Predictive metabolites for incident myocardial infarction: a two-step meta-analysis of individual patient data from six cohorts comprising 7897 individuals from the COnsortium of METabolomics Studies.

Aims: Myocardial infarction (MI) is a major cause of death and disability worldwide. Most metabolomics studies investigating metabolites predicting MI are limited by the participant number and/or the demographic diversity. We sought to identify biomarkers of incident MI in the COnsortium of METabolomics Studies.

Prognostic microRNA signature for estimating survival in patients with hepatocellular carcinoma.

Objective: Hepatocellular carcinoma (HCC) is one of the leading cancer types with increasing annual incidence and high mortality in the USA. MicroRNAs (miRNAs) have emerged as valuable prognostic indicators in cancer patients. To identify a miRNA signature predictive of survival in patients with HCC, we developed a machine learning-based HCC survival estimation method, HCCse, using the miRNA expression profiles of 122 patients with HCC.

Prediction of extranodal extension in head and neck squamous cell carcinoma by CT images using an evolutionary learning model.

Background: Extranodal extension (ENE) in head and neck squamous cell carcinoma (HNSCC) correlates to poor prognoses and influences treatment strategies. Deep learning may yield promising performance of predicting ENE in HNSCC but lack of transparency and interpretability. This work proposes an evolutionary learning method, called EL-ENE, to establish a more interpretable ENE prediction model for aiding clinical diagnosis.

Identification of the HNSC88 Molecular Signature for Predicting Subtypes of Head and Neck Cancer.

Head and neck squamous cell carcinoma (HNSC) exhibits genetic heterogeneity in etiologies, tumor sites, and biological processes, which significantly impact therapeutic strategies and prognosis. While the influence of human papillomavirus on clinical outcomes is established, the molecular subtypes determining additional treatment options for HNSC remain unclear and inconsistent. This study aims to identify distinct HNSC molecular subtypes to enhance diagnosis and prognosis accuracy.

Inhibitory Perturbations of Fluvastatin on Afterhyperpolarization Current, Erg-mediated K Current, and Hyperpolarization-activated Cation Current in Both Pituitary GH Cells and Primary Embryonic Mouse Cortical Neurons.

Fluvastatin (FLV), the first synthetically derived 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitor, is a potent inhibitor of cholesterol biosynthesis. While its primary mechanism of action is to reduce cholesterol levels, there is some evidence suggesting that it may also have effects on K channels. However, the overall effects of fluvastatin on ionic currents are not yet well understood.

DoSurvive: A webtool for investigating the prognostic power of a single or combined cancer biomarker.

We present DoSurvive, a user-friendly survival analysis web tool and a cancer prognostic biomarker centered database. DoSurvive is the first database that allows users to perform multivariant survival analysis for cancers with customized gene/patient list. DoSurvive offers three survival analysis methods, Log rank test, Cox regression and accelerated failure time model (AFT), for users to analyze five types of quantitative features (mRNA, miRNA, lncRNA, protein and methylation of CpG islands) with four survival types, i.

DeepAFP: An effective computational framework for identifying antifungal peptides based on deep learning.

Fungal infections have become a significant global health issue, affecting millions worldwide. Antifungal peptides (AFPs) have emerged as a promising alternative to conventional antifungal drugs due to their low toxicity and low propensity for inducing resistance. In this study, we developed a deep learning-based framework called DeepAFP to efficiently identify AFPs.

Persistence and dynamic structures of diverse cephalosporinase genes in nontyphoidal Salmonella in cross-sectional surveillance in Taiwan.

Objectives: Nontyphoidal Salmonella (NTS) is a major foodborne pathogen causing from acute gastroenteritis to bacteraemia, particularly in paediatric and elderly patients. Antimicrobial resistance of NTS, especially resistance to extended-spectrum cephalosporins, has emerged over the past decades.

Methods: Thirteen NTS isolates resistant to ceftriaxone or cefotaxime were collected from a teaching hospital in Taipei, and another three from a tertiary hospital, in New Taipei City, Taiwan, from September 2018 to December 2019.

StackTHPred: Identifying Tumor-Homing Peptides through GBDT-Based Feature Selection with Stacking Ensemble Architecture.

One of the major challenges in cancer therapy lies in the limited targeting specificity exhibited by existing anti-cancer drugs. Tumor-homing peptides (THPs) have emerged as a promising solution to this issue, due to their capability to specifically bind to and accumulate in tumor tissues while minimally impacting healthy tissues. THPs are short oligopeptides that offer a superior biological safety profile, with minimal antigenicity, and faster incorporation rates into target cells/tissues.

Survival associated miRNA signature in patients with head and neck carcinomas.

Head and neck carcinoma (HNSC) is often diagnosed at advanced stage, incurring poor patient outcome. Despite of advances in chemoradiation and surgery approaches, limited improvements in survival rates of HNSC have been observed over the last decade. Accumulating evidences have demonstrated the importance of microRNAs (miRNAs) in carcinogenesis.

Combining virtual screening with cis-/trans-cleavage enzymatic assays effectively reveals broad-spectrum inhibitors that target the main proteases of SARS-CoV-2 and MERS-CoV.

The main protease (M) of SARS-CoV-2 is essential for viral replication, which suggests that the M is a critical target in the development of small molecules to treat COVID-19. This study used an in-silico prediction approach to investigate the complex structure of SARS-CoV-2 M in compounds from the United States National Cancer Institute (NCI) database, then validate potential inhibitory compounds against the SARS-CoV-2 M in cis- and trans-cleavage proteolytic assays. Virtual screening of ∼280,000 compounds from the NCI database identified 10 compounds with highest site-moiety map scores.

Transcriptomic analysis of World Trade Center particulate Matter-induced pulmonary inflammation and drug treatments.

