Cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC) have shown poor effectiveness in treating peritoneal carcinomatosis (PC) of gastric origin with a high tumor burden , though there are scarce therapy alternatives that are able to improve survival. In experimental studies, chimeric antigen receptor-T (CAR-T) cell therapy has shown encouraging results in gastric cancer and is currently being evaluated in several clinical trials. Regarding PC, CAR-T cell therapy has also proven useful in experimental studies, especially when administered intraperitoneally, as this route improves cell distribution and lifespan. Although these results need to be supported by ongoing clinical trials, CAR-T cells are a promising new therapeutic approach to peritoneal metastases from gastric cancer. In this review, we summarize the current evidence of the use of CAR-T cells in gastric cancer and PC of gastric origin. Gastric cancer (GC) is the fifth most frequently diagnosed cancer and the third leading cause of cancer-related death. More than one million new cases are diagnosed every year worldwide . The worThe peritoneum is a common site of metastasis, and peritoneal metastases are associated with low rates of patient survival . FollowiConventional treatment with chemotherapy, radiotherapy, or surgery is the therapy of choice in most cancer patients . HoweverCurrently, advances in immunotherapy have enabled the clinical use of monoclonal antibodies. Human epidermal growth factor receptor 2 (HER2) is commonly used as a target in immunotherapy, as it is overexpressed in 10% to 20% of GC cases. Trastuzumab is a monoclonal antibody that targets this receptor and is mainly used in breast and gastric cancer, improving patient survival ,14.The use of chimeric antigen receptor-T (CAR-T) cells is a new type of immunotherapy developed over recent decades and consists of modifying patients’ own T-lymphocytes to attack a specific target. By means of genetic engineering using viral vectors, the CAR is introduced into the T cell, which enables the cell to recognize a selected tumor-associated antigen (TAA) in a major histocompatibility class-independent manner .Over the last 5 years, four types of CAR-T cells targeting CD19 have shown promising results in hematological malignancies and have since been approved by the FDA: axicabtagene ciloleucel , tisagenlecleucel , lisocabtagene maraleucel , and brexucabtagene autoleucel  ,17,18,19The development of CAR-T cells began in the late 1980s. Early investigations resulted in the first generation of cells which had a structure that consisted of a single chain variable fragment (ScFv) region and the CD3ζ intracellular domain. These cells were unsuccessful in clinical trials as they were able to activate but did not proliferate, which indicated low efficacy ,21,22,23However, tumors can escape from CAR-T cell activity due to their immunosuppressive microenvironment, which inhibits CAR-T function. As a consequence, the fourth generation of CAR-T cells, or TRUCKs , are currently being designed. These are capable of secreting proinflammatory cytokines that can block the inhibitory effect of the tumor microenvironment, thereby enhancing their antitumoral activity in an immunosuppressive microenvironment  23,24,2,224,25.Selecting an optimal target is essential to the success of CAR-T therapy. The right target must meet two requirements: Firstly, the target should be expressed in all tumor cells; if not, antigen-negative tumor cells can escape the action of CAR-T cells and result in tumor recurrence. Secondly, the target should be present only in tumor cells and be absent from healthy cells in order to avoid an on-target, off-tumor response in which CAR-T cells attack non-tumor cells .CD19 in hematologic malignancies  meets these criteria. CAR-T cells targeting CD19 have achieved a complete response in 70% to 90% of patients ,32,33,34However, solid tumors have some peculiarities that hematologic malignancies do not. Firstly, TAAs are more heterogeneous in solid tumors, and finding an antigen that fits with the target selection criteria is difficult . SecondlCytokine-release syndrome is the most common side effect in patients undergoing CAR-T cell therapy and is sometimes associated with fatal outcomes. Between 50% and 90% of patients who receive anti-CD19 CAR-T cell therapy could develop cytokine-release syndrome during the first week after infusion. This effect is related to treatment response and tumor burden. Cytokine-release syndrome results from immunologic over-activation caused by CAR-T cells, which receive an exaggerated signal owing to receptor stimulation. This produces cytokine release, activating myeloid cells (mainly monocytes and macrophages) that generate a systemic inflammatory response mediated by IL-6 and IL-1. The symptoms of cytokine-release syndrome vary widely. General malaise and nausea are the most frequent symptoms, though the first symptom to present is often fever. Nonetheless, the disease may progress clinically to acute respiratory distress, acute renal failure, disseminated intravascular coagulation, cardiomyopathy, or even arrhythmia [CAR-T cell-related encephalopathy or immune effector cell-associated neurotoxicity syndrome (ICANS): Between 30% and 90% of patients who receive anti-CD19 CAR-T cell therapy develop neurotoxicity manifesting as mild confusion or, in severe cases, cerebral edema [On-target, off-tumor response: Caused by the reaction of the immune system, this response triggers the activation of CAR-T cells against healthy tissues expressing the target antigen. This response is more common in solid tumors. The intensity and frequency of this response vary according to the target and route of administration used. To avoid it, a highly specific receptor for the tumor target must be selected, CAR-T cells must have high affinity and specificity for the receptor chosen, and the CAR-T cells must be administered at an appropriate dose .A potential solution for decreasing the severity of the adverse effect is the local administration of CAR-T cells, i.e intratumoral or intraperitoneal. The peritoneal route increases the local concentration of effector cells, which triggers a local immune response in the peritoneal cavity and minimizes the adverse systemic effects. This lower systemic toxicity is related to the binding of CAR-T cells and tumor cells on the peritoneal surface (extravascular tissue) which involves limited cytokine release into the bloodstream. Furthermore, we hypothesize that the systemic release of cytokines is hindered by the existence of a fibrous stroma with a collagen-rich extracellular matrix, characteristic of peritoneal metastases.The peritoneal surface is a frequent site of involvement in GC, conferring poor prognosis. Therapeutic approaches in patients with peritoneal metastases of gastric origin and high tumor burden have shown poor results .Multiple studies have explored the role of CAR-T in gastrointestinal cancers, obtaining promising results regarding their capacity to eliminate tumor cells and their metastases. However, most are preclinical studies in animal models. The choice of an experimental in vivo model of peritoneal carcinomatosis to evaluate the efficacy and potential side effects of CAR-T therapy is critical before initiating a phase I/II clinical trial. Xenograft, humanized, syngeneic or transgenic are available murine in vivo models for CAR-T cell therapy research.CAR-T cell therapies designed for a number of different targets in GC have proven useful in experimental studies and currently are being evaluated in registered clinical trials with advanced GC patients.The main trials related to CAR-T cell therapies in GC with available data are shown in Carcinoembryonic antigen (CEA) is a glycoprotein related to GC and other gastrointestinal tumors. In clinical practice it is used to estimate the severity of gastrointestinal cancer and to detect recurrence among patients in follow-up. This antigen is an attractive target for CAR-T cell therapy as it is not detected in healthy adult tissues and is only expressed on the luminal face of gastrointestinal and lung cells. However, tumor cells lose this polarity and express CEA on their entire surface. Since normal cells express CEA on the luminal side of the cell, they remain invisible to immune cells. This attribute enables CAR-T cells to distinguish normal cells from tumor cells . When adAnother phase I clinical trial studied the efficacy of second-generation anti-CEA CAR-T cells administered systemically in escalating doses to treat colorectal liver metastasis. The authors of the study demonstrated that these cells are well-tolerated, even at high dose levels, with no toxic effects .Although the clinical efficacy of first-generation carcinoembryonic antigen (CEACAM5)-specific CAR-T cells was limited due to respiratory toxicity and short persistence , new genHER2, or ERBB2, is an antigen present on the cell surface. When activated, this antigen promotes cell proliferation and inhibits apoptosis. Amplification or overexpression of HER2 has been associated with several cancers, including GC. Approximately 10% to 30% of GCs have HER2 amplification; however, this characteristic confers a poor prognosis in terms of tumor growth, lymph-node involvement, and metastases ,43. ThusA preclinical study in a murine model by Han et al. demonstrated that systemic HER2 CAR-T cell therapy was effective in treating HER2-positive GC and did not affect tissues expressing low levels of HER2, thus suggesting no on-target, off-tumor effect. Furthermore, the authors observed Tcell persistence suggestive of protection against recurrence .ICAM, another cell surface glycoprotein, is involved in cell––cell and cell–extracellular matrix adhesion. ICAM is overexpressed in 40% of GC patients and is related to a worse prognosis. ICAM is found more frequently in advanced stages, such as lymph-node metastases and systemic metastases . In miceCLDN18.2 is a stomach-specific isoform of claudin-18, and plays an important role in cell junctions. It is highly expressed in healthy gastric cells, but only within the differentiated epithelial cells of gastric mucosa, and is also expressed in gastric tumor cells, their metastases, and in pancreatic and in esophageal adenocarcinoma . Jiang et al. showed that CLDN18.2 CAR-T could produce a complete response in a xenograft murine model. Furthermore, despite the fact that healthy cells express CLDN18.2, no obvious toxicities were found . Note thA phase I pilot study used CAR-T anti-CLDN18.2 in seven patients with gastric cancer and five with pancreatic cancer. One gastric patient achieved a complete response, and there were two partial responses. No severe adverse effects were observed, indicating that this treatment is safe and well-tolerated .Epithelial cell adhesion molecule (EpCAM) is a transmembrane glycoprotein expressed on epithelial cells and is involved in cell–cell adhesion as well as signaling, migration, proliferation, and cell differentiation. EpCAM is overexpressed in more than 90% of GCs and is associated with a poor prognosis. Clinical trials with EpCAM as a target for CAR-T cell therapy in advanced gastric cancer are currently underway .Meanwhile, other targets for CAR-T therapy in gastric cancer studied in clinical trials include mesothelin, MUC1, CD276, CD44v6 (transmembrane glycoproteins), and ROR2 (tyrosine-protein kinase transmembrane receptor) .The peritoneal surface is frequently involved in GC, conferring a poor prognosis, and peritoneal disease will develop in up to 50% of patients with advanced GC . CurrentThe discovery and development of new biomarkers, such as exosomes produced by tumor cells which are detectable in ascitic fluid or circulating blood, can help us to choose the appropriate therapeutic strategy for each patient. New nanotechnologies, such as templated plasmonic exosomes (TPEX), make it possible to detect exosomes in patients’ body fluids . MeasureThese patients would be suitable candidates for new target therapies