BACKGROUND:Osteosarcoma has a complex pathogenesis and a poor prognosis.While advancements in medical technology have led to some improvements in the 5-year survival rate,substantial progress in its treatment has not yet been achieved. OBJECTIVE:To screen key molecular markers in osteosarcoma,analyze their relationship with osteosarcoma treatment drugs,and explore the potential disease mechanisms of osteosarcoma at the molecular level. METHODS:GSE99671 and GSE284259(miRNA)datasets were obtained from the Gene Expression Omnibus database.Differential gene expression analysis and Weighted Gene Co-expression Network Analysis(WGCNA)on GSE99671 were performed.Functional enrichment analysis was conducted using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes separately for the differentially expressed genes and the module genes with the highest positive correlation to the disease.The intersection of these module genes and differentially expressed genes was taken as key genes.A Protein-Protein Interaction network was constructed,and correlation analysis on the key genes was performed using CytoScape software,and hub genes were identified.Hub genes were externally validated using the GSE28425 dataset and text validation was conducted.The drug sensitivity of hub genes was analyzed using the CellMiner database,with a threshold of absolute value of correlation coefficient|R|>0.3 and P<0.05. RESULTS AND CONCLUSION:(1)Differential gene expression analysis identified 529 differentially expressed genes,comprising 177 upregulated and 352 downregulated genes.WGCNA analysis yielded a total of 592 genes with the highest correlation to osteosarcoma.(2)Gene Ontology enrichment results indicated that the development of osteosarcoma may be associated with extracellular matrix,bone cell differentiation and development,human immune regulation,and collagen synthesis and degradation.Kyoto Encyclopedia of Genes and Genomes enrichment results showed the involvement of pathways such as PI3K-Akt signaling pathway,focal adhesion signaling pathway,and immune response in the onset of osteosarcoma.(3)The intersection analysis revealed a total of 59 key genes.Through Protein-Protein Interaction network analysis,8 hub genes were selected,which were LUM,PLOD1,PLOD2,MMP14,COL11A1,THBS2,LEPRE1,and TGFB1,all of which were upregulated.(4)External validation revealed significantly downregulated miRNAs that regulate the hub genes,with hsa-miR-144-3p and hsa-miR-150-5p showing the most significant downregulation.Text validation results demonstrated that the expression of hub genes was consistent with previous research.(5)Drug sensitivity analysis indicated a negative correlation between the activity of methotrexate,6-mercaptopurine,and pazopanib with the mRNA expression of PLOD1,PLOD2,and MMP14.Moreover,zoledronic acid and lapatinib showed a positive correlation with the mRNA expression of PLOD1,LUM,MMP14,PLOD2,and TGFB1.This suggests that zoledronic acid and lapatinib may be potential therapeutic drugs for osteosarcoma,but further validation is required through additional basic experiments and clinical studies.

Adipose tissue is a critical energy reservoir in animals and humans, with multifaceted roles in endocrine regulation, immune response, and providing mechanical protection. Based on anatomical location and functional characteristics, adipose tissue can be categorized into distinct types, including white adipose tissue (WAT), brown adipose tissue (BAT), beige adipose tissue, and pink adipose tissue. Traditionally, adipose tissue research has centered on its morphological and functional properties as a whole. However, with the advent of single-cell transcriptomics, a new level of complexity in adipose tissue has been unveiled, showing that even under identical conditions, cells of the same type may exhibit significant variation in morphology, structure, function, and gene expression——phenomena collectively referred to as cellular heterogeneity. Single-cell transcriptomics, including techniques like single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq), enables in-depth analysis of the diversity and heterogeneity of adipocytes at the single-cell level. This high-resolution approach has not only deepened our understanding of adipocyte functionality but also facilitated the discovery of previously unidentified cell types and gene expression patterns that may play key roles in adipose tissue function. This review delves into the latest advances in the application of single-cell transcriptomics in elucidating the heterogeneity and diversity within adipose tissue, highlighting how these findings have redefined the understanding of cell subpopulations within different adipose depots. Moreover, the review explores how single-cell transcriptomic technologies have enabled the study of cellular communication pathways and differentiation trajectories among adipose cell subgroups. By mapping these interactions and differentiation processes, researchers gain insights into how distinct cellular subpopulations coordinate within adipose tissues, which is crucial for maintaining tissue homeostasis and function. Understanding these mechanisms is essential, as dysregulation in adipose cell interactions and differentiation underlies a range of metabolic disorders, including obesity and diabetes mellitus type 2. Furthermore, single-cell transcriptomics holds promising implications for identifying therapeutic targets; by pinpointing specific cell types and gene pathways involved in adipose tissue dysfunction, these technologies pave the way for developing targeted interventions aimed at modulating specific adipose subpopulations. In summary, this review provides a comprehensive analysis of the role of single-cell transcriptomic technologies in uncovering the heterogeneity and functional diversity of adipose tissues.

