An integrative data-driven model simulating C. elegans brain, body and environment interactions.

The behavior of an organism is influenced by the complex interplay between its brain, body and environment. Existing data-driven models focus on either the brain or the body-environment. Here we present BAAIWorm, an integrative data-driven model of Caenorhabditis elegans, which consists of two submodels: the brain model and the body-environment model.

Durian: A Comprehensive Benchmark for Structure-Based 3D Molecular Generation.

Three-dimensional (3D) molecular generation models employ deep neural networks to simultaneously generate both topological representation and molecular conformations. Due to their advantages in utilizing the structural and interaction information on targets, as well as their reduced reliance on existing bioactivity data, these models have attracted widespread attention. However, limited training and testing data sets and the unexpected biases inherent in single evaluation metrics pose a significant challenge in comparing these models in practical settings.

Signatures of chronic pain in multiple sclerosis: a machine learning approach to investigate trigeminal neuralgia.

Chronic pain is a pervasive, disabling, and understudied feature of multiple sclerosis (MS), a progressive demyelinating and neurodegenerative disease. Current focus on motor components of MS disability combined with difficulties assessing pain symptoms present a challenge for the evaluation and management of pain in MS, highlighting the need for novel methods of assessment of neural signatures of chronic pain in MS. We investigate chronic pain in MS using MS-related trigeminal neuralgia (MS-TN) as a model condition focusing on gray matter structures as predictors of chronic pain.

Olfactory neurofeedback: current state and possibilities for further development.

This perspective considers the novel concept of olfactory neurofeedback (O-NFB) within the framework of brain-computer interfaces (BCIs), where olfactory stimuli are integrated in various BCI control loops. In particular, electroencephalography (EEG)-based O-NFB systems are capable of incorporating different components of complex olfactory processing - from simple discrimination tasks to using olfactory stimuli for rehabilitation of neurological disorders. In our own work, EEG theta and alpha rhythms were probed as control variables for O-NFB.

Activation of HSPA5 contributes to pazopanib-induced hepatotoxicity through l-ornithine metabolism pathway and endoplasmic reticulum stress.

Objectives: The clinical application of Pazopanib (Paz) is often accompanied by hepatotoxicity. However, the mechanisms of hepatic toxicity induced by pazopanib are not entirely clarified.

Methods: Male C57BL/6J mice were treated with pazopanib every day for 2, 4, or 8 weeks.

Screening social anxiety with the Social Artificial Intelligence Picture System.

Social anxiety disorder (SAD) is a prevalent anxiety disorder marked by strong fear and avoidance of social scenarios. Early detection of SAD lays the foundation for the introduction of early interventions. However, due to the nature of social avoidance in social anxiety, the screening is challenging in the clinical setting.

Intratumoral delivery of mRNA encoding the endogenous TLR2/6 agonist UNE-C1 induces immunogenic cell death and enhances antitumor activity.

Introduction: Recent investigations have highlighted the intratumoral administration of Toll-like receptor (TLR) ligands as a promising approach to initiate localized immune responses and enhance antitumor immunity. However, the clinical application of these ligands is limited by their rapid dissemination from the tumor microenvironment, raising concerns about reduced effectiveness and systemic toxicity.

Methods: To address these challenges, our study focused on the intratumoral delivery of mRNA encoding UNE-C1, a TLR2/6 ligand known for its efficacy and low toxicity profile.

Speech in noise listening correlates identified in resting state and DTI MRI images.

This study presents an examination of the neural connectivity associated with processing speech in noisy environments, an ability that declines with age. We correlated subjects' speech-in-noise (SIN) ability with resting-state MRI scans and Fractional Anisotropy (FA) values from the auditory section of the corpus callosum, both with and without correcting for age. The results revealed that subjects who performed poorly on the right ear SIN test (QuickSIN, MedRx) had higher correlations between the primary auditory cortex and regions of the brain that process language.

Descriptor-Free Collective Variables from Geometric Graph Neural Networks.

Enhanced sampling simulations make the computational study of rare events feasible. A large family of such methods crucially depends on the definition of some collective variables (CVs) that could provide a low-dimensional representation of the relevant physics of the process. Recently, many methods have been proposed to semiautomatize the CV design by using machine learning tools to learn the variables directly from the simulation data.

Fully automatic quantification of pulmonary fat attenuation volume by CT: an exploratory pilot study.

Background: Non-malignant chronic diseases remain a major public health concern. Given the alterations in lipid metabolism and deposition in the lung and its association with fibrotic interstitial lung disease (fILD) and chronic obstructive pulmonary disease (COPD), this study aimed to detect those alterations using computed tomography (CT)-based analysis of pulmonary fat attenuation volume (CTpfav).

