since their body mass was above the lowest possible accurate reading with minimal body mass (> 4.8 g) as indicated in the results Table . The snoGeckos were anesthetized using cotton balls wet with Isofluran which was inserted with the gecko in a sealed jar for a minute or less until the gecko stopped moving. To obtain the DXA measurements, each gecko was scanned twice using a Lunar PIXImus® 2 densitometer . Each sThe DXA machine protocol was designed for 10–50 g. laboratory mice , but it R2 and P values. For statistical analysis, we used SPSS (version 20).We used backward selection mode of a step-wise regression to test the relation between DXA values of body mass and the values determined from the chemical analysis of body composition. Single and multivariate regression models were then constructed to predict the fat mass from DXA readings. We report the results of our statistical analysis in terms of their N = 6, R2 = 0.834, P = 0.011; Fig. N = 5, R2 = 0.995, P < 0.001; Fig. R2 = 0.995) and a lack of significant difference between fat content by chemical extraction and by DXA estimation .Six geckos were used for the validation portion of this study Table . The valN = 30, R2 = 0.984, P < 0.001; Fig. N = 5, R2 = 0.995, P < 0.001.To determine the accuracy of the DXA in calculating mass compared to actual weighed mass, we compared all lizards above 4.8 g. The DXA body fat readings were highly correlated to live wet mass (linear regression: P = 0.045) but the percentage of explained variation was low (R2 = 0.136). We regressed a multivariate regression model, constructed with mass and DXA machine fat percentage  as variants to predict the correct fat percentage: N = 30, R2 = 0.847, R2adj. = 0.836, P < 0.001; y = − 30.412 ± 3.669 + 1.523 ± 0.157(xFat%) + 1.289 ± 0.151(xMass)  + 1.903 ± 0.179(xMass).A simple linear regression of calculated  vs. DXA machine’s predicted fat percentage was significant ( R2 = 0.16. We regN = 29), and did not significantly depart from a normal distribution . Females tended to have a higher percentage of their body mass as fat  compared to males (7.94 ± 4.19); however, this difference was not significant .Average total body wet mass was 11.1 ± 7.0% body fat  mass/SVL3.We applied five different morphological condition indices that are commonly used on reptiles to the 30 geckos in the dataset in order to determine their accuracy. The indices tested were 1) width of tail base; 2) ordinary least squares (OLS) linear regression of body mass (g) against SVL (mm); 3) mass/SVL; 4) mass/SVLP < 0.05). The correlations between calibrated body fat percentage and body condition indices was largest for the mass/SVL index. The correlation coefficient was highest for the Mass/SVL index > mass/SVL2 > width of tail base > Mass/SVL3 > OLS  and not significant for females.All body indices were highly correlated with each other , a non-destructive method that accurately measures fat mass in humans and other mammals, could be used to determine the fat mass in the small, common Israeli fan-toed gecko. We found that the lowest value for body mass calculation by the DXA machine and that was still accurate was 4.8 g. Thus, when we exclude the smallest gecko (4.2 g), we found that DXA and chemical extraction of fat mass were significantly correlated. It should be stressed that the validation sample size was small and there were not enough individuals to explore sex differences, so further exploration of the technique is warranted. Regression equations using DXA values were able to predict the fat mass and total gravimetric body mass accurately with an average error of 8.55 and 3.5% respectively. Using the predicted DXA values, we were able to test common body condition indices against both actual and predicted DXA values and show that mass/SVL was the most accurate method for estimating the condition index of these small lizards. Measuring the tail base width was not correlated with females body percentage; however for males, it was more accurate than mass/SVL. Females and males may differ in their patterns of fats storage, which might explain why the tail base width was not correlated with fat percentage in females, while it was the best indicator for fat percentage in males.2) predict the fat mass of diamondback water snakes (Nerodia rhombifer) with significantly different values from the DXA and chemical analysis results (mean error ranging from 20 to 41%). Weatherhead & Brown [N. sipedon). Other methods for the estimation of fat percentage  have been fairly successful in predicting body condition, but again they lack accuracy  and DXA (this study). The mean error percentage for fat mass we calculated for using DXA was 8.55% when the animal body mass was more than 4.8 g. This percentage designated the difference between the fat mass predicted by the model and the fat mass that was extracted chemically. The mean error of the wet body mass was even lower, at 3.5%. Thus, the body index method we used—measurement of body fat% by DXA— was proved to be most reliable and highly correlated to chemically extracted body fat.Previous work on reptile body condition indices and non-destructive sampling have been notoriously inaccurate. Secor and Nagy  found th & Brown  found anccuracy . Total ote that  found thWe found that DXA was a reliable rapid and accurate means to predict fat mass and body mass in live small lizards. It enables a researcher to accurately track seasonal and ontogenetic changes in an individual and to correlate these changes to fitness traits. The DXA machine and software were easy to use, and such machines are widely available due to DXA use in human medicine. The cost for such a machine solely for use on wildlife would be prohibitive , but as a secondary use for machines already purchased for medical or veterinary laboratories, DXA becomes a cost-effective option. While cost is always a drawback for DXA, we suggest its primary benefit is to select the most accurate CI of the non-invasive methods. A validation study is required to generate the predictive models necessary to know which CI is the most accurate; the sacrifice of study animals becomes then a serious liability for studies on rare species. Another disadvantage is the necessity that subjects remain absolutely still, which requires anesthesia. In a pilot trial, we attempted to place the lizards in the freezer prior to the trial to prevent them from moving during the scanning phase, but this proved unreliable and had to be substituted with an anesthesia which can prove fatal in overdose.r = 0.961). This result concerning tail width should be explored in other species of lizards in order to understand if this specific measurement is a general basic physiological difference between the sexes.As a final note on practicalities, when comparing five common traditional CI’s, we found that mass/SVL was the index with the highest correlation coefficient to the DXA estimates. We conclude that when DXA is not available, mass/SVL is the most reliable alternative index for this species and should be used unless actual fat mass is required. In situations where more accuracy is needed, DXA is a suitable method for small lizards. An interesting question raised here is the insignificant correlation between females tail base width and fat percentage, while in males it was the best predictor (