Transport of a comb-like polymer across a nanochannel subject to a pulling force.

We investigate the dynamics of comb-like polymer translocation through a nanochannel using three-dimensional Langevin dynamics simulations based on a coarse-grained chain model. A comprehensive set of simulations are performed to examine the effects of system parameters such as the grafting densityof the side chains, the polymer chain length, the nanochannel dimensions, and the magnitude of the pulling force on the translocation dynamics. For a given polymer chain length, keeping the backbone length is constant while varying, we have found that the dependence of the mean translocation time⟨τ⟩onis non-monotonic, with a maximum translocation time for a specificat which the translocation is the slowest.

Grit and academic resilience during the COVID-19 pandemic.

Grit, a non-cognitive skill that indicates perseverance and passion for long-term goals, has been shown to predict academic achievement. This paper provides evidence that grit also predicts student outcomes during the challenging period of the Covid-19 pandemic. We use a unique dataset from a digital learning platform in the United Arab Emirates to construct a behavioral measure of grit.

Machine learning-driven diagnosis of multiple sclerosis from whole blood transcriptomics.

Multiple sclerosis (MS) is a neurological disorder characterized by immune dysregulation. It begins with a first clinical manifestation, a clinically isolated syndrome (CIS), which evolves to definite MS in case of further clinical and/or neuroradiological episodes. Here we evaluated the diagnostic value of transcriptional alterations in MS and CIS blood by machine learning (ML).

SCRIB: Set-Classifier with Class-Specific Risk Bounds for Blackbox Models.

Despite deep learning (DL) success in classification problems, DL classifiers do not provide a sound mechanism to decide when to refrain from predicting. Recent works tried to control the overall prediction risk with . However, existing works overlook the different significance of different classes.

Evidence synthesis and pooled analysis of vaccine effectiveness for COVID-19 mRNA vaccine BNT162b2 as a heterologous booster after inactivated SARS-CoV-2 virus vaccines.

Introduction of primary COVID-19 vaccination has helped reduce severe disease and death caused by SARS-CoV-2 infection. Understanding the protection conferred by heterologous booster regimens informs alternative vaccination strategies that enable programmatic resilience and can catalyze vaccine confidence and coverage. Inactivated SARS-CoV-2 vaccines are among the most widely used vaccines worldwide.

CHEER: Rich Model Helps Poor Model via Knowledge Infusion.

There is a growing interest in applying deep learning (DL) to healthcare, driven by the availability of data with multiple feature channels in environments (e.g., intensive care units).

MOLER: Incorporate Molecule-Level Reward to Enhance Deep Generative Model for Molecule Optimization.

The goal of molecular optimization is to generate molecules similar to a target molecule but with better chemical properties. Deep generative models have shown great success in molecule optimization. However, due to the iterative local generation process of deep generative models, the resulting molecules can significantly deviate from the input in molecular similarity and size, leading to poor chemical properties.

Development of a personalized shared decision-making tool for knee osteoarthritis and user-testing with African American and Latina women.

Background: Patients with chronic knee pain are often unaware of treatment options and likely outcomes-information that is critical to decision-making. A consistent framework for communicating patient-personalized information enables clinicians to provide consistent, targeted, and relevant information. Our objective was to user-test a shared decision-making (SDM) tool for chronic knee pain.

Applications of Computer Vision on Automatic Potato Plant Disease Detection: A Systematic Literature Review.

In most developing countries, the contribution of agriculture to gross domestic product is significant. Plant disease is one of the major factors that adversely affect crop yield. Traditional plant disease detection techniques are time-consuming, biased, and ineffective.

Artificial intelligence foundation for therapeutic science.

Artificial intelligence (AI) is poised to advance therapeutic science. Therapeutics Data Commons is an initiative to access and evaluate AI capability across therapeutic modalities and stages of discovery, establishing the foundation of which AI methods are most suitable and why.

Assessment of prescribed vs. achieved fluid balance during continuous renal replacement therapy and mortality outcome.

Background: Fluid management during continuous renal replacement therapy (CRRT) requires accuracy in the prescription of desired patient fluid balance (FBGoal) and precision in the attainable patient fluid balance (FBAchieved). Herein, we examined the association of the gap between prescribed vs. achieved patient fluid balance during CRRT (%FBGap) with hospital mortality in critically ill patients.

HINT: Hierarchical interaction network for clinical-trial-outcome predictions.

Clinical trials are crucial for drug development but often face uncertain outcomes due to safety, efficacy, or patient-recruitment problems. We propose the Hierarchical Interaction Network (HINT) to predict clinical trial outcomes. First, HINT encodes multi-modal data (drug molecule, target disease, trial eligibility criteria) into embeddings.

Analysis of eGFR index category and annual eGFR slope association with adverse clinical outcomes using real-world Japanese data: a retrospective database study.

Objectives: Real-world clinical outcome data of patients with an above-normal estimated glomerular filtration rate (eGFR) and increasing eGFR over time (eGFR slope) are scarce. Although eGFR is commonly recorded, eGFR slopes are rarely used for adverse outcome risk categorisation in clinical practice. We investigated the association of above-normal/below-normal eGFR ranges and increasing/declining eGFR slopes with clinical outcomes in Japan.

