Objective: To investigate the impact of RhE antigen-matched transfusion during liver transplantation on early postoperative recovery and complications. Methods: In this retrospective cohort study, ninety-five patients undergoing liver transplantation at Kunming First People's Hospital between January 2022 and July 2025 were enrolled. Patients were divided into two groups: Group 1 (RhE-mismatched transfusion, n=57) and Group 2 (RhE-matched transfusion, n=38). The baseline data, complete blood counts, hepatic and renal function, coagulation parameters, and complication rates between the two groups were compared at postoperative days 1, 3, 5, 7, and 10. Survival analysis was performed using the Kaplan-Meier method. Results: The baseline characteristics were well-balanced and comparable between the two groups (all P>0.05). The early postoperative mortality rate in the mismatched group (31.58%, 18/57) was significantly higher than that in the matched group (10.53%, 4/38) (P=0.017). The incidence of postoperative hepatic encephalopathy was significantly higher in the mismatched group (50.88%, 29/57) than in the matched group (10.53%, 4/38) (P<0.001). The incidence of postoperative haemorrhage in the mismatched group (24.56%, 14/57) was higher than that in the matched group (5.26%, 2/38), with a statistically significant difference (P=0.014). The incidence of perioperative infection in the mismatched group (28.07%, 16/57) was higher than that in the matched group (10.53%, 4/38), with a statistically significant difference (P=0.04). Corresponding odds ratios (OR) and 95% confidence intervals indicated a lower risk of these adverse events in the matched group. On postoperative day 1, the change in activated partial thromboplastin time (-1.6, 20.5) in the mismatched group was greater than in the matched group (-0.2, 5.5). The change in international normalised ratio (-0.56, 1.22) in the mismatched group was greater than in the matched group (-0.18, 0.32), while the change in albumin (-4.0, 4.8) was smaller in the mismatched group than in the matched group (-2.5, 8.8). On postoperative day 5, the change in albumin (-0.41±7.83) in the mismatched group was smaller than in the matched group (2.68±4.53). At postoperative day 7, the change in albumin in the mismatched group (-0.61±7.38) was smaller than that in the matched group (2.51±5.85), while the change in D-dimer in the mismatched group (0.73, 7.4) was greater than that in the matched group (-1.6, 4.3). On postoperative day 10, the mismatched group exhibited significantly higher fibrinogen levels (-1.21, 1.78) than the matched group (-0.49, 0.97), and significantly longer prothrombin times (-11.3, -2.7) than the matched group (-6.2, -0.8) (all P<0.05). The matched group exhibited a mean overall survival (OS) of 32.803 months (95% CI:29.171-36.436 months), significantly exceeding the mismatched group's 28.996 months (95% CI:24.202-33.790 months). The log-rank test yielded statistically significant results (χ =4.307, P=0.038). Conclusion: Implementing RhE blood group-matched transfusion during liver transplantation may help reduce early postoperative mortality and the incidence of major complication rates, promote faster recovery of coagulation and liver function, and thereby improve short-term patient outcomes.

Objective: To provide evidence for improving emergency blood supply protocols by analyzing the clinical characteristics and disease distribution of emergency transfusion patients, especially those receiving≥10 units of red blood cells (RBCs). Methods: The data of 4 157 patients who urgently applied for large-volume blood transfusion in various hospitals in Shanghai from May 2024 to April 2025 were selected and analyzed statistically. Results: Tertiary gradeA hospitals accounted for the largest proportion of total transfusion volume (U) (48.79%, 8 420/17 256.5), with no statistically significant differences in RBC transfusion volumes among hospitals of different grades (P>0.05). All blood products are most widely used in tertiary hospitals. Obstetric blood transfusion (U)(19.07%, 3 277.5/17 190.5) was the most frequent. A-mong the hospitals of patients who received emergency blood transfusion with red blood cell suspension≥10 U, tertiary gradeA hospitals also had the largest transfusion volume (U)(47.19%, 1 107/2 346). In terms of disease types, the top three diseases in terms of blood transfusion volume (U) were obstetric transfusion (24.59%, 572/2 326), digestive diseases (14.53%, 338/2 326) and tumors (14.19%, 330/2 326). Conclusion: Tertiary grade A hospitals are the main demand units for emergency blood transfusion, with pregnant women and cancer patients being the core blood-using groups. It is suggested that the safety, timeliness and sufficiency of emergency blood transfusion be guaranteed by establishing a hierarchical blood supply mechanism, formulating single-disease blood transfusion plans and promoting precise blood transfusion guided by thromboelastography.

