Eravacycline, an antibacterial drug, repurposed for pancreatic cancer therapy: insights from a molecular-based deep learning model.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) remains a serious threat to health, with limited effective therapeutic options, especially due to advanced stage at diagnosis and its inherent resistance to chemotherapy, making it one of the leading causes of cancer-related deaths worldwide. The lack of clear treatment directions underscores the urgent need for innovative approaches to address and manage this deadly condition. In this research, we repurpose drugs with potential anti-cancer activity using machine learning (ML). METHODS: We tackle the problem by using a neural network trained on drug-target interaction information enriched with drug-drug interaction information, which has not been used for anti-cancer drug repurposing before. We focus on eravacycline, an antibacterial drug, which was selected and evaluated to assess its anti-cancer effects. RESULTS: Eravacycline significantly inhibited the proliferation and migration of BxPC-3 cells and induced apoptosis. CONCLUSION: Our study highlights the potential of drug repurposing for cancer treatment using ML. Eravacycline showed promising results in inhibiting cancer cell proliferation, migration and inducing apoptosis in PDAC. These findings demonstrate that our developed ML drug repurposing models can be applied to a wide range of new oncology therapeutics, to identify potential anti-cancer agents. This highlights the potential and presents a promising approach for identifying new therapeutic options.

Effectiveness of nirmatrelvir plus ritonavir treatment for patients with chronic lymphocytic leukemia during the Omicron surge.

Patients with chronic lymphoid leukemia (CLL), even in the Omicron era and after vaccination, suffer from persistent COVID-19 infection, higher complications, and mortality compared with the general population. In this study, we evaluated retrospectively the effectiveness of nirmatrelvir + ritonavir among 1080 patients with CLL who were infected with severe acute respiratory syndrome coronavirus 2. Nirmatrelvir administration was associated with a reduction in COVID-19-related hospitalization or death by day 35. Specifically, the rate of COVID-19-related hospitalization or death in the treated group compared with the untreated group was 4.8% (14 out of 292) vs 10.2% (75 out of 733), respectively. Moreover, we report a 69% relative risk reduction in COVID-19-related hospitalization or death in patients with CLL at the age of ≥65 years. Multivariate analysis indicates that patients aged >65 years, patients who received heavy treatment (>2 previous treatments), patients with recent hospitalizations, intravenous immunoglobulin (IVIG) treatment, and comorbidity had significant improvement outcomes after treatment with nirmatrelvir.

Pretrained transformer models for predicting the withdrawal of drugs from the market.

MOTIVATION: The process of drug discovery is notoriously complex, costing an average of 2.6 billion dollars and taking ∼13 years to bring a new drug to the market. The success rate for new drugs is alarmingly low (around 0.0001%), and severe adverse drug reactions (ADRs) frequently occur, some of which may even result in death. Early identification of potential ADRs is critical to improve the efficiency and safety of the drug development process. RESULTS: In this study, we employed pretrained large language models (LLMs) to predict the likelihood of a drug being withdrawn from the market due to safety concerns. Our method achieved an area under the curve (AUC) of over 0.75 through cross-database validation, outperforming classical machine learning models and graph-based models. Notably, our pretrained LLMs successfully identified over 50% drugs that were subsequently withdrawn, when predictions were made on a subset of drugs with inconsistent labeling between the training and test sets. AVAILABILITY AND IMPLEMENTATION: The code and datasets are available at https://github.com/eyalmazuz/DrugWithdrawn.

Predicting molecular mechanisms of hereditary diseases by using their tissue-selective manifestation.

How do aberrations in widely expressed genes lead to tissue-selective hereditary diseases? Previous attempts to answer this question were limited to testing a few candidate mechanisms. To answer this question at a larger scale, we developed "Tissue Risk Assessment of Causality by Expression" (TRACE), a machine learning approach to predict genes that underlie tissue-selective diseases and selectivity-related features. TRACE utilized 4,744 biologically interpretable tissue-specific gene features that were inferred from heterogeneous omics datasets. Application of TRACE to 1,031 disease genes uncovered known and novel selectivity-related features, the most common of which was previously overlooked. Next, we created a catalog of tissue-associated risks for 18,927 protein-coding genes (https://netbio.bgu.ac.il/trace/). As proof-of-concept, we prioritized candidate disease genes identified in 48 rare-disease patients. TRACE ranked the verified disease gene among the patient's candidate genes significantly better than gene prioritization methods that rank by gene constraint or tissue expression. Thus, tissue selectivity combined with machine learning enhances genetic and clinical understanding of hereditary diseases.