Over 400,000 people are estimated to have been exposed to World Trade Center particulate matter (WTC) since the attack on the Twin Towers in Lower Manhattan on September 11, 2001. Epidemiological studies have found that exposure to dust may cause respiratory ailments and cardiovascular diseases. However, limited studies have performed a systematic analysis of transcriptomic data to elucidate the biological responses to WTC exposure and the therapeutic options.

MFI2 upregulation promotes malignant progression through EGF/FAK signaling in oral cavity squamous cell carcinoma.

Oral squamous cell carcinoma (OSCC) is the predominant histological type of the head and neck squamous cell carcinoma (HNSCC). By comparing the differentially expressed genes (DEGs) in OSCC-TCGA patients with copy number variations (CNVs) that we identify in OSCC-OncoScan dataset, we herein identified 37 dysregulated candidate genes. Among these potential candidate genes, 26 have been previously reported as dysregulated proteins or genes in HNSCC.

In Silico and In Vitro Screening of Serine Racemase Agonist and In Vivo Efficacy on Alzheimer's Disease .

The NMDA receptor hypofunction has been implicated in schizophrenia, memory impairment, and Alzheimer's disease. Modulating the abundance of D-serine, a co-agonist of the NMDA receptor, is a strategy to treat symptoms of the NMDA receptor hypofunction. In contrast to D-amino acid oxidase (DAAO) inhibitors, which aim at decreasing the loss of D-serine, this study tried to identify serine racemase (SRR) agonists, which boost the conversion of L-serine to D-serine.

Structural basis for water modulating RNA duplex formation in the CUG repeats of myotonic dystrophy type 1.

Secondary structures formed by expanded CUG RNA are involved in the pathobiology of myotonic dystrophy type 1. Understanding the molecular basis of toxic RNA structures can provide insights into the mechanism of disease pathogenesis and accelerate the drug discovery process. Here, we report the crystal structure of CUG repeat RNA containing three U-U mismatches between C-G and G-C base pairs.

Erinacine S from Hericium erinaceus mycelium promotes neuronal regeneration by inducing neurosteroids accumulation.

Erinacines derived from Hericium erinaceus have been shown to possess various health benefits including neuroprotective effect against neurodegenerative diseases, yet the underlying mechanism remains unknown. Here we found that erinacine S enhances neurite outgrowth in a cell autonomous fashion. It promotes post-injury axon regeneration of PNS neurons and enhances regeneration on inhibitory substrates of CNS neurons.

Artificial intelligence-driven pan-cancer analysis reveals miRNA signatures for cancer stage prediction.

The ability to detect cancer at an early stage in patients who would benefit from effective therapy is a key factor in increasing survivability. This work proposes an evolutionary supervised learning method called Cancer to identify cancer stage-specific microRNA (miRNA) signatures for early cancer predictions. Cancer established a compact panel of miRNA signatures as potential markers from 4,667 patients with 15 different types of cancers for the cancer stage prediction, and achieved a mean performance: 10-fold cross-validation accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of 84.

Statistical learning of protein elastic network from positional covariance matrix.

Positional fluctuation and covariance during protein dynamics are key observables for understanding the molecular origin of biological functions. A frequently employed potential energy function for describing protein structural variation at the coarse-gained level is elastic network model (ENM). A long-standing issue in biomolecular simulation is thus the parametrization of ENM spring constants from the components of positional covariance matrix (PCM).

The evolution and structure of snake venom phosphodiesterase (svPDE) highlight its importance in venom actions.

For decades, studies of snake venoms focused on the venom-ome-specific toxins (VSTs). VSTs are dominant soluble proteins believed to contribute to the main venomous effects and emerged into gene clusters for fast adaptation and diversification of snake venoms. However, the conserved minor venom components, such as snake venom phosphodiesterase (svPDE), remain largely unexplored.

Transcriptome landscape reveals the chronic inflammatory response in kidneys affected by the combinatory effect of leptospirosis and nephrotoxic injury.

Leptospirosis can cause chronic kidney damage, putting patients at risk of additional kidney injury due to other factors that can lead to renal failure. To understand the combined effect, the transcriptome profiles of kidneys of mice with adenine-induced and chronically Leptospira-infected kidneys were analysed. Chronic inflammation and T-helper 17 immune responses were activated and a high-level expression of Indoleamine 2,3-dioxygenase 1 protein was found.

Data-Driven Two-Stage Framework for Identification and Characterization of Different Antibiotic-Resistant Escherichia coli Isolates Based on Mass Spectrometry Data.

In clinical microbiology, matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) is frequently employed for rapid microbial identification. However, rapid identification of antimicrobial resistance (AMR) in Escherichia coli based on a large amount of MALDI-TOF MS data has not yet been reported. This may be because building a prediction model to cover all E.

Modulating motor cortical oscillation with coordinated reset multifocal transcranial magnetic stimulation.

According to the theory of coordinated reset (CR) stimulation, multifocal bursts of stimuli delivered in a random order with a specific interval may reduce the resonance power of the oscillatory generator in the epicenter. We develop a noninvasive coordinated multifocal burst stimulation (COMBS) with three repetitive transcranial stimulation machines based on CR theory to modulate the target frequency in the primary motor cortex and to assess its effect on motor cortical excitability in separate experiments. Electroencephalography and electromyography were recorded in 16 healthy participants during a finger-tapping task, both before and after the intervention.

Holistic similarity-based prediction of phosphorylation sites for understudied kinases.

Phosphorylation is an essential mechanism for regulating protein activities. Determining kinase-specific phosphorylation sites by experiments involves time-consuming and expensive analyzes. Although several studies proposed computational methods to model kinase-specific phosphorylation sites, they typically required abundant experimentally verified phosphorylation sites to yield reliable predictions.