Objective: To examine the changing trajectories of sleep problems and teacher support among first year middle school students and their covariant relationship,so as to provide theoretical basis for the prevention strategy of sleep problems for the first year junior high school students. Methods: In September 2020, a multistage cluster random sampling method was used to select 1 027 first year junior high school students from two schools of Anshun and Guiyang cities in Guizhou Province for survey and follow up assessments (T1:September 2020, T2:October 2020, T3:November 2020, T4:December 2020). The Student Perceived Teacher Support Behavior Questionnaire and Pittsburgh Sleep Quality Index Scale were administered to assess sleep problems and teacher support among first year junior high school students. Spearman correlation analysis was used to examine the relationship between sleep problems and teacher support. A multivariate latent growth curve model was employed to analyze the changing trajectories and covariant relationship between teacher support and sleep problems, followed by a multi group analysis. Results: For first year junior high school students, teacher support scores at T1-T4 were 4.00 (3.47, 4.53), 4.00 (3.47, 4.58), 3.95 (3.47, 4.61) and 4.00 (3.48, 4.67), respectively; sleep problem scores were 0.83 (0.50, 1.17), 0.67 (0.50, 1.17), 0.83 (0.50, 1.17) and 0.67 (0.33, 1.17), respectively. Spearman correlation analysis revealed that teacher support and sleep problems were negatively correlated across all four period ( r =-0.28 to -0.14, all P <0.01). Teacher support perceived by students showed a linear increasing trend (intercept=3.98, slope=0.02), while sleep problems showed a linear decreasing trend (intercept=0.86, slope= -0.02 ) (all P <0.05). The multivariate latent growth model indicated that the rate of increase in teacher support after enrollment effectively predicted the rate of decrease in sleep problem levels ( β=-0.34, P <0.01). Multigroup analysis showed that the covariant relationship was not moderated by gender or boarding status (both P >0.05). Conclusions The increase in teacher support experienced by first year junior high school students during the transition period after enrollment, accompanied by a reduction in sleep problems, constitutes a dynamic protective process. The process is not moderated by gender or boarding status.

Risks of food safety induced by small molecule drug residues in animal food and environment have become an increasing public concern,so it is necessary to develop highly sensitive and easy-to-operate techniques to detect small molecules.Herein,a metasurface plasma resonance(MetaSPR)sensor chip coupled with gold nanoparticles(AuNPs)was developed for detection of enrofloxacin(ENR)based on competitive immunoassay.The detection range of the sensor for ENR was 0.025-3.2 ng/mL,and the detection limit(3σ)was 20 pg/mL.The biosensor showed excellent performance including high selectivity,good stability,ease to operate and high throughput,etc.The developed method was applied to detection of ENR residues in real samples,with recoveies of 96.0% -105.0%.The proposed sensing strategy provided new technique reference for detection of other small molecules in the field of residue analysis in food safety and environment monitoring.