Improving severity classification of Hebrew PET-CT pathology reports using test-time augmentation.

Classifying medical reports written in Hebrew is challenging due to the ambiguity and complexity of the language. This study proposes Text Test Time Augmentation (TTTA), a novel method to improve the classification accuracy of cancer severity levels from PET-CT diagnostic reports in Hebrew. Hebrew, being a morphologically rich language, often leads to each word having multiple ambiguous interpretations. TTTA leverages test-time augmentation to enhance text information retrieval and model robustness. During training and testing phases, this method generates and evaluates sets of augmentations to enhance the semantics extracted from each report. Experiments utilize a large institutional report repository from Ziv hospital, Israel, where physicians manually labeled the reports. The results demonstrate that the proposed TTTA approach achieves superior performance over baseline models without TTA, improving PR-AUC by 15.18% on classifying cancer severity levels. The study highlights the efficacy of TTTA in extracting essential medical concepts from free text reports and accurately classifying the severity of cancer. The approach addresses the limitations of prior methods and contributes towards improved automated analysis of Hebrew medical reports. TTTA has the potential to assist physicians in cancer diagnosis and treatment planning.

Explainable multimodal machine learning model for classifying pregnancy drug safety.

MOTIVATION: Teratogenic drugs can cause severe fetal malformation and therefore have critical impact on the health of the fetus, yet the teratogenic risks are unknown for most approved drugs. This article proposes an explainable machine learning model for classifying pregnancy drug safety based on multimodal data and suggests an orthogonal ensemble for modeling multimodal data. To train the proposed model, we created a set of labeled drugs by processing over 100 000 textual responses collected by a large teratology information service. Structured textual information is incorporated into the model by applying clustering analysis to textual features. RESULTS: We report an area under the receiver operating characteristic curve (AUC) of 0.891 using cross-validation and an AUC of 0.904 for cross-expert validation. Our findings suggest the safety of two drugs during pregnancy, Varenicline and Mebeverine, and suggest that Meloxicam, an NSAID, is of higher risk; according to existing data, the safety of these three drugs during pregnancy is unknown. We also present a web-based application that enables physicians to examine a specific drug and its risk factors. AVAILABILITY AND IMPLEMENTATION: The code and data is available from https://github.com/goolig/drug_safety_pregnancy_prediction.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The course of patients with hairy cell leukemia during the omicron surge of the Covid-19 pandemic.

In this study, we aim to explore the outcomes of Covid-19 infection in patients with Hairy cell leukemia (HCL). The cohort is based on data obtained from electronic medical records. It includes 218 consecutive patients diagnosed with HCL between 16 June 1998, and 20 September 2022, out of which the coronavirus has infected 85 patients during the Omicron surge. Out of 85 patients with HCL who were infected by Covid-19; 7 patients (8.2%) have been hospitalized, and the mortality rate was 2.3% (two patients). Thirteen of the 85 patients had been infected by Covid-19 in previous waves, including 9/13 after vaccination, and none of them developed a severe disease. Humoral immune response after three doses of the BNT162b2 mRNA vaccination regimen was evaluated in 40 patients and was attained in 67.5%. Based on multivariate analysis: unfavorable outcome was significantly more common in patients with HCL above 65 years old, who had at least one cytopenia, and with comorbidity of cardiovascular disease or asplenia. Our results indicates that the course of COVID-19 in patients with HCL during the Omicron wave has been improved relatively favorable.

Pregnancy Course of 10 Women Diagnosed with Chronic Lymphocytic Leukemia.

