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.

Objective:To construct a nomogram model for predicting the incidence of hepatocellular carcinoma based on serum abnormal prothrombin and alpha-fetoprotein and evaluate the predictive effect.Methods:Retrospective analysis of data from 351 patients with liver disease who received treatment at the First Affiliated Hospital of Zhengzhou University from January 2021 to December 2023, including 285 males and 66 females, aged (52.9±11.9) years. Among the 351 patients, there were 229 cases (65.2%) of hepatocellular carcinoma, 87 cases (24.8%) of liver cirrhosis, and 35 cases (10.0%) of chronic hepatitis B. All patients were randomly divided into a training set ( n=245) and a testing set ( n=106) in a 7∶3 ratio without replacement sampling. The training set was used to construct the model, and the testing set was used to evaluate the model. At the same time, gender, age, disease type, and other indicators were compared between the two sets. The risk factors of hepatocellular carcinoma were analyzed by univariate and multivariate logistic regression based on the training set, and a nomogram was constructed to predict the incidence of hepatocellular carcinoma based on the multivariate results. Receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the predictive performance of nomogram, and decision curve analysis was used to evaluate the clinical applicability of the model. Results:There was no statistically significant difference in age, gender, disease type, etc. between the training and testing sets of patients (all P>0.05). Univariate logistic regression analysis showed that age, abnormal prothrombin logarithm (LnPIVKA-Ⅱ), alpha-fetoprotein logarithm (LnAFP), and diabetes were associated with hepatocellular carcinoma (all P<0.05). Multivariate logistic regression analysis showed that older age ( OR=1.07, 95% CI: 1.03-1.12), higher LnPIVKA-Ⅱ ( OR=2.97, 95% CI: 1.97-4.46), higher LnAFP ( OR=1.43, 95% CI: 1.11-1.84) and diabetes ( OR=5.17, 95% CI: 1.02-26.17) were risk factors for hepatocellular carcinoma (all P<0.05). Based on the above variables, a nomogram model for predicting the incidence of hepatocellular carcinoma was constructed. The area under the ROC curve analysis of the nomogram for predicting the incidence of hepatocellular carcinoma was 0.920 (95% CI: 0.886-0.953) in the training set and 0.934 (95% CI: 0.891-0.977) in the testing set. The calibration curve fit well with the standard curve, and the prediction was basically consistent with the actual situation. The decision curve analysis showed that the net benefit of the model was greater than 0 under most thresholds (0.1-1.0). Conclusion:The nomogram constructed based on age, LnPIVKA-Ⅱ, LnAFP and diabetes can effectively predict the incidence of hepatocellular carcinoma and has clinical applicability.

Objective To design a training device of the flight crew for plateau normobaric low-oxygen acclimatization so as to enhance the flight crew's ability to adapt to the low oxygen environment after rushing into the plateau and reduce the incidence of acute plateau reaction.Methods The training device comprised a plateau environment simulation controller,a multimodal physiological acquisition system and hypoxia exercise training evaluation software.The plateau environment simulation controller was composed of an environment monitor for plateau acclimatization,two composite sensor sets,a control valve and an alarm device;the multimodal physiological acquisition system was made up of 20 groups of vital signs acquisi-tion devices,with a wearable dynamic ECG and respiration recorder,a wrist oximeter and an arm sphygmomano-meter included in each group.The hypoxia exercise training evaluation software was developed with a B/S architecture,Java language and JetBrains 2020.3.Results The training device proved to have the simulation altitude ranging from 0 to 6 000 m and facilitated simultaneous training of 20 persons for normobaric low-oxygen acclimatization,screening for hypoxia endurance,real-time monitoring of physiological parameters and assessment of training effect,with none of the trainees having acute plateau reaction.Conclusion The training device assists the flight crew for plateau normobaric low-oxygen acclimatization,and can be used for acclimatization training before plateau missions.[Chinese Medical Equipment Journal,2025,46(8):18-24]

Diabetic nephropathy(DN)is a serious complication of diabetes mellitus and a leading cause of end-stage renal diseases.In DN patients,key pathological mechanisms include proteinuria,glomerulo-sclerosis,and fibrosis,largely driven by poor glycemic control and oxidative stress caused by prolonged hyperglycemia.This stress damages renal podocytes and triggers inflammatory mesenchymal infiltration of renal tubular cells,exacerbating the progression of proteinuria and fibrosis.Human umbilical cord-de-rived mesenchymal stem cells(hUC-MSCs)offer promising potential for treating DN due to their strong anti-oxidative properties.In this study,we developed a DN mouse model and treated the mouse via tail vein injections of hUC-MSCs(1×106 cells/mouse).The results indicated that hUC-MSCs significantly lowered fasting blood glucose levels(22.5±3.0 vs 14.7±1.1,P＜0.01)and improved glucose toler-ance,as shown by intraperitoneal glucose tolerance test(IPGTT)results(P＜0.05).Additionally,the renal function improved in hUC-MSCs-treated mice,with marked reductions in oxidative stress markers,including blood urea nitrogen(BUN),urinary creatinine(Ucr),urinary protein(PRO),superoxide dismutase(SOD),and malondialdehyde(MDA)(P＜0.05).Histological analyses through hematoxy-lin-eosin(H&E),Periodic Acid-Schiff(PAS),and Sirius red staining demonstrated alleviation of glo-merular mesangial hyperplasia,glomerular hypertrophy,and tubular inflammation.Furthermore,hUC-MSCs treatment downregulated the expression of oxidative stress-related proteins,such as NADPH oxi-dase 4(NOX4)and thioredoxin-interacting protein(TXNIP),and reduced reactive oxygen species(ROS)production(P＜0.05).Meanwhile,human renal cortical proximal tubule epithelial cells(HK-2 cells)were selected for validation in vitro experiments using high glucose treatment followed by super-natants of hUC-MSCs(MSC-CM),and Western blotting showed that the expression of both NOX4 and TXNIP was inhibited(P＜0.05)and ROS expression was reduced.In conclusion,hUC-MSC treatment effectively lowered blood glucose levels and improved renal function in DN mice,likely through the sup-pression of NOX4 expression and TXNIP-mediated oxidative stress.