BACKGROUND:Knee osteoarthritis(KOA)is a common chronic inflammatory disease that causes damage to joint cartilage and surrounding tissues.Immune cells play an important role in the immune-inflammatory response in knee osteoarthritis,but the specific mechanisms involved are still not fully understood. OBJECTIVE:To evaluate the potential causal relationship between 731 immune cell phenotypes and the risk of knee osteoarthritis using Mendelian randomization. METHODS:Summary statistics of genome-wide association studies(GWAS)for 731 immune cell phenotypes(from GCST0001391 to GCST0002121)obtained from the GWAS catalog and GWAS data for knee osteoarthritis from the IEUGWAS database(ebi-a-GCST007090)were used.Inverse variance-weighted method,MR-Egger regression,weighted median method,weighted mode method,and simple mode method were employed to investigate the causal relationship between immune cells and knee osteoarthritis.Sensitivity analyses were conducted to assess the reliability of the Mendelian randomization results.Reverse Mendelian randomization analysis was also performed using the same methods. RESULTS AND CONCLUSION:The forward MR analysis indicated significant causal relationships(FDR<0.20)between knee osteoarthritis and four immune cell phenotypes,namely CD27 on CD24+CD27+in B cells(OR=1.026,P=0.000 26,Pfdr=0.18),CD33 on CD33dim HLA DR-in myeloid cells(OR=1.014,P=0.000 50,Pfdr=0.18),and CD45RA+CD28-CD8br%CD8br in Treg cells(OR=1.001,P=0.000 78,Pfdr=0.18),and PDL-1 on monocytes in mononuclear cells(OR=0.952,P=0.000 98,Pfdr=0.18).These immune cell phenotypes showed direct positive or negative causal associations with the risk of knee osteoarthritis.Reverse Mendelian randomization analysis revealed no significant causal relationships(FDR<0.20)between knee osteoarthritis as exposure and any of the 731 immune cell phenotypes.The results of sensitivity analysis show that the P-values of the Cochran's Q test and the MR-Egger regression method for bidirectional Mendelian randomization were both greater than 0.05,indicating that there is no significant heterogeneity and pleiotropy in the causal effect analysis between immune cell phenotypes and knee osteoarthritis.To conclude,there may be four potential causal relationships between immune cell phenotypes,such as CD27 on CD24+CD27+cells,CD33 on CD33dim HLA DR-cells,CD45RA+CD28-CD8br%CD8br cells,and PDL-1 on monocytes,and knee osteoarthritis.These findings provide valuable clues for studying the biological mechanisms of knee osteoarthritis and exploring early prevention and treatment strategies.They also offer new directions for the development of intervention drugs.

Rubi Fructus has a long history of medicinal and edible use in China. It contains chemical components such as terpenes, flavonoids, phenolic acids, fatty acids, and alkaloids, and possesses various pharmacological activities, including antioxidant, anti-inflammatory, hypoglycemic, anti-tumor, anti-osteoporosis, and liver-protective effects. Rubi Fructus is widely applied in medical, health, and food fields. The quality of Rubi Fructus can directly affect the safety and effectiveness of clinical medication. Therefore, this article reviews the research progress on the chemical constituents and pharmacological effects of Rubi Fructus. Based on the concept of traditional Chinese medicine(TCM) quality markers(Q-markers), the article explores the screening and determination of Q-markers for Rubi Fructus from various aspects, including plant kinship, traditional efficacy, medicinal properties, measurability of chemical composition, different processing methods, producing areas, harvesting periods, and planting conditions. The components ellagic acid, kaempferol, quercetin, kaempferol-3-O-rutinoside, rutin, astragalin, tiliroside, and hyperoside are preliminarily proposed as Q-markers for Rubi Fructus, providing a reference for the quality control of Rubi Fructus.
Drugs, Chinese Herbal/pharmacology* ; Humans ; Rubus/chemistry* ; Fruit/chemistry* ; Quality Control ; Animals

Eucommiae Cortex, the dried bark of Eucommia ulmoides( Eucommiaceae), has both medicinal and edible values.Modern research has shown that Eucommiae Cortex contains various components such as flavonoids, lignans, iridoids, phenolic acids,terpenoids, and steroids, which have anti-osteoporosis, antioxidant, anti-inflammatory, blood glucose-lowering, and gastrointestinal tract-protecting effects. Eucommiae Cortex has applications in multiple fields such as healthcare, industry, and animal husbandry,demonstrating broad development prospects. This article reviews the chemical constituents, pharmacological effects, and quality control status of Eucommiae Cortex. Furthermore, according to the concept of quality marker(Q-marker), this article predicts the Q-markers of Eucommiae Cortex from traditional medicinal properties, traditional medicinal effects, new medicinal effects, measurability of chemical components, compatibility, harvesting periods, and geographical origins. The components such as pinoresinol diglucoside,chlorogenic acid, caffeic acid, quercetin, baicalein, baicalin, olivil, coniferyl ferulate, and kaempferol can be used as Q-markers for Eucommiae Cortex, which provide reference for establishing a systematic quality control system for Eucommiae Cortex.
Eucommiaceae/chemistry* ; Drugs, Chinese Herbal/pharmacology* ; Quality Control ; Humans ; Animals

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.

