AI-guided virtual biopsy: Automated differentiation of cerebral gliomas from other benign and malignant MRI findings using deep learning.

Background: This study aimed to develop an automated algorithm to noninvasively distinguish gliomas from other intracranial pathologies, preventing misdiagnosis and ensuring accurate analysis before further glioma assessment.

Methods: A cohort of 1280 patients with a variety of intracranial pathologies was included. It comprised 218 gliomas (mean age 54.

Exploring the Active Constituents of in Protecting the Skin Barrier and the Synergistic Effects with Collagen XVII.

is mainly used to treat skin inflammations, wounds, and infections. In this study, Andrographis Herba, the aerial part of the plant, was proven to increase the viability of UVB-damaged HaCat cells and reduce reactive oxygen species levels. The chemical composition of Andrographis Herba extract (AHE) was analyzed using UPLC-Q-TOF-MS, and diterpene lactones were identified as its primary constituents.

The impact of solute carrier proteins on disrupting substance regulation in metabolic disorders: insights and clinical applications.

Carbohydrates, lipids, bile acids, various inorganic salt ions and organic acids are the main nutrients or indispensable components of the human body. Dysregulation in the processes of absorption, transport, metabolism, and excretion of these metabolites can lead to the onset of severe metabolic disorders, such as type 2 diabetes, non-alcoholic fatty liver disease, gout and hyperbilirubinemia. As the second largest membrane receptor supergroup, several major families in the solute carrier (SLC) supergroup have been found to play key roles in the transport of substances such as carbohydrates, lipids, urate, bile acids, monocarboxylates and zinc ions.

Lipids, lipid-lowering drug target genes and pancreatic cancer: a Mendelian randomization study.

Background: Pancreatic cancer (PC) is a malignant tumor with a low survival rate. Lipid modifiers show potential for PC therapy, but evidence is lacking.

Aim: This Mendelian randomization (MR) study aimed to explore the relationship between lipid traits, and lipid-lowering drug target genes with PC risk.

ADMET evaluation in drug discovery: 21. Application and industrial validation of machine learning algorithms for Caco-2 permeability prediction.

The Caco-2 cell model has been widely used to assess the intestinal permeability of drug candidates in vitro, owing to its morphological and functional similarity to human enterocytes. While Caco-2 cell assay is considered safe and cost-effective, it is also characterized by being time-consuming. Therefore, computational models that achieve high accuracies in predicting Caco-2 permeability are crucial for enhancing the efficiency of oral drug development.

Staging of prostate Cancer with ultra-fast PSMA-PET scans enhanced by AI.

Purpose: PSMA-PET is a reference standard examination for patients with prostate cancer, but even using recently introduced digital PET detectors image acquisition with standard field-of-view scanners is still in the range of 20 min. This may cause limited access to examination slots because of the growing demand for PSMA-PET. Ultra-fast PSMA-PET may enhance throughput but comes at the cost of poor image quality.

JOSD2 promotes breast cancer metastasis by deubiquitinating and stabilizing SMAD4.

Breast cancer is one of the most common malignant tumors among women worldwide, and its high degree of metastasis significantly impacts treatment effectiveness leading to poor prognosis. The potential molecular mechanisms underlying breast cancer metastasis remain to be further elucidated. In this study, via database analysis, we revealed that the deubiquitinase josephin domain containing 2 (JOSD2) was abnormally amplified in patients with metastatic breast cancer, and was significantly negatively correlated with patient prognosis.

A Transformer-Based Pipeline for German Clinical Document De-Identification.

Objective:  Commercially available large language models such as Chat Generative Pre-Trained Transformer (ChatGPT) cannot be applied to real patient data for data protection reasons. At the same time, de-identification of clinical unstructured data is a tedious and time-consuming task when done manually. Since transformer models can efficiently process and analyze large amounts of text data, our study aims to explore the impact of a large training dataset on the performance of this task.

Judged by your neighbors: Brain structural normativity profiles for large and heterogeneous samples.

The detection of norm deviations is fundamental to clinical decision making and impacts our ability to diagnose and treat diseases effectively. Current normative modeling approaches rely on generic comparisons and quantify deviations in relation to the population average. However, generic models interpolate subtle nuances and risk the loss of critical information, thereby compromising effective personalization of health care strategies.