Objective:To explore the efficacy and prognosis of preoperative treatment based on arterial infusion chemotherapy (PTAC) in patients with advanced gastric cancer.Methods:Clinical and follow-up data of 821 patients with advanced gastric cancer who received PTAC treatment at the General Hospital of the Eastern Theater Command of the People's Liberation Army from January 2001 to January 2021 were collected. According to the treatment regimen, patients were divided into the FLEEOX group (89 cases), the XEEOX group (196 cases), the SEEOX group (406 cases), and the SEEOX+PD-1 group (130 cases). The primary endpoint was the 3-year progression-free survival rate. Secondary endpoints included the 3-year overall survival rate, objective response rate, radical resection rate, major pathological response rate, and incidence of treatment associated adverse events.Results:After PTAC treatment, the objective response rate was 74.9% (615/821). A total of 671 patients underwent radical surgery, with a radical resection rate of 81.7% and an R0 resection rate of 70.2% (576/821). The pathological complete response rate was 16.7% (112/671), and the major pathological response rate was 32.2% (216/671). With an average follow-up of 27.7 months, the 3-year progression-free survival rate was 52.2%, and the 3-year overall survival rate was 55.8%. The 3-year progression-free survival rate of patients in the SEEOX+PD-1 group was 66.9%, the objective response rate was 83.8% (109/130), the major pathological response rate was 45.3% (53/117), and the radical resection rate was 90.0% (117/130), all of which were better than those in the XEEOX and SEEOX groups (all P＜0.05). However, during the treatment period, three patients in the SEEOX+PD-1 group died from immune-related adverse events. Conclusion:PTAC treatment is an effective preoperative treatment method for advanced gastric cancer, and is expected to further improve the treatment effect when combined with immunotherapy such as PD-1 monoclonal antibodies.

Insulin is an important protein hormone that participates in multiple metabolic pathways.Biosynthetic insulin has been widely used in the treatment of type 1 and type 2 diabetes.Currently,the number of reported cases of insulin overdose both at home and abroad is gradually increasing,and insulin homicide is no longer a means of"committing murder without leaving a trace".At present,there are no systematic protocols for the identification of insulin overdose in the field of forensic medi-cine in China.This article introduces the causes,toxicological characteristics,forensic examination,labo-ratory testing methods and indicator reference of insulin overdose.Based on the identification practice and research results and referring to relevant studies on insulin overdose at home and abroad,this pa-per aims to provide recommendations and references for the formulation of forensic identification guide-lines for insulin overdose cases.

Objective:To compare the differences in repair of rabbit bone defects between allogeneic platelet rich plasma (PRP) gel and autologous PRP gel.Methods:Thirty-six healthy New Zealand white rabbits were selected and randomly divided into an autologous group, an allogeneic group, and a control group ( n=12). A model of bilateral forelimb bone defects was established in each group. The autologous group was repaired with self-made deproteinized bone scaffold materials + autologous bone marrow mesenchymal stem cells (BMSCs) + autologous PRP gel, the allogeneic group with self-made deproteinized bone scaffold materials + autologous BMSCs + allogeneic PRP gel, and the control group with only self-made deproteinized bone scaffold materials + autologous BMSCs. At postoperative 1, 2, and 3 months, 4 animals were euthanized in each group, respectively, for gross observation, X-ray examination, Micro-CT examination, biomechanical testing and histological analysis (HE staining for tissue morphology) to compare the differences in repair of bone defects. Results:The formation of trabecular bone, cortical reconstruction, and medullary recanalization occurred earlier in the autologous and allogeneic groups than in the control group. Micro-CT analysis at postoperative 2 months showed that bone mineral density [(281.51±33.69) mg/mL and (266.13±37.13) mg/mL], bone volume fraction (23.52%±2.81% and 21.91%±1.94%), and trabecular number [(1.68±0.29) mm -1 and (1.63±0.22) mm -1] in the autologous and allogeneic groups were significantly higher than those in the control group [(197.47±18.61) mg/mL, 16.54%±3.06%, and (1.06±0.11) mm -1] ( P<0.05). No significant differences were found among the 3 groups in trabecular thickness [(0.33±0.09) mm, (0.42±0.16) mm, and (0.28±0.13) mm] or in the maximum compressive load ( P>0.05). HE staining revealed a significantly greater number and earlier formation of chondrocytes and osteoblasts in the autologous and allogeneic groups than in the control group. Conclusion:Since allogeneic PRP exhibits similar efficacy in promoting new bone formation compared with autologous PRP in a rabbit bone defect model, it may serve as a viable substitute for autologous PRP.

