Prediction of Cytochrome P450 Inhibition Using a Deep Learning Approach and Substructure Pattern Recognition.

Cytochrome P450 (CYP) is a family of enzymes that are responsible for about 75% of all metabolic reactions. Among them, CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4 participate in the metabolism of most drugs and mediate many adverse drug reactions. Therefore, it is necessary to estimate the chemical inhibition of Cytochrome P450 enzymes in drug discovery and the food industry.

Modeling and Insights into the Structural Characteristics of Chemical Mitochondrial Toxicity.

Mitochondria are the energy metabolism center of cells and are involved in a number of other processes, such as cell differentiation and apoptosis, signal transduction, and regulation of cell cycle and cell proliferation. It is of great significance to evaluate the mitochondrial toxicity of drugs and other chemicals. In the present study, we aimed to propose easily available artificial intelligence (AI) models for the prediction of chemical mitochondrial toxicity and investigate the structural characteristics with the analysis of molecular properties and structural alerts.

Modeling and insights into the structural characteristics of endocrine-disrupting chemicals.

Endocrine-disrupting chemicals (EDCs) can cause serious harm to human health and the environment; therefore, it is important to rapidly and correctly identify EDCs. Different computational models have been proposed for the prediction of EDCs over the past few decades, but the reported models are not always easily available, and few studies have investigated the structural characteristics of EDCs. In the present study, we have developed a series of artificial intelligence models targeting EDC receptors: the androgen receptor (AR); estrogen receptor (ER); and pregnane X receptor (PXR).

Risk-factor analysis and predictive-model development of acute kidney injury in inpatients administered cefoperazone-sulbactam sodium and mezlocillin-sulbactam sodium: a single-center retrospective study.

Acute kidney injury (AKI) is a common adverse reaction observed with the clinical use of cefoperazone-sulbactam sodium and mezlocillin-sulbactam sodium. Based upon real-world data, we will herein determine the risk factors associated with AKI in inpatients after receipt of these antimicrobial drugs, and we will develop predictive models to assess the risk of AKI. Data from all adult inpatients who used cefoperazone-sulbactam sodium and mezlocillin-sulbactam sodium at the First Affiliated Hospital of Shandong First Medical University between January 2018 and December 2020 were analyzed retrospectively.

Association of proton pump inhibitors with gastric and colorectal cancer risk: A systematic review and meta-analysis.

Proton pump inhibitors (PPI) are generally considered to be one of the well-established prescription drug classes and are commonly used to treat most acid-related diseases. However, a growing body of literature showing an association between gastric and colorectal cancer risk and PPI use continues to raise concerns about the safety of PPI use. Therefore, we aimed to investigate the association between proton pump inhibitor use and risk of gastric and colorectal cancer.

Machine Learning Modeling and Insights into the Structural Characteristics of Drug-Induced Neurotoxicity.

Neurotoxicity can be resulted from many diverse clinical drugs, which has been a cause of concern to human populations across the world. The detection of drug-induced neurotoxicity (DINeurot) potential with biological experimental methods always required a lot of budget and time. In addition, few studies have addressed the structural characteristics of neurotoxic chemicals.

Modeling and insights into the structural characteristics of drug-induced autoimmune diseases.

The incidence and complexity of drug-induced autoimmune diseases (DIAD) have been on the rise in recent years, which may lead to serious or fatal consequences. Besides, many environmental and industrial chemicals can also cause DIAD. However, there are few effective approaches to estimate the DIAD potential of drugs and other chemicals currently, and the structural characteristics and mechanism of action of DIAD compounds have not been clarified.

Modeling and insights into the structural basis of chemical acute aquatic toxicity.

It has become a top global regulatory priority to prevent and control pollution from the release of synthetic chemicals, which continues to affect the aquatic communities. In the past decades, computational tools were largely used to significantly reduce the budget and time cost of chemical acute aquatic toxicity assessment. But the structural basis of toxic compounds was rarely analyzed.

The incidence and risk factors analysis of acute kidney injury in hospitalized patients received diuretics: A single-center retrospective study.

Diuretics have been one of the well-known nephrotoxic drugs which can lead to acute kidney injury (AKI). However, there are few real-world studies on the incidence of AKI in hospitalized patients received diuretics. In the present study, a single-center retrospective study was conducted in our center.

SApredictor: An Expert System for Screening Chemicals Against Structural Alerts.

The rapid and accurate evaluation of chemical toxicity is of great significance for estimation of chemical safety. In the past decades, a great number of excellent computational models have been developed for chemical toxicity prediction. But most machine learning models tend to be "black box", which bring about poor interpretability.

In Silico Prediction and Insights Into the Structural Basis of Drug Induced Nephrotoxicity.

Drug induced nephrotoxicity is a major clinical challenge, and it is always associated with higher costs for the pharmaceutical industry and due to detection during the late stages of drug development. It is desirable for improving the health outcomes for patients to distinguish nephrotoxic structures at an early stage of drug development. In this study, we focused on in silico prediction and insights into the structural basis of drug induced nephrotoxicity, based on reliable data on human nephrotoxicity.

An Evaluation of Posture Recognition Based on Intelligent Rapid Entire Body Assessment System for Determining Musculoskeletal Disorders.

Determining the potential risks of musculoskeletal disorders through working postures in a workplace is expensive and time-consuming. A novel intelligent rapid entire body assessment (REBA) system based on convolutional pose machines (CPM), entitled the Quick Capture system, was applied to determine the risk levels. The aim of the study was to validate the feasibility and reliability of the CPM-based REBA system through a simulation experiment.