Objective To develop GTKO (α-1,3-galactosyltransferase gene-knockout, GTKO)/hCD55 (human CD55) gene-edited xenotransplant donor pigs and verify their function. Methods In this study, CRISPR (clustered regularly interspaced short palindromic repeats)/Cas9 (CRISPR-associated nuclease 9), PiggyBac transposon technology and somatic cell nuclear transfer technology were used to construct GTKO/hCD55 gene-edited Diannan miniature pigs. The phenotype and function of GTKO/hCD55 pigs were analyzed by Sanger sequencing, real-time fluorescence quantitative PCR, flow cytometry, immunofluorescence, bisulfite sequencing, antigen-antibody binding assays, and complement-dependent cytotoxicity assays. Results After transfection of PX458 and PiggyBac gene editing vectors into wild-type fetal pig fibroblasts, 48 single-cell colonies were obtained through puromycin drug screening. Two single-cell colonies were selected for somatic cell nuclear transfer, resulting in two fetal pigs at 33 days of gestation. The GGTA1(α-1,3-galactosyltransferase) genotypes of fetal pig F01 were -17 bp and wild type (WT), while the GGTA1 genotypes of fetal pig F02 were -26 bp/+2 bp and -3 bp. The hCD55 mRNA expression levels of both fetal pigs were significantly higher than those of WT pigs (P＜0.01). The fetal pig F02 was selected as the donor cell source for recloning, 11 surviving piglets were obtained, all identified as GTKO/hCD55 gene-edited pigs. These pigs showed absence of α-Gal antigen expression, but weak or no expression of hCD55 was observed. Methylation analysis of the hCD55 gene's CpG island showed hypermethylation in kidney tissue lacking hCD55 expression, whereas it was not methylated or partially methylated in kidney tissue expressing hCD55. Moreover, codon optimization of the CpG island of the hCD55 gene to reduce CG content could achieve stable expression of the hCD55 gene. In addition, antigen-antibody binding experiment showed that the amount of human IgM binding to GTKO/hCD55 gene-edited pig fibroblasts was significantly lower than that of WT pigs (P＜0.01). Complement-dependent cytotoxicity experiment showed that the survival rate of fibroblasts in GTKO/hCD55 pigs was significantly higher than that in WT pigs (P＜0.01). Conclusion This study demonstrates the successful generation of GTKO/hCD55 gene-edited xenotransplant donor pigs. Methylation-induced gene silencing of the hCD55 gene can be effectively avoided by reducing the CG content of the CpG island through codon optimization. This study provides a reference for the development of xenotransplant donor pigs and guides subsequent research on xenotransplantation.

The identification and optimization of mutations in nanobodies are crucial for enhancing their therapeutic potential in disease prevention and control. However, this process is often complex and time-consuming, which limit its widespread application in practice. In this study, we developed a workflow, named Evolutionary-Nanobody (EvoNB), to predict key mutation sites of nanobodies by combining protein language models (PLMs) and molecular dynamic (MD) simulations. By fine-tuning the ESM2 model on a large-scale nanobody dataset, the ability of EvoNB to capture specific sequence features of nanobodies was significantly enhanced. The fine-tuned EvoNB model demonstrated higher predictive accuracy in the conserved framework and highly variable complementarity-determining regions of nanobodies. Additionally, we selected four widely representative nanobody-antigen complexes to verify the predicted effects of mutations. MD simulations analyzed the energy changes caused by these mutations to predict their impact on binding affinity to the targets. The results showed that multiple mutations screened by EvoNB significantly enhanced the binding affinity between nanobody and its target, further validating the potential of this workflow for designing and optimizing nanobody mutations. Additionally, sequence-based predictions are generally less dependent on structural absence, allowing them to be more easily integrated with tools for structural predictions, such as AlphaFold 3. Through mutation prediction and systematic analysis of key sites, we can quickly predict the most promising variants for experimental validation without relying on traditional evolutionary or selection processes. The EvoNB workflow provides an effective tool for the rapid optimization of nanobodies and facilitates the application of PLMs in the biomedical field.

The identification and optimization of mutations in nanobodies are crucial for enhancing their thera-peutic potential in disease prevention and control.However,this process is often complex and time-consuming,which limit its widespread application in practice.In this study,we developed a work-flow,named Evolutionary-Nanobody(EvoNB),to predict key mutation sites of nanobodies by combining protein language models(PLMs)and molecular dynamic(MD)simulations.By fine-tuning the ESM2 model on a large-scale nanobody dataset,the ability of EvoNB to capture specific sequence features of nanobodies was significantly enhanced.The fine-tuned EvoNB model demonstrated higher predictive accuracy in the conserved framework and highly variable complementarity-determining regions of nanobodies.Additionally,we selected four widely representative nanobody-antigen complexes to verify the predicted effects of mutations.MD simulations analyzed the energy changes caused by these mu-tations to predict their impact on binding affinity to the targets.The results showed that multiple mu-tations screened by EvoNB significantly enhanced the binding affinity between nanobody and its target,further validating the potential of this workflow for designing and optimizing nanobody mutations.Additionally,sequence-based predictions are generally less dependent on structural absence,allowing them to be more easily integrated with tools for structural predictions,such as AlphaFold 3.Through mutation prediction and systematic analysis of key sites,we can quickly predict the most promising variants for experimental validation without relying on traditional evolutionary or selection processes.The EvoNB workflow provides an effective tool for the rapid optimization of nanobodies and facilitates the application of PLMs in the biomedical field.