Methods: This observational retrospective single-center study involved 716 chest CT scans from three subcohorts: control (n = 279), COPD (n = 283), and fILD (n = 154).

[An integrated curriculum for epidemiology and medical statistics teaching in undergraduate students majoring in clinical medicine: lesson learned from teaching reform].

Epidemiology and medical statistics are essential courses for undergraduate students majoring in clinical medicine. By studying the two courses, they can obtain the core skills for their future clinical practice. High-level medical schools both at home and abroad have accumulated successful experiences in curriculum, teaching methods and teaching models of the two disciplines.

Ivacaftor, a CFTR potentiator, synergizes with osimertinib against acquired resistance to osimertinib in NSCLC by regulating CFTR-PTEN-AKT axis.

Osimertinib, a third-generation epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI), has demonstrated significant clinical benefits in the treatment of EGFR-mutated non-small cell lung cancer (NSCLC). However, inevitable acquired resistance to osimertinib limits its clinical utility, and there is a lack of effective countermeasures. Here, we established osimertinib-resistant cell lines and performed drug library screening.

Memristor-based model of neuronal excitability and synaptic potentiation.

In this manuscript, we investigate the memristor-based implementation of neuronal ion channels in a mathematical model and an experimental circuit for a neuronal oscillator. We used a FitzHugh-Nagumo equation system describing neuronal excitability. Non-linearities introduced by the voltage-gated ion channels were modeled using memristive devices.

Deciphering white matter microstructural alterations in catatonia according to ICD-11: replication and machine learning analysis.

Catatonia is a severe psychomotor disorder characterized by motor, affective and cognitive-behavioral abnormalities. Although previous magnetic resonance imaging (MRI) studies suggested white matter (WM) dysconnectivity in the pathogenesis of catatonia, it is unclear whether microstructural alterations of WM tracts connecting psychomotor regions might contribute to a better classification of catatonia patients. Here, diffusion-weighted MRI data were collected from two independent cohorts (whiteCAT/replication cohort) of patients with (n = 45/n = 13) and without (n = 56/n = 26) catatonia according to ICD-11 criteria.

Impact of immunological aging on T cell-mediated therapies in older adults with multiple myeloma and lymphoma.

The treatment landscape for lymphoma and multiple myeloma, which disproportionally affect older adults, has been transformed by the advent of T cell-mediated immunotherapies, including immune checkpoint inhibition, T cell-engaging bispecific antibodies, and chimeric antigen receptor (CAR) T cell therapy, during the last decade. These treatment modalities re-enable the patient's own immune system to combat malignant cells and offer the potential for sustained remissions and cure for various diseases.Age profoundly affects the physiological function of the immune system.

Identification and validation of WDR5 WIN-site ligands via DNA-encoded chemical library screening.

WD repeat-containing protein 5 (WDR5) is a scaffolding protein involved in critical protein-protein interactions and a promising target for therapeutic development. Novel small-molecule ligands targeting WDR5 were identified using the DELopen platform, a free-access DNA-encoded chemical library (DEL) for academic research. Through off-DNA structure-activity relationship studies and photoaffinity labeling, two promising initial leads, DBL-6-13 and DBL-6-33, were identified as new binders of WDR5.

The crucial involvement of gamma-Mangostin and CYP1B1 in the mechanism underlying the toxicity caused by cigarette smoke extract: In silico and in vitro insights.

Cigarette smoke extracts (CSE) contain harmful substances that significantly contribute to respiratory conditions. Previous studies have primarily focused on the presence of carcinogens in CSE. However, it should be noted that other compounds may also synergistically contribute to a greater impact.

Mutations in the essential outer membrane protein BamA contribute to Escherichia coli resistance to the antimicrobial peptide TAT-RasGAP.

Antimicrobial peptides (AMPs) are promising alternatives to classical antibiotics against antibiotic-resistant pathogens. TAT-RasGAP is an AMP with broad range antibacterial activity, but its mechanism of action is unknown. In this study, we analyzed a strain of Escherichia coli with extensive resistance to TAT-RasGAP but not to other AMPs that we obtained after twenty passages during an in vitro resistance selection experiment.

SurfDock is a surface-informed diffusion generative model for reliable and accurate protein-ligand complex prediction.

Accurately predicting protein-ligand interactions is crucial for understanding cellular processes. We introduce SurfDock, a deep-learning method that addresses this challenge by integrating protein sequence, three-dimensional structural graphs and surface-level features into an equivariant architecture. SurfDock employs a generative diffusion model on a non-Euclidean manifold, optimizing molecular translations, rotations and torsions to generate reliable binding poses.