AIM: To explore the risk factors for postoperative anterior chamber exudation in cataract patients and construct a nomogram prediction model.METHODS: Retrospective study. From July 2019 to October 2024, 450 patients(467 eyes)with age-related cataract who underwent surgery in our hospital were collected as the study subjects. They were randomly grouped into a modeling group(315 cases, 327 eyes)and a validation group(135 cases, 140 eyes)roughly estimated at a 7:3 ratio using the random number table method. Both groups were separated into a non-exudative group and an exudative group based on whether anterior chamber exudation occurred after surgery. Clinical basic data was collected; multivariate Logistic regression was applied to analyze the influencing factors of anterior chamber exudation in patients with age-related cataract after surgery; R software was applied to draw a nomogram prediction model of anterior chamber exudation in patients with age-related cataract after surgery; the calibration curve and Hosmer Lemeshow(H-L)test were applied to evaluate the calibration of the column plot model in predicting the occurrence of anterior chamber exudation in patients with age-related cataract after surgery; ROC was applied to evaluate the efficacy of anterior chamber exudation in patients with age-related cataract after surgery.RESULTS:The clinical characteristics of the modeling group and the validation group were comparable. The high myopia, history of uveitis, preoperative intraocular pressure, lens nuclear grade, intraoperative cumulative dissipated energy, and intraoperative posterior capsular rupture of the lens were the influencing factors for postoperative anterior chamber exudation in patients with age-related cataract(all P<0.05). The results of the modeling group verifying the occurrence of anterior chamber exudation in patients with age-related cataract after surgery showed that the area under the ROC curve(AUC)was 0.986(95% CI: 0.966-0.996), the H-L test was χ2=6.494, P=0.592, indicating that the risk of anterior chamber exudation in patients with age-related cataract after surgery predicted by model had good consistency with actual risks, the AUC of postoperative anterior chamber exudation in patients with age-related cataract based on external validation was 0.982(95% CI: 0.960-0.994); and the H-L test suggested that the risk of anterior chamber exudation in CAT patients after surgery predicted by model had good consistency with actual risks(χ2=6.117, P=0.634).CONCLUSION:High myopia, history of uveitis, preoperative intraocular pressure, lens nuclear grade, intraoperative cumulative dissipated energy, and intraoperative posterior capsular rupture of the lens are risk factors for postoperative anterior chamber exudation in patients with age-related cataract; the nomogram prediction model constructed based on this has high predictive value, and can provide reference for individualized prevention of anterior chamber exudation in patients with age-related cataract after surgery.

Objective: To evaluate the clinical efficacy and safety of injectable platelet-rich fibrin (i-PRF) in the treatment of mild to moderate female pattern hair loss (FPHL), and to analyze the related clinical factors affecting the therapeutic efficacy of i-PRF. Methods: This was a secondary analysis of a prospective, randomized controlled trial. A total of 29 FPHL patients with Sinclair grade Ⅱ-Ⅲ, who attended the outpatient clinics of Beijing Friendship Hospital, Capital Medical University and Peking University Third Hospital from March 2024 to April 2025, were enrolled. All patients received i-PRF treatment once every 4 weeks for a total of 4 sessions, with follow-up up to 24 weeks. Standardized clinical photographs were taken at baseline, week 12 and week 24. Trichoscopic examination was performed to measure the non-vellus target area hair count (TAHC) and hair diameter (HD) in the target area, and all adverse events were recorded. The Mann-Whitney U test was used to compare the intergroup differences in efficacy for categorical influencing factors; Spearman rank correlation analysis was employed to assess the correlation between continuous influencing factors and efficacy; and multiple linear regression (enter method) was performed to identify independent influencing factors of efficacy. Results: A total of 27 patients completed the trial. At week 12, TAHC significantly increased from 112 (105, 120) hairs/cm at baseline to 128 (110, 150) hairs/cm , with a median increase of 12 (5, 28) hairs/cm and a median growth rate of 10.5 (4.2, 24.5)% (P=0.045); hair diameter significantly increased from 42.5 (39.8, 45.2) μm to 52.0 (47.0, 58.0) μm, with a median increase of 7.5 (3.0, 12.5) μm and a median growth rate of 18.0 (7.5, 29.0)% (P<0.001). At week 24, TAHC further significantly increased to 141 (125, 158) hairs/cm (P<0.001), with a median growth rate of 24.8 (18.5, 32.0)%; hair diameter continued to improve to 55.0 (50.0, 61.0) μm (P=0.008), with a median growth rate of 21.5 (16.0, 27.0)%. The overall incidence of adverse events was 10.34% (3/29), mainly presenting as mild scalp pruritus and transient headache, without severe adverse effects. Multiple linear regression analysis revealed that baseline TAHC was an independent influencing factor for i-PRF efficacy (P<0.05), while age, BMI, disease duration, Sinclair grade, family history of AGA, and baseline HD showed no significant independent effect on efficacy in this model (P>0.05). Conclusion: This preliminary study indicates that i-PRF can improve hair density and hair shaft diameter in patients with FPHL with a favorable safety profile. Patients with FPHL and lower baseline TAHC may achieve greater clinical improvement.

