Muscle-Bone Biochemical Crosstalk in Osteosarcopenia: Focusing on Mechanism and Potential Therapeutic Strategies.

Osteosarcopenia (OS) is a syndrome defined by the concurrent presence of sarcopenia and osteoporosis in the elderly population, which markedly elevates the risk of falls, fractures, and mortality. Recent studies demonstrate that disruption of muscle-bone biochemical crosstalk emerges as a key driver of OS pathogenesis, and that targeting pivotal mediators and pathways can concurrently restore musculoskeletal homeostasis. However, the precise molecular mechanisms and targeted therapeutic strategies remain inadequately explored.

MCAMEF-BERT: an efficient deep learning method for RNA N7-methylguanosine site prediction via multi-branch feature integration.

Accurate identification of N7-methylguanosine (m7G) modification sites plays a critical role in uncovering the regulatory mechanisms of various biological processes, including human development, tumor initiation, and progression. However, existing prediction methods still suffer from limited representational power, redundant feature fusion, insufficient utilization of biological prior knowledge, and poor interpretability. In this study, we propose a novel deep learning model named MCAMEF-BERT.

DiffusionST: a deep generative diffusion model-based framework for enhancing spatial transcriptomics data quality and identifying spatial domains.

Recent advancements in spatial transcriptomics (ST) technology have generated substantial volumes of spatial transcriptome data. However, the quality of this data is often compromised due to the limitations of current sequencing technologies. To address this issue, DiffusionST proposes a method for imputing ST data and clustering the imputed data.

Dual-energy CT to evaluate the infiltration status of tumor-associated macrophage (TAM) in gastric cancer: a preliminary study.

Purpose: To explore the relationship between tumor-associated macrophages (TAMs) in gastric cancer and dual-energy computed tomography (DECT) parameters.

Methods: This retrospective study was conducted with patients who underwent both DECT examination and gastric cancer resection. 89 patients (mean age ± standard deviations, 64.

EDNTOM: An Ensemble Learning and Weight Mechanism-Based Nanopore Methylation Detection Tool.

DNA methylation is an epigenetic modification that plays a crucial role in genome stability and cellular specialization, essential for maintaining normal cellular function and development, also a manifestation indicator of some diseases. Various tools have been proposed for methylation detection, typically leveraging a third-generation sequencing technology called nanopore sequencing, which provides more accurate DNA sequencing data. However, existing tools have their own limitations and advantages in terms of computational resources and information processing, without achieving a good balance.

Research on RNA modification in disease diagnosis and prognostic biomarkers: current status and challenges.

RNA modification, as a crucial post-transcriptional regulatory mechanism, plays a pivotal role in normal physiological processes and is closely associated with the onset and progression of various human diseases. Recent studies have highlighted significant alterations in the level of RNA modifications, including m6A, m6Am, m1A, m5C, m7G, ac4C, Ψ, and A-to-I editing, across multiple diseases. These findings suggest the potential of RNA modifications and their regulatory factors as biomarkers for early disease diagnosis and prognosis.

Immune checkpoint inhibitor therapy for advanced HPV-related penile squamous cell carcinoma: a rare case report.

Background: Human papillomavirus type 16 (HPV-16)-associated penile squamous cell carcinoma (PSCC) poses considerable therapeutic challenges, especially in its advanced stages. Although surgery continues to be the cornerstone of treatment, immunotherapeutic approaches hold a promising alternative for patients unable to endure conventional chemotherapy.

Case Summary: A 69-year-old male presented with progressive ulceration of the foreskin over the course of one year, which ultimately extended to the glans, accompanied by inguinal lymph node metastasis.

Susceptibility and shedding in Mx1 and Mx1 female mice experimentally infected with dairy cattle A(H5N1) influenza viruses.

Background: Clade 2.3.4.

Deep Learning in Antimicrobial Peptide Prediction.

Antimicrobial peptides (AMPs) have garnered significant attention from researchers as effective alternatives to antibiotics. In recent years, deep learning has demonstrated unique advantages in AMP prediction, surpassing traditional machine learning methods and offering new avenues to address the issue of antibiotic resistance. This review introduces the research foundations of deep learning in AMP prediction, covering data set status, processing methods, and representation learning approaches.