Membrane proteins are integral components of cellular membranes, accounting for approximately 30% of the mammalian proteome and serving as targets for 60% of FDA-approved drugs. They are critical to both physiological functions and disease mechanisms. Their functional protein-protein interactions form the basis for many physiological processes, such as signal transduction, material transport, and cell communication. Membrane protein interactions are characterized by membrane environment dependence, spatial asymmetry, weak interaction strength, high dynamics, and a variety of interaction sites. Therefore, in situ analysis is essential for revealing the structural basis and kinetics of these proteins. This paper introduces currently available in situ analytical techniques for studying membrane protein interactions and evaluates the characteristics of each. These techniques are divided into two categories: label-based techniques (e.g., co-immunoprecipitation, proximity ligation assay, bimolecular fluorescence complementation, resonance energy transfer, and proximity labeling) and label-free techniques (e.g., cryo-electron tomography, in situ cross-linking mass spectrometry, Raman spectroscopy, electron paramagnetic resonance, nuclear magnetic resonance, and structure prediction tools). Each technique is critically assessed in terms of its historical development, strengths, and limitations. Based on the authors’ relevant research, the paper further discusses the key issues and trends in the application of these techniques, providing valuable references for the field of membrane protein research. Label-based techniques rely on molecular tags or antibodies to detect proximity or interactions, offering high specificity and adaptability for dynamic studies. For instance, proximity ligation assay combines the specificity of antibodies with the sensitivity of PCR amplification, while proximity labeling enables spatial mapping of interactomes. Conversely, label-free techniques, such as cryo-electron tomography, provide near-native structural insights, and Raman spectroscopy directly probes molecular interactions without perturbing the membrane environment. Despite advancements, these methods face several universal challenges: (1) indirect detection, relying on proximity or tagged proxies rather than direct interaction measurement; (2) limited capacity for continuous dynamic monitoring in live cells; and (3) potential artificial influences introduced by labeling or sample preparation, which may alter native conformations. Emerging trends emphasize the multimodal integration of complementary techniques to overcome individual limitations. For example, combining in situ cross-linking mass spectrometry with proximity labeling enhances both spatial resolution and interaction coverage, enabling high-throughput subcellular interactome mapping. Similarly, coupling fluorescence resonance energy transfer with nuclear magnetic resonance and artificial intelligence (AI) simulations integrates dynamic structural data, atomic-level details, and predictive modeling for holistic insights. Advances in AI, exemplified by AlphaFold’s ability to predict interaction interfaces, further augment experimental data, accelerating structure-function analyses. Future developments in cryo-electron microscopy, super-resolution imaging, and machine learning are poised to refine spatiotemporal resolution and scalability. In conclusion, in situ analysis of membrane protein interactions remains indispensable for deciphering their roles in health and disease. While current technologies have significantly advanced our understanding, persistent gaps highlight the need for innovative, integrative approaches. By synergizing experimental and computational tools, researchers can achieve multiscale, real-time, and perturbation-free analyses, ultimately unraveling the dynamic complexity of membrane protein networks and driving therapeutic discovery.

Objective: This study aimed to explore the relationships between various exercise components (frequency, intensity, duration) and social anxiety. Methods: A sample of 844 college students in China participated in this study. The Physical Activity Rating Scale-3 assessed participants’ daily physical activity. Social anxiety levels were measured using the Liebowitz Social Anxiety Scale. A questionnaire was developed to collect demographic information and examine the relationships between exercise components and social anxiety levels. Results: One-way analysis of variance revealed significant differences in social anxiety levels across varying physical activity intensities. Specifically, students engaging in high levels of physical activity exhibited the lowest social anxiety. Post hoc analyses identified that exercise frequency F3 (p<0.01), exercise duration D5 (p<0.01), and exercise intensity I3 (p<0.01) were significantly associated with the lowest social anxiety levels. Among these components, regression analysis indicated that exercise duration (p<0.01) had the most substantial impact on social anxiety levels, followed by exercise frequency (p<0.05). In contrast, exercise intensity (p>0.05) did not significantly affect social anxiety levels. Conclusion The most influential factors associated with decreased social anxiety were: 1) moderate to high exercise intensity, 2) exercise duration of at least one hour, and 3) exercise frequency of at least 1–2 times per week. Among these factors, exercise duration and frequency demonstrated significantly stronger associations with reduced social anxiety. Therefore, it is advisable to prioritize exercise duration and frequency in physical activity programs for college students to reduce social anxiety and achieve more substantial outcomes.