such as CAR-T cells. As mentioned previously, multiple studies have explored the role of CAR-T in GC, obtaining promising results in terms of their capacity to eliminate tumor cells and their metastases. Currently, there is an urgent need for new therapeutic approaches to treat gastric PC, and CAR-T cells could be a potential treatment.Treatment of PC using CAR-T was first described by Katz et al., who used CAR-T cells targeting CEA to treat colorectal PC in an animal model. The authors observed that intraperitoneal delivery was superior to systemic administration. Intraperitoneal infusion was associated with higher tumor reduction and a more durable effect compared to systemic administration, thus suggesting a protection against recurrence and against other distant metastases .Another group designed a second-generation CAR-T cell strategy targeting TAG72 in a murine model with ovarian PC. Intraperitoneal administration was superior to intravenous administration, showing increased antitumor activity and better overall survival, which was further enhanced with repeated infusions .Ang et al. created a murine model of PC and developed CAR-T cells against EpCAM by means of mRNA transfection. This type of transfection resulted in temporary action, which offers a higher safety in case of adverse effects. Due to the transient effect, repeated infusions are needed for an optimal effect .Therefore, these results suggest that local administration allows better CAR-T cell trafficking and infiltration, greater antitumor activity, and added protection against recurrence and extraperitoneal antitumor effect while decreasing systemic adverse effects. However, there are still challenges inherent to solid tumors and PC that must be overcome. The most important hurdle is the tumor microenvironment, which confers a physical barrier and an immunosuppressive microenvironment. One of the physical barrier compounds is the tumor stroma, which contain high levels of collagen in the extracellular matrix. This stroma prevents systemic and local treatments from reaching the tumor cells. However, this collagen can be destroyed using collagenase, therefore facilitating drug penetration .Furthermore, intraperitoneal administration of CAR-T cells has shown antitumor activity at distant sites such as subcutaneous nodules. This is not a result of the direct action of CAR-T cells; rather, it is the consequence of an effect similar to the abscopal effect that occurs in radiotherapy . This meThough there are several studies investigating the usefulness of CAR-T in PC, studies focused on peritoneal carcinomatosis from GC are scarce.Third-generation CAR-T cells targeting mesothelin as treatment for GC and PC in a murine model showed that intravenous administration could produce tumor regression and even elimination, and reports have shown persistence of CAR-T cells in peripheral blood after 2 weeks. This resulted in prolonged survival in mice treated with CAR-T compared with no CAR-T. The researchers also observed that peritumoral injection in subcutaneous implants significantly reduced tumor growth compared with intravenous administration . LikewisThe CAR-T cell therapy approach in PC originating from GC is being evaluated in two phase I clinical trials . The tarCAR-T cell therapies have been widely investigated and have achieved extraordinary results in hematological malignancies. However, the application of this approach in solid tumors has fallen short of expectations, and its efficacy is still unclear. Solid tumors contain certain hurdles not present in hematological malignancies, which explains the lack of efficacy of CAR-T cell therapy in these tumors. The main obstacle is related to the tumor microenvironment, which provides the tumor with a physical barrier and plays an immunosuppressive role. Many studies are evaluating new types of CAR-T cells and optimizing them for greater efficacy, persistence, and migration. In addition to modifying CAR-T cells to improve their action, these cells are also being combined with other therapies such as monoclonal antibodies, chemotherapy, collagenase, or surgery, which may improve their results.The use of CAR-T cells in GC and PC appears to be safe and feasible. The results of preclinical studies with these cells are promising and several targets may be used. Additionally, since patients with GC undergo gastrectomy, specific antigens present in healthy gastric cells can be used as targets without an on-target, off-tumor response, thereby expanding the number of potential targets.Intraperitoneal delivery of CAR-T cells is the best route of administration as it promotes migration to, and infiltration of, the tumor cells, as well as averting the systemic toxicity of the microenvironment. Despite the existence of ongoing clinical trials needed to confirm the results of preclinical studies, regional CAR-T cell therapy for the treatment of PC provides promising evidence for its use and a new hope in the search for a treatment for this lethal disease. The possible selection of patients with gastric peritoneal carcinomatosis for CAR-T cell therapy is shown in a flowchart .


Next-Generation-Sequencing (NGS) enables detection of microorganisms present in biological and other matrices of various origin and nature, allowing not only the identification of known phyla and strains but also the discovery of novel ones. The large amount of metagenomic shotgun data produced by NGS require comprehensive and user-friendly pipelines for data analysis, that speed up the bioinformatics steps, relieving the users from the need to manually perform complex and time-consuming tasks.We describe here HOME-BIO , an exhaustive pipeline for metagenomics data analysis, comprising three independent analytical modules designed for an inclusive analysis of large NGS datasets.HOME-BIO is a powerful and easy-to-use tool that can be run also by users with limited computational expertise. It allows in-depth analyses by removing low-complexity/ problematic reads, integrating the analytical steps that lead to a comprehensive taxonomy profile of each sample by querying different source databases, and it is customizable according to specific users’ needs. Metagenomics is an interdisciplinary research field combining molecular genetics, microbial ecology, and data processing . NowadayHere we describe HOME-BIO , a dockerized solution for the analysis of WMG shotgun datasets that aims at addressing and solving the above mentioned limitations and problems. HOME-BIO is a modular and flexible pipeline that allows taxonomic profiling by allowing the operator to choose between two analytical approaches commonly used in WMG shotgun analysis. The first, defined Metagenomic shotgun module, performs taxonomic characterization and abundance estimation based on exact k-mer matches, to achieve high accuracy and fast classification speeds. The Assembly de novo module, instead, provides the assembly of sequences in contigs in order to perform an unbiased analysis of entire genomes sequenced in the sample. Furthermore, the performance of species classification in metagenomics applications can be improved using long reads, assembled from short reads . The twogreedy, with an e-value cutoff of 0.001. All the entities classified with Kaiju are then processed with Krona [’ module analysis, HOME-BIO uses SPAdes [-meta). It takes in input unmapped reads resulting from the ‘Quality Control’ module, generating sequence contigs that are classified with Kraken2, and protein-validated with Kaiju as described before. As mentioned above, HOME-BIO takes advantage of several reference databases. This allows users to investigate in depth the content of their biological samples. We linked NCBI taxonomy information from complete RefSeq bacterial, viral, and protozoal genomes/proteins. If it ran in end-to-end mode, the pipeline provides comprehensive profiling of specimens by querying the above-mentioned databases. This aspect makes HOME-BIO a powerful tool in the hands of users dealing with metagenomic data.HOME-BIO is implemented using both tools mainly used in metagenomic analyses and custom python scripts to produce tables and charts for immediate and easier interpretation of the results. The central core of this pipeline is its modularity, being composed of three main blocks: ‘Quality Control’, ‘Metagenomic Shotgun’ and ‘Assembly de novo’ modules Fig. . They cath Krona , to obtas SPAdes  in metaghttps://anaconda.com). This allows that all the required tools are always compatible with each other, automatically updated, and handled by Anaconda itself. All the parameters are set using a configuration file that is given as input file when the pipeline is launched.To make its installation easier, HOME-BIO is inserted in a Docker  image, wHOME-BIO processes NGS data to characterize the composition of host-associated microbial communities. The pipeline is implemented to be used by biologists with limited computational experience who intend to explore the pathogens’ content in their samples., representing 26.17% and 41.63% of the total reads in Control and Case, respectively. Subsequently, for each sample, the reads were analyzed with the Shotgun Metagenomic module, in order to obtain the taxonomy profile. HOME-BIO queried bacterial, viral, and protozoal databases. The Quality Control and Metagenomic results are summarized in Table To test the performance of HOME-BIO, we considered the public dataset of Mitra et al. , comprisRunning the Assembly de novo module, 567.37957 contigs for Cases samples and 238.7888 contigs for Control samples were obtained. The results are summarized in the Additional file For a further test with a different species, HOME-BIO was used to analyze a small dataset derived in our lab by sequencing four samples from canine heart biopsies, two consisting of formalin-fixed, paraffin-embedded (FFPE) samples and two of snap-frozen biopsies (raw data available on Array Express with Accession n.er E-MTAB-9189 and E-MTAB-9191). Results confirmed the good performance of the tool in all these datasets, leading as expected to the identification of several microorganisms independent from the nature and pre-treatment of the tissue samples. The results from all these samples are included in Additional file Here, we describe HOME-BIO, a user-friendly pipeline based on a dockerized solution, designed for analyzing shotgun metagenomic data avoiding time-consuming and error-prone installation and configuration steps. This modular pipeline provides a quality control step and the two main analysis approaches commonly used in metagenomic studies. By querying bacterial, viral, protozoal, and protein databases, HOME-BIO generates also an exhaustive taxonomic profiling of the biological entities in specimens.Project name: HOME-BIOhttps://github.com/carlferr/HOME-BIOProject home page: Operating system(s): AnyProgramming language: PythonOther requirements: DockerLicense: GNU GPLAny restrictions to use by non-academics: noneAdditional file 1: Table S1. Characteristics of Shotgun Pipelines.Additional file 2: Table S1–S24. HOME-BIO reports for bacteria identified with the metagenomic module in the twelve samples analyzed.Additional file 3: Table S1–S24. HOME-BIO reports for viruses identified with the metagenomic module in the twelve samples analyzed.Additional file 4: Table S1–S24. HOME-BIO reports for protozoa identified with the metagenomic module in the twelve samples analyzed.Additional file 5: Table S1–S24. HOME-BIO reports for bacteria identified with the Assembly de novo module in the twelve samples analyzed.Additional file 6: Table S1–S24. HOME-BIO reports for viruses identified with the Assembly de novo module in the twelve samples analyzed.Additional file 7: Table S1–S24. HOME-BIO reports for protozoa identified with the Assembly de novo module in the twelve samples analyzed.Additional file 8: Table S1–S2. HOME-BIO reports for bacteria, viruses, and protozoa identified Can_Fam samples.Additional file 9. Tutorial and use examples of HOME-BIO.