Self-amplifying mRNA expressing COBRA hemagglutinin elicits long-lasting, broadly reactive antibodies against seasonal influenza A viruses.

Development of universal or broadly-reactive influenza virus vaccines is critical for addressing emerging pandemic strains, as well as improving the effectiveness and longevity of annual, seasonal influenza virus vaccines. The next generation of influenza vaccines need to address expanding the breadth of vaccine induced immune response to neutralize drifted variants, enhance the longevity of elicited immunity, and preferably use single-shot platforms that will reduce the number of vaccinations and expand the number of doses available. In this report, influenza hemagglutinin sequences, developed using computationally optimized broadly-reactive antigen (COBRA) methodology, were expressed from a self-amplifying mRNA (samRNA) vector to elicit broadly-reactive, protective immunity following a single vaccination of mice or ferrets.

Polaron-Polariton Bose-Einstein Condensation Lasing via Longitudinal Optical Phonon Scattering in GaN-Based Microcavity.

Exciton-polaritons, hybrid states composed of excitons and photons under strong coupling, play a crucial role in investigating Bose-Einstein condensation (BEC) and optoelectronics. However, the relaxation bottleneck in exciton-polaritons, arising from reduced scattering efficiency with acoustic phonons, hinders the achievement of thermal equilibrium Bose-Einstein condensation (BEC). Here, by invoking the polaronic effect of the exciton, we present the first observation of polaron-polariton BEC in a GaN-based microcavity.

Associations of plasma protein levels with risk of colorectal cancer: a proteome-wide Mendelian randomization study.

Background: The treatment of advanced or metastatic colorectal cancer (CRC) poses a global challenge. Mendelian Randomization (MR) has been primarily applied for repurposing licensed drugs and uncovering new therapeutic targets.

Objective: This study aims to systematically identify potential plasma protein targets for CRC using proteome-wide Mendelian randomization and evaluate their potential side effects through phenome-wide association studies (Phe-WAS).

Identification of novel susceptibility loci for testicular germ cell tumors through multi-omics analysis.

Objective: This study aimed to identify potential susceptibility loci for testicular germ cell tumors (TGCT) through a comprehensive multi-omics analysis.

Methods: A Mendelian randomization (MR) analysis was conducted to investigate the causal relationships between immune cell phenotypes and TGCT. Summary-based Mendelian Randomization (SMR) and colocalization analyses were subsequently performed to examine candidate phenotypes and TGCT using quantitative trait locis from multiple datasets.

Taco-DDI: accurate prediction of drug-drug interaction events using graph transformer-based architecture and dynamic co-attention matrices.

Drug-drug interactions (DDIs) are critical in pharmaceutical research, as adverse interactions can pose significant risks for patient treatment plans. Accurate prediction of DDI events risk levels can provide valuable guidance for designing safer and more effective medical regimens. However, existing approaches often focus on interaction networks while overlooking the inherent molecular properties of drugs.

GICL: A Cross-Modal Drug Property Prediction Framework Based on Knowledge Enhancement of Large Language Models.

Deep learning models have demonstrated their potential in learning effective molecular representations critical for drug property prediction and drug discovery. Despite significant advancements in leveraging multimodal drug molecule semantics, existing approaches often struggle with challenges such as low-quality data and structural complexity. Large language models (LLMs) excel in generating high-quality molecular representations due to their robust characterization capabilities.

Uncovering Potential Susceptibility Genes for Multiple Sclerosis-Induced Neuropathic Bladder: A Mendelian Randomization Analysis.

Despite lacking a genetic explanation for the causal link between multiple sclerosis (MS) and neuropathic bladder (NPB), our study aims to explore this causality and identify novel susceptibility genes for both phenotypes. We performed linkage disequilibrium score regression to assess SNP heritability for both phenotypes. Two-sample bidirectional Mendelian randomization (MR) analyses were conducted to evaluate causal relationships between MS and NPB.