Objectives: This study aimed to achieve expert consensus on menopause management in the Asia-Pacific region, taking into account patient diversity, the latest evidence, and current treatment options. Methods: A focused literature search was performed to identify clinical practice statements on menopause management. Menopause experts were nominated by members of the Asia-Pacific Menopause Federation (APMF) society. A modified Delphi methodology, involving iterative rounds of anonymous surveys, was employed until consensus was reached for each statement. Consensus was defined as ≥ 70% of experts voting ‘agree’ or ‘strongly agree’ for a given clinical practice statement. Results: A total of 39 participants from 14 different APMF member societies were involved. Eighty-five clinical practice statements reached a consensus. Based on the clinical practice statements, an algorithm was created as a tool to guide clinicians on menopause management. APMF experts agreed that, in addition to vasomotor symptoms, Asian women experiencing somatic or psychological symptoms may also benefit from treatment with menopausal hormone therapy (MHT). MHT should also be considered for the prevention of osteoporosis in asymptomatic peri- and postmenopausal women. Conclusions This APMF consensus statement supersedes the previous one published in 2008. It provides guidance to gynecologists, endocrinologists, family physicians, and other healthcare professionals in delivering optimal care to menopausal women in the ethnically and culturally diverse Asia-Pacific region.

ObjectiveTo investigate the mechanism of Acanthopanacis Senticosi Radix et Rhizoma seu Caulis extract （ASH） in treating Parkinson's disease （PD） in mice by Data-Independent Acquisition （DIA） proteomics. MethodsThe α-Synuclein （α-Syn） transgenic PD mice were selected as suitable models for PD， and they were randomly assigned into PD， ASH （61.25 mg·kg^-1）， and Madopar （97.5 mg·kg^-1） groups. Male C57BL/6 mice of the same age were selected as the control group， with eight mice in each group. Mice were administrated with corresponding drugs by gavage once a day for 20 days. The pole climbing time and the number of autonomic activities were recorded to evaluate the exercise ability of mice. Hematoxylin-eosin staining was employed to observe neuronal changes in the substantia nigra of PD mice. Immunohistochemistry （IHC） was employed to measure the tyrosine hydroxylase （TH） activity in the substantia nigra and assess the areal density of α-Syn in the striatum. DIA proteomics was used to compare protein expression in the substantia nigra between groups. IHC was utilized to validate key differentially expressed proteins， including Lactotransferrin， Notch2， Ndrg2， and TMEM 166. The cell counting kit-8 （CCK-8） method was used to investigate the effect of ASH on the viability of PD cells with overexpression of α-Syn. Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） and Western blot were employed to determine the protein and mRNA levels of Lactotransferrin， Notch2， Ndrg2， and TMEM 166 in PD cells. ResultsCompared with the control group， the model group showed prolonged pole climbing time， diminished coordination ability， reduced autonomic activities （P<0.01）， and reduced swelling neurons. Compared with the model group， ASH and Madopar reduced the climbing time， increased autonomic activities （P<0.01）， and ameliorated neuronal damage. Compared with the control group， the model group showed a decrease in TH activity in the substantia nigra and an increase in α-Syn accumulation in the striatum （P<0.01）. Compared with the model group， the ASH group showed an increase in TH activity and a reduction in α-Syn accumulation （P<0.05）. DIA proteomics revealed a total of 464 differentially expressed proteins in the model group compared with the control group， with 323 proteins being up-regulated and 141 down-regulated. A total of 262 differentially expressed proteins were screened in the ASH group compared with the model group， including 85 proteins being up-regulated and 177 down-regulated. Kyoto encylopedia of genes and genomes （KEGG） pathway analysis indicated that ASH primarily regulated the Notch signaling pathway. The model group showed up-regulation in protein levels of Notch2， Ndrg2， and TMEM 166 and down-regulation in the protein level of Lactotransferrin compared with the control group （P<0.01）. Compared with the model group， ASH down-regulated the protein levels of Notch2， Ndrg2， and TMEM 166 （P<0.05） while up-regulating the protein level of Lactotransferrin （P<0.01）. The IHC results corroborated the proteomics findings. The cell experiment results showed that compared with the control group， the modeling up-regulated the mRNA and protein levels of Notch2， Ndrg2， and TMEM 166 （P<0.01）， while down-regulating the mRNA and protein levels of Lactotransferrin （P<0.01）. Compared with the model group， ASH reduced the mRNA and protein levels of Notch2， Ndrg2， and TMEM 166 （P<0.01）， while increasing the mRNA and protein levels of Lactotransferrin （P<0.05， P<0.01）. ConclusionASH may Synergistically inhibit the Notch signaling pathway and mitigate neuronal damage by down-regulating the expression of Notch2 and Ndrg2. Additionally， by up-regulating the expression of Lactotransferrin and down-regulating the expression of TMEM166， ASH can address brain iron accumulation， intervene in ferroptosis， inhibit mitophagy， and mitigate reactive oxygen species damage， thereby protecting nerve cells and contributing to the treatment of PD.

