Modern machine learning (ML) technologies have great promise for automating diverse clinical and research workflows; however, training them requires extensive hand-labelled datasets. Disambiguating abbreviations is important for automated clinical note processing; however, broad deployment of ML for this task is restricted by the scarcity and imbalance of labeled training data. In this work we present a method that improves a model’s ability to generalize through novel data augmentation techniques that utilizes information from biomedical ontologies in the form of related medical concepts, as well as global context information within the medical note. We train our model on a public dataset (MIMIC III) and test its performance on automatically generated and hand-labelled datasets from different sources . Together, these techniques boost the accuracy of abbreviation disambiguation by up to 17% on hand-labeled data, without sacrificing performance on a held-out test set from MIMIC III. Disambiguating abbreviations is important for automated clinical note processing; however, deploying machine learning for this task is restricted by lack of good training data. Here, the authors show novel data augmentation methods that use biomedical ontologies to improve abbreviation disambiguation in many datasets. Semi-supervised approaches also took root during this time: Pakhomov et al. improved the contextual representation of senses in clinical notes by augmenting them with text from the Web and biomedical abstracts, but were only able to validate their methods on eight abbreviations2.Health care practitioners typically abbreviate complex medical terminology when preparing clinical records, saving time of writing out long terms/phrases, while making the text clear to an experienced professional in the context. Correctly disambiguating medical abbreviations is important to build comprehensive patient profiles, link clinical notes to ontological concepts, and allow for easier interpretation of the unstructured text by practitioners from other disciplines. Expanding abbreviated terms into their long forms is nontrivial since abbreviations can have many expansions. For example, “ra” can mean right atrium, rheumatoid arthritis, or room air depending on both its local (adjoining words) and global (type of note and other information in it) context. While disambiguating abbreviations is typically simple for an expert in the field, it is a challenging task for automated processing, which has been addressed by a number of methods going back at least 20 years. These methods largely rely on supervised algorithms such as Naive Bayes classifiers trained on co-occurrence counts of senses with automatically tagged medical concepts in biomedical abstracts6. More recently, abbreviation disambiguation models have been fine-tuned using contextualized embeddings generated from BERT and ELMo model derivatives8. However, the development and deployment of methods for automated abbreviation disambiguation are limited by the availability of appropriate training data. Creating hand-labeled medical abbreviation datasets to train and test ML models is costly and difficult, and to the best of our knowledge, the only such publicly available dataset with training data and labels is the Clinical Abbreviation Sense Inventory (CASI)9, which contains just 75 abbreviations. The sparsity of these datasets makes methods built based on them vulnerable to overfitting and inapplicable to abbreviations not present in the training data. This is evident in studies where training and testing models on different corpora can result in performance drops of 20–40%6. Moreover, the same studies typically disambiguate 50–2000 abbreviations, compared to >80,000 medical abbreviations that are in AllAcronyms, a crowd-sourced database of abbreviations and their possible expansions10.Modern methods that disambiguate abbreviations rely on the local context of the abbreviation to discern its meaning. A number of supervised machine learning (ML) models have been built for abbreviation disambiguation in medical notes, including ones based on support vector machines (SVM), Naive Bayes classifiers, and neural networks11 utilized reverse substitution (RS) to auto-generate training data by replacing expansions with their corresponding abbreviations. For example, the phrase “Patient was administered intravenous fluid” in the training data was transformed to “Patient was administered ivf”, and the label for this instance of the abbreviation “ivf” was “intravenous fluid”. RS, however, creates imbalanced training sets because the distributions of terms in their abbreviated and long forms are often different. Some phrases, due to their obvious meaning, or because they are too long, are rarely written out fully; for example, milligrams (mg) next to medication dosage, or “in vitro fertilization” . Although additional work has improved RS by hand-labeling of specific instances6, none of the existing methods for abbreviation disambiguation scale to tens of thousands of medical abbreviations listed in resources such as AllAcronyms.Finley et al.12, and Kirchoff and Turner demonstrated that document contexts are useful in medical abbreviation disambiguation tasks13. A study by Li et al.14 represented acronyms in scientific abstracts using the embeddings of words with the highest term frequency–inverse document frequency (TF-IDF) weights within a collection of documents. This was motivated by the idea that acronym expansions are related to the topic of the abstract and that topics can be described by words with the highest TF-IDF weights.An additional problem with medical abbreviation disambiguation is that the local context of a word is not always sufficient to disambiguate its meaning. For example, “rt” could represent “radiation therapy” or “respiratory therapy”, and the phrase “the patient underwent rt” cannot be disambiguated without further information. Huang et al. showed that words can be better represented by jointly considering their local and global contextsWe used information from related medical concepts to create more balanced and representative examples of training data for RS approaches. We did this by sampling sentences of related concepts in the immediate vector space and adding them to our training cohort, which is especially beneficial for medical concepts that are rare or not written in the training text.15 by constraining medical concepts to be in the same vector space as their neighbors.We leveraged structural relationships in biomedical ontologies such as the unified medical language system (UMLS) to pre-train our modelsWe defined a simple global context that combines medical knowledge from the entire note and used it in conjunction with the local context of an abbreviation to further improve the accuracy of abbreviation disambiguation.In this work, we tackle the problem of disambiguating medical abbreviations in the absence of training data for any specific abbreviation, thus dramatically increasing the ability of such models to generalize to new texts. We took the following three-pronged approach:16, we show a 2% accuracy improvement on i2b2, and over a 7% increase on abbreviations with little training data. Finally, we recruited medical professionals and students to hand label abbreviations in i2b2. We tested our model on these abbreviations and found a 16% improvement compared to the baseline.Using these three techniques, we achieve an overall 17% improvement on CASI. Using automatically generated testing samples from i2b2, a collection of patient discharge summaries17. We then map medical concepts in UMLS to the resulting vector space to generate a word embedding for every medical concept. Then, for a given abbreviation, we augment the training samples for each expansion with sentences containing closely related medical concepts determined using embedding distance  to perform the classification task of predicting the correct expansion for an abbreviation given its local context (the neighboring words) and global context . Further details on each step are provided in the “Methods” section.To evaluate the contribution of each component of our model, we compared its performance to models trained without the critical sub-components. The first model (Control) uses training samples acquired using RS without any alterations. The second  is similar to the first, but samples training sentences with replacement such that each expansion has an equivalent number of training samples. The third model (Relatives) incorporates our novel data sampling technique by including relatives of expansions in the biomedical ontology (UMLS) into the training set. We sample concepts with replacement so that all expansions have an equivalent number of training samples. To evaluate whether using the structure of the ontology can improve the results we initiated it with weights learned from the hierarchical training task . We also trained the models using both only the local neighborhood of the abbreviation (default) and by incorporating global context information for each sample . We used bootstrapping to obtain the mean for each abbreviation by resampling our predicted values and true values 999 times.We consider two forms of accuracy. Micro accuracy is the total number of abbreviations correctly disambiguated divided by the total number of samples in the test set across all abbreviations with two or more possible expansions. Macro accuracy is the average of individual abbreviation accuracies and gives a better reflection on the performance of imbalanced datasets. A Wilcoxon signed-rank test was used to compare the macro accuracy results of different models (micro accuracy is a point estimate).a held-out test set consisting of RS samples of abbreviation expansions from MIMIC III (20% of the dataset),an orthogonal dataset of 65 abbreviations from CASI with gold-standard annotations,1116 abbreviations from i2b2 generated by finding sentences with expansions from AllAcronyms using RS, and24 abbreviations from i2b2 hand-labeled by medical students.We evaluated our model on four datasets (see “Methods” for further detail):Table p = 1.2e−03) increase in accuracy on CASI compared to the control. Incorporating global context increased this value to 15% (p = 5.3e−05) and hierarchical pretraining improved it by another 2% (p = 1.4e−05). This demonstrates that the global context in which related terms appeared and hierarchical information aided disambiguation.The p-values and performance differences between all models are displayed in Fig. 11(https://github.com/gpfinley/towards_comprehensive) and utilized it on the CASI dataset. We found that our baseline model (“Control”) has a 3% improvement .While the main goal of this paper is to evaluate the data augmentation and the use of ontological information for pre-training, which can be applied to any method, as an additional baseline we downloaded and installed the codebase of Finley et al.Figure p-values and performance differences of the various model iterations tested on the larger test set of 1116 abbreviations using another orthogonal dataset, i2b2, with labels generated using RS. of all the models. While the full model outperformed the control, the total performance gain was more modest (2%). Higher overall performance and smaller improvement indicate that i2b2 more closely resembles MIMIC III with respect to the frequency of different disambiguations. For example, in the case of “ivf”, there are significantly fewer instances of fully spelled out “in vitro fertilization” compared to “intravenous fluids” in both MIMIC III (zero versus 2503) and i2b2 (2 versus 49). At the same time, “in vitro fertilization” is the more common expansion in CASI (294 versus 181). This could indicate either a difference between the datasets, or human behavior: the RS method relies on the long form of an abbreviation to be written out fully, and