Age-related sarcopenia is a progressive, systemic skeletal muscle disorder associated with aging. It is primarily characterized by a significant decline in muscle mass, strength, and physical function, rather than being an inevitable consequence of normal aging. Despite ongoing research, there is still no globally unified consensus among physicians regarding the diagnostic criteria and clinical indicators of this condition. Nonetheless, regardless of the diagnostic standards applied, the prevalence of age-related sarcopenia remains alarmingly high. With the global population aging at an accelerating rate, its incidence is expected to rise further, posing a significant public health challenge. Age-related sarcopenia not only markedly increases the risk of physical disability but also profoundly affects patients’ quality of life, independence, and overall survival. As such, the development of effective prevention and treatment strategies to mitigate its dual burden on both societal and individual health has become an urgent and critical priority. Skeletal muscle regeneration, a vital physiological process for maintaining muscle health, is significantly impaired in age-related sarcopenia and is considered one of its primary underlying causes. Skeletal muscle satellite cells (MSCs), also known as muscle stem cells, play a pivotal role in generating new muscle fibers and maintaining muscle mass and function. A decline in both the number and functionality of MSCs is closely linked to the onset and progression of sarcopenia. This dysfunction is driven by alterations in intrinsic MSC mechanisms—such as Notch, Wnt/β‑Catenin, and mTOR signaling pathways—as well as changes in transcription factors and epigenetic modifications. Additionally, the MSC microenvironment, including both the direct niche formed by skeletal muscle fibers and their secreted cytokines, and the indirect niche composed of extracellular matrix proteins and various cell types, undergoes age-related changes. Mitochondrial dysfunction and chronic inflammation further contribute to MSC impairment, ultimately leading to the development of sarcopenia. Currently, there are no approved pharmacological treatments for age-related sarcopenia. Nutritional intervention and exercise remain the cornerstone of therapeutic strategies. Adequate protein intake, coupled with sufficient energy provision, is fundamental to both the prevention and treatment of this condition. Adjuvant therapies, such as dietary supplements and caloric restriction, offer additional therapeutic potential. Exercise promotes muscle regeneration and ameliorates sarcopenia by acting on MSCs through various mechanisms, including mechanical stress, myokine secretion, distant cytokine signaling, immune modulation, and epigenetic regulation. When combined with a structured exercise regimen, adequate protein intake has been shown to be particularly effective in preventing age-related sarcopenia. However, traditional interventions may be inadequate for patients with limited mobility, poor overall health, or advanced sarcopenia. Emerging therapeutic strategies—such as miRNA mimics or inhibitors, gut microbiota transplantation, and stem cell therapy—present promising new directions for MSC-based interventions. This review comprehensively examines recent advances in MSC-mediated muscle regeneration in age-related sarcopenia and systematically discusses therapeutic strategies targeting MSC regulation to enhance muscle mass and strength. The goal is to provide a theoretical foundation and identify future research directions for the prevention and treatment of this increasingly prevalent condition.

In recent years,significant breakthroughs have been achieved in the immunotherapy for Alzheimer's disease.In line with global advancements,two anti-amyloid-β monoclonal antibodies have been approved and successfully launched in China for clinical use.Lecanemab and Donanemab were officially used in June 2024 and April 2025 in China,respectively.In order to standardize the rational and safe application of anti-amyloid-β monoclonal antibodies for Alzheimer's disease in China,this article integrates recom-mendations from the clinical trials and real-world experience from the author's team and domestic peers to further update the recom-mendations for the clinical use of anti-amyloid-β monoclonal antibody based on the 2024 version.It includes indications for therapy,pre-treatment evaluation and preparation,administration protocols and safety measures during treatment,and post-treatment monitor-ing strategies.