Trends in Demographic Characteristics and Risk Factors among Individuals with Syphilis in Colorado from 2011-2020.

Background: Identify opportunities to improve syphilis screening by describing changes in patient characteristics and risk factors among individuals with syphilis and by comparing cases with and without an indication for syphilis screening.

Methods: This retrospective cohort study used Colorado public health surveillance data to identify 8,326 syphilis diagnoses from 2011-2020. Demographics, clinical characteristics, and risk factors were compared across 2-year groups and between individuals with and without an indication for screening.

VC dimension of Graph Neural Networks with Pfaffian activation functions.

Graph Neural Networks (GNNs) have emerged in recent years as a powerful tool to learn tasks across a wide range of graph domains in a data-driven fashion. Based on a message passing mechanism, GNNs have gained increasing popularity due to their intuitive formulation, closely linked to the Weisfeiler-Lehman (WL) test for graph isomorphism, to which they were demonstrated to be equivalent (Morris et al., 2019 and Xu et al.

A topological description of loss surfaces based on Betti Numbers.

In the context of deep learning models, attention has recently been paid to studying the surface of the loss function in order to better understand training with methods based on gradient descent. This search for an appropriate description, both analytical and topological, has led to numerous efforts in identifying spurious minima and characterize gradient dynamics. Our work aims to contribute to this field by providing a topological measure for evaluating loss complexity in the case of multilayer neural networks.

Composite Graph Neural Networks for Molecular Property Prediction.

Graph Neural Networks have proven to be very valuable models for the solution of a wide variety of problems on molecular graphs, as well as in many other research fields involving graph-structured data. Molecules are heterogeneous graphs composed of atoms of different species. Composite graph neural networks process heterogeneous graphs with multiple-state-updating networks, each one dedicated to a particular node type.

Protein-Protein Interfaces: A Graph Neural Network Approach.

Protein-protein interactions (PPIs) are fundamental processes governing cellular functions, crucial for understanding biological systems at the molecular level. Compared to experimental methods for PPI prediction and site identification, computational deep learning approaches represent an affordable and efficient solution to tackle these problems. Since protein structure can be summarized as a graph, graph neural networks (GNNs) represent the ideal deep learning architecture for the task.

A Mobile App for Detecting Potato Crop Diseases.

Artificial intelligence techniques are now widely used in various agricultural applications, including the detection of devastating diseases such as late blight () and early blight () affecting potato ( L.) crops. In this paper, we present a mobile application for detecting potato crop diseases based on deep neural networks.

Patient-Level Exposure to Actionable Pharmacogenomic Medications in a Nationally Representative Insurance Claims Database.

Background: The prevalence of exposure to pharmacogenomic medications is well established but little is known about how long patients are exposed to these medications.

Aim: Our objective was to describe the amount of exposure to actionable pharmacogenomic medications using patient-level measures among a large nationally representative population using an insurance claims database.

Methods: Our retrospective cohort study included adults (18+ years) from the IQVIA PharMetrics Plus for Academics claims database with incident fills of 72 Clinical Pharmacogenetics Implementation Consortium level A, A/B, or B medications from January 2012 through September 2018.

A Deep Learning Approach to the Prediction of Drug Side-Effects on Molecular Graphs.

Predicting drug side effects before they occur is a critical task for keeping the number of drug-related hospitalizations low and for improving drug discovery processes. Automatic predictors of side-effects generally are not able to process the structure of the drug, resulting in a loss of information. Graph neural networks have seen great success in recent years, thanks to their ability of exploiting the information conveyed by the graph structure and labels.

Postintubation Sedation Practices Within Multiple Emergency Departments Across a Large Pediatric Health Care Organization.

Objective: Children requiring rapid or standard sequence intubation are at risk of experiencing paralysis without adequate sedation when the duration of neuromuscular blockade exceeds the duration of sedation provided by the induction agent. The objective of this study was to evaluate the rate of appropriately timed postintubation sedation (PIS; defined as the administration of PIS before the clinical effects of the induction agent have dissipated) in patients requiring intubation across multiple emergency department/urgent care sites within a large pediatric health care organization.

Methods: This retrospective cohort study included patients admitted to 1 of 6 affiliated pediatric emergency department or urgent care sites who were intubated with an induction agent and neuromuscular blocker between January 2016 and December 2021.

Risk Factors Associated With Opioid/Benzodiazepine Iatrogenic Withdrawal Syndrome in COVID-19 Acute Respiratory Distress Syndrome.

BackgroundMechanically ventilated COVID-19 acute respiratory distress syndrome (ARDS) patients often receive deeper sedation and analgesia to maintain respiratory compliance and minimize staff exposure, which incurs greater risk of iatrogenic withdrawal syndrome (IWS) and has been associated with worse patient outcomes. To identify potential risk factors and differences in patient outcomes associated with the development of IWS in COVID-19 ARDS patients. Retrospective analysis of ventilated COVID-19 ARDS intensive care unit (ICU) patients who received continuous intravenous (IV) analgesia and sedation for ≥5 days from March 2020-May 2021.

A Bioinformatics Approach to Investigate Structural and Non-Structural Proteins in Human Coronaviruses.

Recent studies confirmed that people unexposed to SARS-CoV-2 have preexisting reactivity, probably due to previous exposure to widely circulating common cold coronaviruses. Such preexistent reactivity against SARS-CoV-2 comes from memory T cells that can specifically recognize a SARS-CoV-2 epitope of structural and non-structural proteins and the homologous epitopes from common cold coronaviruses. Therefore, it is important to understand the SARS-CoV-2 cross-reactivity by investigating these protein sequence similarities with those of different circulating coronaviruses.

Modular Multi-Source Prediction of Drug Side-Effects With DruGNN.