Objective To screen the risk factors for hypoglycemia in adult intensive care unit(ICU)patients,construct a risk prediction model,and validate its predictive effect.Methods A retrospective study was conducted on adult critically ill patients admitted to the general ICU of Hospital of Chengdu University of Traditional Chinese Medicine from December 2023 to September 2024.Patients admitted from December 2023 to June 2024 served as the modeling group,and those from July to September 2024 as the validation group.A total of 928 patients were included,with 650 in the modeling group and 278 in the validation group.After literature review and expert consultation,27 potential risk factors for hypoglycemia in ICU patients were initially screened,and data were collected including general information[gender,age,acute physiology and chronic health evaluation Ⅱ(APACHEⅡ)score,sequential organ failure assessment(SOFA)score,nutrition risk in critically ill(NUTRIC)score,mechanical ventilation status,hemodialysis status,enteral nutrition status],disease data(sepsis,liver disease history,kidney disease history,diabetes history,hypoglycemia history),blood glucose-related indicators[mean blood glucose,blood glucose coefficient of variation,insulin dosage,intravenous insulin titration use,inotropic drug use,insulin secretagogues(Sulfonylureas and Glinides),and combined use of hypoglycemic drugs(two or more)],and laboratory indicators[serum creatinine(SCr),blood urea nitrogen(BUN),serum albumin(Alb),alanine aminotransferase(ALT),aspartate aminotransferase(AST),total bilirubin(TBil),glomerular filtration rate(GFR)].The patients were divided into a hypoglycemia group and a non-hypoglycemia group based on the occurrence of hypoglycemia.Univariate analysis and binary Logistic regression analysis were used to identify influencing factors of hypoglycemia in adult ICU patients,and a nomogram prediction model was constructed.The area under the receiver operator characteristic curve(AUC)and calibration curves were employed to evaluate the discrimination and calibration of the model.Results The modeling cohort included 552 non-hypoglycemic patients and 98 hypoglycemic patients,with an ICU hypoglycemia incidence rate of 15.1％.Compared with the hypoglycemia group,the non-hypoglycemia group showed significantly lower proportions of patients with renal disease history,diabetes history,hypoglycemia history,undergoing hemodialysis,using intravenous insulin titration,and combined use of hypoglycemic drugs,as well as lower blood glucose coefficient of variation,lower APACHEⅡ scores,and significantly elevated GFR(all P<0.05).Binary Logistic regression analysis was performed using the 9 variables with statistically significant differences in univariate analysis as independent variables and hypoglycemia occurrence as the dependent variable.The results indicated that a history of diabetes,a history of hypoglycemia,APACHEⅡ score,GFR,blood glucose coefficient of variation,and combined use of hypoglycemic drugs were independent risk factors for hypoglycemia in ICU patients[odds ratios(OR)were 1.761,2.095,1.048,0.990,1.029,and 1.975,respectively,and 95％confidence intervals(95％CI)were 1.052-2.949,1.220-3.600,1.022-1.074,0.982-0.997,1.013-1.046,and 1.145-3.408,respectively.The corresponding Pvalues were 0.031,0.007,0.000,0.009,<0.001,0.014].A nomogram prediction model for hypoglycemia in ICU patients was constructed using six independent predictors selected through binary logistic regression analysis.The ROC curve AUC for the modeling group was 0.884(95％CI 0.826-0.941,P=0.250),with a maximum Youden index of 0.713,sensitivity of 92.1％,and specificity of 79.2％.The validation cohort included 38 patients with hypoglycemia and 240 patients without hypoglycemia.Compared with the hypoglycemia group,the non-hypoglycemia group showed significantly lower proportions of patients with a history of diabetes,a history of hypoglycemia,and combined use of hypoglycemic drugs,as well as lower APACHEⅡ scores and lower blood glucose coefficient of variation,with significantly increased GFR(all P<0.05).The ROC curve AUC for the validation cohort was 0.803(95％CI was 0.757-0.849,P=0.138),indicating high discriminatory ability.The predicted probability at the diagnostic cutoff point was P=0.138.The model's diagnostic threshold for predicted probability was P=0.138,while the optimal cut-off value based on the Youden index was 0.513,yielding a sensitivity of 76.5％and specificity of 74.8％,indicating predictive value for hypoglycemia in adult ICU patients.The mean absolute error(MAE)results for the modeling group and validation group were<0.05.The calibration curves of both the modeling and validation groups showed close alignment with the ideal curve,indicating excellent calibration performance of the model.Conclusion The constructed hypoglycemia risk prediction model for adult ICU patients has good predictive performance,which can quickly identify high-risk populations of hypoglycemia in ICU and provide reference for clinical preventive nursing.