this may be less likely with abbreviations that are either clearer in the context, or longer and hence rarely written out.Table Further breaking down our model’s performance on the i2b2 test set, we see that it performed better on abbreviations with little training data Fig. . Almost p-values between all models. Table As an orthogonal metric, we manually labeled a dataset of 270 abbreviations from the i2b2 corpus. Of the 270, only 24 had multiple expansions, illustrating the strong bias in which abbreviations are written out fully (see “Methods”). Figure 19. One advantage of using such embeddings is that a single word can have a different embedding depending on its context. Specifically, attention-based models such ClinicalBERT have shown dramatic improvements in clinical NLP tasks20. We fine-tuned the ClinicalBERT model for our abbreviation disambiguation task and compared it to our baseline CNN model (see “Experiments” for training specifications). We find that in our case, using embeddings from ClinicalBERT does not offer any improvement on our baseline. We believe that this is because the clinical notes in both the training and test sets lack structure typical of training corpa for BERT such as Wikipedia and that the average number of expansions per abbreviation is small enough that a single embedding per term is sufficient. The benefit of using a simpler model such as our baseline is that BERT models are memory intensive; because we train one model per abbreviation, this adds up quickly and becomes impractical.Impressive advances have been made in a variety of natural language processing (NLP) tasks by learning context-dependent embedding representationsTo evaluate whether our data augmentation technique (Relatives) is applicable to more complex models, we applied clinicalBERT to the CASI dataset and find that there is a significant gain in performance . We have also observed a significant performance increase for the i2b2 hand-labeled dataset . This demonstrates that our novel data augmentation technique can be useful regardless of the underlying model architecture.p < 0.05 using the standard bootstrap method), showing that there is value in disambiguating abbreviations in clinical notes before using them in downstream tasks, at least in this specific setting. See “Methods” for additional details on this experiment. This work will be more fully described in a separate paper.We tested the impact of expanding abbreviations in clinical notes on the prediction of required medical tests in an Emergency Department setting. We developed a model that extracts UMLS concepts in clinical notes from the Hospital for Sick Children  and predicts whether the patient received a specific clinical test . We found that without expanding abbreviations, the model achieved an accuracy of 78.09% on an independent test set. After training the model on clinical notes with expanded abbreviations, the performance increased to 78.51% . Overall, our approach overcomes the lack of training data and achieves additional improvements by considering the relationship of the terms in the ontology, introducing a pre-training step to help embed concepts. For all samples, we are also able to generate better representations by considering the global context in which an abbreviation appears. Because of these improvements, our overall framework demonstrates up to 17% higher accuracy of abbreviation disambiguation on auxiliary datasets.In this work, we demonstrate a general algorithm for disambiguating medical abbreviations that scales to previously unseen medical acronyms by utilizing biomedical ontologies as prior medical knowledge. Our approach is based on the ideas introduced by us in an extended conference abstract6, we train one model per abbreviation; the training cost of this is significantly more expensive using our data augmentation technique than the baseline model since we do 25 rounds of Bayesian optimization to search for the optimal temperature . To give a concrete example, training 65 abbreviations (the size of CASI dataset) on a single Tesla V-100 GPU takes ~25 h using our data augmentation technique but ~1 h for the baseline. Furthermore, given the variability among the various datasets used in this study, more work is required in creating a unified corpus for possible abbreviations and related medical terms. While AllAcronyms give an idea for what senses are possible, there exist senses in CASI that are not in AllAcronyms. Finally, to make the pipeline fully end-to-end, better abbreviation detection models should be developed. While some publically available models do exist, they are trained on a small fraction of all possible medical abbreviations5.One notable limitation of our approach is runtime. As was done in the previous work23 and word sense disambiguation in biomedical texts24. We believe that such approaches can be useful for addressing a wide variety of biomedical problems.Our approach has immediately led to better results for the abbreviation disambiguation problem and has further implications for the development of other ML-based methods. Utilizing examples of closely related concepts from an ontology has already shown improved results for named entity recognitionWe used clinical notes from MIMIC III as our training set. We collected sentences from MIMIC III containing abbreviation expansions, as well as concepts in UMLS to augment our training set. We also used MIMIC-III to pre-train word embeddings using FastText and IDF weights.We augmented our training sets based on relationships between expansions and concepts defined by UMLS Metathesaurus.We used the medical section of AllAcronyms, a crowd-sourced database, to obtain a list of 80,000 medical abbreviations and 200,000 potential expansions. We removed abbreviations with only one disambiguation and those that do not appear in UMLS, resulting in 30,974 abbreviations.We used the CASI dataset as an orthogonal test set to measure model generalizability. We removed expansions that are the same as the abbreviation  and abbreviations with one expansion since the disambiguation task is trivial in that case. This left us with 65 abbreviations. On average, each abbreviation has 4 expansions with 459 test sentences.As another test set, we used i2b2. This dataset does not have hand-labeled annotations, so we used RS to generate labels. There are 1116 abbreviations in i2b2 containing more than one expansion. On average, each abbreviation has 4 expansions with 97 test sentences.To ensure the in-distribution performance was not compromised with our augmentation techniques and demonstrate the level of overfitting, we tested our model on a small test set from MIMIC III. We test our model on the same 1116 abbreviations as in the i2b2 test set.We also generated a hand-labeled dataset to better reflect the frequency of abbreviations used in practice. Starting with 270 abbreviations whose expansions occurred with a similar frequency in i2b2, we sampled up to 50 sentences containing each abbreviation. We developed a website that presented this sentence and possible expansions . The temperature T is a “sharpening” function25. For each abbreviation, we searched for a temperature that minimizes the loss on the MIMIC III validation set using Bayesian optimization, constraining the temperature to be between 2−1 and 2. We found that smaller values overfit to MIMIC III, while larger ones added too much noise. For each abbreviation, we performed 25 iterations of Bayesian optimization using the Tree-structured Parzen Estimator algorithm and took the model with the lowest validation loss26.For each expansion for a given abbreviation, we augmented the training samples with the ten most related medical concepts. Figure 23. The network consists of one convolution layer with a filter size of one word, followed by ELU activation27. Max-overtime pooling was used to combine the output into a single vector, vt. W1 and b correspond to the weight matrices and bias vectors, respectively, which we learned through training.We mapped an input sentence to a vector representation using a simple encoder similar to that used by Arbabi et al.x to the final encoded sentence representation:g, we took the weighted average of the embedding vectors for each word in the document. The embeddings were weighted using IDF weights trained on the MIMIC III corpus. The vector g was calculated as follows:j is the index of the abbreviation, i is the index of the ith word in the document, and d is the number of words in the document. ith word.A fully connected layer with ReLU activation followed by L2 normalization was used to map g with the encoded sentence vector, v, and normalized it to produce the final encoded sample embeddingWe then concatenated Our model was trained to minimize the distance between a target expansion embedding and its context Fig. .Fig. 5OvH, where each row, Hc, corresponds to the embedding of an expansion for a given abbreviation. To do the classification task of assigning an expansion label, c, to an input sentence, e, we took the dot-product of H and e and apply a softmax function, such thatp(c|e).Our model represents expansion embeddings with an embedding matrix, 23) and has been shown to improve vector space representations for general language tasks28.Ontologies are structured medical terminologies that link related concepts together. Incorporating structure in this form can entangle embeddings of related concepts and generate more refined embedding clusters in the medical domainδ (we treated δ as a hyperparameter and found δ = 2.6 to work best). We linked these concepts using the lowest common ancestor in the UMLS hierarchy and trained a model to predict which concepts from UMLS best fit the context. This is similar to our abbreviation model; however, to take structural information into account, we first learned a matrix, c. Hc is then derived by taking the sum of c’s ancestors’ embeddings, c project c to a global location, while the raw embedding of c learns a local location. During training, we backpropagated through We linked related concepts together using a hierarchical medical ontology as a prior. The benefit of this is that concepts with insufficient training data are constrained to be close to their relatives. For an abbreviation, we took all expansions and closest medical concepts within a Euclidean distance of 20. In general, we find that ClinicalBERT performs significantly better than the original BERT model for this application. We finetune the ClinicalBERT model on our task by stacking two hidden layers joined by a nonlinear activation function which takes as input the embedding from the ClinicalBERT model and outputs the probability of each expansion. We experiment with the number of hidden layers and find that two work best. BERT-style architectures output an embedding for each token in the input, as well as a “start of sentence” token that serves to encode the entire sentence18. We experiment with using the “start of the sentence” token and the abbreviation token, and find that the “start of sentence” token performs better. We also tune other hyperparameters such as sentence length, batch size, and learning rate.We compare our embeddings to contextualized word embeddings generated from ClinicalBERT5. While this model is publically available, it was trained to detect only 500 medical abbreviations, which is a small proportion of all existing abbreviations and may have missed abbreviations in our notes. 