Computational models for prediction of m6A sites using deep learning.

RNA modifications play a crucial role in enhancing the structural and functional diversity of RNA molecules and regulating various stages of the RNA life cycle. Among these modifications, N6-Methyladenosine (m6A) is the most common internal modification in eukaryotic mRNAs and has been extensively studied over the past decade. Accurate identification of m6A modification sites is essential for understanding their function and underlying mechanisms.

Multivalent H3 COBRA-based influenza vaccine elicits enhanced immune response in a pre-immune elderly ferret model.

Influenza viruses cause significant mortality in humans, especially among people 65 years and older. The outbreaks of A(H3N2) influenza viruses or viruses of vaccine-mismatched strains are usually associated with more severe diseases in elderly population. Vaccination is the practical countermeasure for controlling influenza virus infection in humans.

NeuroPred-AIMP: Multimodal Deep Learning for Neuropeptide Prediction via Protein Language Modeling and Temporal Convolutional Networks.

Neuropeptides are key signaling molecules that regulate fundamental physiological processes ranging from metabolism to cognitive function. However, accurate identification is a huge challenge due to sequence heterogeneity, obscured functional motifs and limited experimentally validated data. Accurate identification of neuropeptides is critical for advancing neurological disease therapeutics and peptide-based drug design.

UMPPI: Unveiling Multilevel Protein-Peptide Interaction Prediction via Language Models.

Protein-peptide interactions are essential to cellular processes and disease mechanisms. Identifying protein-peptide binding residues is critical for understanding peptide function and advancing drug discovery. However, experimental methods are costly and time-intensive, while existing computational approaches often predict interactions or binding residues separately, lack effective feature integration, or rely heavily on limited high-quality structural data.

A single dose of inactivated influenza virus vaccine expressing COBRA hemagglutinin elicits broadly-reactive and long-lasting protection.

Influenza virus infections present a pervasive global health concern resulting in millions of hospitalizations and thousands of fatalities annually. To address the influenza antigenic variation, the computationally optimized broadly reactive antigen (COBRA) methodology was used to design influenza hemagglutinin (HA) or neuraminidase (NA) for universal influenza vaccine candidates. In this study, whole inactivated virus (WIV) or split inactivated virus (SIV) vaccine formulations expressing either the H1 COBRA HA or H3 COBRA HA were formulated with or without an adjuvant and tested in ferrets with pre-existing anti-influenza immunity.

Research on Concrete Crack Detection in Hydropower Station Burial Engineering Based on Quantum Particle Technology.

Cracking in hydraulic buried engineering can cause localized damage or complete structural failure, potentially resulting in catastrophic project outcomes. Traditional methods for detecting cracks in hydraulic concrete buried engineering are often insufficient in terms of reliability and accuracy. With the development and application of particle-based technology, it has been widely used in the field of crack detection.

Establishment of Real-Time PCR Method to Differentiate (Diptera, Psychodidae) from s.s. Based on Whole Mitochondrial Genome Analysis.

, considered a potential vector for visceral leishmaniasis (VL), is distributed in the southern Gansu and northern Sichuan regions in China. However, the high similarity in the morphology of and s.s.

Dual-energy CT for predicting serosal invasion in gastric cancer and subtype analysis.

Purpose: To predict the serosal invasion of gastric cancer (GC) using dual-energy CT (DECT)-based parameters and analyze the diagnostic performance according to different subtypes.

Methods: The patients were divided into the T1-3 group and T4a group. The irregular region of interest (ROI) was manually delineated on the largest cross-section of the lesion.

OCTN2 expression and function in the Sertoli cells of testes from patients with non-obstructive azoospermia.

Background: Among couples, male factors account for approximately 50% of infertility cases, with nonobstructive azoospermia (NOA) representing one of the most clinically common and severe categories of male infertility, affecting approximately 10-15% of patients. Currently, L-carnitine is clinically used to improve spermatogenesis by regulating Sertoli cell function. Multiple clinical trials have described the efficacy of L-carnitine in treating NOA.