Turning to critical illness is a common stage of various diseases and injuries before death. Patients usually have complex health conditions, while the treatment process involves a wide range of content, along with high requirements for doctor′s professionalism and multi-specialty teamwork, as well as a great demand for time-sensitive treatments. However, this is not matched with critical care professionals and the current state of medical care in China. Telemedicine, which shortens the distance of medical professionals and the gap of disease diagnosis and treatments in various regions through electronic information, can effectively solve the current problem. Therefore, there is an urgent need to develop a standardized, high-quality visualization telemedicine round system .Therefore, experts have been organized to search domestic and foreign literature on telemedicine round for critically ill patients and to form this consensus based on clinical experiences so as to further improve the level of critical care treatments in regions.

Objective:To assess the predictive performance of a nomogram model integrating endoscopic ultrasound（EUS）radiomic features with clinical variables for distinguishing early esophageal squamous cell carcinoma（ESCC）from non-cancerous lesions.Methods:Clinical and imaging data from 454 patients who underwent EUS for suspected esophageal malignancies were retrospectively collected in the First Affiliated Hospital of Nanjing Medical University（training cohort， n = 323）and Dongyang People's Hospital（external validation cohort， n = 131）from January 2020 to November 2023. Independent clinical predictors of early ESCC were identified using univariable and multivariable Logistic regression analyses to establish a clinical model. Pearson correlation and Least Absolute Shrinkage and Selection Operator（LASSO）algorithms were used to construct a radiomics model. A combined model integrating radiomics scores and clinical predictors was developed and visualized as a nomogram. The predictive performance of each model was assessed using the area under the ROC curve（AUC），and calibration curves were used to evaluate the model's fitting capability. Results:The training set and validation set indicated that there were statistically significant differences in age，smoking history and lesion location between the early ESCC group and the non-cancerous lesion change group（all P < 0.05）. According to univariate and multivariate Logistic regression analysis，age（ OR = 1.039，95% CI = 1.003–1.077， P = 0.036）and smoking（ OR = 2.358，95% CI = 1.270 - 4.376， P = 0.007）were identified as independent predictors and used to develop the clinical model，with AUCs of 0.608 and 0.694 in the training and validation cohorts，respectively. Fourteen optimal radiomic features were selected to construct the radiomics model，with AUCs of 0.881 and 0.807 in the training and validation cohorts，respectively. The combined nomogram model demonstrated superior predictive performance with AUCs of 0.893 and 0.830，sensitivities of 82.5% and 79.1%，and specificities of 82.2% and 81.3% in the training and validation cohorts，respectively. Conclusions:The EUS-based nomogram model demonstrates optimal predictive performance and can serve as a non-invasive tool to assist endoscopists in distinguishing early ESCC from non-cancerous lesions.

Objective:To investigate the role of calcium-binding protein S100A9 in acute lung injury induced by hepatic ischemia-reperfusion (HIR) in mice, and to explore its relationship with nuclear factor erythroid 2-related factor 2 (Nrf2).Methods:A total of 12 specific pathogen-free (SPF) male wild-type (WT) and 12 S100A9 knockout (S100A9 KO) C57BL/6J mice aged 6~8 weeks and weighing 20-25 g were randomly divided into four groups using a random number table: WT+Sham group, S100A9 KO+Sham group, WT+HIR group, and S100A9 KO+HIR group ( n=6 per group). The HIR model was established by clamping the portal vein and hepatic artery of the left and median liver lobes for 60 minutes followed by reperfusion. At 6 hours post-reperfusion, mice were anesthetized again, and blood samples were collected from the inferior vena cava. Both lungs were harvested. The lung wet-to-dry (W/D) weight ratio was measured. Hematoxylin and eosin (HE) staining was used to assess histopathological changes and calculate lung injury scores. The levels of inflammatory markers—S100A9, tumor necrosis factor-α (TNF-α), and interleukin-6 (IL-6) —as well as oxidative stress indicators including myeloperoxidase (MPO), reactive oxygen species (ROS), malondialdehyde (MDA), and superoxide dismutase (SOD) in serum and lung tissue were measured. Western blotting was used to assess the expression levels of nuclear and cytoplasmic Nrf2, and cytoplasmic HO-1. Results:Compared with the WT+Sham group, both the WT+HIR and S100A9 KO+HIR groups showed significantly increased lung injury scores, W/D ratio, TNF-α, IL-6, ROS, MPO, and MDA levels (all P<0.05). Compared with the WT+HIR group, the S100A9 KO+HIR group exhibited significantly reduced levels of these indicators (all P<0.05). Moreover, the S100A9 KO+HIR group showed elevated nuclear Nrf2 expression and decreased cytoplasmic Nrf2 expression, accompanied by increased expression of HO-1, Gclm, Gclc, and Nqo1 (all P<0.05). Conclusion:Upregulation of S100A9 is involved in the development of HIR-induced acute lung injury, possibly through inhibition of Nrf2 nuclear translocation.

