Objective To describe the significance of the pretreatment inflammatory indicators in predicting the prognosis of patients with esophageal squamous cell carcinoma (ESCC) after undergoing radical radiotherapy. Methods The data of 246 ESCC patients who underwent radical radiotherapy were retrospectively collected. Receiver operating characteristic (ROC) curves were drawn to determine the optimal cutoff values for platelet-lymphocyte ratio (PLR), neutrophil-lymphocyte ratio (NLR), and systemic immune-inflammation index (SII). The Kaplan-Meier method was used for survival analysis. We conducted univariate and multivariate analyses by using the Cox proportional risk regression model. Software R (version 4.2.0) was used to create the nomogram of prognostic factors. Results The results of the ROC curve analysis showed that the optimal cutoff values of PLR, NLR, and SII were 146.06, 2.67, and 493.97, respectively. The overall response rates were 77.6% and 64.5% in the low and high NLR groups, respectively (P<0.05). The results of the Kaplan-Meier survival analysis revealed that the prognosis of patients in the low PLR, NLR, and SII group was better than that of patients in the high PLR, NLR, and SII group (all P<0.05). The results of the multivariate Cox regression analysis showed that gender, treatment modalities, T stage, and NLR were independent factors affecting the overall survival (OS). In addition, T stage and NLR were independent factors affecting the progression-free survival (PFS) (all P<0.05). The nomogram models of OS and PFS prediction were established based on multivariate analysis. The C-index values were 0.703 and 0.668. The calibration curves showed excellent consistency between the predicted and observed OS and PFS. Conclusion The pretreatment values of PLR, NLR, and SII are correlated with the prognosis of patients with ESCC who underwent radical radiotherapy. Moreover, NLR is an independent factor affecting the OS and PFS of ESCC patients. The NLR-based nomogram model has a good predictive ability.

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.

OBJECTIVE To explore the construction of selection system for drug directory of the county-level medical community based on multi-criteria decision analysis， and provide decision-making basis for the selection of drug directory of medical community. METHODS Taking county-level medical community in Chongqing as an example，Delphi method and analytic hierarchy process were employed to construct the selection system for drug directory of the county-level medical community. Selected drugs were quantitatively scored based on the constructed index system， and the drug directory was selected according to the drug’s comprehensive score. The implementation effect of the directory was then evaluated through questionnaire surveys one year after the implementation of the directory. RESULTS The expert authority coefficients of the two rounds of consultation were＞ 0.8， with Kendall’s W values of 0.213 and 0.196， respectively （P＜0.001）. Finally， the selection system for drug directory of the medical community was determined to include five evaluation dimensions： safety， effectiveness， economy， accessibility， and innovation， along with eight evaluation indicators. In the drug directory selected according to the above method， the proportions of centrally procured drugs， medical insurance drugs， and essential drugs had all increased compared to before the selection； the comprehensive scores of chemical drugs ranged from 50.25 to 96.31 scores， and the proportion of drugs scoring between 70 and 100 scores had increased from 78.06% before selection to 85.82%. Among them， antiparasitic drugs had the highest comprehensive scores， while drugs for the digestive tract and metabolism were the most numerous. The evaluation scores of each indicator and the comprehensive scores of drugs in the drug directory after the selection process increased significantly than before selection （P＜ 0.05）. CONCLUSIONS The selection system for drug directory of the county-level medical community constructed in this study is scientific， objective and operable. This process facilitates the promotion of standardized and unified management of drugs in the medical community.

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.

Objective To evaluate the efficacy and safety of brexpiprazole in treating acute schizophrenia.Methods Patients with schizophrenia were randomly divided into treatment group and control group.The treatment group was given brexpiprozole 2-4 mg·d-1 orally and the control group was given aripiprazole 10-20 mg·d-1orally,both were treated for 6 weeks.Clinical efficacy of the two groups,the response rate at endpoint,the changes from baseline to endpoint of Positive and Negative Syndrome Scale(PANSS),Clinical Global Impression-Improvement(CGI-S),Personal and Social Performance scale(PSP),PANSS Positive syndrome subscale,PANSS negative syndrome subscale were compared.The incidence of treatment-related adverse events in two groups were compared.Results There were 184 patients in treatment group and 186 patients in control group.After treatment,the response rates of treatment group and control group were 79.50％(140 cases/184 cases)and 82.40％(150 cases/186 cases),the scores of CGI-I of treatment group and control group were(2.00±1.20)and(1.90±1.01),with no significant difference(all P＞0.05).From baseline to Week 6,the mean change of PANSS total score wese(-30.70±16.96)points in treatment group and(-32.20±17.00)points in control group,with no significant difference(P＞0.05).The changes of CGI-S scores in treatment group and control group were(-2.00±1.27)and(-1.90±1.22)points,PSP scores were(18.80±14.77)and(19.20±14.55)points,PANSS positive syndrome scores were(-10.30±5.93)and(-10.80±5.81)points,PANSS negative syndrome scores were(-6.80±5.98)and(-7.30±5.15)points,with no significant difference(P＞0.05).There was no significant difference in the incidence of treatment-related adverse events between the two group(69.00％vs.64.50％,P＞0.05).Conclusion The non-inferiority of Brexpiprazole to aripiprazole was established,with comparable efficacy and acceptability.