Background: s/Aims: Sarcomatoid hepatocellular carcinoma (HCC) is a rare histological subtype of HCC characterized by extremely poor prognosis; however, its molecular characterization has not been elucidated. Methods: In this study, we conducted an integrated multiomics study of whole-exome sequencing, RNA-seq, spatial transcriptome, and immunohistochemical analyses of 28 paired sarcomatoid tumor components and conventional HCC components from 10 patients with sarcomatoid HCC, in order to identify frequently altered genes, infer the tumor subclonal architectures, track the genomic evolution, and delineate the transcriptional characteristics of sarcomatoid HCCs. Results: Our results showed that the sarcomatoid HCCs had poor prognosis. The sarcomatoid tumor components and the conventional HCC components were derived from common ancestors, mostly accessing similar mutational processes. Clonal phylogenies demonstrated branched tumor evolution during sarcomatoid HCC development and progression. TP53 mutation commonly occurred at tumor initiation, whereas ARID2 mutation often occurred later. Transcriptome analyses revealed the epithelial–mesenchymal transition (EMT) and hypoxic phenotype in sarcomatoid tumor components, which were confirmed by immunohistochemical staining. Moreover, we identified ARID2 mutations in 70% (7/10) of patients with sarcomatoid HCC but only 1–5% of patients with non-sarcomatoid HCC. Biofunctional investigations revealed that inactivating mutation of ARID2 contributes to HCC growth and metastasis and induces EMT in a hypoxic microenvironment. Conclusions We offer a comprehensive description of the molecular basis for sarcomatoid HCC, and identify genomic alteration (ARID2 mutation) together with the tumor microenvironment (hypoxic microenvironment), that may contribute to the formation of the sarcomatoid tumor component through EMT, leading to sarcomatoid HCC development and progression.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Sarcomatoid hepatocellular carcinoma (HCC) is a rare histological subtype of HCC characterized by extremely poor prognosis; however, its molecular characterization has not been elucidated. Methods: In this study, we conducted an integrated multiomics study of whole-exome sequencing, RNA-seq, spatial transcriptome, and immunohistochemical analyses of 28 paired sarcomatoid tumor components and conventional HCC components from 10 patients with sarcomatoid HCC, in order to identify frequently altered genes, infer the tumor subclonal architectures, track the genomic evolution, and delineate the transcriptional characteristics of sarcomatoid HCCs. Results: Our results showed that the sarcomatoid HCCs had poor prognosis. The sarcomatoid tumor components and the conventional HCC components were derived from common ancestors, mostly accessing similar mutational processes. Clonal phylogenies demonstrated branched tumor evolution during sarcomatoid HCC development and progression. TP53 mutation commonly occurred at tumor initiation, whereas ARID2 mutation often occurred later. Transcriptome analyses revealed the epithelial–mesenchymal transition (EMT) and hypoxic phenotype in sarcomatoid tumor components, which were confirmed by immunohistochemical staining. Moreover, we identified ARID2 mutations in 70% (7/10) of patients with sarcomatoid HCC but only 1–5% of patients with non-sarcomatoid HCC. Biofunctional investigations revealed that inactivating mutation of ARID2 contributes to HCC growth and metastasis and induces EMT in a hypoxic microenvironment. Conclusions We offer a comprehensive description of the molecular basis for sarcomatoid HCC, and identify genomic alteration (ARID2 mutation) together with the tumor microenvironment (hypoxic microenvironment), that may contribute to the formation of the sarcomatoid tumor component through EMT, leading to sarcomatoid HCC development and progression.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.