Objective:To evaluate the mid-term efficacy of sleeve gastrectomy combined with single anastomosis gastric-ileal bypass (SASI) for treating gastroesophageal reflux disease (GERD) after laparoscopic sleeve gastrectomy (LSG).Methods:Clinical data of 10 patients with post-LSG GERD undergoing SASI at the Department of Hernia and Bariatric Surgery, the First Affiliated Hospital of University of Science and Technology of China between Jan 2022 and Oct 2024 was retrospectively analyzed. Surgical safety and mid-term outcomes were evaluated.Results:The mean follow-up period was (25.40±17.33) months. The GerdQ score significantly decreased from (14.00±2.05) preoperatively to (5.70±1.49) postoperatively ( t=10.330, P<0.001), with a GERD remission rate of 90 % (9/10). Postoperative body weight and body mass index (BMI) both showed statistically significant reductions compared to preoperative values. Weight dropped from (110.29±22.92) kg to (84.95±15.89) kg ( t=5.889, P<0.001), and BMI decreased from (38.98±7.16) kg/m2 to (30.02±4.88) kg/m2 ( t=6.086, P<0.001). The percentage of excess weight loss was 65.88%±32.85%, and the percentage of total weight loss was 22.43%±9.65%. Only one patient experienced transient postoperative diarrhea, which resolved spontaneously, and no severe malnutrition cases were observed. Conclusion:SASI effectively improves GERD symptoms after LSG with favorable safety, serving as a suitable revisional surgical option for those patients.

Objectives:There is a lack of long-term follow-up study results on severe microvascular complications in a larger Chinese population with diabetes.This study aims to explore long-term impact of newly diagnosed diabetes(NDD)on the incidence and risk of severe microvascular complications.Methods:A total of 598 NDD and 493 normal glucose tolerance(NGT)subjects were included in this study in 1986.By questionnaire and systematic case review,the occurrence of severe microvascular complications,including severe diabetic retinopathy,severe diabetic nephropathy,and severe diabetic neuropathy,was followed up and collected over a period of 34 years.Results:The cumulative incidence of severe microvascular complications in the NDD population was 65.03%(95%CI:58.90%-70.48%)over 34 years,significantly higher than that in the NGT population(16.8%,95%CI:12.64%-20.11%).After adjusting for related risk factors,the risk of severe microvascular complications in the NDD population was 7.08 times than that of the NGT population(HR=7.08,95%CI:5.09-9.84,P<0.0001).Stratified analysis by sex showed that the cumulative incidence and risk of severe microvascular complications were slightly higher in male NDD population(68.02%,95%CI:57.27%-76.61%;HR=9.45,95%CI:5.78-15.47,P<0.0001)than in female NDD population(63.37%,95%CI:55.69%-70.09%;HR=5.86,95%CI:3.75-9.16,P<0.0001);however,the cumulative incidence increased more rapidly in women during the follow-up period of 10-25 years.Conclusions:The incidence and risk of severe microvascular complications in diabetes were significantly higher than those in the NGT population;and the incidence of severe vascular complications increased rapidly after the duration of diabetes exceeded 10 years,indicating that strict control of blood glucose in the early stage of diabetes is of vital importance.

Plant-derived bioactive peptides have become a research hotspot in the fields of food and medicine due to their high source safety,easy absorption and utilization by the human body,and potential edible and medicinal value.Bioactive peptides can be classified into antihypertensive,hypoglycemic,hypolipidemic,anticancer,antioxidant,antimicrobial,and anti-inflammatory peptides according to their functions.Among these,antihypertensive,hypoglycemic,and hypolipidemic peptides are collectively referred to as cardiovascular active peptides,which can be used for the treatment and prevention of cardiovascular diseases and have an important role in the development of modem biomedicine.This review focused on the preparation methods,separation,purification,and identification techniques of bioactive peptides,as well as their mechanisms of action and applications in regulating cardiovascular diseases,aiming to provide a reference for further development and application of plant-derived cardiovascular peptides.