Review of Neurologic Comorbidities in Hospitalized Patients With Opioid Abuse.

Purpose Of Review: To determine the prevalence and burden of neurologic comorbidities in hospitalized patients with opioid abuse.

Recent Findings: From 1 year of hospital discharges, 2,182 patients with opioid abuse were identified (prevalence 6.3%), with abuse greater among younger patients ( < 0.

Mobility Levels With Physical Rehabilitation Delivered During and After Extracorporeal Membrane Oxygenation: A Marker of Illness Severity or an Indication of Recovery?

Objective: The aims of this study were to determine whether physical rehabilitation intervention for individuals who required extracorporeal membrane oxygenation (ECMO) is associated with clinical outcomes and to assess whether the patient mobility response over initial rehabilitation sessions early in the intensive care unit (ICU) course predicts or is associated with survival, lengths of stay, discharge disposition, and 30-day readmissions.

Methods: This study was a 10-year retrospective practice analysis of adults who were critically ill and required ECMO for >72 hours in the cardiothoracic ICU at an academic medical center. Physical rehabilitation implemented during or following the initiation of ECMO was quantified on the basis of timing, frequency, and change in mobility level in response to the intervention over the first 4 consecutive sessions.

Developing and Demonstrating the Viability and Availability of the Multilevel Implementation Strategy for Syncope Optimal Care Through Engagement (MISSION) Syncope App: Evidence-Based Clinical Decision Support Tool.

Background: Syncope evaluation and management is associated with testing overuse and unnecessary hospitalizations. The 2017 American College of Cardiology/American Heart Association (ACC/AHA) Syncope Guideline aims to standardize clinical practice and reduce unnecessary services. The use of clinical decision support (CDS) tools offers the potential to successfully implement evidence-based clinical guidelines.

Machine learning applications for therapeutic tasks with genomics data.

Thanks to the increasing availability of genomics and other biomedical data, many machine learning algorithms have been proposed for a wide range of therapeutic discovery and development tasks. In this survey, we review the literature on machine learning applications for genomics through the lens of therapeutic development. We investigate the interplay among genomics, compounds, proteins, electronic health records, cellular images, and clinical texts.

Predicting in-hospital mortality after an in-hospital cardiac arrest: A multivariate analysis.

Aim Of The Study: Most survivors of an in-hospital cardiac arrest do not leave the hospital alive, and there is a need for a more patient-centered, holistic approach to the assessment of prognosis after an arrest. We sought to identify pre-, peri-, and post-arrest variables associated with in-hospital mortality amongst survivors of an in-hospital cardiac arrest.

Methods: This was a retrospective cohort study of patients ≥18 years of age who were resuscitated from an in-hospital arrest at our University Medical Center from January 1, 2013 to September 31, 2016.

STELAR: Spatio-temporal Tensor Factorization with Latent Epidemiological Regularization.

Accurate prediction of the transmission of epidemic diseases such as COVID-19 is crucial for implementing effective mitigation measures. In this work, we develop a tensor method to predict the evolution of epidemic trends for many regions simultaneously. We construct a 3-way spatio-temporal tensor (location, attribute, time) of case counts and propose a nonnegative tensor factorization with latent epidemiological model regularization named STELAR.

5th generation vs 4th generation troponin T in predicting major adverse cardiovascular events and all-cause mortality in patients hospitalized for non-cardiac indications: A cohort study.

Objective: The frequency and implications of an elevated cardiac troponin (4th or 5th generation TnT) in patients outside of the emergency department or presenting with non-cardiac conditions is unclear.

Methods: Consecutive patients aged 18 years or older admitted for a primary non-cardiac condition who had the 4th generation TnT drawn had the 5th generation TnT run on the residual blood sample. Primary and secondary outcomes were all-cause mortality (ACM) and major adverse cardiovascular events (MACE) respectively at 1 year.

Direct Medical Costs Associated With Treatment Nonpersistence in Patients With Higher-Risk Myelodysplastic Syndromes Receiving Hypomethylating Agents: A Large Retrospective Cohort Analysis.

Background: Suboptimal use of hypomethylating agents (HMAs) among higher-risk myelodysplastic syndrome (HR-MDS) patients can translate into worse health outcomes and economic burden. We estimated the direct medical costs associated with HMA treatment nonpersistence among HR-MDS patients.

Patients And Methods: Using the Surveillance, Epidemiology, and End Results-Medicare linked database, a retrospective cohort of patients diagnosed with refractory anemia with excess blasts (RAEB), a diagnosis that substantially overlaps with HR-MDS, between January 2011 and December 2015 was analyzed.

SkipGNN: predicting molecular interactions with skip-graph networks.

Molecular interaction networks are powerful resources for molecular discovery. They are increasingly used with machine learning methods to predict biologically meaningful interactions. While deep learning on graphs has dramatically advanced the prediction prowess, current graph neural network (GNN) methods are mainly optimized for prediction on the basis of direct similarity between interacting nodes.

Use of hospital resources in the care of patients with intermediate risk pulmonary embolism.

Background: Risk stratification and management of hemodynamically stable pulmonary embolism remains challenging. Professional societies have published stratification schemes, but little is known about the management of patients with intermediate risk pulmonary embolism. We describe the care of these patients at an academic health system.