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.

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: 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: 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.

Central retinal vein occlusion(CRVO)is a retinal vascular disorder that significantly impairs vision, with its underlying mechanisms involving complex interactions across multiple biological systems. This article provides a systematic review of the pathological mechanisms associated with CRVO, emphasizing critical factors such as endothelial dysfunction, arteriosclerosis, thrombophilia, inflammation, and oxidative stress. The pathological mechanisms of CRVO are characterized by arteriosclerosis, which obstructs venous return through a dual mechanism involving mechanical compression and endothelin-1-mediated contraction; endothelial dysfunction, which exacerbates disturbances in blood flow; genetic and acquired coagulation abnormalities that disrupt hemostatic balance and promote thrombosis; and the synergistic effects of inflammation and oxidative stress that activate cytokines, thereby aggravating ischemia and vascular leakage. Innovatively, this review explores emerging mechanisms such as miRNA-mediated vascular regulation via exosomes, gut microbiota-retina crosstalk through the “gut-eye axis,” and systemic metabolic interactions that link local retinal lesions to broader dysregulation of CRVO. These insights underscore the importance of integrated eye-system interventions and provide a theoretical foundation for advancing early biomarker discovery, multitarget therapeutics, and personalized treatment paradigms. By bridging localized pathology and systemic mechanisms, this work promotes a transformative shift toward an integrative medicine model in the diagnosis and management of CRVO.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have been included in various neoadjuvant therapy (NAT) regimens for non-small cell lung cancer (NSCLC). However, due to the relatively short period for the use of ICIs in NAT, patients' clinical outcomes with different regimens are uncertain. Our study aims to examine the efficacy of neoadjuvant immunotherapy (NAIT) for NSCLC patients and compare the overall survival (OS) and event-free survival (EFS) of patients receiving different NAT regimens. METHODS: This study retrospectively included 308 NSCLC patients treated with different NAT regimens and subsequent surgery in National Cancer Center between August 1, 2016 and July 31, 2022. Kaplan-Meier survival analysis and Cox proportional hazards regression analysis were conducted to evaluate the prognosis of patients. RESULTS: With a median follow-up of 27.5 months, the 1-year OS rates were 98.8% and 96.2%, and the 2-year OS rates were 96.6% and 85.8% in patients of the NAIT and neoadjuvant chemotherapy (NACT) group, respectively (hazard ratio [HR], 0.339; 95% confidence interval [CI], 0.160-0.720; P = 0.003). The 1-year EFS rates were 96.0% and 88.0%, and the 2-year EFS rates were 92.0% and 77.7% for patients in the NAIT and NACT groups, respectively (HR, 0.438; 95% CI, 0.276-0.846; P = 0.010). For patients who did not achieve pathological complete response (pCR), significantly longer OS ( P = 0.012) and EFS ( P = 0.019) were observed in patients receiving NAIT than those receiving NACT. Different NAT regimens had little effect on surgery and the postoperative length of stay (6 [4, 7] days vs . 6 [4, 7] days, Z = -0.227, P = 0.820). CONCLUSIONS NAIT exhibited superior efficacy to NACT for NSCLC, resulting in longer OS and EFS. The OS and EFS benefits were also observed among patients in the NAIT group who did not achieve pCR.
Humans ; Carcinoma, Non-Small-Cell Lung/mortality* ; Male ; Female ; Lung Neoplasms/mortality* ; Middle Aged ; Immune Checkpoint Inhibitors/therapeutic use* ; Neoadjuvant Therapy/methods* ; Retrospective Studies ; Aged ; Adult ; Kaplan-Meier Estimate ; Treatment Outcome ; Immunotherapy/methods*