156,801 sentences (89%) were found to contain at least one abbreviation using the CARD model. We removed all sentences that did not contain any abbreviations (11%), since we are comparing the performance of expanded vs. non-expanded sentences. We then expanded the detected abbreviations using our pipeline. We then extracted CUIs from both the original and expanded notes and encoded them using a one-hot representation. We trained a simple neural network consisting of two fully connected layers linked by a ReLU nonlinear activation function that took in the one-hot-encoded CUIs and predicted which of the tests the patient should have received. The model was evaluated based on whether the corresponding tests were actually ordered by the physicians.We acquired 176,140 clinical notes from the emergency department at the Hospital for Sick Children as well as the corresponding codes for tests that the patients received. The possible tests were: . First, we detected abbreviations in the clinical notes using the CARD abbreviation detection model developed in ref. 17. For the classification task, we built one model for each abbreviation. To train our model, we used a maximum of 1000 samples per expansion and found a context window of 3 words to work best. On average, each abbreviation had 3.46 expansions. We trained our models on 60% of the sample set, validated it on 20%, and kept 20% as a held-out test set. We trained all concept embedding models for 100 epochs with a learning rate of 0.01 and saved the epoch with the lowest validation loss. We ran the Bayesian Optimization acquisition function using 15 random seeds and used the one with median validation loss to get typical model performance. Only 1 random seed was used for the i2b2-RS dataset due to extremely long runtimes.We trained our model on sentences from MIMIC III. We collected sentences containing expansions from CASI and medical concepts from UMLS using RS. In total, 105,161 concepts in UMLS were found in MIMIC III. To learn word vectors, we trained a FastText model using a skip-gram architecture with an embedding size of 100To pre-train our model using structural relationships from UMLS, we first learned a 1-D CNN encoder similar to our abbreviation model that predicted what medical concept is present given its context. The embeddings for each concept were calculated by summing the raw embeddings of itself and its ancestors . We collected sentences for every concept in UMLS using the RS technique. To train our model, we used 1000 samples per concept and incorporated both a local context of 3 words and the global context. On average, the global context is 60 words per clinical note. We split the dataset into a training set (90%) and a validation set (10%). We used a learning rate of 0.002 and a batch size of 2048.We initialized the weights of the convolution and fully connected layers with corresponding weights from the hierarchy model. If an expansion for a given abbreviation has a concept code in UMLS, we also initialized the expansion embedding in the abbreviation model with the corresponding embedding from the hierarchy.Further information on research design is available in the Supplementary InformationSupplementary Dataset 1Description of Additional Supplementary FilesReporting SummaryPeer Review File


Animals were examined at 3, 5 and 7 days post-infection (DPI) for lung histopathology, viral load and production of proteins regulating the progression of SARS-CoV-2 infection. Results indicated that oral administration of PT150 caused a dose-dependent decrease in replication of SARS-CoV-2 in lung, as well as in expression of ACE2 and TMPRSS2. Lung hypercellularity and infiltration of macrophages and CD4+ T-cells were dramatically decreased in PT150-treated animals, as was tissue damage and expression of IL-6. Molecular docking studies suggest that PT150 binds to the co-activator interface of the ligand-binding domain of both AR and GR, thereby acting as an allosteric modulator and transcriptional repressor of these receptors. Phylogenetic analysis of AR and GR revealed a high degree of sequence identity maintained across multiple species, including humans, suggesting that the mechanism of action and therapeutic efficacy observed in Syrian hamsters would likely be predictive of positive outcomes in patients. PT150 is therefore a strong candidate for further clinical development for the treatment of COVID-19 across variants of SARS-CoV-2.Despite significant research efforts, treatment options for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remain limited. This is due in part to a lack of therapeutics that increase host defense to the virus. Replication of SARS-CoV-2 in lung tissue is associated with marked infiltration of macrophages and activation of innate immune inflammatory responses that amplify tissue injury. Antagonists of the androgen (AR) and glucocorticoid (GR) receptors have shown efficacy in models of COVID-19 and in clinical studies because the cell surface proteins required for viral entry, angiotensin converting enzyme 2 (ACE2) and the transmembrane protease, serine 2 (TMPRSS2), are transcriptionally regulated by these receptors. We postulated that the GR and AR modulator, PT150, would reduce infectivity of SARS-CoV-2 and prevent inflammatory lung injury in the Syrian golden hamster model of COVID-19 by down-regulating expression of critical genes regulated through these receptors. Animals were infected intranasally with 2.5 × 10 Following the emergence of a number of idiopathic cases of severe pneumonia in December 2019 in Wuhan, China, deep sequencing of lower respiratory samples from these patients revealed a novel beta-coronavirus that was identified as the causative agent of COVID-19  and contNidovirales; Family: Coronaviridae) are enveloped, non-segmented, positive-sense RNA viruses that contain very large genomes up to 33.5 kilobases (kb). The four genera of these viruses – Alphacoronavirus, Beta-coronavirus, Gamma-coronavirus, and Delta-coronavirus – share a highly conserved genome organization comprising a large replicase gene followed by structural and accessory genes. The organization of the SARS-CoV-2 coronavirus genome is arranged from the 5’-leader-UTR, replicase, S (spike), E (envelope), M (membrane), N (nucleocapsid) to the 3’ UTR poly (A) tail (Coronaviruses (Order: (A) tail . Notably(A) tail . The spi(A) tail . Viral i(A) tail . Proteol(A) tail .Targeting ACE2 and TMPRSS2 has therefore emerged as an important therapeutic strategy for the treatment of COVID-19 by preventing entry of SARS-CoV-2 into cells, thereby limiting viral replication. Both ACE2 and TMPRSS2 are highly expressed in bronchiolar epithelial cells and are transcriptionally regulated by the androgen receptor (AR) through 5’-flanking promoter elements . In anotcis-acting transcription factors  could also be problematic, due to excessive blockade of cortisol function. Allosteric modulators of both AR and GR that could dampen transcriptional activation through these receptors would be preferable as a means of downregulating expression of AR- and GR-regulated genes. Ligands that act as allosteric modulators of nuclear receptors tend to favor stabilization of transcriptional co-repressor proteins on chromatin, such as CoREST, HDAC2/3/4 and NCoR2, that prevent binding of co-activator proteins in response to activation of  factors , 17. Thu factors , 19 thatin vitro . Based o2. Virus titrations from animal tissues were performed by plaque assay as described previously  at 37°C with 5% COeviously . Plaquesad libitum prior to being moved to the BSL-3 containment facility for experimental infection. Hamsters were anesthetized by inhalation with isoflurane and then intranasally inoculated with 2.5 × 104 TCID50/ml equivalents of SARS-CoV-2 in sterile Dulbecco’s modified Eagles medium (DMEM). Hamsters not receiving SARS-CoV-2 were given a sham inoculation with the equivalent volume of DMEM vehicle. To assess activity of PT150  against SARS-CoV-2, experimental groups  were as follows: control (sham inoculation + miglyol vehicle), SARS-CoV-2 + miglyol, SARS-CoV-2 + 30 mg/Kg PT150, SARS-CoV-2 + 100 mg/Kg PT150. The experimental drug (PT150) was dissolved in 100% miglyol 812 and delivered by oral gavage at 8μL/g body weight under isoflurane anesthesia. Animals were weighed daily to deliver an accurate dose of drug and were monitored for clinical severity of disease through daily health checks according to an approved clinical scoring matrixs. The SARS-CoV-2 + vehicle group also received miglyol 812 by oral gavage. Animals were observed for clinical signs of disease at time of dosing each day . Groups of animals were euthanized at 3, 5 and 7 days post-infection (DPI). Eighteen hamsters  were euthanized at 3 and 5 DPI. On day 7 post-infection (7 DPI) the remaining 24 hamsters were euthanized . Animals were euthanized by decapitation under isoflurane anesthesia and tissue was collected for immunohistochemistry, viral isolation, RNA analysis and histopathology.All animal protocols were approved by the Institutional Animal Care and Use Committee at Colorado State University (IACUC Protocol No. 996). Hamsters were used in compliance with the PHS Policy and Guide for the Care and Use of Laboratory Animals and procedures were performed in accordance with National Institutes of Health guidelines. Male and female Syrian hamsters were divided equally and randomly assigned to treatment groups at 8 weeks of age . The animals were housed in the CSU animal facility and allowed access to standard pelleted feed and water via Integrated DNA Technologies : forward primer nCOV_N1, probe nCOV_N1 , and reverse primer nCOV_N1  (2 = 0.991) equation generated.The 2019-nCoV CDC qPCR Probe Assay, which targets regions within the nCoV nucleocapsid gene, was used and adapted here. In brief, RNA was isolated from hamster lung by homogenizing tissue in trizol reagent using 5mm stainless steel beads and Qiagen TissueLyser II homogenizer under BSL-3 conditions. RNA was then extracted from individual samples using previously described methods . RNA pur0569191) . Each qRen bloc and fixed whole in 10% neutral buffered formalin under BSL-3 containment for at least 72 hours before being transferred to CSU Veterinary Diagnostic Laboratory, BSL-2 necropsy area for tissue trimming and sectioning. Four transverse whole-lung sections were stained with hematoxylin and eosin (H&E). Tissue was sectioned at 5µm thickness and were mounted onto poly-ionic slides. Sections were then deparaffinized and immunostained using the Leica Bond RXm automated robotic staining system. Antigen retrieval was performed by using Bond Epitope Retrieval Solution 1 for 20 minutes in conjunction with base plate heat application. Sections were then permeabilized (0.1% Triton X in 1X TBS) and blocked with 1% donkey serum. Primary antibodies were diluted to their optimized dilutions in tris-buffered saline and incubated on the tissue for 1 hour/antibody: Rabbit SARS nucleocapsid protein , goat ionized calcium binding adaptor molecule 1 , goat angiotensin converting enzyme 2 , rabbit transmembrane serine protease 2 , mouse interleukin 6 . Sections were then stained for DAPI (ThermoFisher) and were mounted on glass coverslips using ProLong Gold Anti-Fade medium and stored at 4°C until imaging.Lungs from 60 hamsters were extirpated + cells were determined per 1 mm2 areas given the previously drawn ROI overall area.The studies described here were conducted by a single investigator. Images were captured using an automated stage Olympus BX63 fluorescent microscope equipped with a Hamamatsu ORCA-flash 4.0 LT CCD camera and collected using Olympus CellSens software. Quantification of protein was performed by acquiring five randomized images encompassing the pseudostratified columnar epithelium around bronchi at 400x magnification  all from different lung lobes. Regions of interest were then drawn to enclose the epithelial layer and exclude the lumen, in order to accurately obtain average intensity measurements. The Count and Measure function on Olympus CellSens software was then used to threshold the entirety of the ROI and measure the given channel signal. Quantification of invading inflammatory cells was performed by generating whole lung montages by compiling 100x images acquired using automated stage coordinate mapping with an Olympus 10X air objective (0.40 N.A.) All images were obtained and analyzed under the same conditions for magnification, exposure time, lamp intensity, camera gain, and filter application. ROIs were drawn around the lung sections and the co-localization function of Count and Measure within Olympus CellSens software was applied the sections. IBA12) was determined in hematoxylin and eosin (H&E) -stained histological sections by digital image analysis. A digital montage was compiled at 100X magnification using an Olympus X-Apochromat 10X air objective (N.A. 0.40) consisting of approximately 1,200 individual frames per lung lobe. Affected regions of interest (ROI) were subsequently automatically identified using Olympus CellSens software by quantifying whole-lung montages scanned from each hamster for total number of nuclei or nucleated cells (to exclude erythrocytes) stained with H&E, relative to the total area of the ROI for each lung.Quantification of the total affected pulmonary parenchyma as well as counting of inflammatory cells per area  , and thePhylogenetic analysis was performed by aligning protein coding sequences across multiple species within the protein of interest. FASTA files were downloaded from National Center for Biotechnology Institute’s (NCBI) gene databases and were then input into Molecular Evolutionary Genetic Analysis  software for alignment. Muscle alignment was performed, and the evolutionary history was inferred using Neighbor-Joining methodology  resultinn=6/group) were quantified independently in technical triplicate. The resultant Cp values were used to calculate relative gene expression by use of the Pfaffl method  was used as the internal standard reference gene. Primer pairs used in qRT-PCR are listed in l method . Relativl method  where hel method  was perfGenes of interest were entered into STRING protein-protein interaction database (V11.5)  and view+ T-cells as a percent of lung area, the overall intensity of IL-6 expression as a percent of lung area, and the overall percent of lung area occupied by IBA1+ macrophages. These responses were normalized to control values according to the following equation:To model the temporal sequence of each cellular response in SARS-CoV-2-infected hamsters, normalized pathological overlays were generated for each parameter encompassing all time points examined . Parameters modeled included SARS-CoV-2 viral load , the number of CD4. The conpost hoc test for multiple comparisons. Differences between two variables were identified using a two-way ANOVA following a Tukey post hoc multiple comparisons test. Significance is denoted as *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. All statistical analysis was conducted using Prism.All data is presented as mean +/- SEM, unless otherwise noted. Experimental values from each mean were analyzed with a ROUT (α=0.05) test for exclusion of significant outliers. Differences between each experimental group were analyzed using an unpaired t-test or a one-way ANOVA with Tukey’s 4 TCID50 SARS-CoV-2 (USA-WA1-2020 strain) by intranasal inoculation. Body weights were monitored daily for each animal, with a noted decline in average body weight in each experimental group that reached a maximum loss by day 5 with a total overall loss of eight-percent body weight =6.231) and time post-infection =16.39). Hamsters treated with PT150 at 30 and 100 mg/Kg/day did not show a statistically significant difference in body weight from control hamsters at day 5 post-infection, at which the maximal extent of body weight loss is observed in Syrian hamsters infected with SARS-CoV-2 and treated with vehicle only , metabolism, half-life, etc.  and examined by a veterinary pathologist blinded to the treatment groups. Representative lung sections from each experimental group are presented in Lungs were examined for the extent of immune cell infiltration at 3, 5 and 7 DPI by quantitative digital image analysis  and the glucocorticoid receptor (PDB: 3CLD), using the Glide module within Schrödinger  [9-11]. For the androgen receptor  between clustering pairs is visualized by purple boxes outlining groupings generated =14.71; Timepoint, p<0.0001, F=29.99), with the greatest differences evident at 3 DPI, where both PT150-treated groups were different from the SARS-CoV-2 + vehicle group, as well as from each other, indicating dose-dependent effects on reduction of IL-6 in the bronchiolar epithelial layer of infected hamsters  cells and activated macrophages, which is known to be a critical mediator of lung injury in COVID-19 patients . This cyfunction , 80. Thi+ T-cell proliferation and further recruitment of circulating macrophages that initiate a severe innate immune response leading to broncho-interstitial pneumonia and consolidation of the lung parenchyma. Expression of IL-6 is necessary for recruitment of immune cells to the site of infection, which leads to the ‘cytokine storm’ observed in patients that is associated with poorer outcomes , National Institute of Allergy and Infectious Disease R01 AI140442 (TS), and the National Science Foundation 2033260 (TS).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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


BackgroundNosocomial infections are a significant health concern. Following surgery, infections are most commonly associated with the surgical site, yet there are other potential sources for infections after surgical interventions. Identification of the source of infections can be very challenging.MethodologyCitrobacter freundii. The only connection between all cases was the anesthesiologist. An epidemiological inquiry could not definitively identify the source of the outbreak. Therefore, we utilized an RNA sequencing technique to evaluate the microbiome of the anesthesiologist and compared the results to bacteria cultured from the bloodstream of the two patients.An outbreak of postoperative infections following surgery led to intensive care unit (ICU) admission of patients immediately after the surgical procedure. The blood cultures of two patients were positive for ResultsCitrobacter freundii is an uncommon source of bloodstream infections, and in the normal human microbiome, the results establish the source of a cluster of infections to the anesthesiologist.The anesthesiologist’s microbiome contained amplicons that were identical to those of the bacteria in the patient’s bloodstream. Because ConclusionsIn cases of nosocomial infections, when conventional microbiological techniques do not clearly establish the source of the infection, using 16S RNA sequencing should be considered. Various sources cause outbreaks of infections following surgery. These include healthcare personnel, environmental sources, contaminated medications, or the patients themselves. Several studies have examined various operating room parameters, including staff attire , hand hyWhen several infectious episodes occur over a short period of time, an investigation into the source of the infections should be performed, and an epidemiological effort to identify the cause of the outbreak should be made. If a clear source is found, actions can be taken to prevent further risk. In some cases, identifying the source of the infections using conventional microbiological tools may not be sufficient, necessitating the use of more advanced molecular techniques .Citrobacter freundii, in his microbiome. This enabled us to establish the source of the outbreak and provide further training to the clinician to improve compliance with infection prevention guidelines.In this study, when conventional microbiological methods failed, we used 16S RNA sequencing to establish the source of infection during an outbreak following surgery. Using this technique, we found that the anesthesiologist who was involved in the care of all patients carried the organism causing the infection, Description of the episodeA patient who underwent dilatation and curettage (D&C) on January 16, 2020, for a missed abortion, developed a clinical picture of sepsis including fever (temperature >38°C) and hypotension . She was admitted to the intensive care unit (ICU) and treated with antibiotics for suspected sepsis due to the surgical procedure. A few hours later, another patient who had also undergone a D&C was admitted to the ICU due to sepsis. The following day, we learned that two more women who underwent the same procedure had signs of an infectious episode and were being treated at two other hospitals for a clinical picture suggestive of sepsis.On January 17th, a male patient who had a revision of total knee replacement surgery developed a septic clinical picture shortly after the surgery, was admitted to the ICU, and required intubation and mechanical ventilation due to acute respiratory distress syndrome. On the following day, two patients who had undergone colporrhaphy the day before complained of fever, weakness, and hypotension. They were both admitted to the ICU and diagnosed with sepsis based on their clinical presentation and laboratory results of white blood cell count, C-reactive protein level, and procalcitonin levels. Details of the patients are presented in Table An epidemiological investigation was initiated to determine a possible connecting link between the cases. Staff members were questioned regarding personal medical issues, hand hygiene, procedures involving equipment, and medications. There was no change from usual practice.The investigation revealed that the anesthesiologist in all cases was the same physician. No other clinician was involved in the care of all patients. When questioned, he denied any current illness or unusual exposure. He had anesthetized six women on the same day as the four patients with D&C before these cases. None of them had any complaints either immediately after the procedures or when questioned three days later by phone. The two operating rooms in which the cases occurred are located on different floors of the hospital. The sterile equipment used in the procedures was similar for the first four cases but different in the last three cases.As part of the clinical assessment of the patients, the Sequential Organ Failure Assessment (SOFA) score was calculated. The maximal score of the patients was correlated with the duration of surgery. Statistical analysis was performed with JMP software .Citrobacter freundii. Patient seven also had an aerobic Gram-negative bacillus: Acinetobacter baumannii, cultured from the same blood specimen. Because the anesthesiologist was the only identified common factor involved in the care of all patients, we decided to test the possibility of him being the source of the infection. Cultures of the anesthesiologist’s mouth, nares, and hands were taken three times in the days following the events and were negative for Gram-positive or Gram-negative pathogenic bacteria.Blood cultures were obtained from the five patients who were treated in our hospital. Patients five and seven had positive cultures. Both cultures grew the same anaerobic Gram-negative organism: Citrobacter freundii (two isolates from patients five and seven) and Acinetobacter baumannii from patient seven.We then obtained samples from different body sites to characterize his microbial composition using V4 16S rRNA amplicon metagenomic sequencing. Swabs were obtained from his nares, buccal area, hands, and feces (physician A), and from another anesthesiologist who served as control (physician B). We also sequenced using V4 16S rRNA amplicon metagenomic sequencing the three isolated bacteria that were grown and identified as 16S rRNA gene amplicon sequencing and analysesDNA extraction and polymerase chain reaction (PCR) amplification of the variable region 4 (V4) of the 16S rRNA gene using Illumina adapted universal primers 515F/806R was conducted using the direct PCR protocol [Extract-N-Amp Plant PCR Kit ], as previously described [Citrobacter freundii, and “TACAGAGGGTGCGAGCGTTAATCGGATTTACTGGGCGTAAAGCGTGCGTAGGCGGCTTTTTAAGTCGGATGTGAAATCCCCGAGCTTAACTTGGGAATTGCATTCGATACTGGGAAGCTAGAGTATGGGAGAGGATGGTAGAATTCCAGG” for Acinetobacter baumannii. A heatmap was generated using Calour version 2018.10.1 with default parameters [Reads were processed in a data curation pipeline implemented in QIIME 2 version 2019.4 . Reads wWe have uploaded the 16S files to NCBI SRA. The SRA accession is SPRJNA803025.Approval for publication of this epidemiological event was provided by the Institutional Review Board of Assuta Medical Centers .Clinical descriptionThe clinical information regarding the patients is presented in Table Citrobacter freundii were the same as the obtained 16S sequence in the NCBI blast.As shown in Figure Acinetobacter baumannii ASV was identified only in the culture but not in physician A or B samples or in any of the procedural blank samples.Moreover, this identical ASV was identified in physician A hands and fecal samples but not in physician B samples or in any of the negative procedural controls Figure . This clNosocomial infections are a common occurrence, and the source of infection is frequently ascribed to bacterial transfer from patient to patient, most commonly by staff. Other possible sources are the patient’s flora, and, in some cases, the possibility of a clinician being the source of the infectious organism has also been reported.The possible contribution of anesthetic practice to nosocomial infections has received less attention than other possible sources. Although perioperative nosocomial infections are a significant portion of hospital-acquired infections, these are most commonly due to surgical technique, staff involved directly with the surgical wound, patients themselves, or poorly sterilized equipment. There have been several reports of anesthetic medications, particularly propofol, as a source of infection, particularly if single patient vials of the medication are used in more than one patient -12. An eDuring routine anesthetic care, the most probable mechanism by which an anesthesiologist can cause infection in a patient is through contamination of the anesthesia workplace, particularly the anesthesia machine after airway management, and especially the areas which are frequently touched during anesthesia, namely, the adjustable pressure limit valve and the ventilatory settings controls ,17. The In many cases, it may not be feasible to comply with the guidelines because of the sheer number of hand hygiene opportunities in a fast-paced scenario such as an anesthetic induction . For exaAn expert guidance protocol for the prevention of contamination of anesthesia workstations was recently published . This guAn interesting observation in our series is the fact that the severity of the infection was correlated with the length of the procedure Figure , Table 2Citrobacter freundii is an uncommon pathogen of nosocomial infections, although it is frequently present in the environment. In hospitalized patients, Citrobacter freundii is the source of infection in only 0.7% of those with bacteremia [cteremia . It is aLimitationsA major limitation of our epidemiological inquiry and assessment of the events is the fact that we have no clear explanation for the mechanism by which this infection occurred. Although breaches in the aseptic technique were identified in the physician’s work, we could not explain the occurrence of a cluster of patients over two days, without any change in practice just before the outbreak. We could not identify any special event regarding the anesthesiologist’s health in the days before the outbreak. Despite the fact that we have no mechanistic explanation for the infection of all patients, there is no doubt that the source of the infecting organism was the anesthesiologist.The correlation between the severity of the infection and the duration of surgery, that is, the time when the anesthesiologist was caring for the patient invariably leads to the conclusion that the number of contacts between the anesthesiologist and the patient was a contributing factor to the development of sepsis. This indicates that the source of infection was through the intravenous line or through the airways. During anesthesia delivery, both airway management maneuvers and intravenous medication administration increase as the duration of the procedure increases. Therefore, we believe that the source of the infection was most likely intravenously, although we cannot rule out contact through the airways as a source.Alverdy et al. recently reported that the fact that breaches of aseptic technique occur regularly in the operating room, yet infections following surgery are not that common, suggesting that the source of infection should be sought elsewhere. They propose an approach of sequencing organisms that were cultured from infected wounds and comparing them to the patient’s microbiome, which may lead to identifying the source of the infection in many cases and perhaps direct us toward different approaches to deal with surgical site infections .The complexity of infection control during anesthesia delivery is due to the intensity of the clinical activities, as well as the unique setup of the anesthesia workplace. When an infection occurs following surgery, its source may not always be apparent even when an epidemiological investigation is conducted. Others have reported on the role of anesthesia personnel in the transmission of pathogenic organisms from one patient to another due to lapses in sterile techniques. Our report establishes the role of the clinician’s microbiome as the source of infections in patients. This source of infection will usually be missed when utilizing conventional microbiological techniques when performing an epidemiological inquiry.When there is an outbreak of infections following surgery, we propose that, in addition to exploring a patient as a source of infection, and performing standard cultures of the patients, environment, and the clinicians who were involved in patient care, an additional approach should be utilized. This involves the evaluation of the microbiome of the clinicians involved in the patient’s care. As in the outbreak described here, this may provide valuable information which can be significant in identifying the source of infections and decreasing nosocomial infections.


Westermanniadifficilis Dohrn, 1860 , currently included in Dohrnemesa Wygodzinsky, 1945, is transferred to the genus Polauchenia McAtee & Malloch, 1925 with the resulting new combination: Polaucheniadifficilis , comb. nov. An updated key to the species of Polauchenia is provided.Based on the examination of its lectotype (here designated),  Emesinae classified in four tribes in the Neotropics   Dohrn” and “tenerrima (Westermannia) Dohrn” as unplaced species in the genus Emesa. Because ([1821]) , KirkaldDohrnemesa, Westermanniadifficilis because the types were not examined again. However, he considered that, considering the generic synonyms of Westermannia and Westermannias with Emesa proposed by Dohrnemesa in the future.When describing Dohrnemesa close to Polauchenia McAtee & Malloch, 1925 and pointed out that the main differences between them were the absence of tubercles or spined humeri and the presence of a free short vein emitted from the base of the basal cell of the forewing in Dohrnemesa. In the key to genera of the Emesini presented by Dohrnemesa and Polauchenia, respectively, is the main character that separates these genera. Additionally, based on this characteristic alone, Polaucheniareimoseri Wygodzinsky, 1950 to Dohrnemesa, assuming that the possession of two veins emitted from the base of the basal cell indicated that this species belonged to the latter genus , comb. nov. The main differences are that in P.difficilis, comb. nov. the base of basal cell is pointed, emitting a single longitudinal vein, with the absence of a short free vein at this base and the humeri are spined. Both differences are sufficient to show that P.difficilis, comb. nov. does not belong to Dohrnemesa but to Polauchenia. Additionally, as commented below, several other diagnostic characteristics of Polauchenia are present in the specimen examined.In the current work, we confirm that the lectotype (here designated) of Westermanniadifficilis Dohrn, deposited in the Hemimetabola Collection of the Museum für Naturkunde Berlin, Leibniz Institute for Evolution and Biodiversity Science, Berlin, Germany (MFNB), was directly examined  of ned Figs , 4–14.W.difficilis Dohrn, 1860 were taken with a Canon EOS 6D  of  6D Fig. –12, 14 wFigure Reduviidae  separates the lines and a double slash (//) different labels.EmesinaeSubfamily Taxon classificationAnimaliaHemipteraReduviidaecomb. nov.E0BAD3D1-3D24-55C7-9A60-A76373FABAE8WestermanniadifficilisEmesa]; Dohrnemesa], 1949: 34 . Dohrn, 1860: 251 [description], 1863: 47–48 [redescription]; DohrnemesadifficilisW.difficilis figured by  ; Westermanniadifficilis, male lectotype (here designated): [handwritten label]: Leptol. / difficilis / Dohrn // [blue underlined handwritten label]: Columb; Moritz. // [printed label]: 3326 // [printed label]: [at right side]: QR CODE, [at left side]: http://coll.mfn-berlin.de/u/ /123b88 // [printed red label]: LECTOTYPE / Westermanniadifficilis Dohrn, 1860 / designated by H. R. Gil-Santana & / J. Deckert 2020 (MFNB).Leptolemusdifficilis Dohrn , collected in the Caribbean islands and Venezuela, but there is a disagreement among some authors if he collected in Colombia. It is possible that the records of his collecting from Colombia originated from the confusion between Venezuela and Colombia, parts of the former having once belonged to the ancient vice-kingdom of “Nueva Granada” . In thisPolaucheniadifficilis, comb. nov. can be separated from other species of the genus by the combination of characters presented in the key below. Polaucheniadifficilis, comb. nov. shares similarities with P.paraprotentor Gil-Santana & Ferreira, 2017 but differs from this species in several characteristics, such as: the pale markings of the antenna, middle and hind femora are simple (P.paraprotentor) or bordered by darker markings , those on antenna narrow, with the pale annuli as long or only slightly longer (P.paraprotentor) or quite longer (four and seven times) than the width of the segment ; fore coxa with a median pale annulus  or two pale annuli at submedian basal portion and approximately midportion of distal half of the segment (P.paraprotentor); distal portion of forewings with  or without (P.paraprotentor) a large whitish subdistal marking; petiole approximately 1.5 (P.paraprotentor) or 1.3  times as long as fore lobe; humeri spined  or not (P.paraprotentor); spine of scutellum obliquely directed upwards  or backwards (P.paraprotentor); spines of scutellum and metanotum mostly pale  or brownish (P.paraprotentor).Male. Measurements (mm): total length: to tip of abdomen 10.0; to tip of forewings 10.6. Coloration: brownish to light brown, with yellowish to pale markings or portions , allowing us to state 10.6 mm as the actual minimum of the genus. Moreover, it is noteworthy that W.difficilis. It is not possible to know how accurately A. Dohrn measured the specimen or if he rounded the measurement to an exact number. It is possible that the specimen was originally 11 mm in length when examined by him but due to the passing of time, the specimen may have shortened a little.The only small difference is the total length, which was recorded as being 10.6 mm for Dohrnemesa, the genus where it was currently included  are absent in this species. Therefore, it becomes clear that W.difficilis, providing an additional argument to disregard his placement of P.difficilis, comb. nov. in Dohrnemesa.On the other hand, some characteristics which Westermanniadifficilis was designated here as a lectotype following the Art. 74.1 of ICZN.The type specimen of Polauchenia and nine in Dohrnemesa , D.santosi Wygodzinsky, 1945)  , 2017.


