Dietary cinnamon promotes longevity and extends healthspan via mTORC1 and autophagy signaling.

Cinnamon, renowned for its aromatic flavor, represents one of the most widely used spices worldwide. Cinnamon is also considered beneficial to human health with therapeutic potential for treating various diseases, ranging from diabetes and cancer to neurodegenerative diseases. However, the mechanisms underlying cinnamon's health benefits remain elusive.

Covalent Inhibitor Screening for Targeting LOXL2: Studied by Virtual Screening and Experimental Validation.

Background: Lysyl oxidase-like 2 (LOXL2) is a metalloenzyme that catalyzes oxidative deamination ε-amino group of lysine. It has been found that LOXL2 is a promotor for the metastasis and invasion in kinds of tumors. Previous studies show that disulfide bonds are important components in LOXL2, and their bioactivity can be regulated by those bonds.

Differences in the composition of plasma metabolites and intestinal flora of piglets with different weaning weights revealed by untargeted metabolomics and 16S rRNA gene sequencing.

Background: Piglets with different weaning body weights exhibit varying growth performance. This study explores the relationship between their plasma metabolites and gut microbiota to reveal differences in metabolic regulation and microbial composition.

Results: Plasma and colon content samples from piglets of different weaning weights were collected.

RBM47 is a novel immunotherapeutic target and prognostic biomarker in gliomas.

The role of RBM47, an RNA-binding protein, in shaping the immune landscape of gliomas and tumor immune responses is yet to be fully studied. Therefore, a comprehensive investigation into the immunomodulatory roles of RBM47 in gliomas was conducted, leveraging gene expression data from multi-omic datasets. The prognosis of patients with gliomas considering RBM47 was elucidated using bioinformatics methods and clinical data, with results validated using immunohistochemistry and immunofluorescence analyses.

Unveiling novel antimicrobial peptides from the ruminant gastrointestinal microbiomes: A deep learning-driven approach yields an anti-MRSA candidate.

Introduction: Antimicrobial peptides (AMPs) present a promising avenue to combat the growing threat of antibiotic resistance. The ruminant gastrointestinal microbiome serves as a unique ecosystem that offers untapped potential for AMP discovery.

Objectives: The aims of this study are to develop an effective methodology for the identification of novel AMPs from ruminant gastrointestinal microbiomes, followed by evaluating their antimicrobial efficacy and elucidating the mechanisms underlying their activity.

Bioinformatics Exploration of the Therapeutic Potential of Lotus Seed Compounds in Multiple Sclerosis: A Network Analysis of c-Jun Pathway.

The central nervous system is affected by multiple sclerosis (MS), a chronic autoimmune illness characterized by axonal destruction, demyelination, and inflammation. This article summarizes the state of the field, highlighting its complexity and significant influence on people's quality of life. The research employs a network pharmacological approach, integrating systems biology, bioinformatics, and pharmacology to identify biomarkers associated with MS.

Identification of fatty acid anabolism patterns to predict prognosis and immunotherapy response in gastric cancer.

Gastric cancer (GC), one of the most common and heterogeneous malignancies, is the second leading cause of cancer death worldwide and is closely related to dietary habits. Fatty acid is one of the main nutrients of human beings, which is closely related to diabetes, hypertension and other diseases. However, the correlation between fatty acid metabolism and the development and progression of GC remains largely unknown.

Proteomic analysis of the nonstructural protein 2-host protein interactome reveals a novel regulatory role of SH3 domain-containing kinase-binding protein 1 in porcine reproductive and respiratory syndrome virus replication and apoptosis.

Virus-host protein interaction is critical for successful completion of viral replication cycles. As the largest nonstructural protein (NSP) of porcine reproductive and respiratory syndrome virus (PRRSV), NSP2 plays multiple and critical roles in viral replication, antiviral immunity, cellular tropism and virulence. An interactome of this protein with host proteins would be instrumental in full understanding of these multifunctional roles.