Pregnancies following diagnosis of chronic lymphocytic leukemia (CLL) are rare events, mainly because the disease is typically diagnosed in the elderly. Literature on the topic is based only on case reports, and limited data are available on the influence of pregnancy on CLL course. In this retrospective study, we aimed to summarize the clinical and laboratory course of 10 women with CLL who became pregnant. None of the patients had significant changes in blood count during or after pregnancy or had complications such as infection, autoimmune phenomenon, or preeclampsia. Four out of 10 pregnancies were terminated with an early miscarriage. Following labor, 1 patient started anti-CLL treatment due to preexisting anemia, but none of the women required therapy during CLL progression during the first 2 years of follow-up. We conclude that based on our serial, pregnancy does not negatively impact on CLL course.

Clinical Outcomes of COVID-19 in Mycosis Fungoides and Sezary Syndrome.

INTRODUCTION: Haemato-oncologic patients are more susceptible to severe infections with SARS-CoV-2. We aimed to assess the clinical outcomes of SARS-CoV-2 infection among patients with Mycosis Fungoides and Sezary Syndrome (MF/SS). METHODS: The data were retrieved from anonymized electronic medical records of Maccabi Healthcare Services (MHS), the second-largest healthcare organization in Israel. Patients diagnosed with MF/SS were included in the study. COVID-19 PCR test results together with sociodemographic and clinical data were extracted and analyzed to evaluate the association of COVID-19 with clinical outcomes. RESULTS: In the period of 2020-2022, 1,472 MF/SS patients were included in the study. Among them, 768 (52%) had SARS-CoV-2 infection. The hospitalization rate was 2.9% and infection by the Delta variant was associated with the highest hospitalization rate (7.7%). The hospitalization rate was lower among fully vaccinated patients (p = 0.032) but higher for patients older than 65 (p < 0.001) and patients with SS (vs. MF) (p < 0.001) or COPD (p = 0.024) diagnosis. There was a tendency for decreased hospitalization among patients treated with nirmatrelvir + ritonavir within 5 days of infection, with a 79% risk reduction, although it was not statistically significant (p = 0.164). CONCLUSION: Patients with MF/SS do not necessarily have worse COVID-19 outcomes compared to the general population.

TTANAD: Test-Time Augmentation for Network Anomaly Detection.

Machine learning-based Network Intrusion Detection Systems (NIDS) are designed to protect networks by identifying anomalous behaviors or improper uses. In recent years, advanced attacks, such as those mimicking legitimate traffic, have been developed to avoid alerting such systems. Previous works mainly focused on improving the anomaly detector itself, whereas in this paper, we introduce a novel method, Test-Time Augmentation for Network Anomaly Detection (TTANAD), which utilizes test-time augmentation to enhance anomaly detection from the data side. TTANAD leverages the temporal characteristics of traffic data and produces temporal test-time augmentations on the monitored traffic data. This method aims to create additional points of view when examining network traffic during inference, making it suitable for a variety of anomaly detector algorithms. Our experimental results demonstrate that TTANAD outperforms the baseline in all benchmark datasets and with all examined anomaly detection algorithms, according to the Area Under the Receiver Operating Characteristic (AUC) metric.

Predicting drug characteristics using biomedical text embedding.

BACKGROUND: Drug-drug interactions (DDIs) are preventable causes of medical injuries and often result in doctor and emergency room visits. Previous research demonstrates the effectiveness of using matrix completion approaches based on known drug interactions to predict unknown Drug-drug interactions. However, in the case of a new drug, where there is limited or no knowledge regarding the drug's existing interactions, such an approach is unsuitable, and other drug's preferences can be used to accurately predict new Drug-drug interactions. METHODS: We propose adjacency biomedical text embedding (ABTE) to address this limitation by using a hybrid approach which combines known drugs' interactions and the drug's biomedical text embeddings to predict the DDIs of both new and well known drugs. RESULTS: Our evaluation demonstrates the superiority of this approach compared to recently published DDI prediction models and matrix factorization-based approaches. Furthermore, we compared the use of different text embedding methods in ABTE, and found that the concept embedding approach, which involves biomedical information in the embedding process, provides the highest performance for this task. Additionally, we demonstrate the effectiveness of leveraging biomedical text embedding for additional drugs' biomedical prediction task by presenting text embedding's contribution to a multi-modal pregnancy drug safety classification. CONCLUSION: Text and concept embeddings created by analyzing a domain-specific large-scale biomedical corpora can be used for predicting drug-related properties such as Drug-drug interactions and drug safety prediction. Prediction models based on the embeddings resulted in comparable results to hand-crafted features, however text embeddings do not require manual categorization or data collection and rely solely on the published literature.

minMLST: machine learning for optimization of bacterial strain typing.