OBJECTIVE To evaluate the clinical efficacy of Chaihu anxin capsules in the treatment of mild-to-moderate depression， analyze the influencing factors of its efficacy, construct a nomogram prediction model.METHODS Patients with mild-to-moderate depression treated at The First Hospital of Hebei Medical University from May 1， 2023 to March 10， 2025 were retrospectively included and categorized into Chaihu anxin capsules group and milnacipran group according to treatment regimen. Propensity score matching （PSM） was used to balance baseline characteristics， and treatment efficacy was compared after 8 weeks， including 24 Hamilton Depression Scale （HAMD-24） scores and changes， Hamilton Anxiety Scale （HAMA） scores and changes， and treatment response rate after 8 weeks of treatment. Patients receiving Chaihu anxin capsules were further classified as responders or non-responders based on the occurrence of treatment response. Univariate and multivariate Logistic regression analyses were conducted to identify the influencing factors of treatment response， and a nomogram model was constructed to predict the probability of treatment response， its predictive performance was evaluated using receiver operating characteristic （ROC） curves， calibration curves， and decision curve analysis.RESULTS A total of 297 patients were included before PSM， including 196 patients in the Chaihu anxin capsules group and 101 patients in the milnacipran group. After PSM， each group included 44 patients. After PSM， the HAMD-24 scores and HAMA scores at 8 weeks after treatment in the Chaihu anxin capsules group， as well as the HAMD-24 scores in the milnacipran group， were significantly lower than their respective baseline values （ P ＜0.05）； the change in HAMA score in the Chaihu anxin capsules group was significantly greater than that in the milnacipran group （ P ＝0.04）. Multivariable Logistic regression identified age， total bilirubin， and baseline HAMD-24 score as independent predictors of treatment response （ P ＜0.05）. The area under the ROC curve of the constructed nomogram prediction model was 0.72 （95% confidence interval： 0.637-0.799）， and the corrected Brier score was 0.143 （95% confidence interval： 0.112-0.176）； compared with the “all intervention” and “all non intervention” strategies， applying this nomogram prediction model could achieve higher net benefits within the threshold probability range of 8% to 37%.CONCLUSIONS The efficacy of Chaihu anxin capsules in improving mild-to-moderate depression is comparable to that of milnacipran， and it has certain advantages in improving anxiety symptoms； age， total bilirubin， and baseline HAMD-24 score are independent influencing factors for the treatment response of Chaihu anxin capsules. The nomogram prediction model constructed based on these factors has good discrimination， calibration， and clinical applicability.