Owing to the nature of health data, their sharing and reuse for research are limited by legal, technical, and ethical implications. In this sense, to address that challenge and facilitate and promote the discovery of scientific knowledge, the Findable, Accessible, Interoperable, and Reusable (FAIR) principles help organizations to share research data in a secure, appropriate, and useful way for other researchers.The objective of this study was the FAIRification of existing health research data sets and applying a federated machine learning architecture on top of the FAIRified data sets of different health research performing organizations. The entire FAIR4Health solution was validated through the assessment of a federated model for real-time prediction of 30-day readmission risk in patients with chronic obstructive pulmonary disease (COPD).The application of the FAIR principles on health research data sets in 3 different health care settings enabled a retrospective multicenter study for the development of specific federated machine learning models for the early prediction of 30-day readmission risk in patients with COPD. This predictive model was generated upon the FAIR4Health platform. Finally, an observational prospective study with 30 days follow-up was conducted in 2 health care centers from different countries. The same inclusion and exclusion criteria were used in both retrospective and prospective studies.Clinical validation was demonstrated through the implementation of federated machine learning models on top of the FAIRified data sets from different health research performing organizations. The federated model for predicting the 30-day hospital readmission risk was trained using retrospective data from 4.944 patients with COPD. The assessment of the predictive model was performed using the data of 100 recruited (22 from Spain and 78 from Serbia) out of 2070 observed (records viewed) patients during the observational prospective study, which was executed from April 2021 to September 2021. Significant accuracy (0.98) and precision (0.25) of the predictive model generated upon the FAIR4Health platform were observed. Therefore, the generated prediction of 30-day readmission risk was confirmed in 87% (87/100) of cases.Implementing a FAIR data policy in health research performing organizations to facilitate data sharing and reuse is relevant and needed, following the discovery, access, integration, and analysis of health research data. The FAIR4Health project proposes a technological solution in the health domain to facilitate alignment with the FAIR principles. FAIR4Health is a project that received funding from the European Union’s (EU) Horizon 2020 research and innovation program under grant 824666. This project started in December 2018 and ended in November 2021. The main objective of this European project was to promote and encourage the EU health research community to apply the Findable, Accessible, Interoperable, and Reusable (FAIR) principles [open data [FAIR data [Despite strong concerns and challenges regarding data sharing in health research ,3 and fopen data ,5 and FAAIR data ,7, it isAIR data ,9. GivenAIR data .The purpose of the FAIR4Health project  was to dThe aim of the FAIR data principles  is to enSince its formal release via the FORCE11 community , the FAIFAIR principles are being adopted by a diverse range of research disciplines, such as economics, semantic web, and environment. Several groups have assessed the uptake to date and the challenges encountered. FAIR4Health  and otheFAIR4Health adds to the analysis and experience of the application of FAIR principles in the health research field, specifically in health research data sets on COPD.COPD is a respiratory disease characterized by persistent symptoms and chronic limitation of airflow. This disease is known to be underdiagnosed even though it affects almost 10% of adults worldwide  and its Previous studies have shown that there are several risk factors associated with readmission in patients with COPD, such as significant deterioration of lung function, low oxygen saturation in pulse oximetry, decreased activity levels, comorbidities, and the absence of medication reconciliation during hospitalization . HospitaRegarding the comorbidities, it is noted that several studies agree that the greater the number of comorbidities, the greater the risk of readmission for patients with COPD ,29. The Therefore, COPD is a major health problem that must be addressed and analyzed . SeveralIn this paper, the clinical validation of the FAIR4Health solution is described, including the development and selection of the most appropriate model for predicting 30-day readmission risk in patients with COPD and the assessment of such a model. This study builds upon the FAIRification of health research data sets of different health research performing organizations and a federated machine learning architecture on top of the FAIRified data sets of different organizations. The entire FAIR4Health solution was validated in real-world settings with the clinical use case described in this paper.The use case that was designed in this study to validate the FAIR4Health solution was composed of two phases: (1) a retrospective multicenter observational study, including the training of the predictive models in the FAIR4Health platform, and (2) an observational prospective study with a 30-day follow-up.In the retrospective study, the population included patients aged >18 years diagnosed with COPD, considering that COPD-related comorbidities are observed at a younger age . PatientIn the first phase, which is to train the federated machine learning models, three different organizations participated with their health care  and health research data sets: (1) Universite De Geneve from Switzerland provided health care data from the electronic health record (EHR) of the University Hospitals of Geneva; (2) Virgen del Rocío University Hospital as part of the Andalusian Health Service  from Spain provided health care data from the EHR of the Virgen del Rocío University Hospital in Seville; and (3) Instituto Aragonés de Ciencias de la Salud and Instituto de Investigación Sanitaria Aragón from Spain provided a health research data set based on the EpiChron Cohort ,35, a stFor organizations contributing with health research data sets from previous research projects, the sample size was defined by taking into account the original size of the data sets in the previous research, whereas for organizations contributing with health care data sets from the EHRs, it was defined from the number of patients fulfilling the inclusion and exclusion criteria.The variables for the training and prediction processes were related to demographic, multimorbidity, comorbidities, polypharmacy, laboratory, and hospitalization data. The principal dependent variable was readmission, defined as unplanned hospitalization for any cause related to COPD within 30 days of hospital discharge.Following the clinical protocol defined in the study, an observational prospective study with a 30 days follow-up was carried out after the retrospective study to assess the impact of the early predictive model by collecting data from a cohort of recruited patients. Patients aged ≥18 years with a diagnosis of COPD who were admitted to the hospital for this disease  and who signed the informed consent form (ICF) were included in the observational prospective study, complying with the same inclusion and exclusion criteria as described for the retrospective study.Two health care organizations participated in the observational prospective study in which the trained predictive model was tested: (1) Internal Medicine Department of the Virgen del Rocío University Hospital in Seville as part of the Andalusian Health Service (SAS) from Spain and (2) Clinic for Obstructive Pulmonary Diseases and Acute Pneumopathies of the Institute for Pulmonary Diseases of Vojvodina (IPBV) from Serbia. In both cases, the sample size was defined by considering the number of patients admitted to the hospital during the prospective study period, thus fulfilling the inclusion and exclusion criteria.Regarding the study variables, the same variables were collected at the time of inclusion of each patient during the prospective study as in the retrospective study. As a monitoring variable, aiming to assess the prediction performance of the model on the patient’s risk of readmission, it was analyzed whether the patient with COPD had a readmission within the 30 days of discharge.Ethical approval for this study was obtained from all participating health research organizations based on regional regulations before involving them in the execution of the case studies .Technical and organizational measures were defined to safeguard the rights and freedoms of the data participants, including the data minimization principle. Informed consent procedures were defined, including informed consent and information sheets. A data protection officer was appointed at each data owner institution. To reinforce the appropriate coverage of these ethical aspects, at the beginning of the study, an external ethics advisory board was made up, which involved reviewing deliverables, generating reports, and performing presentations to support the FAIR4Health Consortium.Making health data FAIR opens up new horizons, especially for the secondary use of health care and reuse of health research data sets. The FAIR4Health project proposed a FAIRification workflow  to be usTo address the challenges of the health domain, the proposed workflow adapted the generic FAIRification process defined by GO FAIR . First, These steps were (1) raw data analysis, (2) data curation and validation, (3) data deidentification and anonymization, (4) semantic modeling, (5) making data linkable, (6) license attribution, (7) data versioning, (8) indexing, (9) metadata aggregation, and (10) publishing.Steps 2, 3, 7, and 8 were newly introduced in the FAIR4Health FAIRification workflow. The FAIRification workflow was based on the HL7 Fast Healthcare Interoperability Resources (FHIR) . Making Along with the onFHIR.io repositories, at each organization, a DCT and a DPT were installed, and these tools were used by the data managers and FAIR4Health researchers to FAIRify their existing data sets, collaborating to appropriately treat the databases. Following the FAIRification workflow, the raw data were first transformed into HL7 FHIR by creating the associated FHIR resources through the DCT. It was shown that the DCT is a valid software tool that meets the challenges of raw data analysis, curation, and validation steps . Once thThe FAIR4Health project implemented the PPDDM philosophy by designing and implementing a federated machine learning architecture. The ultimate aim of this architecture is to address the challenging security and privacy concerns of health data owners. The PPDDM architecture does not allow data to leave their servers. Partial machine learning models were trained on each FAIRified data set at each organization, and then these partial models were used to develop a boosted machine learning model on the central FAIR4Health platform. The platform provides a web-based graphical user interface to the researchers so that they can define their features, create distributed data sets, and then train federated models. The PPDDM architecture was composed of the agent implementation. Then, the agents were