ObjectiveTriple-negative breast cancer (TNBC) is the breast cancer subtype with the worst prognosis, and lacks effective therapeutic targets. Colony stimulating factors (CSFs) are cytokines that can regulate the production of blood cells and stimulate the growth and development of immune cells, playing an important role in the malignant progression of TNBC. This article aims to construct a novel prognostic model based on the expression of colony stimulating factors-related genes (CRGs), and analyze the sensitivity of TNBC patients to immunotherapy and drug therapy. MethodsWe downloaded CRGs from public databases and screened for differentially expressed CRGs between normal and TNBC tissues in the TCGA-BRCA database. Through LASSO Cox regression analysis, we constructed a prognostic model and stratified TNBC patients into high-risk and low-risk groups based on the colony stimulating factors-related genes risk score (CRRS). We further analyzed the correlation between CRRS and patient prognosis, clinical features, tumor microenvironment (TME) in both high-risk and low-risk groups, and evaluated the relationship between CRRS and sensitivity to immunotherapy and drug therapy. ResultsWe identified 842 differentially expressed CRGs in breast cancer tissues of TNBC patients and selected 13 CRGs for constructing the prognostic model. Kaplan-Meier survival curves, time-dependent receiver operating characteristic curves, and other analyses confirmed that TNBC patients with high CRRS had shorter overall survival, and the predictive ability of CRRS prognostic model was further validated using the GEO dataset. Nomogram combining clinical features confirmed that CRRS was an independent factor for the prognosis of TNBC patients. Moreover, patients in the high-risk group had lower levels of immune infiltration in the TME and were sensitive to chemotherapeutic drugs such as 5-fluorouracil, ipatasertib, and paclitaxel. ConclusionWe have developed a CRRS-based prognostic model composed of 13 differentially expressed CRGs, which may serve as a useful tool for predicting the prognosis of TNBC patients and guiding clinical treatment. Moreover, the key genes within this model may represent potential molecular targets for future therapies of TNBC.

Analyze the changes in phenolic components and gene expression profiles of Tetrastigma hemsleyanum leaves with supplemental blue light, and screen differentially expressed genes related to phenolic metabolism, providing a basis for improving the quality of T. hemsleyanum leaves. Using the leaves of T. hemsleyanum under supplemental blue light and visible light as research materials, the content of phenolic components such as neochlorogenic acid, chlorogenic acid, and 3-O-coumaroyl quinic acid was significantly increased by supplemental blue light. A total of 102 949 unigenes were obtained from the transcriptome, including 14 564 differentially expressed unigenes. They can be divided into 52 subclasses by gene ontology (GO) function, kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis showed that these differentially expressed unigenes were significantly enriched in phenolic metabolic pathways such as phenylpropane biosynthesis, flavonoid biosynthesis and isoflavone biosynthesis. A total of 63 differentially expressed unigenes were identified in the pathways related to the biosynthesis of phenolic cpmponents, including 19 key enzymes such as phenylalanine ammonia-lyase (PAL), cinnamyl-alcohol dehydrogenase (CAD), flavonol synthase (FLS), and so on. This study greatly enriched the genetic data information of T. hemsleyanum and laid a foundation for analyzing the mechanism of blue light promoting the biosynthesis of phenols and molecular breeding of T. hemsleyanum.

Objective To observe the clinical efficacy of joint needling method combined with ultrasound in the treatment of qi stagnation and blood stasis type of patellofemoral pain syndrome(PFPS).Methods Eighty-six patients with qi stagnation and blood stasis type of PFPS were randomly divided into observation group and control group,with 43 cases in each group.The control group was given western medicine conventional treatment combined with functional exercise,and the observation group was given joint needling method combined with ultrasound treatment on the basis of the control group.Both groups were treated for 2 consecutive weeks.After 2 weeks of treatment,the clinical efficacy of the two groups was evaluated,and the changes in the Visual Analogue Scale(VAS)scores of knee pain and the Kujala scale scores of the two groups were observed before and after treatment.The changes in active range of motion(AROM)of the affected knee joint were compared before and after treatment between the two groups.Results(1)After treatment,the VAS scores of the two groups of patients were significantly improved(P＜0.05),and the observation group was significantly superior to the control group in improving the level of VAS scores,and the difference was statistically significant(P＜0.05).(2)After treatment,the Kujala scores of patients in the two groups were significantly improved(P＜0.05),and the observation group was significantly superior to the control group in improving the level of Kujala scores,and the difference was statistically significant(P＜0.05).(3)After treatment,the AROM of patients in the two groups were significantly improved(P＜0.05),and the observation group was significantly superior to the control group in improving the level of AROM,and the difference was statistically significant(P＜0.05).(4)The total effective rate was 95.35%(41/43)in the observation group and 81.40%(35/43)in the control group.The efficacy of the observation group was superior to that of the control group,and the difference was statistically significant(P＜0.05).Conclusion The joint needling method combined with ultrasound can significantly relieve the pain symptoms of patients with PFPS and promote the recovery of knee joint function,and the clinical efficacy is remarkable.

