CD93: A Promising NETs-Related Biomarker for Diagnosis and Therapy in Actinic Keratosis.

Background: Actinic keratosis (AK), a UV-induced precancerous skin condition potentially progressing to cutaneous squamous cell carcinoma (cSCC) with undefined mechanisms, was analyzed for neutrophil extracellular traps (NETs)-related biomarkers to identify key clinical targets.

Methods: Transcriptomic profiles of AK retrieved from the GEO database were analyzed using the "limma" package to screen differentially expressed genes (DEGs), which were intersected with a curated NETs-related gene set to extract differentially expressed NETs-related genes (DE-NRGs). Functional enrichment analyses via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations identified enriched biological processes and pathways.

Deep learning for sub-ångström-resolution imaging in uncorrected scanning transmission electron microscopy.

Achieving sub-ångström resolution has long been restricted to sophisticated aberration-corrected scanning transmission electron microscopy (AC-STEM). Recent advances in computational super-resolution techniques, such as deconvolution and electron ptychography, have enabled uncorrected STEM to achieve sub-ångström resolution without the need for delicate aberration correctors. However, these methods have strict requirements for sample thickness and thus have yet to be widely implemented.

DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials.

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks, such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of the DeePMD-kit exemplified these limitations.

End-to-End Crystal Structure Prediction from Powder X-Ray Diffraction.

Powder X-ray diffraction (PXRD) is a prevalent technique in materials characterization. While the analysis of PXRD often requires extensive human manual intervention, and most automated method only achieved at coarse-grained level. The more difficult and important task of fine-grained crystal structure prediction from PXRD remains unaddressed.

Accurate Conformation Sampling via Protein Structural Diffusion.

Accurate sampling of protein conformations is pivotal for advances in biology and medicine. Although there has been tremendous progress in protein structure prediction in recent years due to deep learning, models that can predict the different stable conformations of proteins with high accuracy and structural validity are still lacking. Here, we introduce UFConf, a cutting-edge approach designed for robust sampling of diverse protein conformations based solely on amino acid sequences.

Bridging Machine Learning and Thermodynamics for Accurate p Prediction.

Integrating scientific principles into machine learning models to enhance their predictive performance and generalizability is a central challenge in the development of AI for Science. Herein, we introduce Uni-p , a novel framework that successfully incorporates thermodynamic principles into machine learning modeling, achieving high-precision predictions of acid dissociation constants (p ), a crucial task in the rational design of drugs and catalysts, as well as a modeling challenge in computational physical chemistry for small organic molecules. Uni-p utilizes a comprehensive free energy model to represent molecular protonation equilibria accurately.

Data-driven quantum chemical property prediction leveraging 3D conformations with Uni-Mol.

Quantum chemical (QC) property prediction is crucial for computational materials and drug design, but relies on expensive electronic structure calculations like density functional theory (DFT). Recent deep learning methods accelerate this process using 1D SMILES or 2D graphs as inputs but struggle to achieve high accuracy as most QC properties depend on refined 3D molecular equilibrium conformations. We introduce Uni-Mol+, a deep learning approach that leverages 3D conformations for accurate QC property prediction.

Node-Aligned Graph-to-Graph: Elevating Template-free Deep Learning Approaches in Single-Step Retrosynthesis.

Single-step retrosynthesis in organic chemistry increasingly benefits from deep learning (DL) techniques in computer-aided synthesis design. While template-free DL models are flexible and promising for retrosynthesis prediction, they often ignore vital 2D molecular information and struggle with atom alignment for node generation, resulting in lower performance compared to the template-based and semi-template-based methods. To address these issues, we introduce node-aligned graph-to-graph (NAG2G), a transformer-based template-free DL model.

A comprehensive transformer-based approach for high-accuracy gas adsorption predictions in metal-organic frameworks.

Gas separation is crucial for industrial production and environmental protection, with metal-organic frameworks (MOFs) offering a promising solution due to their tunable structural properties and chemical compositions. Traditional simulation approaches, such as molecular dynamics, are complex and computationally demanding. Although feature engineering-based machine learning methods perform better, they are susceptible to overfitting because of limited labeled data.

DPA-2: a large atomic model as a multi-task learner.

The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with the accuracy of electronic structure methods. However, the model generation process remains a bottleneck for large-scale applications.

Explore the impact of hypoxia-related genes (HRGs) in Cutaneous melanoma.

Background: Cutaneous melanoma (CM) has an overall poor prognosis due to a high rate of metastasis. This study aimed to explore the role of hypoxia-related genes (HRGs) in CM.

Methods: We first used on-negative matrix factorization consensus clustering (NMF) to cluster CM samples and preliminarily analyzed the relationship of HRGs to CM prognosis and immune cell infiltration.

Cucurbitacin E inhibits cellular proliferation and induces apoptosis in melanoma by suppressing HSDL2 expression.

Background: Melanoma is among the most aggressive types of skin malignancy and can have an unpredictable clinical course. Exploration of novel therapeutic targets and their regulators remains essential for the prevention and treatment of melanoma.

Methods: HSDL2 protein levels were examined by immunohistochemistry.

Deep Subdomain Adaptation Network for Image Classification.

For a target task where the labeled data are unavailable, domain adaptation can transfer a learner from a different source domain. Previous deep domain adaptation methods mainly learn a global domain shift, i.e.

Estradiol inhibits Th17 cell differentiation through inhibition of RORγT transcription by recruiting the ERα/REA complex to estrogen response elements of the RORγT promoter.

The symptoms of vaginal candidiasis exacerbate in the second half of the menstrual cycle in premenopausal women when the serum estradiol level is elevated. Estradiol has been shown to inhibit Th17 differentiation and production of antifungal IL-17 cytokines. However, little is known about the mechanisms.