deployed at each data source organization on top of their FAIRified data sets. These agents communicated with their associated onFHIR.io repositories at each deployment site. A manager was deployed as a backend to the FAIR4Health platform graphical user interface so that these agents can be orchestrated to build distributed data sets and federated predictive models on top of those distributed data sets. During the retrospective study, the researchers of the data owner organizations used the platform to train federated machine learning models on the retrospective data sets that were previously made FAIR using the FAIRification tools. The PPDDM implementation provided a set of machine learning algorithms to the researchers to be executed in a federated manner. These algorithms were grouped as (1) support vector machine, (2) logistic regression, (3) decision trees, (4) random forest, and (5) gradient-boosted trees.During the retrospective study, a number of machine learning models were generated by using the prediction algorithms listed above as well as trying out various values for different parameters . More focus was given to the tree-based algorithms because the data in the agents were skewed in one direction, and tree-based methods produced better results than the others when the data were unbalanced. In addition, k-fold cross-validation was used to split the data into a set of nonoverlapping training and test sets to obtain more accurate results.In the experiments, better results were obtained with the predictive models generated using the random forest algorithm. An example screenshot of the platform is shown in 3-fold cross-validation with area under the curve of the receiver operating characteristic evaluation metricImputation strategy: median—replaces the missing values using the approximate median value of the featureMaximum depth of a tree: 5Minimum information gain: 0.0Impurity: giniNumber of trees: 50Feature subset strategy: auto-calculates the number of features at each tree node as the square root of the total number of features in the classification algorithm.After the parameters of the algorithm were selected, the predictive model was generated using retrospective data sets of 4.944 patients with COPD. Subsequently, an observational prospective study was conducted to validate and evaluate an early predictive model for 30-day readmission risk in patients with COPD.In total, 100 patients were recruited and included in the observational prospective study with a 30-day follow-up, from April 2021 to September 2021, including recruitment and follow-up. During that period, the study participants were recruited by performing weekly prevalence cuts in which all patients hospitalized because of COPD conditions were systematically evaluated, offering inclusion in this study to all those who met the inclusion criteria and did not meet any exclusion criteria.Clinicians and researchers performed functional and clinical validations of the FAIR4Health solution during the observational prospective study. As this was a multicenter observational study, the recruitment and inclusion of patients in the study were carried out as mentioned below.For SAS, the clinical team reviewed 711 hospitalized patients during the study period, and 53 (7.5%) of them fulfilled the inclusion criteria and did not meet any exclusion criteria. Finally, 22 patients with COPD signed the ICF and were included in this observational prospective study. Out of the total recruited patients in SAS, 18% (4/22) were female and 82% (18/22) were male.In the case of IPBV, out of 2070 hospitalized patients, 113 (5.46%) patients were hospitalized because of COPD exacerbation, and 83 (73.5%) patients met all inclusion criteria and did not meet any exclusion criteria and signed the ICF. A total of 78 patients were included in this observational prospective study.Of the total patients recruited during the study period, 47% (37/78) were female and 53% (41/78) were male.All data gathered from patients with COPD were entered into the FAIR4Health platform to obtain the prediction generated by the predictive model for 30-day readmission risk and to assess its performance.When the prediction was obtained using the FAIR4Health platform, a concordance analysis was performed to compare the real data with the predicted values. Concerning the reality of readmissions among the 100 patients recruited, in both cases, the patients were followed up during hospitalization, and the follow-up was performed during the following 30 days. Out of a total of 22 patients recruited from SAS, 3 (14%) were readmitted within 30 days of discharge . Out of a total of 78 patients recruited from IPBV, 10 (15%) were readmitted during the follow-up period. Finally, from the 100 recruited patients, (1) the accuracy of predictions generated by the FAIR4Health platform was confirmed in 87% (87/100) of the cases; that is, either the patient was readmitted to the hospital because of COPD in real life and the algorithm predicted that there was early 30-day hospital readmission risk or the patient was not readmitted and the algorithm predicted that there was no early 30-day hospital readmission risk and (2) the prediction generated was not confirmed in 13% (13/100) of the cases; that is, in real life, the patient was readmitted within 30 days and the platform predicted that there was no early 30-day hospital readmission risk or the patient was not readmitted and the platform predicted that there was early 30-day hospital readmission.The application of the FAIR principles in health research data sets of health research performing organizations from different countries allowed the federated data analysis to accelerate the discovery of scientific outputs. Therefore, the analysis of legal, technical, and ethical requirements of health research data were addressed during data FAIRification. Furthermore, a clinical decision support model for predicting 30-day readmission risk in patients with COPD at discharge based on the risk factors uncovered previously, using data mining approaches, was implemented, deployed, and validated. Finally, through a multicenter study in which the rate of readmission of patients with COPD within 30 days after hospital discharge was analyzed, clinical partners could reach use case objectives and obtain an early 30-day hospital readmission risk predictive model. Further details of the FAIR4Health pathfinder case studies can be found in the FAIR4Health public report on the demonstrators’ performance .It is important to highlight that the FAIR4Health solution was implemented following a practical extensibility capacity, so that other research questions can be covered using the solution without the need to perform adaptations. Furthermore, to improve the reusability capacity of the study, using both the open-source code and the generated metadata freely available in GitHub , the stuFirst, significant cross-cutting data-related challenges were addressed during data collection. Data extraction from EHRs and other types of health care sources aligning this extraction with a FAIR4Health common data model was not trivial and required a lot of conceptual and technical efforts because of (1) the complexity of the raw data , (2) free text used in some fields in the raw data sources, and (3) differences between the types of raw data sources. To address the complexity of the raw data, each health research organization from different countries that participated in the data extraction involved colleagues who were experts in each source data model. To handle the information in free text fields, natural language processing techniques were assessed. Finally, in some cases, manual natural language processing to extract structured information from unstructured information was performed. To manage the differences between the nature of the raw data sources, each raw data set was analyzed in depth in a collaborative effort between each clinical partner and the technical partners to reach the required configuration in the FAIR4Health solution, achieving the FAIRification of all raw data and finally achieving the PPDDM models’ generation using all sources.Second, concerning the predictive model generated in this study, it can be stated that it is possible to generate more efficient prediction parameters  if the distribution of the readmission variable in the data sets is better adjusted. The readmission variable, which was the dependent variable, was not balanced in the data sets of the retrospective studies (data sets used to generate the predictive model for this prospective study), which resulted in the generated results being good but not perfect as desired. For more effective models, in the future, a better adjustment of the distribution of the readmission variable using data sets with more patients will be addressed to boost the application of predictive models in clinical practice. Most studies of predictive models based on machine learning show poor methodological quality and are at a high risk of bias. The small study size, poor management of missing data, and failure to address overfitting are factors that contribute to the risk of bias .In contrast, it is crucial to add that this study was carried out while these 2 health care organizations were experiencing the consequences of the COVID-19 pandemic, and clinical researchers had to make significant efforts to properly conclude the prospective study:IPBV as a health care institution was included in the national COVID-19 system of health care institutions caring for COVID-19 positive patients with severe clinical difficulties. Owing to this reorganization of the Serbian health care system, the likelihood of hospitalization of patients with COPD has been reduced since March 2020. Many of the researchers responsible for patient recruitment in the prospective study were engaged in COVID-19 departments, and the remaining researchers were overworked during the study period.On the side of SAS, this health care institution was involved in the care of patients with suspicion of COVID-19 and COVID-19–positive patients with severe clinical difficulties. All health professionals in SAS had a higher workload in health care. In fact, different clinical researchers participating in this observational study were transferred during the project to the COVID-19 Emergency Hospital in Seville (Spain), relieving each other, with an essential health care priority and looking after patients who did not meet the inclusion criteria of this study and could not be recruited. The clinical researchers identified a low use of health care services (both urgencies and consultancies) by patients with COPD; presumably, the patients waited for more severe symptoms to go to the health care centers because of the fear of having contact with COVID-19–positive patients. In addition, hospitalizations of patients with COPD were restricted, similar to what has happened in other pathologies, to avoid patient flow through health care centers.Considering the final version of the FAIR4Health solution and the main outcomes of this study, some future advances can be taken into account:Both the FAIRification tools and the FAIR4Health platform were validated using the FAIR4Health common data model. The solution has been designed and developed by considering the extensive capacity of other data models, so it is appropriate to continue the validation and testing with other data models in