Background: Remodeling due to myocardial infarction (MI) significantly increases patient arrhythmic risk. Simulations using patient-specific models have shown promise in predicting personalized risk for arrhythmia. However, these are computationally- and time- intensive, hindering translation to clinical practice. Classical machine learning (ML) algorithms  as well as neural network techniques, shown to increase prediction accuracy, can be used to predict occurrence of arrhythmia as predicted by simulations based solely on infarct and ventricular geometry. We present an initial combined image-based patient-specific in silico and machine learning methodology to assess risk for dangerous arrhythmia in post-infarct patients. Furthermore, we aim to demonstrate that simulation-supported data augmentation improves prediction models, combining patient data, computational simulation, and advanced statistical modeling, improving overall accuracy for arrhythmia risk assessment.Methods: MRI-based computational models were constructed from 30 patients 5 days post-MI (the “baseline” population). In order to assess the utility biophysical model-supported data augmentation for improving arrhythmia prediction, we augmented the virtual baseline patient population. Each patient ventricular and ischemic geometry in the baseline population was used to create a subfamily of geometric models, resulting in an expanded set of patient models (the “augmented” population). Arrhythmia induction was attempted via programmed stimulation at 17 sites for each virtual patient corresponding to AHA LV segments and simulation outcome, “arrhythmia,” or “no-arrhythmia,” were used as ground truth for subsequent statistical prediction  models. For each patient geometric model, we measured and used choice data features: the myocardial volume and ischemic volume, as well as the segment-specific myocardial volume and ischemia percentage, as input to ML algorithms. For classical ML techniques (ML), we trained k-nearest neighbors, support vector machine, logistic regression, xgboost, and decision tree models to predict the simulation outcome from these geometric features alone. To explore neural network ML techniques, we trained both a three - and a four-hidden layer multilayer perceptron feed forward neural networks (NN), again predicting simulation outcomes from these geometric features alone. ML and NN models were trained on 70% of randomly selected segments and the remaining 30% was used for validation for both baseline and augmented populations.Results: Stimulation in the baseline population (30 patient models) resulted in reentry in 21.8% of sites tested; in the augmented population  reentry occurred in 13.0% of sites tested. ML and NN models ranged in mean accuracy from 0.83 to 0.86 for the baseline population, improving to 0.88 to 0.89 in all cases.Conclusion: Machine learning techniques, combined with patient-specific, image-based computational simulations, can provide key clinical insights with high accuracy rapidly and efficiently. In the case of sparse or missing patient data, simulation-supported data augmentation can be employed to further improve predictive results for patient benefit. This work paves the way for using data-driven simulations for prediction of dangerous arrhythmia in MI patients. Ventricular arrhythmia, resulting from abnormal impulse propagation in the heart, is a leading cause of death in the industrialized world , has emerged as remarkably successful in tackling a wide variety of challenges in healthcare over the last decade, including in cardiology Topol, . In contIndeed, machine learning has been used extensively in cardiovascular medicine, not least in the automatic interpretation and classification of ECG signals  are available. The expanded, augmented population from biophysically-detailed computational cardiology simulations can then be used to train AI models for a downstream task .in silico and machine learning methodology to assess risk for dangerous arrhythmia in post-infarct patients. Furthermore, we aim to demonstrate that simulation-supported data augmentation improves prediction models, combining patient data, computational simulation, and advanced statistical modeling, improving overall accuracy for arrhythmia risk assessment. We present a semi-automated image-based patient-specific modeling and simulation pipeline and well as data-augmentation and machine learning techniques, and show that a combined approach can provide key clinical insights with high accuracy rapidly and efficiently. In the case of sparse or missing patient data, simulation-supported data augmentation can be employed to further improve predictive results for patient benefit. This work paves the way for using data-driven simulations for prediction of dangerous arrhythmia in MI patients.In this study, we present an initial combined image-based patient-specific Several prior studies have developed pipelines generating personalized heart models, . Described in Engblom et al. , resulting in a total of 129 ventricular models (the augmented population).The ten Tusscher model represented healthy ventricular cell membrane electrophysiology  as based on simple virtual patient model-derived features. In each patient model (baseline plus augmented), global ischemia volume and global ventricular volume was measured, and for each of the 17 AHA LV segments the ischemic percentage and tissue volume was measured : K-nearest neighbors (knn), Gaussian support vector machine (SVM), logistic regression, decision tree (tree), xgboost, and 3- and 4-hidden layer multilayer perceptrons .For all ML models, a chosen data set was shuffled randomly and split into train and test sets. The train and test sets were further individually standardized prior to model training and post-run model performance evaluation via the test set: the population mean was first subtracted then divided by standard deviation. Each model was trained on 70% of randomly selected segments and the remaining 30% of data (test set) was used for validation. This procedure was repeated for 100 runs of each ML model on each data set (both baseline and baseline + augmented).k-nearest neighbors was implemented using sklearn's KNeighborsClassifier (k was set to 5). Support vector machine (SVM) was implemented using sklearn's SVC (C was set to 2). Logistic regression was implemented using sklearn's LogisticRegression. Tree was implemented using sklearn's DecisionTreeClassifier (max depth was set to 3). Xgboost was implemented using xgboost's XGBClassifier. 3-hl NN and 4-hl NN were implemented using Keras sequential (an API built on tensorflow). 3-hl NN used 32, 17 and 8 nodes, respectively, in each layer. Activation functions were the rectifier linear unit (ReLU) on hidden layers and the normalized exponential function (softmax) on output. Batch normalization was applied between each layer. The learning rate schedule was exponential decay with an initial learning rate of 0.01, with decay steps set to 100,000 and the decay rate to 0.9. We employed the gradient-based optimization methods RMSProp with zero momentum during training, as well as categorical crossentropy as the loss function. Twenty-five epochs, batch size = 20. 4-hl NN employed the same implementation as 3-hl NN, but with an additional layer of 8 nodes at its end.Other than specified, default parameter values from sklearn, xgboost, and Keras sequential were used.recall), defined as Accuracy, equal to the number of correct predictions divided by the number of all predictions for the test data, was computed for all ML models for both baseline and augmented data sets. Precision was also calculated at a threshold of 0.5 for all ML models for both baseline and augmented data sets, to determine the proportion of positive arrhythmia identifications that were actually correct, defined as Receiver operating characteristic (ROC) curves were calculated for all models for both baseline and augmented population results Melo, . The AreP-values were calculated using a t-test to test whether per-model prediction accuracy improved when including the augmented patient population's simulated arrhythmia outcomes, as well as an F-test to test whether the per-model variance was smaller when including augmented population results.In the baseline patient model population, 17 segments in 30 patients were evaluated for global arrhythmic vulnerability. Of these 510 segments, arrhythmia appeared in 111 segments during the protocol . In the augmented patient model population, 17 segments in 129 total patient models were evaluated. Of these 2,193 segments, global arrhythmia appeared in 285 segments  during our protocol. A summary of the results are given in The full set of results of the virtual vulnerability protocol for all patient models as specified in Materials and Methods can be found in Segment-specific myocardial volume and segment-specific ischemic percentage as well as total myocardial volume and total estimated ischemic volume were calculated for each patient model in the baseline and augmented populations as detailed in Methods and Materials. Statistics on these features as well as associated arrhythmia outcomes can be found in p-values shown in Accuracy of all ML models trained and tested on data from both the baseline and augmented populations is shown in SVM, which decreased slightly. Sensitivity and precision of all ML models trained and tested on data from both the baseline and augmented populations at a threshold of 0.5 is additionally shown in Average precision (AP) of all ML models trained and tested on data from both the baseline and augmented populations is shown in in silico and machine learning methodology to assess risk for dangerous arrhythmia in post-infarct patients. We have aimed to briefly demonstrate that simulation-supported data augmentation can improve prediction models and overall accuracy for arrhythmia risk assessment. Briefly, we used a semi-automated image-based patient-specific modeling and simulation pipeline to create both baseline and augmented patient populations, and assessed vulnerability to reentry in both populations via a virtual programmed stimulation protocol. We then calculated specific geometric features in all patient models, and trained seven machine learning algorithms  to predict arrhythmia outcome directly from these geometric patient model features alone.Here, we have presented a combined p-values shown in We found that this combined approach can provide insight with high accuracy, rapidly and efficiently, with accuracy ranging from 83 to 86% for all ML models for the baseline population. Furthermore, all ML models improved in accuracy to 88–89%  include shape modeling approaches , European, and/or other funding source. ProCardio Norwegian Center for Research-Based Innovation, Research Council of Norway, NO. MI-RISK Novo Nordisk Interdisciplinary Synergy Grant, Novo Nordisk Fond, DK.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.