MOTIVATION: High-resolution microbial strain typing is essential for various clinical purposes, including disease outbreak investigation, tracking of microbial transmission events and epidemiological surveillance of bacterial infections. The widely used approach for multilocus sequence typing (MLST) that is based on the core genome, cgMLST, has the advantage of a high level of typeability and maximal discriminatory power. Yet, the transition from a seven loci-based scheme to cgMLST involves several challenges, that include the need by some users to maintain backward compatibility, growing difficulties in the day-to-day communication within the microbiology community with respect to nomenclature and ontology, issues with typeability, especially if a more stringent approach to loci presence is used, and computational requirements concerning laboratory data management and sharing with end-users. Hence, methods for optimizing cgMLST schemes through careful reduction of the number of loci are expected to be beneficial for practical needs in different settings. RESULTS: We present a new machine learning-based methodology, minMLST, for minimizing the number of genes in cgMLST schemes by identifying subsets of informative genes and analyzing the trade-off between gene reduction and typing performance. The results achieved with minMLST over eight bacterial species show that despite the reduction in the number of genes up to a factor of 10, the typing performance remains very high and significant with an Adjusted Rand Index that ranges between 0.4 and 0.93 in different species and a P-value < 10-3. The identification of such optimized MLST schemes for bacterial strain typing is expected to improve the implementation of cgMLST by improving interlaboratory agreement and communication. AVAILABILITY AND IMPLEMENTATION: The python package minMLST is available at https://PyPi.org/project/minmlst/PyPI and supported on Linux and Windows. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Safety and efficacy of the BNT162b mRNA COVID-19 vaccine in patients with chronic lymphocytic leukemia.

Patients with chronic lymphocytic leukemia (CLL) have a suboptimal humoral response to vaccination. Recently, BNT162b2, an mRNA COVID-19 vaccine with a high efficacy of 95% in immunocompetent individuals, was introduced. We investigated the safety and efficacy of the BNT162b2 mRNA COVID-19 vaccine in patients with CLL from nine medical centers in Israel, Overall 400 patients were included, of whom 373 were found to be eligible for the analysis of antibody response. The vaccine appeared to be safe and only grade 1-2 adverse events were seen in 50% of the patients. Following the second dose, an antibody response was detected in 43% of the cohort. Among these CLL patients, 61% of the treatment-na ve patients responded to the vaccine, while responses developed in only 18% of those with ongoing disease, 37% of those previously treated with a BTK inhibitor and 5% of those recently given an anti-CD20 antibody. Among patients treated with BCL2 as monotherapy or in combination with anti-CD20, 62% and 14%, respectively, developed an immune response. There was a high concordance between neutralizing antibodies and positive serological response to spike protein. Based on our findings we developed a simple seven-factor score including timing of any treatment with anti-CD20, age, treatment status, and IgG, IgA, IgM and hemoglobin levels. The sum of all the above parameters can serve as a possible estimate to predict whether a given CLL patient will develop sufficient antibodies. In conclusion, the BNT162b2 mRNA COVID-19 vaccine was found to be safe in patients with CLL, but its efficacy is limited, particularly in treated patients.

Virtual Breathalyzer: Towards the Detection of Intoxication Using Motion Sensors of Commercial Wearable Devices.

Driving under the influence of alcohol is a widespread phenomenon in the US where it is considered a major cause of fatal accidents. In this research, we present Virtual Breathalyzer, a novel approach for detecting intoxication from the measurements obtained by the sensors of smartphones and wrist-worn devices. We formalize the problem of intoxication detection as the supervised machine learning task of binary classification (drunk or sober). In order to evaluate our approach, we conducted a field experiment and collected 60 free gait samples from 30 patrons of three bars using a Microsoft Band and Samsung Galaxy S4. We validated our results against an admissible breathalyzer used by the police. A system based on this concept successfully detected intoxication and achieved the following results: 0.97 AUC and 0.04 FPR, given a fixed TPR of 1.0. Our approach can be used to analyze the free gait of drinkers when they walk from the car to the bar and vice versa, using wearable devices which are ubiquitous and more widespread than admissible breathalyzers. This approach can be utilized to alert people, or even a connected car, and prevent people from driving under the influence of alcohol.