Drug Side-Effects (DSEs) have a high impact on public health, care system costs, and drug discovery processes. Predicting the probability of side-effects, before their occurrence, is fundamental to reduce this impact, in particular on drug discovery. Candidate molecules could be screened before undergoing clinical trials, reducing the costs in time, money, and health of the participants.

Cost-Effectiveness Analysis of New Beta-Lactam Beta-Lactamase Inhibitor Antibiotics Versus Colistin for the Treatment of Carbapenem-Resistant Infections.

Carbapenem-resistant organisms (CROs) present a serious public health problem. Limited treatment options has led to increased use of colistin and polymyxin. Since 2014, the US Food and Drug Administration approved 4 new beta-lactam beta-lactamase inhibitor (BLBLI) combination antibiotics with activity against CROs.

Structural bioinformatics survey on disease-inducing missense mutations.

Understanding the molecular mechanisms that correlate pathologies with missense mutations is of critical importance for disease risk estimations and for devising personalized therapies. Thus, we have performed a bioinformatic survey of ClinVar, a database of human genomic variations, to find signals that can account for missense mutation pathogenicity. Arginine resulted as the most frequently replaced amino acid both in benign and pathogenic mutations.

On Inductive-Transductive Learning With Graph Neural Networks.

Many real-world domains involve information naturally represented by graphs, where nodes denote basic patterns while edges stand for relationships among them. The graph neural network (GNN) is a machine learning model capable of directly managing graph-structured data. In the original framework, GNNs are inductively trained, adapting their parameters based on a supervised learning environment.

Multi-Modal Siamese Network for Diagnostically Similar Lesion Retrieval in Prostate MRI.

Multi-parametric prostate MRI (mpMRI) is a powerful tool to diagnose prostate cancer, though difficult to interpret even for experienced radiologists. A common radiological procedure is to compare a magnetic resonance image with similarly diagnosed cases. To assist the radiological image interpretation process, computerized Content-Based Image Retrieval systems (CBIRs) can therefore be employed to improve the reporting workflow and increase its accuracy.

Structural Bioinformatic Survey of Protein-Small Molecule Interfaces Delineates the Role of Glycine in Surface Pocket Formation.

With a structural bioinformatic approach, we have explored amino acid compositions at PISA defined interfaces between small molecules and proteins that are contained in an optimized subset of 11,351 PDB files. The use of a series of restrictions, to prevent redundancy and biases from interactions between amino acids with charged side chains and ions, yielded a final data set of 45,230 protein-small molecule interfaces. We have compared occurrences of natural amino acids in surface exposed regions and binding sites for all the proteins of our data set.

AKUImg: A database of cartilage images of Alkaptonuria patients.

ApreciseKUre is a multi-purpose digital platform facilitating data collection, integration and analysis for patients affected by Alkaptonuria (AKU), an ultra-rare autosomal recessive genetic disease. We present an ApreciseKUre plugin, called AKUImg, dedicated to the storage and analysis of AKU histopathological slides, in order to create a Precision Medicine Ecosystem (PME), where images can be shared among registered researchers and clinicians to extend the AKU knowledge network. AKUImg includes a new set of AKU images taken from cartilage tissues acquired by means of a microscopic technique.

Clinical characteristics and predictors of survival in adults with coronavirus disease 2019 receiving tocilizumab.

Coronavirus disease 2019 (COVID-19) can progress to cytokine storm that is associated with organ dysfunction and death. The purpose of the present study is to determine clinical characteristics associated with 28 day in-hospital survival in patients with coronavirus disease 2019 (COVID-19) that received tocilizumab. This was a retrospective observational cohort study conducted at a five hospital health system in Michigan, United States.

A possible strategy to fight COVID-19: Interfering with spike glycoprotein trimerization.

The recent release of COVID-19 spike glycoprotein allows detailed analysis of the structural features that are required for stabilizing the infective form of its quaternary assembly. Trying to disassemble the trimeric structure of COVID-19 spike glycoprotein, we analyzed single protomer surfaces searching for concave moieties that are located at the three protomer-protomer interfaces. The presence of some druggable pockets at these interfaces suggested that some of the available drugs in Drug Bank could destabilize the quaternary spike glycoprotein formation by binding to these pockets, therefore interfering with COVID-19 life cycle.

Image generation by GAN and style transfer for agar plate image segmentation.

Background And Objectives: Deep learning models and specifically Convolutional Neural Networks (CNNs) are becoming the leading approach in many computer vision tasks, including medical image analysis. Nevertheless, the CNN training usually requires large sets of supervised data, which are often difficult and expensive to obtain in the medical field. To address the lack of annotated images, image generation is a promising method, which is becoming increasingly popular in the computer vision community.

Glycine-induced formation and druggability score prediction of protein surface pockets.

Nowadays, it is well established that most of the human diseases which are not related to pathogen infections have their origin from DNA disorders. Thus, DNA mutations, waiting for the availability of CRISPR-like remedies, will propagate into proteomics, offering the possibility to select natural or synthetic molecules to fight against the effects of malfunctioning proteins. Drug discovery, indeed, is a flourishing field of biotechnological research to improve human health, even though the development of a new drug is increasingly more expensive in spite of the massive use of informatics in Medicinal Chemistry.

Discharge Delays and Costs Associated With Outpatient Parenteral Antimicrobial Therapy for High-Priced Antibiotics.

Background: Outpatient parenteral antimicrobial therapy (OPAT) is a widely used, safe, and cost-effective treatment. Most public and private insurance providers require prior authorization (PA) for OPAT, yet the impact of the inpatient PA process is not known. Our aim was to characterize discharge barriers and PA delays associated with high-priced OPAT antibiotics.