future clinical validations.The whole FAIR4Health solution covers alignment with relevant standards: HL7 FHIR, International Classification of Diseases, SNOMED Clinical Terms, Logical Observation Identifiers Names and Codes, and the Anatomical Therapeutic Chemical classification system. Other standards such as other HL7 standards, epidemiological standards, and W3C standards could be considered to be integrated if viable.The FAIR4Health platform was validated using the following machine learning algorithms: frequent pattern growth, support vector machine, logistic regression, decision trees, random forest, and gradient-boosted trees. Deep learning algorithms such as neural networks can be considered in future studies to improve the capabilities of the FAIR4Health platform.From a scientific point of view, some researchers of the FAIR4Health Consortium contribute to the application of the FAIR principles in the health research field, being involved in international working groups part of the European Open Science Cloud, the European Federation for Medical Informatics, the Research Data Alliance, the GO FAIR initiative, and HL7 International.Despite the limitations mentioned above, the objective of this study was achieved: to validate the FAIR4Health solution through the assessment of a federated model that was generated by applying a federated machine learning architecture on top of the FAIRified data sets of different health research performing organizations for real-time prediction of 30-day readmission risk in patients with COPD.The clinical, technical, and functional validation of the FAIR4Health solution was achieved through (1) the application of FAIR principles through the FAIR4Health FAIRification tools in health research data sets of different health research performing organizations and FAIRifying data from 4.944 patients with COPD; (2) development and use of federated machine learning architecture on top of the FAIRified data sets; and (3) clinical, technical, and functional development and assessment of a federated model for predicting 30-day readmission risk in patients with COPD, with an accuracy of 0.98, a precision of 0.25, and a confirmed prediction in 87% (87/100) of the cases.In the retrospective study where 3 different organizations participated with their health care  and health research data sets, the federated model was generated with an accuracy of 98.6% and a precision of 25%. In the observational prospective study in which 2 health care organizations participated, 100 patients were recruited for the federated model to predict their readmission risk to the hospital within 30 days because of COPD. Therefore, the accuracy of predictions generated by the model, and hence the FAIR4Health platform, was confirmed in 87% (87/100) of the cases.Health research performing organizations are aware of the need to implement a FAIR data policy to facilitate data sharing and reuse following the discovery, access, integration, and analysis of health research data. One obvious example would be the COVID-19 pandemic, where international cooperation allowed the rapid sequencing and epidemiological studies to be carried out, thus demonstrating the need and importance of data sharing to accelerate health research ,47. For The FAIR4Health project proposes a technological solution in the health domain to facilitate the use of larger and more heterogeneous data sets, thus increasing the variability of the data and the size of the data sets. Therefore, an increase in the scope of the research will be obtained and a significant improvement in the ability to generate more accurate predictive models.


Secreted phospholipases of type A2 (sPLA2s) are proteins of 14–16 kDa present in mammals in different forms and at different body sites. They are involved in lipid transformation processes, and consequently in various immune, inflammatory, and metabolic processes. sPLA2s are also major components of snake venoms, endowed with various toxic and pharmacological properties. The activity of sPLA2s is not limited to the enzymatic one but, through interaction with different types of molecules, they exert other activities that are still little known and explored, both outside and inside the cells, as they can be endocytosed. The aim of this review is to analyze three features of sPLA2s, yet under-explored, knowledge of which could be crucial to understanding the activity of these proteins. The first feature is their disulphide bridge pattern, which has always been considered immutable and necessary for their stability, but which might instead be modulable. The second characteristic is their ability to undergo various post-translational modifications that would control their interaction with other molecules. The third feature is their ability to participate in active molecular condensates both on the surface and within the cell. Finally, the implications of these features in the design of anti-inflammatory drugs are discussed. A phospholipase A2 (PLA2) is an enzyme that hydrolyzes the second ester bond of a phospholipid releasing a fatty acid and a lysophospholipid. Eucaryotic PLA2s are classified in intra- and extra-cellular or secreted. Secretory PLA2s (sPLA2s) are translated with a N-terminal signal peptide that is removed in the endoplasmic reticulum, and then they are translocated through the Golgi and secretory vesicle to the extracellular space. They are abundant in numerous human body fluids, in pancreatic juice, tears, seminal fluid, and some of them are detectable also in blood serum ,2.sPLA2s are also present in prokaryotes, and procaryotic and eukaryotic sPLA2s share the same mechanism of action, in which a water molecule, activated by the histidine residue H48) of the active site, performs a nucleophilic attack on the sn2 carbonyl oxygen of the phospholipid . The rea8 of the The best-known sPLA2 functions are the digestion of lipids, in the digestive system, and the antibacterial defense in various body sites, e.g., in the intestines, in tears. However, these proteins are involved in various inflammatory and lipid metabolism processes and consequently in numerous diseases, from metabolic and cardiovascular, to neurodegenerative and neuromuscular diseases. Important toxic components of the snake venoms are sPLA2s of group I and II. They are endowed with several toxic properties, among which the most widespread are hemotoxicity, myotoxicity, and neurotoxicity . The homThe biological activity of PLA2s, although they are involved in numerous pathologies, has not yet been fully understood. Firstly, because their enzymatic activity, well characterized in vitro, is more difficult to know in vivo because it is influenced by many local parameters, such as the type of lipids present, their accessibility, the micro-local calcium concentration, and interactions with other proteins and carbohydrates. Secondly, because sPLA2s perform other activities besides their enzymatic function. Natural sPLA2 homologs of snake venom lacking catalytic activity, as the aspartic acid residue (D49) in the active site is replaced by lysine (K49) or other amino acids, possess equal or even higher toxicity than their catalytically active counterparts. This non-catalytic activity of sPLA2s is even less known because it depends on a complex network of interactions with many other types of molecules. Finally, the activity of sPLA2s is not only expressed outside the cell because, following interaction with various surface receptors and co-receptors, sPLA2s are internalized .In recent years, many reviews have been focused on these proteins, some addressing one group in particular, group IB , includiConventional sPLA2 share a pattern of disulphide, comprising five to eight bridges . The cenBothrops pirajai, piratoxin-I, as it is present in some but not all structures of the proteins. This suggests that it can be an allosteric bridge, i.e., a bridge that confers different activities to the protein depending on its state, open or closed  phosphorylation-regulated prolyl isomerase . These dIn conclusion, to investigate the biological action of sPLA2s, it is important to consider that, when they are imported into the cytosol, they may undergo PTMs that affect their molecular interactions and biological activity. The pathological action of human variants of sPLA2s, and of toxic sPLA2s present in animal venoms, could also be due to mutations of their PTM sites.The sPLA2s interact with different types of proteins, both extra- and intracellular . Basic pTwo-dimensional condensates can also be formed on the membrane of the cell or internal organelles . RecentlBecause of interaction with the surface receptors described above, sPLA2s are internalized and transported to the paranuclear, or in some cases, even nuclear zones ,66,74. TBothrops asper myotoxin-II has a high affinity for phosphatidic acid [The toxic action of snake sPLA2s is not yet fully understood. At the level of the plasma membrane, in the case of catalytically active phospholipases, one hypothesis is that the enzyme action may destabilize membranes, in the case of myotoxins, or induce the fusion of neurotransmitter vesicles with the synaptic terminal, in the case of neurotoxins ,83. In tdic acid  and is adic acid .To summarize, the multiple interactions established by sPLA2s with different types of molecules, lipids, carbohydrates, membrane receptors, co-receptors, and intracellular enzymes mean that they can participate in the formation of molecular condensates both on the cell surface and intracellularly. Understanding how these complexes are formed and broken down, as well as their function, will be crucial to understanding the activity of sPLA2s.Most, if not all, diseases in which sPLA2s are involved have an inflammatory component, and A2-type phospholipases, mainly sPLA2s, are considered an important target in the development of anti-inflammatory drugs, as they arbitrate the release of fatty acids that are then converted into inflammatory mediators. Today, inflammation is mainly treated with cyclooxygenase 1 and 2 inhibitors that act downstream of the action of sPLA2s, or with cortisone drugs that act instead upstream of sPLA2s production, as they regulate the expression of different inflammatory agents. Both types of treatments have important side effects, ranging from gastrointestinal and cardiovascular disorders in the case of cyclooxygenase inhibitors, to disorders of various kinds in the case of cortisone drugs, as this class of proteins regulates various physiological processes that are consequently altered .Inhibitors of sPLA2 enzymatic activity have not yielded the desired results in clinical trials . One posExamples of molecules that interfere with molecular interactions of sPLA2s already exist: an aptamer that binds to NCL, a disordered co-receptor protein of a snake venom sPLA2, preventing its internalization into the cell and protecting it from its toxic effects ; and penIn the future, we will see an increase in the development and creation of molecules acting on molecular condensates , and we To sum up, the development of new anti-inflammatory drugs targeting sPLA2s will have to consider the complexity of their action and regulation, as well as the multiple molecular interactions they can establish. To this end, new experimental models will be required to test the action of compounds that affect the formation and stability of the molecular complexes of which they take part.