The sensitive scalp is a complex syndrome characterised by abnormal nerve sensations, such as stinging, itching, burning, and tightness, and diminished tolerance to physical or chemical stimuli that would not typically cause such unpleasant responses, with its pathology involving physical barrier destruction at the stratum corneum level, neural hyperreactivity and sensory nerve sensitization, and local immunological dysregulation and chronic low‑grade inflammation. The homeostasis of the scalp microenvironment plays a crucial regulatory role in maintaining the hair follicle growth cycle, yet this “soil” factor has long been underappreciated in hair loss research, which has traditionally centered on intrinsic follicular defects, such as genetic predisposition or androgen-driven mechanisms. In this review, we summarized the potential mechanisms underlying the imbalance in the “barrier-neural-immune” network in sensitive scalps. Sensitive scalp is further manifested as impaired skin barrier, including increased transepidermal water loss and abnormal lipid secretion. These alterations are accompanied by increased surface pH, excessive secretion of sebum rich in free fatty acids, reduced microbial diversity, and dysbiosis characterized by a reduction in Cutibacterium and an increase in Staphylococcus colonization. Mechanistically, we also analyzed how neurogenic inflammation, mediated by substances such as substance P (SP) and calcitonin gene-related peptide (CGRP), along with persistent low-grade inflammation, disrupts the microenvironment of the hair follicle stem cell niche through mechanisms involving mechanosensitive ion channels (e.g., Piezo1), oxidative stress, and cytokine cascades. When the barrier is compromised, the weakened stratum corneum may facilitate the penetration of external stimuli, sensitizing cutaneous nerve endings. Those hyperresponsive nerves then fire off neuropeptides like substance P and CGRP. These peptides activate mast cells and other immune cells via the MrgprB2 receptor, prompting degranulation and a wave of pro-inflammatory mediators that kick off neurogenic inflammation. At the same time, oxidative stress and inflammatory cytokines drive a persistent low-grade inflammation that directly damages the hair follicle stem cell niche. Physical compression from shrinking hair shafts activates Piezo1 channels, setting off calcium influx and Caspase‑3‑dependent apoptotic signaling. We primarily considered the interactions between inflammatory signaling and the sensitization of the androgen receptor (AR) pathway, as well as the inhibition of the Wnt/β‑catenin pathway, which may subsequently contribute to hair follicle miniaturization and disruption of the growth cycle. The inflammatory microenvironment appears to boost AR expression in dermal papilla cells, making them more sensitive to dihydrotestosterone (DHT). This heightened sensitivity may promote the expression of transforming growth factor-β1 (TGF-β1), TGF-β2, and Dickkopf-1 (DKK1), which suppress the Wnt/β‑catenin pathway—essential for follicle growth and regeneration—potentially accelerating follicular miniaturization and stalling the growth cycle. Coupled with direct inflammatory apoptosis, oxidative stress, and circadian disruption, these routes may collectively speed up hair follicle stem cell depletion and diffuse hair thinning. These findings point to the need for more mechanistic and translational research to test the proposed pathways and explore therapies that target the microenvironment in sensitive scalp-related hair loss. Ultimately, we hope to provide theoretical support for the intervention of hair loss by restoring the steady state of the scalp microenvironment and combining with new precision delivery technologies such as microneedles.

ObjectiveTo identify protein biomarkers and potential therapeutic targets associated with metabolic dysfunction-associated fatty liver disease （MAFLD） through an integrative multi-omics analysis. MethodsLarge-scale plasma protein quantitative trait locus data were integrated with MAFLD genome-wide association study data， and a proteome-wide Mendelian randomization analysis was performed. Summary data-based Mendelian Randomization （SMR）/heterogeneity in dependent instruments （HEIDI） test and colocalization analysis were used to assess significant proteins. The independent samples t-test was used for comparison of continuous data with homogeneity of variance between two groups， and the Welch-corrected t-test was used for comparison of continuous data with heterogeneity of variance. The transcriptomic validation results were compared with the Mendelian randomization results to evaluate the changing trends of gene expression， and boxplots were used to display the expression distribution of candidate genes between groups. ResultsAmong 7 130 proteins analyzed， 40 showed a causal relationship with MAFLD， among which 26 increased the risk of MAFLD and 14 exerted a protective effect against MAFLD （FDR<0.05）. Nine proteins were supported by SMR/HEIDI （P_SMR<0.05， P_HEIDI>0.05）， and 14 showed a strong colocalization signal （PPH₄>0.9）. The transcriptomic analysis identified 5 differentially expressed proteins （all P<0.05）， i.e.， adenosine diphosphate-ribosylation factor-like GTPase 2， insulin-like growth factor binding protein 2， casein kinase 1 delta （CSNK1D）， leucine-rich repeat neuronal 1， and moesin （MSN）. In vitro validation showed upregulated expression levels of MSN （1.612±0.346 vs 0.569±0.077， P<0.05）， 14-3-3 protein theta （YWHAQ） （1.267±0.126 vs 0.313±0.029， P<0.05）， and CSNK1D （0.871±0.025 vs 0.431±0.080， P<0.05） and a downregulated expression level of histone deacetylase 8 （0.637±0.059 vs 1.097±0.260， P<0.05） in the MAFLD cell model. Functional annotation showed that CSNK1D participated in circadian and metabolic regulation， YWHAQ modulated the Hippo pathway and lipid signal transduction， and MSN was associated with cytoskeletal remodeling and fibrogenic responses. ConclusionThe integrative proteogenomic analysis in this study shows that CSNK1D， YWHAQ， and MSN may be key mediators in the pathogenesis of MAFLD. These proteins have the potential to serve as novel biomarkers and therapeutic targets， providing new directions for research on disease mechanisms and the development of intervention strategies.