Bilateral internal thoracic arteries (BITA) are uncommonly used in the every-day practice due to safety concerns and technical challenges with Y-grafts. We hypothesized that in-situ BITA use during coronary artery by-pass grafting (CABG) for two vessel disease is equally safe to standard strategy with left internal thoracic artery-left anterior descending artery revascularization and venous graft to other target vessels.A propensity score matched analysis was used to compare elective on-pump CABG patients who received in-situ BITA (BITA-group), versus left internal thoracic artery graft to the left anterior descending artery plus vein (SITA-group). Primary end points were 30-days all-cause-mortality, major adverse cardiac events and incidents and deep sternal wound infections.A total of 50 matched pairs (c-statistics 0.769) were selected from patients operated on between January 2015 and April 2020 using BITA (n = 50) and SITA (n = 2170). There were no inter-group differences in demographics and basic clinical characteristics. The total operation time was longer in the BITA-group . The rate of complete revascularization was similar, as was median aortic cross-clamp time, median extracorporeal circulation time, rate of re-explorations for bleeding, deep sternal wound infections or length of stay. One patient died in BITA group, 3 days after surgery, from a non-cardiac cause. After 36 months, the survival rate was 98% for BITA-group and 96% for controls .In-situ use of BITA during coronary revascularization for two-vessel disease is as safe and effective, as use of single ITA and vein graft. In-situ strategy abolishes allows to avoid the technically demanding composite graft configuration. Coronary artery by-pass grafting (CABG) remains the standard of care for the treatment of multivessel coronary artery disease. Despite multiple possible technical modifications, the most frequently used strategy involves implantation of the left internal thoracic artery (LITA) to the left anterior descending artery (LAD) and placing saphenous vein grafts to other target vessels. Because of LITA’s excellent patency rates, growing enthusiasm was observed towards the use of both internal thoracic arteries. Current US and European guidelines encourage the use of multiple arterial grafts in patients with a longer life expectancy , 2.The use of bilateral internal thoracic arteries (BITA) for CABG has been quite low in the USA, however, overall BITA utilization is estimated at 3–10% . There iRetrocaval and transverse sinus routing of the right internal thoracic artery (RITA) may result in the inability to control bleeding from retroaortic RITA branches. Potential difficulties may result from accidental clip removal due to compression of the RITA by aorta, compromised graft patency because of undetected kinks, graft overstretching and/or rotation, which can also facilitate anastomotic site bleeding. On the other hand, the free RITA to the in situ LITA (Y-graft) configuration is more technically demanding  and doesUse of BITA in situ may provide the benefits of multiple arterial grafting, while enabling to avoid the complexity of sequential anastomoses. We hypothesized that in two vessel disease, the perioperative and mid-term follow-up results of BITA in-situ revascularization are similar to those of standard CABG, where the LIMA graft is placed to the LAD, and combined with a venous graft to other target vessel.In a retrospective matched case-control study using the propensity score, we compared outcomes of CABG in elective patients with 2-vessel coronary artery disease managed surgically at the Department of Cardiovascular Surgery and Transplantology, The John Paul II Hospital, Krakow, Poland. The studied individuals were operated on between January 2015 and April 2020. Patients who received both internal thoracic arteries in an in-situ configuration were compared with CABG subjects, who received a LIMA to LAD graft and an additional venous graft. A study flow chart is shown in All patients were sternotomized in a standardized fashion and underwent cardiopulmonary by-pass (CPB) at mild-to-moderate hypothermia  or normothermia, according to surgeon’s preference, using a non-pulsatile roller pump and a 40 μm arterial blood filter  with blood flow at 2.0–2.4 L/min/m2 and mean arterial pressure at 40–60 mmHg. Blood-based cardioplegia was used in all cases. The pleural spaces were routinely opened and drained during ITA harvesting, which was done in a ‘pedicled’ fashion in all cases for all ITAs. In the BITA group, RITA was always used to graft the LAD, while LITA was used to graft the best target vessel on the lateral heart wall . In the SITA group, LITA was always used to graft the LAD, while SVG was used to graft the best target vessel on the lateral heart wall. All ITAs were implanted as in situ grafts. Sternum was sutured using a steel wire. Standard perioperative antibiotic prophylaxis was used in all patients, according to the institutional protocol. All analysed procedures were performed by two experienced attending surgeons, who perform >100 pump cases yearly. Complete revascularization was defined as grafting of all coronary arteries preoperatively assessed by the Heart Team as amenable to surgical revascularization. Intraoperatively, choosing the best target vessel  was at surgeon’s discretion. Throughout the perioperative period, all patients received aspirin orally (75 mg). Whenever any indication to receive dual antiplatelet therapy was present , clopidogrel was introduced as the second antiplatelet drug. Postoperative anticoagulation was started when necessary, based on current ESC guidelines.The statistical analysis was performed with IBM Corp. Released 2019. IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY: IBM Corp. Normal distribution was tested using the Kolmogorov-Smirnov test and all continuous variables were presented as medians and interquartile ranges (IQR). Mann U Whitney and Chi square tests were used to evaluate baseline differences.Propensity scores were calculated using a multivariable logistic regression, where the use of BITA was considered the dependent variable (i.e. BITA vs. LITA+vein). Following variables were included in the statistical model: age, gender, BMI, diabetes, left main stenosis, Euroscore II. Patients were matched one-to-one without replacement using the nearest neighbour method from the pool of patients operated by two experienced surgeons. The c-statistics for the final model equalled 0.769.Paired statistics, including Wilcoxon signed-rank and McNemar tests were used to calculate the differences in outcomes. Sensitivity analysis with inverse probability treatment weighting (IPTW) and general estimating equations was performed for selected end-points. A two-sided p-value <0.05 was considered statistically significant.From January 2015 to April 2020, 2220 patients underwent elective on-pump CABG for two vessel disease. Fifty patients received on-pump in-situ BITA grafting (BITA group). All patients who received BITA were enrolled in the study. Controls were selected from those patients, who received LITA-LAD graft and an additional venous graft (SITA group). Patients who received composite arterial grafts, and those who received LITA-LAD graft and an additional radial artery graft were not included in the analyzed database.A total of 50 propensity score-matched pairs were analyzed. There were no baseline between-group differences with regard to demographic and clinical parameters. Risk factors for sternal instability, such as diabetes mellitus, elevated BMI or chronic obstructive lung disease did not differ between the groups . In all The surgical characteristics are presented in One patient died in BITA group 3 days after surgery .The use of BITA in situ also did not increase the risk of perioperative complications, as compared to LIMA-LAD with vein graft. We did not observe any statistical difference in deaths or major adverse cardiac or vascular events (MACCE) between groups. Our perioperative results are comparable to those published by Raja et al.  and the high-use centers  was similar to single arterial bypass graft group  [Low perioperative mortality was reported by Schwann et al.  based on = 0.32) .The necessity of re-exploration for bleeding was another important argument against the widespread use of BITA. In our study, the need for re-exploration for bleeding was similar in both groups, and compares well with incidences of 2% to 6% mentioned in the literature , 17. Har2, diabetes mellitus, and BITA grafting are, among others, listed as the predictors of DSWI in the literature [Deep sternal wound infection (DSWI) is a major complication of cardiac surgery and significantly affects the postoperative results and quality of life. Female gender, BMI ≥30 kg/mterature , 19. In One of the arguments against using BITA in situ is potential risk of RITA damage during re-do sternotomy. However, it should be noted that in the current era of percutaneous interventions, the rate of redo CABG is low, and it becomes exceedingly rare in the setting of a patent ITA to LAD graft. In addition, BITA are expected to significantly enhance freedom from repeat revascularization because of their excellent patency rate . PercutaFollow-up results are another important element in assessing the results of using both internal thoracic arteries in-situ. Raja proved that RITA to LAD did not increase the risk for late death, the need for repeat revascularization, and the composite of death or repeat revascularization . SimilarThe importance of institutional and surgeon experience, and careful patient selection for BITA bypass grafting should be highlighted. Only experienced BITA surgeons (> 50 BITA cases) participated in the Arterial Revascularization Trial , which sIn conclusion, in situ RITA to LAD is a valid and reproducible revascularization strategy for two vessel coronary artery disease. This study confirms uniformly excellent perioperative CABG outcomes with low rates of mortality and DSWI for BITA in-situ.S1 File(DOCX)Click here for additional data file.S2 File(DOCX)Click here for additional data file.S3 File(DOCX)Click here for additional data file.S1 Data set(XLSX)Click here for additional data file.