Elucidating Binding Selectivity in Cyclin-Dependent Kinases 4, 6, and 9: Development of Highly Potent and Selective CDK4/9 Inhibitors.

CDK4/6 inhibitors are effective in treating HR/HER2 breast cancer but face limitations due to therapeutic resistance and hematological toxicity, particularly from strong CDK6 inhibition. To address these challenges, designing selective inhibitors targeting specific cyclin-dependent kinases (CDK) members could offer clinical advantages and broaden CDK inhibitor indications. However, the highly conserved binding pockets of CDKs complicate selective targeting.

Land Use Change Consistently Reduces α- But Not β- and γ-Diversity of Bees.

Land use change threatens global biodiversity and compromises ecosystem functions, including pollination and food production. Reduced taxonomic α-diversity is often reported under land use change, yet the impacts could be different at larger spatial scales (i.e.

Common biomarkers of idiopathic pulmonary fibrosis and systemic sclerosis based on WGCNA and machine learning.

Interstitial lung disease (ILD) is known to be a major complication of systemic sclerosis (SSc) and a leading cause of death in SSc patients. As the most common type of ILD, the pathogenesis of idiopathic pulmonary fibrosis (IPF) has not been fully elucidated. In this study, weighted correlation network analysis (WGCNA), protein‒protein interaction, Kaplan-Meier curve, univariate Cox analysis and machine learning methods were used on datasets from the Gene Expression Omnibus database.

Identification of acetylcholinesterase inhibitors and stability analysis of THC@HP-β-CD inclusion complex: A comprehensive computational study.

Complexing medications with cyclodextrins can enhance their solubility and stability. In this study, we investigated the host-guest complexation between Tetrahydrocurcumin (THC) and Hydroxypropyl-β-Cyclodextrin (HP-β-CD) using density functional theory (DFT) at the B3LYP-D3/TPZ level of theory in two possible orientations. To determine the reactive sites in both complexes for electrophilic and nucleophilic attacks, we calculated and interpreted the binding energy, HOMO and LUMO orbitals, global chemical reactivity descriptors, natural bond orbital (NBO) analysis, and Fukui indices.

GPSD: a hybrid learning framework for the prediction of phosphatase-specific dephosphorylation sites.

Protein phosphorylation is dynamically and reversibly regulated by protein kinases and protein phosphatases, and plays an essential role in orchestrating a wide range of biological processes. Although a number of tools have been developed for predicting kinase-specific phosphorylation sites (p-sites), computational prediction of phosphatase-specific dephosphorylation sites remains to be a great challenge. In this study, we manually curated 4393 experimentally identified site-specific phosphatase-substrate relationships for 3463 dephosphorylation sites occurring on phosphoserine, phosphothreonine, and/or phosphotyrosine residues, from the literature and public databases.

KPRR: a novel machine learning approach for effectively capturing nonadditive effects in genomic prediction.

Nonadditive genetic effects pose significant challenges to traditional genomic selection methods for quantitative traits. Machine learning approaches, particularly kernel-based methods, offer promising solutions to overcome these limitations. In this study, we developed a novel machine learning method, KPRR, which integrated a polynomial kernel into ridge regression to effectively capture nonadditive genetic effects.

Identification and validation of the diagnostic biomarker MFAP5 for CAVD with type 2 diabetes by bioinformatics analysis.

Introduction: Calcific aortic valve disease (CAVD) is increasingly prevalent among the aging population, and there is a notable lack of drug therapies. Consequently, identifying novel drug targets will be of utmost importance. Given that type 2 diabetes is an important risk factor for CAVD, we identified key genes associated with diabetes - related CAVD via various bioinformatics methods, which provide further potential molecular targets for CAVD with diabetes.

Psychometric properties of an Arabic translation of the 13-item short mood and feelings questionnaire- parent version (SMFQ-P) to screen for depression in children.