Multi-channel learning for integrating structural hierarchies into context-dependent molecular representation.

Reliable molecular property prediction is essential for various scientific endeavors and industrial applications, such as drug discovery. However, the data scarcity, combined with the highly non-linear causal relationships between physicochemical and biological properties and conventional molecular featurization schemes, complicates the development of robust molecular machine learning models. Self-supervised learning (SSL) has emerged as a popular solution, utilizing large-scale, unannotated molecular data to learn a foundational representation of chemical space that might be advantageous for downstream tasks.

PPI-CoAttNet: A Web Server for Protein-Protein Interaction Tasks Using a Coattention Model.

Predicting protein-protein interactions (PPIs) is crucial for advancing drug discovery. Despite the proposal of numerous advanced computational methods, these approaches often suffer from poor usability for biologists and lack generalization. In this study, we designed a deep learning model based on a coattention mechanism that was capable of both PPI and site prediction and used this model as the foundation for PPI-CoAttNet, a user-friendly, multifunctional web server for PPI prediction.

Development of a two-component recombinant vaccine for COVID-19.

Introduction: Though COVID-19 as a public health emergency of international concern (PHEIC) was declared to be ended by the WHO, it continues to pose a significant threat to human society. Vaccination remains one of the most effective methods for preventing COVID-19. While most of the antigenic regions are found in the receptor binding domain (RBD), the N-terminal domain (NTD) of the S protein is another crucial region for inducing neutralizing antibodies (nAbs) against COVID-19.

Analyzing the TotalSegmentator for facial feature removal in head CT scans.

Background: Facial recognition technology in medical imaging, particularly with head scans, poses privacy risks due to identifiable facial features. This study evaluates the use of facial recognition software in identifying facial features from head CT scans and explores a defacing pipeline using TotalSegmentator to reduce re-identification risks while preserving data integrity for research.

Methods: 1404 high-quality renderings from the UCLH EIT Stroke dataset, both with and without defacing were analysed.

Auto-RapTAC: A Versatile and Sustainable Platform for the Automated Rapid Synthesis and Evaluation of PROTAC.

The tedious synthesis and limited throughput biological evaluation remain a great challenge for discovering new proteolysis targeting chimera (PROTAC). To rapidly identify potential PROTAC lead compounds, we report a platform named Auto-RapTAC. Based on the modular characteristic of the PROTAC molecule, a streamlined workflow that integrates lab automation with "click chemistry" joint building-block libraries was constructed.

- a large-scale dataset of 3D medical shapes for computer vision.

Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models).

Mechanisms of low MHC I expression and strategies for targeting MHC I with small molecules in cancer immunotherapy.

Major histocompatibility complex (MHC) class I load antigens and present them on the cell surface, which transduces the tumor-associated antigens to CD8 T cells, activating the acquired immune system. However, many tumors downregulate MHC I expression to evade immune surveillance. The low expression of MHC I not only reduce recognition by- and cytotoxicity of CD8 T cells, but also seriously weakens the anti-tumor effect of immunotherapy by restoring CD8 T cells, such as immune checkpoint inhibitors (ICIs).

Artificial intelligence-enabled discovery of a RIPK3 inhibitor with neuroprotective effects in an acute glaucoma mouse model.

Background: Retinal ganglion cell (RGC) death caused by acute ocular hypertension is an important characteristic of acute glaucoma. Receptor-interacting protein kinase 3 (RIPK3) that mediates necroptosis is a potential therapeutic target for RGC death. However, the current understanding of the targeting agents and mechanisms of RIPK3 in the treatment of glaucoma remains limited.

Multi-Modal CLIP-Informed Protein Editing.

Proteins govern most biological functions essential for life, and achieving controllable protein editing has made great advances in probing natural systems, creating therapeutic conjugates, and generating novel protein constructs. Recently, machine learning-assisted protein editing (MLPE) has shown promise in accelerating optimization cycles and reducing experimental workloads. However, current methods struggle with the vast combinatorial space of potential protein edits and cannot explicitly conduct protein editing using biotext instructions, limiting their interactivity with human feedback.

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.

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.

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).