Although ultrasound-based risk stratification systems (RSSs) including Thyroid Imaging, Reporting and Data Systems (TIRADSs) may play an important role in triaging nodules with nondiagnostic/unsatisfactory cytology, no previous studies have compared ultrasound-based RSSs for these nodules. In this retrospective, longitudinal, real-world study in Korea including 1143 thyroid aspirations with nondiagnostic/unsatisfactory results from 1125 patients, further diagnostic evaluations, including repeat fine-needle aspiration, were conducted more commonly as the categories of ultrasound-based RSSs increased. The American Thyroid Association (ATA) guidelines, Korean (K)-TIRADS, and American College of Radiology (ACR) TIRADS were more competent in predicting malignancy from nondiagnostic/unsatisfactory nodules. The EU-TIRADS, although it was also helpful, demonstrated less effective diagnostic performance in predicting malignancy for nondiagnostic/unsatisfactory nodules in Korea, where iodine intake is more than adequate. These findings have implications for developing and verifying universal guidelines for the ultrasound-based stratification of thyroid nodules and applying these guidelines to nondiagnostic/unsatisfactory nodules.We compared American Thyroid Association (ATA) guidelines, Korean (K)-Thyroid Imaging, Reporting and Data Systems (TIRADS), EU-TIRADS, and American College of Radiology (ACR) TIRADS in diagnosing malignancy for thyroid nodules with nondiagnostic/unsatisfactory cytology. Among 1143 nondiagnostic/unsatisfactory aspirations from April 2011 to March 2016, malignancy was detected in 39 of 89 excised nodules. The minimum malignancy rate was 7.82% in EU-TIRADS 5 and 1.87–3.00% in EU-TIRADS 3–4. In the other systems, the minimum malignancy rate was 14.29–16.19% in category 5 and ≤3% in the remaining categories. Although the EU-TIRADS category ≥ 5 exhibited the highest positive likelihood ratio (LR) of only 2.214, category ≥ 5 in the other systems yielded the highest positive LR of >5. Receiver operating characteristic (ROC) curves of all systems to predict malignancy were located statistically above the diagonal nondiscrimination line . The areas under the ROC curve (AUCs) were not significantly different among the four systems. The ATA guidelines, K-TIRADS, and ACR TIRADS may be useful to guide management for nondiagnostic/unsatisfactory nodules. The EU-TIRADS, although also useful, exhibited inferior performance in predicting malignancy for nondiagnostic/unsatisfactory nodules in Korea, an iodine-sufficient area. For individuals with thyroid nodules, ultrasound (US) is a primary diagnostic modality to evaluate the risk of malignancy (ROM) and to inform decisions regarding the application of fine-needle aspiration (FNA) . For theFNA represents the standard tool to triage patients with thyroid nodules for excision or clinical observation ,14. ThyrThis study was conducted at Samsung Medical Center, a hospital-based tertiary referral center in Korea. This study was approved by the Institutional Review Board (IRB) of Samsung Medical Center (SMC 2020-12-069). The IRB waived the requirement for informed consent because all data were deidentified. TBSRTC was adopted by our pathology department in April 2011 ,18. FromMethods of US examinations and specimen preparation have been described in our previous studies ,18.We retrospectively reviewed all US images obtained on the same day as aspirations and analyzed the sonographic features of the nodules while blinded to the final diagnosis. These features were interpreted following the 2015 revised ATA , 2016 KTParticipants had follow-ups until March 2020, and whether surgical resection was performed was confirmed. The final diagnosis of malignancy was based on the histopathological findings from surgical resection. We calculated the malignancy rate in two ways (maximum and minimum) ,18,22. TFor the final diagnosis of benign nodules, the satisfaction of ≥one of the followings was required : (1) benp < 0.05 for two-tailed tests.The baseline characteristics of the patients and nodules were analyzed with respect to all cases and cases treated with surgical resection. Continuous variables are shown as the median and interquartile range, while categorical variables are expressed as the frequency and percentage. The maximum and minimum malignancy/benign rates were calculated according to the categories of US-based RSSs. After including surgically-resected cases only, the positive and negative likelihood ratios (LRs) for category cutoffs of US-based RSSs in diagnosing malignancy were calculated according to the established method ,26. SincDuring the five-year period, 1143 ND/UNS aspiration samples from 1125 patients were included . The medThe malignancy rates are presented according to the categories of US-based RSSs . Of the The benign rates are summarized according to the categories of US-based RSSs . Among tp for the ROC curve of the EU-TIRADS = 0.0022, all others p = 0.0001). The differences in the AUC of US-based RSSs are summarized in The ROC curves of all US-based RSSs in predicting malignancy were located statistically above the diagonal nondiscrimination line . This level is lower than the suggested overall ROM for ND/UNS specimens in the 2017 TBSRTC (5–10%)  but withIn our study, cytological or histopathological follow-ups for ND/UNS nodules were more common in cases with higher categories of US-based RSSs. This suggests that although US-based RSSs were helpful in predicting benign diagnosis, further evaluations to confirm a benign or malignant status were increased in advanced categories of US-based RSSs. Because of this phenomenon, the benign rates defined through the cytological or histopathological follow-up results did not concordantly match the categories of US-based RSSs. The proportion of benign cases proven through cytological or histopathological follow-up was only 10.06% (minimum benign rate). However, only 18.02% of the ND/UNS nodules in our study were followed-up cytologically or histopathologically. In the remaining 81.98%, repeat FNA, CNB, and/or surgical resection were not applied, and most of them are considered to be managed conservatively due to a lack of worrisome clinical/sonographic features. Although TBSRTC guidelines recommend repeat FNA with US guidance for ND/UNS nodules , in realThe maximum benign rate of category 5 was less than half of that in category 4 of the ATA nodule sonographic pattern system, K-TIRADS, and ACR TIRADS. This phenomenon was not demonstrated when the EU-TIRADS was applied for ND/UNS nodules. This suggests the excellence of the highest category of the ATA nodule sonographic pattern system, K-TIRADS, and ACR TIRADS in predicting thyroid malignancy from ND/UNS nodules.Category 5 in the ATA nodule sonographic pattern system, K-TIRADS, and ACR TIRADS demonstrated a positive LR of >5. Stated in another way, malignant nodules were >5 times more likely to be in category 5 of these three US-based RSSs than were nonmalignant nodules. Therefore, without priority, these three systems may be useful diagnostic tests to guide the further management of ND/UNS nodules. However, category 5 in the EU-TIRADS exhibited a positive LR of only 2.214, demonstrating inferior performance compared with the other systems. The criteria to be classified as category 5 are the least demanding for the EU-TIRADS compared to the other systems. It is sufficient for a nodule to be classified as category 5 of EU-TIRADS if it has one of the following four features: irregular shape , irregular (spiculated/microlobulated) margin, microcalcifications, and marked hypoechogenicity (and solid) . AlthougSeveral limitations of this study should be acknowledged. First, analyses were performed per nodule, not per patient, which may have led to an overestimation of our results. However, when the same analyses were repeated after excluding cases where ≥two nodules were included from one patient, the findings were not changed. Second, we analyzed the positive and negative LRs and ROC curves of US-based RSSs for predicting thyroid malignancy only in surgically-resected ND/UNS nodules since the gold standard diagnosis based on surgical pathology is required to evaluate the performance of diagnostic tests. Therefore, this information is applicable to cases with high clinical suspicion rendered to surgery, not all ND/UNS nodules, most of which are managed conservatively. Third, our study is based on samples from Korea, an iodine-sufficient area. Therefore, caution should be applied before extrapolating our findings to countries with different dietary iodine contents and/or thyroid tumor epidemiology. Fourth, US-based RSSs that include new US parameters such as elastography or 4-dimensional (4D) vascularity  were notIn conclusion, this real-world comparative study demonstrated the following important findings on the diagnostic performance and clinical applications of US-based RSSs for thyroid nodules with ND/UNS cytology. For ND/UNS nodules, although US-based RSSs aided in predicting benign lesions, in real-world practice, further diagnostic evaluations to confirm the benign status, including repeat FNA, were increased in higher categories of US-based RSSs. The ATA nodule sonographic pattern system, K-TIRADS, and ACR TIRADS, especially the highest category of these systems, were more competent in predicting malignancy from ND/UNS nodules. These systems may be useful diagnostic tools to guide the further management of ND/UNS nodules. The EU-TIRADS, although it was also helpful, exhibited relatively less effective diagnostic performance in predicting malignancy for nodules with ND/UNS cytology in Korea, where iodine intake is more than adequate. These findings have implications for developing and verifying the U-TIRADS and applying the U-TIRADS to ND/UNS nodules.


This study aimed to assess the diagnostic performance of deep convolutional neural networks (DCNNs) in classifying breast microcalcification in screening mammograms. To this end, 1579 mammographic images were collected retrospectively from patients exhibiting suspicious microcalcification in screening mammograms between July 2007 and December 2019. Five pre-trained DCNN models and an ensemble model were used to classify the microcalcifications as either malignant or benign. Approximately one million images from the ImageNet database had been used to train the five DCNN models. Herein, 1121 mammographic images were used for individual model fine-tuning, 198 for validation, and 260 for testing. Gradient-weighted class activation mapping (Grad-CAM) was used to confirm the validity of the DCNN models in highlighting the microcalcification regions most critical for determining the final class. The ensemble model yielded the best AUC (0.856). The DenseNet-201 model achieved the best sensitivity (82.47%) and negative predictive value . The ResNet-101 model yielded the best accuracy (81.54%), specificity (91.41%), and positive predictive value . The high PPV and specificity achieved by the ResNet-101 model, in particular, demonstrated the model effectiveness in microcalcification diagnosis, which, in turn, may considerably help reduce unnecessary biopsies. The breast imaging-reporting and data system (BI-RADS) for mammography interpretation has been used extensively to standardize the classification of mammographic findings3. However, substantial inter-reader variability exists, and the positive predictive value (PPV) for mammographic microcalcification usually does not exceed 30%6. Therefore, a biopsy is frequently performed after a mammography, even though most microcalcifications tend to be benign. These unnecessary biopsies cost time and money, and may cause discomfort for some patients. Thus, the improvement of radiographic-diagnosis accuracy to increase the PPV of screening mammography is essential.Mammography is an established method for the early diagnosis of breast cancer. Microcalcifications are vital markers for early breast cancer diagnosis via mammography; they facilitate nonpalpable breast cancer diagnosis of up to 55% of lesions8. In particular, recent studies have reported that applying DCNNs for the detection and classification of microcalcifications and masses in mammograms increases classification accuracy12. Considering the small size and unobtrusive characteristics of microcalcifications in mammograms, we hypothesize that DCNNs can provide additional indicators for appropriate classification and diagnostic accuracies exceeding those achieved by expert radiologists. However, training a deep learning model requires a large amount of labeled data, which is scarce in clinical practice. Moreover, training a DCNN from scratch requires heavy computational power, large memory resources, and considerable time; furthermore, the use of limited data may lead to overfitting.Deep learning methods have facilitated dramatic advances in the fields of computer vision and image analysis. The availability of large datasets and