A primary hallmark of pathological cardiac hypertrophy is excess protein synthesis due to enhanced translational activity. However, regulatory mechanisms at the translational level under cardiac stress remain poorly understood. Here we examined the translational regulations in a mouse cardiac hypertrophy model induced by transaortic constriction (TAC) and explored the conservative networks versus the translatome pattern in human dilated cardiomyopathy (DCM). The results showed that the heart weight to body weight ratio was significantly elevated, and the ejection fraction and fractional shortening significantly decreased 8 weeks after TAC. Puromycin incorporation assay showed that TAC significantly increased protein synthesis rate in the left ventricle. RNA-seq revealed 1,632 differentially expressed genes showing functional enrichment in pathways including extracellular matrix remodeling, metabolic processes, and signaling cascades associated with pathological cardiomyocyte growth. When combined with ribosome profiling analysis, we revealed that translation efficiency (TE) of 1,495 genes was enhanced, while the TE of 933 genes was inhibited following TAC. In DCM patients, 1,354 genes were upregulated versus 1,213 genes were downregulated at the translation level. Although the majority of the genes were not shared between mouse and human, we identified 93 genes, including Nos3, Kcnj8, Adcy4, Itpr1, Fasn, Scd1, etc., with highly conserved translational regulations. These genes were remarkably associated with myocardial function, signal transduction, and energy metabolism, particularly related to cGMP-PKG signaling and fatty acid metabolism. Motif analysis revealed enriched regulatory elements in the 5' untranslated regions (5'UTRs) of transcripts with differential TE, which exhibited strong cross-species sequence conservation. Our study revealed novel regulatory mechanisms at the translational level in cardiac hypertrophy and identified conserved translation-sensitive targets with potential applications to treat cardiac hypertrophy and heart failure in the clinic.
Animals ; Humans ; Cardiomegaly/physiopathology* ; Ribosomes/physiology* ; Protein Biosynthesis/physiology* ; Mice ; Cardiomyopathy, Dilated/genetics* ; Ribosome Profiling

Buyang Huanwu Decoction(BYHWD), as one of the classic formulas in traditional Chinese medicine(TCM) for the treatment of cerebral ischemic stroke(CIS), has demonstrated definite effects in clinical practice. However, the material basis and mechanism of treatment have not been systematically elucidated. This study employed network pharmacology and molecular docking to analyze the potential targets and mechanisms of blood-and brain-penetrating active components of BYHWD in reducing cell apoptosis in CIS. Cell experiments were then carried out to validate the prediction results. In the experiments, five active components including hydroxysafflor yellow A( HSYA), tetramethylpyrazine( TMP), astragaloside Ⅳ( AS-Ⅳ), amygdalin( AMY), and paeoniflorin(PF) were selected to explore the pharmacological effects of BYHWD. HT22 cells were treated with BYHWD, and the cell counting kit-8(CCK-8) method was employed to examine the toxic and side effects of BYHWD. A cell model of oxygen-glucose deprivation/reoxygenation( OGD/R) was constructed, with apoptosis and pyroptosis as the main screening indicators. The levels of lactate dehydrogenase(LDH) and glutathione(GSH) were measured to assess the cell membrane integrity. Flow cytometry was employed to detect apoptosis, and the activities of caspase-3 and caspase-1 were measured to clarify the status of apoptosis and pyroptosis. ELISA was employed to determine the levels of interleukin(IL)-1β and IL-18 to confirm pyroptosis. HSYA and AMY were identified in this study as the active components regulating apoptosis and pyroptosis. TUNEL was employed to detect the apoptosis rate, and Western blot was employed to determine the expression levels of apoptosis-related proteins B-cell lymphoma-2(Bcl-2), Bcl-2-associated X protein(Bax), and caspase-3, which confirmed that the anti-apoptotic effect of the combined component group was superior to that of the single component groups. The molecular docking results revealed strong binding affinity of HSYA and AMY with SDF-1α and CXCR4.AMD3100, a selective antagonist of CXCR4, was then used for intervention. The results of Western blot showed alterations in the expression levels of apoptosis-associated proteins, SDF-1α, and CXCR4. In conclusion, HSYA and AMY influence cellular apoptosis by modulating the SDF-1α/CXCR4 signaling cascade.
Drugs, Chinese Herbal/chemistry* ; Apoptosis/drug effects* ; Animals ; Neurons/cytology* ; Mice ; Molecular Docking Simulation ; Cell Line ; Glucose/metabolism* ; Humans ; Neuroprotective Agents/pharmacology*