Background: Understanding the connection between parental wellbeing and its impact on childhood depression is crucial in order to develop targeted interventions and support systems that can mitigate potential long-term effects on mental health. This study focuses on examining the properties of an Arabic translation of a questionnaire called Short Mood and Feelings Questionnaire Parent Version (SMFQ-P) as a preliminary step toward validating a culturally relevant screening tool for childhood depression in Lebanon.

Methods: A total of 502 parents, recruited through a snowball method, took part in the survey with an age of 36.

Exosome-derived long non-coding RNA AC010789.1 modified by FTO and hnRNPA2B1 accelerates growth of hair follicle stem cells against androgen alopecia by activating S100A8/Wnt/β-catenin signalling.

Background: The increased incidence of androgenic alopecia (AGA) causes adverse physiological and psychological effects on people of all genders. The hair follicle stem cells (HFSCs) have displayed clinical improvements on AGA. However, the molecular mechanism of HFSCs against AGA remains elusive.

Age-related morphological changes of the pubic symphyseal surface: using three-dimensional statistical shape modeling.

Computational analysis of the pubic symphyseal surface is widely used for accurate age estimation, offering quantitative precision through the detection of subtle morphological changes. However, these methods often lack insights into the underlying morphological changes across different age groups. To bridge this gap, the study utilizes statistical shape modeling (SSM), a versatile tool capable of describing diverse morphological variations within populations.

Cellular Feimin enhances exercise performance by suppressing muscle thermogenesis.

Exercise can rapidly increase core body temperature, and research has indicated that elevated internal body temperature can independently contribute to fatigue during physical activity. However, the precise mechanisms responsible for regulating thermogenesis in muscles during exercise have remained unclear. Here, we demonstrate that cellular Feimin (cFeimin) enhances exercise performance by inhibiting muscle thermogenesis during physical activity.

Non-targeted LC-MS metabolomics reveals serum metabolites for high-altitude adaptation in Tibetan donkeys.

Tibetan donkeys inhabit the harsh environment of the Qinghai-Tibet Plateau. Research on serum metabolites related to their high-altitude adaptation is limited compared to other livestock. We used liquid chromatography-mass spectrometry (LC-MS) to analyze serum samples from healthy adult donkeys in Shigatse, Changdu, and Dezhou to evaluate the effects of high altitudes on serum metabolites.

Prognostic signature and therapeutic drug identification for dilated cardiomyopathy based on necroptosis via bioinformatics and experimental validation.

Necroptosis, a type of programmed cell death, has been increasingly linked to cardiovascular disease development, yet its role in dilated cardiomyopathy (DCM) remains unclear. In this study, we analyzed the GSE5406 dataset from the GEO database to explore necroptosis-related prognostic signatures in DCM using LASSO regression. We identified five necroptosis-related genes (BID, CAMK2B, GLUL, HSP90AB1, CHMP5) that define a necroptosis-related signature with strong predictive value, evidenced by ROC curve areas of 0.

Predicting noncoding RNA and disease associations using multigraph contrastive learning.

MiRNAs and lncRNAs are two essential noncoding RNAs. Predicting associations between noncoding RNAs and diseases can significantly improve the accuracy of early diagnosis.With the continuous breakthroughs in artificial intelligence, researchers increasingly use deep learning methods to predict associations.

Pseudogene Lamr1-ps1 Aggravates Early Spatial Learning Memory Deficits in Alzheimer's Disease Model Mice.

Alzheimer's disease (AD), a neurodegenerative disorder with complex etiologies, manifests through a cascade of pathological changes before clinical symptoms become apparent. Among these early changes, alterations in the expression of non-coding RNAs (ncRNAs) have emerged as pivotal events. In this study, we focused on the aberrant expression of ncRNAs and revealed that Lamr1-ps1, a pseudogene of the laminin receptor, significantly exacerbates early spatial learning and memory deficits in APP/PS1 mice.