sizable computing power has boosted improvements in the diagnostic performance of deep convolutional neural networks (DCNNs) in several medical fields13. Furthermore, the DCNN model performance can be validated using the gradient-weighted class activation mapping (Grad-CAM) method, which calculates the weighted sum of the feature map in each convolutional layer14.The transfer learning method helps overcome these challenges—a model pre-trained on the ImageNet dataset can be fine-tuned for application in other tasksTherefore, in this study, we fine-tuned the weights of five pre-trained deep learning models instead of training DCNN models from scratch and used these models to assess the diagnostic performance of DCNNs for classifying suspicious breast microcalcifications in screening mammograms. Grad-CAM was used to confirm the validity of the DCNN models.p values listed in Table p values that show the overall difference between each DCNN model and the ensemble model. As the overall p values indicated a significant difference, pairwise comparisons were performed to calculate the p value for each pair of models. The pairwise diagnostic performance comparisons of the DCNN models at the 1e−4 and 1e−5 learning rates are presented in the Of all 1579 microcalcifications observed, 589 (37.3%) were histologically proven as malignant lesions. Table The Grad-CAM method generated heatmaps, which were activated by the model to provide evidence concerning the malignancy or benignity of microcalcification regions. Figures 6; however, the ResNet-101 PPV obtained herein (81.82%) was considerably higher. This, along with the high specificity (91.41%) of ResNet-101, can be essential for reducing the need for the unnecessary biopsies burdening patients.In this study, we investigated the diagnostic performance of DCNNs for patients with suspicious microcalcification in screening mammograms. The ensemble model AUC was 0.856. The specificity and PPV of ResNet-101 were 91.41% and 81.82%, respectively. The results of this study suggest that DCNNs can help radiologists determine the malignancy or benignity of microcalcifications. Earlier studies reported the existence of substantial inter-reader variability, and the PPV for mammographic microcalcifications in these studies usually did not exceed 30%15. In this study, we utilized five state-of-the-art DCNN models: ResNet-101, Xception, Inception-v3, Inception-ResNet-v2, and DenseNet-20120. Each model was pre-trained on ImageNet21, and the resultant model weights were fine-tuned, instead of training new models from scratch. Through transfer learning, the microcalcification classification performance of the fine-tuned DCNNs could be assessed to improve the differential diagnosis of breast cancer and reduce false positive diagnoses.Transfer learning is commonly used in deep learning applications. It has been very effective in the medical domain, wherein the amount of data is generally limitedThe interpretability of the DCNN models is essential in the medical imaging field. The DCNN model results were visualized for interpretation using the Grad-CAM technique. The superimposed heatmaps generated via Grad-CAM yielded additional information useful in clinical practice use-cases. These Grad-CAM results reflected the microcalcification regions that most affected DCNN model predictions.Although the applicability of DCNN models to classify suspicious microcalcification was demonstrated herein, the limitations of this study should be considered. First, a relatively small population was studied. Although fivefold data augmentation of the training data was performed and the pre-trained networks were adapted to address this limitation, the potential risk in the training may remain. Second, the data analyzed were localized to a single hospital center. Additional investigations involving larger and multi-center populations are necessary for further analysis and improvement of the DCNN model performances, particularly in terms of their PPV. Third, the sensitivities of the models were low compared to the other performance-measure values. This problem may be resolved if more data were used for training. Finally, our study population only sampled the patients recommended for biopsy owing to suspicious microcalcifications. However, a biopsy may not have been recommended for all patients when one was warranted; hence, they may have been excluded from the population despite their eligibility. This selection bias can limit the generalizability of the results obtained.The institutional review board (IRB) of Gangnam Severance Hospital  approved this retrospective, single-center study and the written informed consent of patients was waived. All research was performed in accordance with relevant guidelines and regulations.The cohort comprised patients exhibiting suspicious microcalcifications in screening mammograms, who underwent stereotactic vacuum-assisted biopsy (SVAB) or surgical excisional biopsy for histopathologic confirmation between July 2007 and December 2019. Our study focused only on microcalcifications, and the microcalcification lesions associated with masses were excluded. Furthermore, patients with breast related symptoms, a history of breast cancer, previous breast operations, and implants, and without histopathologic results, were excluded.For each patient, the mammography examination was considered the index examination, followed by histopathologic confirmation, during our study period. For the final analysis, 1579 mammograms from 821 patients were utilized. The dataset was split into three distinct sets. For training and validating the DCNN models, 1319 mammograms obtained from 676 patients between 2007 and 2017 were used. The test dataset consisted of 260 mammograms obtained from 145 patients between 2018 and 2019.All mammographic images were obtained via a full-field digital mammography unit . Standard craniocaudal and mediolateral oblique views were obtained for both breasts along with spot-magnification views over the microcalcification regions.16, Xception17, Inception-v318, Inception-ResNet-v219, and DenseNet-20120, which were pre-trained on the ImageNet dataset, to implement transfer learning from natural images to microcalcification images. The features of the five DCNN models are described in the Over the past few years, DCNN performance has drastically improved on increasing network depth. In this study, we use five state-of-the-art DCNN architectures, ResNet-101The classifier layers in the DCNN models were replaced with a new classifier for microcalcification characterization. To this end, the weights of the existing convolution layers were initialized with the weights of the pre-trained DCNN models, whereas new classifier layers were initialized with random weights. Subsequently, the parameters of the convolution and classifier layers were fine-tuned through backpropagation using the microcalcification-image dataset.Model fine-tuning was performed on a Windows 10 personal computer (PC) with an NVIDIA GTX 2080Ti graphics processing unit (GPU). The five DCNN models were implemented in MATLAB R2020a using the Deep Learning Toolbox.Of the 1579 mammographic images, 1121  were randomly chosen as training data and 198  as validation data. The remaining images  were independently selected as test data. To generate the target classification outcomes in each dataset, a radiologist with more than 10 years of experience in mammography cropped each lesion by applying a square region of interest (ROI) of 512 × 512 pixels using routines written in MATLAB. Each cropped image was classified as either benign or malignant and accordingly saved in the corresponding folder. Data augmentation of the training dataset was used to avoid overfitting and increase the number of training examples, because deep learning shows better results with more data. The 1121 training images were randomly flipped horizontally and vertically, rotated between 1° and 359°, and translated along the x- and y-axis by − 30 to + 30 pixels. The validation and test data were not augmented.When fine-tuning the pre-trained network, we used the adaptive moment estimation (Adam) optimizer, which combines the advantages of the adaptive gradient algorithm (AdaGrad) and root mean square propagation (RMSProp) techniques. The Adam optimizer  and invasive carcinoma were classified as malignant. All other final histopathologic results, including high-risk lesions, were classified as benign. To evaluate the discriminative power of the DCNN models, the following quantitative measures were calculated: overall classification accuracy (ACC), sensitivity (SENS), specificity (SPEC), PPV, NPV, and AUC. The best cut-off point for each DCNN model to differentiate between malignant and benign microcalcification was set based on the maximal Youden index (sensitivity + specificity − 1)24. We applied the non-parametric DeLong test of differences to the six AUCs25. The statistical analysis was performed using SAS software . A p < 0.05 value was considered to indicate a statistically significant difference.The GEE method was used for comparing the diagnostic performances of the individual DCNN models, as well as the ensemble model, in terms of accuracy, sensitivity, specificity, PPV, and NPVSupplementary Information.


Internalized sexual stigma may hamper the development of self-identity, social interaction, and intimate relationship in lesbian, gay, and bisexual (LGB) individuals. The Measure of Internalized Sexual Stigma for Lesbians and Gay Men (MISS-LG) is an important instrument assessing internalized sexual stigma for LGB individuals. However, its psychometric properties have not been examined in LGB populations outside the Italian community. The present study used advanced psychometric testing to evaluate the traditional Chinese version (the MISS-LG) among LGB individuals in Taiwan. LGB individuals  participated in this study and completed the MISS-LG, HIV and Homosexuality Related Stigma (HHRS), and Acceptance and Action Questionnaire-II (AAQ). Confirmatory factor analysis results confirmed the three-factor structure of the MISS-LG; however, two items for males and one item for females had somewhat low factor loadings on the sexuality factor. Each MISS-LG factor was found to be unidimensional in Rasch results, except for the slight misfit in Item 3 and concurrent validity of the MISS-LG was supported by the positive correlations with HHRS and the AAQ. The Chinese MISS-LG has relatively satisfactory psychometric properties. However, further research is needed to investigate the reasons for problematic fitting of several items. A wide range of social stigma derived from heterosexism toward lesbian, gay, and bisexual (LGB) individuals prevails worldwide . StructuYoung adulthood is a phase of the life span from adolescence to full-fledged adulthood where the individuals become more independent and explore various life possibilities . LGB indA review study identified six scales that were developed for measuring internalized sexual stigma and had the reports of psychometric properties . Among tThe MISS-LG was developed using robust procedures, including (i) construct  definition; (ii) scale design; (iii) pilot testing; (iv) measure purification and scale administration; and (v) construct validation verification. Several rounds of psychometric testing with different methods  were applied to the MISS-LG; finally, the MISS-LG was verified to be a three-factor structure  with 17 items . The feaAlthough the MISS-LG was developed using a robust procedure and has been cited more than 120 times since its release, the current literature provides little information on its psychometric properties. To the best of our knowledge, the psychometric properties of the MISS-LG have only been reported in one study , in the Tolerance to sexual minorities in Taiwan has outpaced that in China, Japan, and South Korea over the past two decades . In May In order to supplement the psychometric evidence of the MISS-LG in non-Western societies, the present study aimed to use two types of psychometric testing  ,35 to evWe also examined the concurrent validity of the MISS-LG by testing the correlations between internalized sex stigma and perceived social stigma attitudes toward homosexuality and psychological flexibility. We hypothesized that the MISS-LG factors would have significant correlations with perceived social stigma attitudes toward homosexuality and psychological flexibility because of the following reasons: (1) as internalized sex stigma is the result whereby LGB individuals perceive and internalize social stigma toward sexual minority, internalized sex stigma and perceived social stigma attitudes toward homosexuality should be positively associated; (2) individuals with a higher level of psychological flexibility may cope better with unfriendly environments that may prevent generation of internalized stigma .We recruited participants by posting online advertisements on social media including