Using Machine Learning to Predict Rehabilitation Outcomes in Postacute Hip Fracture Patients.

OBJECTIVE: To use machine learning-based methods in designing a predictive model of rehabilitation outcomes for postacute hip fracture patients. DESIGN: A retrospective analysis using linear models, AdaBoost, CatBoost, ExtraTrees, K-Nearest Neighbors, RandomForest, Support vector machine, XGBoost, and voting of all models to develop and validate a predictive model. SETTING: A university-affiliated 300-bed major postacute geriatric rehabilitation center. PARTICIPANTS: Consecutive hip fracture patients (N=1625) admitted to an postacute rehabilitation department. MAIN OUTCOME MEASURES: The FIM instrument, motor FIM (mFIM), and the relative functional gain on mFIM (mFIM effectiveness) as a continuous and binary variable. Ten predictive models were created: base models (linear/logistic regression), and 8 machine learning models (AdaBoost, CatBoost, ExtraTrees, K-Nearest Neighbors, RandomForest, Support vector machine, XGBoost, and a voting ensemble). R2 was used to evaluate their performance in predicting a continuous outcome variable, and the area under the receiver operating characteristic curve was used to evaluate the binary outcome. A paired 2-tailed t test compared the results of the different models. RESULTS: Machine learning-based models yielded better results than the linear and logistic regression models in predicting rehabilitation outcomes. The 3 most important predictors of the mFIM effectiveness score were the Mini Mental State Examination (MMSE), prefracture mFIM scores, and age. The 3 most important predictors of the discharge mFIM score were the admission mFIM, MMSE, and prefracture mFIM scores. The most contributing factors for favorable outcomes (mFIM effectiveness > median) with higher prediction confidence level were high MMSE (25.7±2.8), high prefacture mFIM (81.5±7.8), and high admission mFIM (48.6±8) scores. We present a simple prediction instrument for estimating the expected performance of postacute hip fracture patients. CONCLUSIONS: The use of machine learning models to predict rehabilitation outcomes of postacute hip fracture patients is superior to linear and logistic regression models. The higher the MMSE, prefracture mFIM, and admission mFIM scores are, the higher the confidence levels of the predicted parameters.

Cyber security threats in the microbial genomics era: implications for public health.

Next generation sequencing (NGS) is becoming the new gold standard in public health microbiology. Like any disruptive technology, its growing popularity inevitably attracts cyber security actors, for whom the health sector is attractive because it combines mission-critical infrastructure and high-value data with cybersecurity vulnerabilities. In this Perspective, we explore cyber security aspects of microbial NGS. We discuss the motivations and objectives for such attack, its feasibility and implications, and highlight policy considerations aimed at threat mitigation. Particular focus is placed on the attack vectors, where the entire process of NGS, from sample to result, could be vulnerable, and a risk assessment based on probability and impact for representative attack vectors is presented. Cyber attacks on microbial NGS could result in loss of confidentiality (leakage of personal or institutional data), integrity (misdetection of pathogens) and availability (denial of sequencing services). NGS platforms are also at risk of being used as propagation vectors, compromising an entire system or network. Owing to the rapid evolution of microbial NGS and its applications, and in light of the dynamics of the cyber security domain, frequent risk assessments should be carried out in order to identify new threats and underpin constantly updated public health policies.

Probability-Based Algorithm for Bearing Diagnosis with Untrained Spall Sizes.

Bearing spall detection and predicting its size are great challenges. Model-based simulation is a well-known traditional approach to physically model the influence of the spall on the bearing. Building a physical model is challenging due to the bearing complexity and the expert knowledge required to build such a model. Obviously, building a partial physical model for some of the spall sizes is easier. In this paper, we propose a machine-learning algorithm, called Probability-Based Forest, that uses a partial physical model. First, the behavior of some of the spall sizes is physically modeled and a simulator based on this model generates scenarios for these spall sizes in different conditions. Then, the machine-learning algorithm trains these scenarios to generate a prediction model of spall sizes even for those that have not been modeled by the physical model. Feature extraction is a key factor in the success of this approach. We extract features using two traditional approaches: statistical and physical, and an additional new approach: Time Series FeatuRe Extraction based on Scalable Hypothesis tests (TSFRESH). Experimental evaluation with well-known physical model shows that our approach achieves high accuracy, even in cases that have not been modeled by the physical model. Also, we show that the TSFRESH feature-extraction approach achieves the highest accuracy.