In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.

Enterobacterales are important pathogens responsible for hospital-acquired infections and can cause various severe infectious diseases. Since their first report, extended-spectrum β-lactamase-producing Enterobacterales(ESBL-E) have become widely prevalent worldwide. These strains often exhibit multidrug resistance, significantly increasing the risk of treatment failure and patient mortality. In China, the prevention and control of ESBL-E face numerous challenges, including high prevalence rates, significant regional distribution disparities, uneven laboratory testing capabilities, and variations in antimicrobial stewardship and prescribing practices. To address these issues, Professors Zhuo Chao, Xu Yingchun and Yu Yunsong initiated the developmentof the Expert Consensus on Clinical Management Strategies for Infections Caused by Extended-Spectrum β-Lactamase-Producing Enterobacterales (2025), in collaboration with multidisciplinary experts. This consensus systematically reviews the global and domestic epidemiological characteristics of ESBL-E, clinical laboratory detection techniques, treatment principles and key therapeutic agents, as well as infection prevention and control measures. Furthermore, it elaborates on the microbiological features and stratified treatment strategies for different infection sources (including bloodstream infections, intracranial infections, respiratory and mediastinal infections, intra-abdominal infections, and urinary tract infections) and special populations (such as neutropenic patients with fever, emergency patients, and pediatric patients). The consensus aims to enhance the detection capacity of clinical laboratories for ESBL-E, standardize diagnosis and treatment strategies in healthcare settings, and provide scientific guidance to curb the spread of ESBL-E in China, thereby reducing associated morbidity and mortality.

Heat acclimation provides cardiovascular protection in high-temperature environments through multilevel mechanisms; however, the complete molecular basis of its effects remains unclear. In this paper, we systematically review the effects of heat acclimation on blood volume, vascular function, cardiac structure, energy metabolism, and anti-stress regulation, revealing their potential mechanisms in cardiovascular adaptive protection. We also summarizes the multilevel responses induced by heat stress and heat acclimation, including the modulatory effects of heat acclimation on heat shock proteins (HSPs), hypoxia inducible factor 1 (HIF-1), and apoptotic pathways. Additionally, we highlights the comprehensive protective effects of heat acclimation across various stressors (e.g., hypoxia, heat stress). This review provides a significant physiological basis for cardiovascular disease management and sports medicine, emphasizing the potential application of heat acclimation in response to multiple stressors and supporting its role as an effective tool in cardiovascular health management and stress protection interventions.
Humans ; Acclimatization/physiology* ; Hot Temperature ; Heat-Shock Proteins/metabolism* ; Animals ; Heat-Shock Response/physiology* ; Hypoxia-Inducible Factor 1/metabolism* ; Apoptosis/physiology*