Facebook, Twitter, and LINE messaging app , the Bulletin Board System, and the home pages of three health promotion and counseling centers for LGB individuals from August 2018 to July 2019. The recruitment criteria were individuals who identified their sexual orientation as homosexual or bisexual, aged between 20 and 30 years, and lived in Taiwan. Anyone interested in participating in this study was to phone the research assistants. Research assistants assessed the eligibility of potential participants based on study criteria, explained the study aims and procedures to them, and scheduled times for completing the study questionnaires individually in the study room. The research assistants evaluated participants face-to-face in the study room to determine whether they had impaired intellect or evidence of alcohol and substance use that might interfere with understanding the study’s purpose or method of completing the questionnaire; if they had, they were excluded from the study. In total, 1000 participants  participated. No participants were excluded. Informed consent was obtained from all participants. This study was approved by the Institutional Review Board of Kaohsiung Medical University Hospital—KMUHIRB-F(II)-20180018.The MISS-LG contains 17 items regarding internalized sexual stigma for lesbian women and gay men . The MISThe MISS-LG has been translated into a Chinese version for Taiwanese LGB individuals using standard forward-, backward-, and pretest-step methods . First, We used two measures for examining the concurrent validity of the MISS-LG. The first measure was the HIV and Homosexuality Related Stigma (HHRS) . We adopThe second measure is the Acceptance and Action Questionnaire-II (AAQ) . The AAQData were collected regarding the participants’ gender , age, education level (high school or below vs. college or above), sexual orientation , paternal and maternal education, and sexual orientation known by family, friends, and online friends (no or few vs. many or a great quantity).Participants’ characteristics and MISS-LG item scores were first analyzed using descriptive statistics. Then, the distributions of the MISS-LG items were checked  before https://lavaan.ugent.be/index.html, accessed on 12 December 2021), except for the Rasch analyses. The Rasch analyses were done using the WINSTEPS  .All psychometric testing for the MISS-LG was conducted separately for male and female participants because of the different gender versions. All statistical analyses were conducted using R software   and 500 females . Most participants had obtained a college diploma or college degree . Nearly three-fourths of male participants (73.0%) and slightly less than half of the female participants (41.0%) were homosexual. Other characteristics, including parental educational levels and whether participants’ sexual orientation was known by others, are presented in p-value < 0.001, CFI = 0.985, TLI = 0.983, RMSEA (90% CI) = 0.045 , and SRMR = 0.062 or females, the fit indices were chi-square (df) = 186.62 (116), p-value < 0.001, CFI = 0.987, TLI = 0.985, RMSEA (90% CI) = 0.035 , and SRMR = 0.056.Results from Rasch analyses  indicater = 0.752 to 0.937 for males; = 0.640 to 0.784 for females; all p-values < 0.001). Moreover, the three MISS-LG factors were positively associated with HHRS-Homosexuality scores  and AAQ scores . Finally, the concurrent validity of the MISS-LG is shown in r = 0.752 to 0.937 for males; = 0.640 to 0.784 for females; all p-values < 0.001). Moreover, the three MISS-LG factors were positively associated with HHRS-Homosexuality scores  and AAQ scores .Finally, the concurrent validity of the MISS-LG is shown in This study used various statistical methods to examine the psychometric properties of a Chinese version of the MISS-LG. The results derived from CFA and Rasch analysis all suggest that the traditional Chinese version of the MISS-LG had satisfactory construct validity across groups. Specifically, CFA results indicated that the factorial structure of the MISS-LG included three factors in both male and female sexual-minority individuals in Taiwan. Therefore, the sexuality, identity, and social discomfort factors are different constructs in the MISS-LG, which echoes findings from Lingiardi et al. . Rasch aThe CFA results indicated that most items had an acceptable factor loading. However, we found that item 11 for males  had a low factor loading (factor loading = 0.167). Therefore, item 11 might not well adhere to its embedded sexuality factor. The sexuality factor indicates the negative attitudes toward sexual behaviors and intimate relationships in lesbian women, gay men, or bisexual individuals. However, item 11 describes a value judgment on a specific sexual behaviors or positions rather than sexual behaviors or intimate relationships in gay men. This may be a possible reason to explain the low factor loadings for item 11 among males. It is also possible that cultural differences relating to individualism versus collectivism in Western and Eastern cultures may in part underlie apparent differences. Another item for males was found to have slightly low factor loadings; specifically, item 8 for males . Item 8 for males does not explicitly describe attitudes toward sexual behaviors or intimate relationships. For example, whether a person “believes in love” could be influenced by past experiences of dating. Therefore, when a person reads this sentence, the focus may not be on the intimate relationship of the individual per se. One more item, item 11 for females, also had a factor loading lower than 0.3 (factor loading = 0.299). However, this item  conceptually fits well in the sexuality factor and its factor loadings is extremely close to 0.3. Nevertheless, future studies are needed to further explore the factor structure of MISS-LG for both the male and female versions.The three factors of the MISS-LG were positively associated with HHRS-Homosexuality and AAQ scores. A high HHRS-Homosexuality score indicates a high level of perceived stigma regarding homosexuality from families. Family is the microsystem in which individuals have typically lived since childhood . LGB indThere are several limitations in this study. First, participants were a group of LGB individuals in early adulthood; thus, our results may not generalize to other age groups. Second, biases inherent to questionnaires  should be considered. Although we assured participants that all questionnaires were anonymous and individual data would be kept confidential, we cannot guarantee that every participant had no other concerns when they completed the questionnaires. Third, we did not conduct test–retest reliability and responsiveness for the MISS-LG. Therefore, it is unclear whether the traditional Chinese version of the MISS-LG has good reproducibility over time and whether it is sensitive to detecting changes in self-stigma. Fourth, the study included only lesbian, gay, and bisexual individuals. How sexual stigma may impact other sexual minority groups  should be considered in future studies. Fifth, this study inquired participants’ gender identities by the binary of male and female but did not include the options of transgender, gender nonbinary, or genderqueer. Research has found that sexual and gender minority identities have intersectional impacts on health  and behaIn conclusion, the MISS-LG has relatively satisfactory psychometric properties in a sample of LGB individuals in Taiwan. The three factors of the MISS-LG were confirmed in the CFA results; however, two items  were found to have relatively low factor loadings in the sexuality factor. Each factor of the three MISS-LG factors were found to be unidimensional and most items fit satisfactorily in their embedded constructs, except for slight misfitting of Item 3 and concurrent validity of the MISS-LG was supported by the positive correlations with HHRS-Homosexuality family subscale and AAQ scores. However, more investigation is needed to understand reasons for the slight misfitting of several MISS-LG items.


MicroRNAs (miRNAs) are a kind of short non-coding ribonucleic acid molecules that can regulate gene expression. The computational identification of plant miRNAs is of great significance to understanding biological functions. In our previous studies, we have put firstly forward and further developed a set of knowledge-based energy features to construct two plant pre-miRNA prediction tools (plantMirP and riceMirP). However, these two tools cannot be used for miRNA prediction from NGS (Next-Generation Sequencing) data. In addition, for further improving the prediction performance and accessibility, plantMirP2 has been developed. Based on the latest dataset, plantMirP2 achieves a promising performance: 0.9968 , 0.9754 (accuracy), 0.9675 (sensitivity) and 0.9876 (specificity). Additionally, the comparisons with other plant pre-miRNA tools show that plantMirP2 performs better. Finally, the webserver and stand-alone version of plantMirP2 are available. Malus × domestica) and Arabidopsis thaliana [MicroRNAs (miRNAs) are small noncoding RNAs with a length of about 20–24 nucleotides . Plant mthaliana . The miRthaliana .Accurate detection and identification of plant miRNAs are the basis of understanding miRNA biological functions. Therefore, many methods have been developed to this area. These methods are roughly divided into two categories: One is based on biological experiments ,7,8,9, ak-mer scheme with knowledge-based potentials derived from Boltzmann formulations, and developed SVM-based plantMirP for predicting plant pre-miRNAs [In 2005, Xue et al. used 32 local structural features to build an SVM (Support Vector Machine) model (triplet-SVM) for predicting human pre-miRNA . In 2007e-miRNAs . AdditioIn 2012, based on a probability model, Friedlaender et al. exploited a probability model to comprehensively score the coincidence degree between sequencing fragments and microRNA production process, and proposed miRDeep , miRDeepThese high-throughput sequencing-based miRNA prediction tools share a familiar flowchart. Firstly, sequence alignment tools are used to align sequencing fragments  to reference genomes. Then, candidate regions are selected and further labeled as known microRNA precursors according to genome annotation data or nominated precursors according to sequence and structure-related characteristics. Based on the model of miRNA biogenesis, the compatibility of the position and frequency of the aligned reads with the microRNA precursors is evaluated in virtue of various different methods. These tools are not taking full advantage of sequence and structure features of miRNA precursors. Moreover, high-throughput sequencing-based methods usually predict superfluous miRNAs. However, the follow-up researchers usually select a few miRNAs to conduct verification of biological functions. Therefore, how to pick these miRNAs is a practical and thorny problem. In addition, those predicted miRNAs are not all genuine. How to control false positive rate is an important problem. We argue that the combination of high-throughput sequencing-based (which is based mainly on model of miRNA biogenesis) with machine learning-based methods  will further boost the performance of miRNA prediction, narrow the range of selection and reduce the false-positive rate.https://github.com/wuqiansibai/plantMiRP2/releases/tag/v1.0/, accessed on 16 August 2021) and a Dockerfile  alongside the web-server version , which is able to directly receive and process NGS data.To do this, we developed plantMirP into plantMirP2. Firstly, we incorporated and optimized knowledge-based energy features, which are firstly proposed in plantMirP and further developed in our recent studies  were used as the positive training dataset, while 5323 pre-miRNAs from the remaining plants were used as the positive testing dataset. On the other hand, pseudo pre-miRNA fragments were extracted from CDS (Coding Sequence) data under the constraint that there are sufficient similarities between pseudo and genuine pre-miRNAs. Then, 8652 pseudo pre-miRNAs were randomly selected as the negative dataset including 5186 negative training dataset and 3466 negative testing dataset. The differences in positive datasets of both tools are shown in Plant pre-miRNA sequence data (positive dataset) used in plantMirP was extracted from the miRBase  databasek-mer scheme in bioinformatics and the distance-specific pair potential in statistical physics. The recognition characteristic of k-mer and the advantages of distance-specific pair potential distinguishing between natural and non-natural structures were well combined. Furthermore, knowledge-based energy features have been firmly demonstrated to have very high discriminatory power [In plantMirP, we firstly designed knowledge-based energy features by combining the ry power ,29,30. MAc), sensitivity (Se), specificity (Sp) and Matthew’s correlation coefficient (MCC) are widely used in the binary classifier. Their definitions