Aim To investigate the protective effect of Shengmai Yin on myocardial ischemia/reperfusion in-jury(MI/RI)in vitro and in vivo and to unravel the underlying mechanism.Methods SD rats were divid-ed into the sham group,model group,and Shengmai Yin group(SM).Rat MI/RI model was established.Cardiac function,infarct area,pathological changes,cardiomyocyte apoptosis,macrophage infiltration,and serum cTnT and CK-MB levels were measured.The mRNA and protein expressions of Calpain-1 and Cal-pain-2 were assessed.The hypoxia/reoxygenation(H/R)model was constructed in H9c2 cells.The active ingredients of Shengmai Yin were screened using net-work pharmacology and verified by CCK-8.In the car-diomyocytes H/R model,Fluo-4 AM staining was used to detect the changes of Ca2+levels.Results Com-pared with model group,LVEF and LVFS of Shengmai Yin-treated rats increased,myocardial infarction area was reduced,while myocardial tissue injury was allevi-ated.Myocardial apoptosis rate and the number of macrophages were reduced.Similarly,cTnT and CK-MB levels decreased.In addition,the expression lev-els of Calpain-1 and Calpain-2 mRNA and protein de-creased in the SM treatment group.Under the H/R model,all the active ingredients of Shengmai decoction had protective effects on cardiomyocytes,and the treat-ment could reduce the level of Ca2+in cardiomyocytes.Conclusions Shengmai Yin has protective effects on MI/RI in rats.This effect may be related to the de-crease in Ca2+levels,as well as Calpain-1 and Calap-in-2 mRNA and protein expression.

Objective:To evaluate the efficacy and safety of telitacicept in the treatment of systemic lupus erythematosus (SLE) .Methods:The clinical data of 25 SLE patients who received standard therapy combined with telitacicept at the Department of Dermatology, Xiangya Second Hospital, Central South University, from 2021 to 2024 were retrospectively collected. Baseline demographic and clinical characteristics were analyzed. Changes in skin lesions, joint pain symptoms, complete blood count, and biochemical parameters at 4, 12, and 24 weeks of treatment were compared with baseline (week 0). The Wilcoxon signed-rank test was used to compare complement C3 and C4 levels before and after treatment, and univariate logistic regression analysis was performed to explore factors influencing the efficacy of telitacicept.Results:Among the 25 SLE patients, 3 were male (12.0%) and 22 were female (88.0%). Based on the SLE Disease Activity Index (SLEDAI) -2000 scores, 8 patients were mild, 13 were moderate, and 4 were severe. Of the 11 SLE patients with rashes before treatment, 6 achieved complete remission at 12 weeks. Among the 7 patients with joint pain before treatment, 4 experienced symptom resolution at 24 weeks. The proportion of patients with leukopenia at baseline and at 4, 12, and 24 weeks was 10/25 (40.0%), 0/24 (0), 1/22 (4.5%), and 2/19 (10.5%), respectively. The proportion of patients with thrombocytopenia was 6/25 (24.0%), 3/24 (12.5%), 1/22 (4.5%), and 1/19 (5.3%), respectively, and the proportion of patients with anemia was 7/25 (28.0%), 3/24 (12.5%), 1/22 (4.5%), and 1/19 (5.3%), respectively. At baseline, 11 out of 25 patients (44.0%) had proteinuria. At 12 weeks, the urinary protein quantification level (0.4 [0, 0.6] g/L) was significantly lower than at baseline (0.9 [0.8, 1.2] g/L). The SLE responder index-4 (SRI4) response rates at 4, 12, and 24 weeks were 14/18, 15/17, and 12/14, respectively. Complement C3 and C4 levels were significantly higher at 4, 12, and 24 weeks compared to baseline (all P < 0.001). Univariate logistic regression analysis showed that age, disease duration, glucocorticoid dosage, baseline complement C4 levels, antinuclear antibody titer, and SLEDAI-2K score did not significantly affect the efficacy of telitacicept (SRI4 response rate at 12 weeks) (all P > 0.05). No serious adverse reactions related to telitacicept were observed in patients. Conclusions:Telitacicept improved skin lesions, complement C3 and C4 levels, and anti-double-stranded DNA antibody levels in SLE patients. No association was found between the efficacy of telitacicept and baseline SLEDAI-2K scores, antinuclear antibody titers, or complement C4 levels, suggesting that telitacicept is an effective and safe treatment for SLE patients.

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.