OPTICAL COHERENCE TOMOGRAPHY BIOMARKERS TO DISTINGUISH DIABETIC MACULAR EDEMA FROM PSEUDOPHAKIC CYSTOID MACULAR EDEMA USING MACHINE LEARNING ALGORITHMS.

PURPOSE: In diabetic patients presenting with macular edema (ME) shortly after cataract surgery, identifying the underlying pathology can be challenging and influence management. Our aim was to develop a simple clinical classifier able to confirm a diabetic etiology using few spectral domain optical coherence tomography parameters. METHODS: We analyzed spectral domain optical coherence tomography data of 153 patients with either pseudophakic cystoid ME (n = 57), diabetic ME (n = 86), or "mixed" (n = 10). We used advanced machine learning algorithms to develop a predictive classifier using the smallest number of parameters. RESULTS: Most differentiating were the existence of hard exudates, hyperreflective foci, subretinal fluid, ME pattern, and the location of cysts within retinal layers. Using only 3 to 6 spectral domain optical coherence tomography parameters, we achieved a sensitivity of 94% to 98%, specificity of 94% to 95%, and an area under the curve of 0.937 to 0.987 (depending on the method) for confirming a diabetic etiology. A simple decision flowchart achieved a sensitivity of 96%, a specificity of 95%, and an area under the curve of 0.937. CONCLUSION: Confirming a diabetic etiology for edema in cases with uncertainty between diabetic cystoid ME and pseudophakic ME was possible using few spectral domain optical coherence tomography parameters with high accuracy. We propose a clinical decision flowchart for cases with uncertainty, which may support the decision for intravitreal injections rather than topical treatment.

Empowering prediction of miRNA-mRNA interactions in species with limited training data through transfer learning.

MicroRNAs (miRNAs) play a crucial role in mRNA regulation. Identifying functionally important mRNA targets of a specific miRNA is essential for uncovering its biological function and assisting miRNA-based drug development. Datasets of high-throughput direct bona fide miRNA-target interactions (MTIs) exist only for a few model organisms, prompting the need for computational prediction. However, the scarcity of data poses a challenge in training accurate machine learning models for MTI prediction. In this study, we explored the potential of transfer learning technique (with ANN and XGB) to address the limited data challenge by leveraging the similarities in interaction rules between species. Furthermore, we introduced a novel approach called TransferSHAP for estimating the feature importance of transfer learning in tabular dataset tasks. We demonstrated that transfer learning improves MTI prediction accuracy for species with limited datasets and identified the specific interaction features the models employed to transfer information across different species.

Vitamin D supplement for patients with early-stage chronic lymphocytic leukemia is associated with a longer time to first treatment.

ABSTRACT: Low levels of vitamin D are associated with a shorter time to first treatment (TTFT) and inferior overall survival in patients with chronic lymphocytic leukemia (CLL). But whether vitamin D supplement affects the clinical course of patients with CLL, remains an open question. In this study, we aimed to retrospectively explore the clinical benefit of vitamin D supplement or one of its analogs, on TTFT and treatment-free survival (TFS) in a large cohort of patients with asymptomatic CLL, who were under watch-and-wait approach. Among the 3474 patients included in the study, 931 patients (26.8%) received either vitamin D supplement or its analog, for a minimum of 6 months. We found that vitamin D supplement was statistically significant for longer TTFT in the young cohort (age ≤65) and was associated with a longer TFS for all ages (P = .004). Among non-vitamin-D users, the median TFS was found to be 84 months, whereas among vitamin D supplement users the median TFS extended to 169 months. In conclusion, our long-term retrospective study demonstrates that the administration of vitamin D to patients with CLL in a watch-and-wait active surveillance is significantly associated with a longer TFS (in any age) and a longer TTFT among young patients (age ≤65). A prospective clinical trial is needed to validate results.