are defined as follows:The AUC value under the ROC (Receiver Operating Characteristic) curve is a global measure for evaluating classification performance. A larger AUC value means a better classification performance. Accuracy (TP (True Positive) and FP  represent the number of real positives and false positives, respectively, while TN (True Negative) and FN  indicate the number of true negatives and false negatives.Here C (which is used to reduce the degree of overfitting) are adjusted based on the results of cross-validation.Here we use the algorithms associated with the sklearn package  in pythoWhen training the SVM model, the input is the 193 feature values of training sequences, and these feature values are normalized using the following formula:The training scalers are saved for processing the feature values of testing sequences. During training the SVM model, the provided target variable y is true (1) or false (0) and this is also the target output during testing.https://github.com/wuqiansibai/plantMiRP2/releases/tag/v1.0/ (accessed on 16 August 2021) and the related Dockerfile is provided at https://github.com/wuqiansibai/plantMiRP2/releases/tag/Dockerfile/ (accessed on 16 August 2021). All scripts have been tested on CentOS. For the convenience of users, we also provide the webserver of plantMirP2  for pre-miRNA prediction, and direct miRNA prediction , 0.9930 (6-fold CVs), 0.9930 (8-fold CVs) and 0.9930 (10-fold CVs), respectively . The ROCk-mer pair potentials. In plantMirP, knowledge-based energy features show very high discriminatory power. Recently, we further developed k-mer pair potentials, and put forward diverse knowledge-based energy features based on position-dependent k-mer pair potentials. Then, riceMirP was implemented specifically for rice pre-miRNAs. Numerous comparisons also demonstrate that riceMirP performs better than existing tools for other plant prediction. In order for performance improvement, we compared plantMirP2 with plantMirP and riceMirP based on the training and testing dataset of plantMirP2. The training dataset was used to train the prediction model for three tools, and the independent testing dataset was used for performance evaluation. The values of Ac, Se, Sp and AUC are displayed in PlantMirP is the first plant pre-miRNA tool with knowledge-based energy features calculated from distance-specific In order to further verify the performance of plantMirP2, 6944 pre-miRNA data in miRbase (release 21) and the corresponding 3472 negative data constructed through CDS sequences were used to construct the training dataset. The testing dataset consisted of 1639 new plant pre-miRNA data in miRbase (release 22.1) and 820 negative data. Based on the above datasets, we compared plantMirP2 with plantMirP and riceMirP. PlantMirP2 shows better performance than the other two tools . In addiMiPlantPreMat  is a repThe training dataset of PlantMiRNAPred, which included 980 real pre-miRNAs and 980 pseudo pre-miRNAs, was applied to training prediction models of plantMirP and plantMirP2. Then, the testing dataset of PlantMiRNAPred, which consisted of three parts, known pre-miRNAs from eight species, updated datasets and the negative testing dataset, was submitted directly into plantMirP and plantMirP2 for prediction with a freshly trained prediction model. Because PlantMiRNAPred is unavailable, the classification results of PlantMiRNAPred, triplet-SVM and microPred, which are reported in the article of PlantMiRNAPred, were used directly for comparisons with results obtained from our tools. It is very evident that plantMirP2 is superior to other tools in the great majority of datasets .Arabidopsis thaliana genome file and a related ncRNAs file for testing, 105 new high-precision miRNAs were successfully obtained [http://plantmi.top/ (accessed on 16 August 2021).In order to predict miRNAs from NGS data, and further improve the prediction performance, speed and usability, we upgraded plantMirP to plantMirP2. The latest database data were used to build the prediction model. The optimized knowledge-based energy features and other effective features were widely combined to further improve the prediction accuracy. After optimizing the program and parameters of the prediction model, the prediction accuracy and running speed were also improved. Two prediction functions can be used through plantMirP2. The first function is pre-miRNA prediction. Users can use the established plant model based on the miRBase database or provide training data to establish a new model to predict the sequences. The second is miRNA prediction. Novel miRNAs can be obtained through NGS data with the help of genome files and ncRNAs files. By using a formatted GSM (Gene Expression Omnibus Sample) sequencing file, an obtained . The ext


To reveal the influence of hypoxia on tumor cells and immune cells in primary IDH-wt glioblastoma patients.Single-cell RNA-seq data and bulk RNA-seq data were acquired from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, respectively. Hypoxia status and subtypes of tumor cells were identified based on single-sample Gene Set Enrichment Analysis (ssGSEA). Regulon network analysis of different subtypes under different conditions was conducted by SCENIC. Within tumor microenvironment, biological process activity analysis and cell–cell communication network were conducted to uncover the inner links between each cell subtype under different hypoxia status.Different types of tumor cell in GBM possessed different hypoxia status, and MES-like subtype was under a more severe hypoxia condition than other subtypes. Hypoxia also induced MES-like signature gene expression within each tumor cell, which could stimulate tumor cell proliferation and invasion by regulating cell–cell communication. Additionally, hypoxia inhibited immune cell activity in the tumor microenvironment by inducing macrophage phenotype polarization and upregulating immune-inhibited cell–cell interaction within immune cells. Interactions between tumor cells and immune cells under hypoxia status also promoted tumor progression.Hypoxia was a poor prognostic marker for primary IDH-wt GBM patients. Meanwhile, it could induce tumor cells’ MES-like transformation trend and inhibit antitumor function of immune cells. Glioblastoma (GBM) is the most fatal tumor in the central nervous system (CNS) with the median overall survival of 12–15 months. A previous study has divided GBM into four subtypes, namely, mesenchymal (ME), classical (CL), proneural (PN), and neural (NE), based on bulk RNA-seq data . To evalHypoxia, a microenvironment feature in solid tumor, is associated with many cancer “hallmarks,” involving impaired immune response, metabolic reprogramming, stimulation of tumor angiogenesis, and promotion of tumor invasion . It has Collectively, the vast majority of studies focusing on hypoxia effect are based on bulk RNA-seq datasets. Due to intratumoral heterogeneity, tumor purity, and single-cell sequencing development, hypoxia evaluation through bulk data is unable to reveal specific hypoxia status of each cell type, which, however, can be complemented by single-cell analysis. To study the influence of hypoxia on single cells, including tumor and non-tumor cells, especially immune cells in the tumor microenvironment, we performed the study on single-cell RNA-seq datasets derived from primary IDH-wild type (IDH-wt) GBM patients. This work provided an opportunity to investigate the influence of hypoxia on single cells and uncover new possible mechanisms for hypoxia as a poor prognostic marker.https://www.ncbi.nlm.nih.gov/geo) (https://tcga-data.nci.nih.gov/) via Xena Browser developed by University of California Santa Cruz (UCSC). The QC process, normalization, and cell-type identification of single-cell RNA-seq data were performed as described in published studies  were downloaded from the GEO database (gov/geo) . We addigov/geo) –12. Six 1928 were studies , 10, 11,Subtypes of GBM cells and glioma stem-like cells (GSCs) were identified by ssgsea.GBM.classification R package . First, According to the hypoxia 15-gene signature evaluated by published papers –20 in paWe used the same method as calculating hypoxia score to acquire the MES-like score of each GBM tumor cell based on MES-like signatures. The deconvolution of TCGA GBM samples were conducted by CIBERSORTx, in which we got the target cell proportion within the bulk samples .We adopted five tumor purity scores in the analysis, namely, the consensus purity estimation (CPE) score, ESTIMATE score, ABSOLUTE score, LUMP score, and IHC score. ESTIMATE was based on specific gene expression profiles consisting of 141 immune genes and 141 stromal genes ; ABSOLUTLineage trajectory was constructed by monocle R package . BrieflyBiological process activity (BPA) analysis was based on gene list in msigdb database to evaluate each pathway’s activity in single cells . We usedDifferentially expressed genes (DEGs) and differentially expressed pathways between the hypoxia and normoxia groups were identified by limma R package. All parameters were set as default.in silico deletion analysis (LISA), based on DEGs, to identify main transcription factors in hypoxia group . Continuous variables that conform to normal distribution between groups were compared by the Student’s t-test with post-hoc pairwise Bonferroni tests. Variables that do not conform to the normal distribution were compared by the Wilcoxon rank-sum test with post-hoc pairwise Bonferroni tests.R packages survival and survminer were used for overall survival analysis. Univariate cox and multivariate cox regression analysis methods were conducted to identify independent prognostic features. In addition, single- and two-factor Kaplan–Meier curves were shown to compare the prognostic results between different groups. All statistical analyses were performed using R software, version 3.6.2 , intermediate state (n = 17), and normoxia (n = 50) based on 15-gene hypoxic signature, which performed the best in evaluating tumor hypoxic condition reported by former study . To inve6-methylguanine DNA methyltransferase (MGMT) promoter methylation status, and purity to perform multivariate Cox analysis, which suggested that hypoxia was an independent risk factor . We first identified four subtypes of GBM cells  and then classified all tumor cells into another two GSC subtypes. Interestingly, mGSCs were derived from MES- and AC-like subtypes, while pGSCs were derived mainly from OPC- and NPC-like cells  scRNA-seq data, which could decrease the impact of dropout events and batch effects to provide a robust description on cellular states, had already been tested well in the additional two single-cell datasets . We obse2) or normoxia condition  for 48 h, respectively. Consistently, two MES-like score of cell lines under hypoxia were higher than normoxia  . We seleand CD44 -related The classification of GBM subtypes according to bulk RNA-seq and scRNA-seq possesses inner link as previously reported that TCGA-CL and TCGA-ME subtypes in bulk correspond to tumors enriched with AC- and MES-like cells, respectively, while TCGA-PN subtype corresponds to the combination of OPC- and NPC-like cells . TCGA GBOne mechanism of hypoxia inducing GSCs PMT is altering DNA methylation pattern at promoter of mesenchymal-related genes, regulators, or enhancers . NeverthHypoxia is a microenvironment feature that could influence tumor cells and other non-tumor cells simultaneously. In our results, hypoxia was identified as an independent risk factor for prognosis of GBM patients. Although the result of multivariate Cox regression analysis denied MES-like ratio as a risk factor, it could be explained that hypoxia had inner contact with MES-like ratio, thereby influencing MES-like ratio’s impact on prognosis. In addition, we found that hypoxia could inhibit immune activity. Since the immune microenvironment of primary GBM mainly consisted of TAMs , we analIn summary, we explored in this study the effects of hypoxia on single cells and on both tumor cells and immune cells from primary IDH-wt GBM. Hypoxia was identified as a poor prognostic marker for primary IDH-wt GBM patients by analyzing bulk RNA-seq data. We also explored the potential mechanism of hypoxia contributing to poor prognosis on scRNA-seq data and found that hypoxia could induce tumor cells’ MES-like transformation trend and inhibit immune antitumor function such as inducing macrophage M1 to M2 polarization, thereby promoting tumor progression.All data was downloaded from the public database. The single-cell data was got from the GEO database and the bulk data was from the TCGA database.ZX designed the workflow, performed the data analysis, and wrote the manuscript. HL and CH assisted with the data analysis, designed the graphic image, and revised the manuscript. XL assisted with the study design and contributed to the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.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.