The mechanism of L-perilla alcohol(L-POH) in intervening hypoxic pulmonary hypertension(HPAH) was discussed based on network pharmacology, and experimental verification. The active components and potential targets of the volatile oil of Rhodiola tangutica(VORA) in the intervention of HPAH were screened by network pharmacology. The biological process of Gene Ontology(GO) and the signaling pathway enrichment of Kyoto Encyclopedia of Genes and Genomes(KEGG) were analyzed for the core targets, and a "component-common target-disease" network was constructed. Four active components were screened from VORA: L-POH, linalool, geraniol, and(-)-myrtenol. The core targets for treating HPAH were HSP90AA1, AKT1, ESR1, PIK3CA, EP300, EGFR, and JAK2. GO enrichment analysis mainly involved biological processes such as reaction to hypoxia, heme binding, and steroid binding. KEGG enrichment analysis mainly involved hypoxia-inducing factor 1(HIF-1) signaling pathway, phosphatidylinositol 3-kinase/protein kinase B(PI3K/AKT) signaling pathway, and Janus kinase/activator of signal transduction and transcription(JAK/STAT) signaling pathway. The vasodilation effects of the four active components were screened by perfusion experiment of extracorporeal vascular rings, and the mechanism of the main active component L-POH was studied by channel blockers. The inhibitory effects of the four active components on the proliferation of pulmonary artery smooth muscle cells(PASMCs) induced by hypoxia were screened by cell proliferation experiment, and the mechanism of the main active component L-POH was studied by flow cytometry, cell cycle experiment, and Western blot. The results showed that L-POH could directly act on vascular smooth muscle to relax pulmonary arterioles, induce ATP-sensitive potassium channels to open, and inhibit extracellular Ca~(2+) influx through voltage-gated calcium channels to relax blood vessels. In addition, L-POH could inhibit the abnormal proliferation of PASMCs induced by hypoxia and promote its apoptosis, and its mechanism may be related to the increase in Bax protein expression and the decrease in p-JAK2, p-STAT3, Bcl-2, and cyclinA2 protein expression. In summary, L-POH can interfere with HPAH by relaxing pulmonary arterioles and inhibiting the proliferation of smooth muscle cells.
Network Pharmacology ; Animals ; Hypertension, Pulmonary/physiopathology* ; Drugs, Chinese Herbal/administration & dosage* ; Rats ; Hypoxia/metabolism* ; Rhodiola/chemistry* ; Signal Transduction/drug effects* ; Humans ; Monoterpenes/chemistry* ; Male ; Cell Proliferation/drug effects* ; Rats, Sprague-Dawley

Ginsenoside Rg_2(GRg2) is a triterpenoid compound found in Panax notoginseng. This study explored its effects and mechanisms on diabetic retinopathy and angiogenesis. The study employed endothelial cell models induced by glucose or vascular endothelial growth factor(VEGF), the chorioallantoic membrane(CAM) model, the oxygen-induced retinopathy(OIR) mouse model, and the db/db mouse model to evaluate the therapeutic effects of GRg2 on diabetic retinopathy and angiogenesis. Transwell assays and endothelial tube formation experiments were conducted to assess cell migration and tube formation, while vascular area measurements were applied to detect angiogenesis. The impact of GRg2 on the retinal structure and function of db/db mice was evaluated through retinal thickness and electroretinogram(ERG) analyses. The study investigated the mechanisms of GRg2 by analyzing the activation of Yes-associated protein(YAP) and Toll-like receptors(TLRs) pathways. The results indicated that GRg2 significantly reduced cell migration numbers and tube formation lengths in vitro. In the CAM model, GRg2 exhibited a dose-dependent decrease in the vascular area ratio. In the OIR model, GRg2 notably decreased the avascular and neovascular areas, ameliorating retinal structural disarray. In the db/db mouse model, GRg2 increased the total retinal thickness and enhanced the amplitudes of the a-wave, b-wave, and oscillatory potentials(OPs) in the ERG, improving retinal structural disarray. Transcriptomic analysis revealed that the TLR signaling pathway was significantly down-regulated following YAP knockdown, with PCR results consistent with the transcriptome sequencing findings. Concurrently, GRg2 downregulated the expression of Toll-like receptor 4(TLR4), TNF receptor-associated factor 6(TRAF6), and nuclear factor-kappaB(NF-κB) proteins in high-glucose-induced endothelial cells. Collectively, GRg2 inhibits cell migration and tube formation and significantly reduces angiogenesis in CAM and OIR models, improving retinal structure and function in db/db mice, with its pharmacological mechanism likely involving the down-regulation of YAP expression.
Animals ; Ginsenosides/pharmacology* ; Diabetic Retinopathy/physiopathology* ; Mice ; YAP-Signaling Proteins ; Humans ; Male ; Signal Transduction/drug effects* ; Cell Movement/drug effects* ; Adaptor Proteins, Signal Transducing/genetics* ; Mice, Inbred C57BL ; Neovascularization, Pathologic/metabolism* ; Drugs, Chinese Herbal/administration & dosage* ; Panax notoginseng/chemistry* ; Endothelial Cells/metabolism* ; Transcription Factors/genetics* ; Angiogenesis