Objective: To understand characteristics of men who have sex with men (MSM) who applied for HIV antibody self-testing reagents through "AIDS self-testing" column of a WeChat official account named "Dark Blue Public Health Center" in Tianjin City, so as to provide insights into exploring online modes of HIV antibody self-testing for MSM. Methods: Data of MSM who were 18 years old or above, currently lived in Tianjin City, had sex with men in the past six months and applied for HIV antibody self-testing reagents through "AIDS self-testing" column from May 2018 to December 2022 were collected. Demographic characteristics, results return and positive findings were descriptively analyzed. Results: Data of 2 064 MSM were collected, including 1 052 MSM aged 20 to 29 years (50.97%), 1 522 unmarried MSM (73.74%), 545 workers (26.41%), 1 385 MSM with college education or above (67.10%), and 315 MSM without testing for HIV antibody in the past (15.26%). A total of 6 470 self-testing reagents were applied, and 5 942 testing results were returned, with a return rate of 91.84%. There were 33.28% (687/2 064) of the applicants applying for 66.32% (4 291/6 470) reagents multiple times. There were 73 MSM with positive results, accounting for 1.23%. Conclusions The MSM applying for HIV antibody self-testing reagents through "AIDS self-testing" are mainly young and highly educated, including some who have never tested for HIV. However, attention should be paid to duplicate applications and the return rate should be increased.

Objective To investigate the efficacy and safety of camrelizumab monoclonal antibody combined with molecular-targeted therapy in elderly patients with unresectable or advanced hepatocellular carcinoma(HCC).Methods A retrospective analysis was performed for the patients with unresectable/advanced HCC who attended six hospitals from January 1,2019 to March 31,2021,and all patients received camrelizumab monoclonal antibody treatment,among whom 84.8%also received targeted therapy.According to the age of the patients,they were divided into elderly group(≥65 years)and non-elderly group(＜65 years).The two groups were assessed in terms of overall survival(OS),progression-free survival(PFS),objective response rate(ORR),disease control rate(DCR),and immune-related adverse events(irAE).The chi-square test or the Fisher's exact test was used for comparison of categorical data between groups;the independent samples t-test was used for comparison of normally distributed continuous data,and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups.The Kaplan-Meier method was used for survival analysis,and the log-rank test was used for comparison of survival curves.Univariate and multivariate Cox proportional hazards regression analyses were used to determine the independent influencing factors for PFS and DCR at 6 months.Results A total of 99 HCC patients were enrolled,with 27 in the elderly group and 72 in the non-elderly group.The elderly group had an OS rate of 67.8%,an ORR of 44.4%,and a DCR of 74.1%at 12 months and a median PFS of 6.4(95%confidence interval[CI]:3.0-12.4)months,with no significant differences compared with the non-elderly group(all P＞0.05).The median OS was unavailable for the elderly group,while the non-elderly group had an OS of 18.9(95%CI:13.0-24.8)months;there was no significant difference between the two groups(P=0.485).The univariate and multivariate Cox regression analyses showed that major vascular invasion(MVI)was an independent risk factor for PFS(hazard ratio[HR]=2.603,95%CI:1.136-5.964,P=0.024)and DCR(HR=3.963,95%CI:1.671-9.397,P=0.002)at 6 months,while age,sex,etiology of HBV infection,presence of extrahepatic metastasis,Child-Pugh class B,and alpha-fetoprotein＞400 ng/mL were not associated with PFS or DCR at 6 months.For the elderly group,the incidence rates of any irAE and grade 3/4 irAE were 51.9%and 25.9%,respectively,with no significant differences compared with the non-elderly group(P＞0.05),and skin disease was the most common irAE in both groups(39.4%).Conclusion Camrelizumab monoclonal antibody combined with molecular-targeted therapy has similar efficacy and safety in patients with unresectable/advanced HCC aged≥65 years and those aged＜65 years.MVI is associated with suboptimal response to immunotherapy and poor prognosis.