ObjectiveTo observe the effect of Shenshu Fujian decoction on platelet-derived growth factor （PDGF）/naked cuticle homolog 2 （NKD2） /Wnt signaling pathway in rats with chronic renal failure （CRF）. MethodsSixty male SD rats were randomly divided into normal group， model group， Niaoduqing group （5 g·kg^-1）， low-dose Shenshu Fujian decoction group （5.5 g·kg^-1）， medium-dose Shenshu Fujian decoction group （11 g·kg^-1）， and high-dose Shenshu Fujian decoction group （22 g·kg^-1）， with 10 rats in each group. A CRF rat model was established by feeding a 0.5% adenine diet for 21 days. After successful modeling， intragastric administration was given once daily for 28 consecutive days. After treatment， the renal morphology of rats was observed. Serum creatinine （SCr） and blood urea nitrogen （BUN） levels were detected. Hematoxylin-eosin （HE） staining and Masson staining were used to detect renal histopathological changes， and collagen volume fraction （CVF） was calculated. Serum levels of inflammatory markers interleukin （IL）-1β and IL-6 were measured using enzyme-linked immunosorbent assay （ELISA）. The expressions of fibronectin 1 （FN1）， type Ⅰ collagen （ColⅠ）， α-smooth muscle actin （α-SMA）， platelet-derived growth factor receptor-β （PDGFR-β）， NKD2， dishevelled protein 2 （DVL2） and β-catenin in renal tissue were detected by immunohistochemistry and Western blot. ResultsCompared with the normal group， the model group showed significant renal pathological changes， a markedly increased kidney weight/body weight ratio （P<0.01）， significantly elevated CVF （P<0.01）， and notably increased serum levels of SCr， BUN， IL-1β， and IL-6 （P<0.01）. Expression levels of FN1， ColⅠ， α-SMA， PDGFR-β， NKD2， DVL2， and β-catenin in renal tissue were also significantly increased （P<0.01）. Compared with the model group， all treatment groups showed significantly decreased kidney weight/body weight ratios and CVF （P<0.01）， as well as markedly decreased serum SCr， BUN， IL-1β， and IL-6 levels. Protein expression levels of FN1， ColⅠ， α-SMA， PDGFR-β， NKD2， DVL2， and β-catenin in renal tissue were decreased， with more pronounced effects observed in the Niaoduqing， medium-dose， and high-dose Shenshu Fujian decoction groups （P<0.05， P<0.01）. ConclusionShenshu Fujian decoction improves renal function， reduces inflammation， and reverses renal fibrosis in CRF rats， possibly by downregulating the expression of PDGF/NKD2/Wnt signaling pathway-related proteins.

Objective: To investigate the protective effects and underlying mechanisms of sodium pyruvate (SP) on RBC storage lesions using an oxidative damage model. Methods: Six units of leukocyte-depleted suspended RBCs (discarded for non-infectious reasons within three days post-collection) were randomly assigned to four groups: negative control (NS), positive control (PS), experimental group 1 (SP1), and experimental group 2 (SP2). Oxidative stress was induced in the PS group by the addition of hydrogen peroxide (H O ), while SP1 and SP2 received SP supplementation at different concentrations (25 mM and 50 mM, respectively) in the presence of H O . After 1 hour of incubation, RBC morphology was assessed microscopically, and biochemical indicators including glutathione (GSH), malondialdehyde (MDA), methemoglobin (MetHb), adenosine triphosphate (ATP), and Na /K -ATPase activity were measured. Results: RBCs in the PS group exhibited pronounced morphological damage, including cell shrinkage and echinocyte formation, whereas both SP-treated groups showed significantly reduced structural injury. SP treatment led to elevated GSH levels and decreased concentrations of MDA and MetHb, suggesting attenuation of oxidative stress. Additionally, SP enhanced intracellular ATP levels and Na /K -ATPase activity, thereby contributing to membrane stability. Notably, the SP2 group (50 mM) demonstrated superior protective effects compared to SP1 (25 mM). Conclusion: Sodium pyruvate effectively attenuates oxidative storage lesions in RBCs, primarily through its antioxidant properties, energy metabolism supporting ability, and celluar membrane stabilizing function. These findings suggest SP as a promising additive for enhancing the quality and safety of stored RBCs.

Objective To investigate the value of cranial CT cisternal grading combined with D-dimer(D-D)and Glasgow Coma Scale(GCS)score in predicting the short-term postoperative prog-nosis of patients with severe traumatic brain injury.Methods A total of 165 patients with severe trau-matic brain injury who were treated in the hospital from January 2019 to May 2024 were selected as study subjects,all underwent craniotomy surgery.Postoperative follow-up was conducted for 3 months to analyze the differences in clinical data and preoperative indicators such as cranial CT cisternal grad-ing,D-D levels,and GCS scores between patients with poor and good prognosis.The value of cranial CT cisternal grading,D-D levels,and GCS scores in predicting short-term postoperative poor prognosis in patients with severe traumatic brain injury was also analyzed.Results Compared with patients with good prognosis,patients with poor prognosis had higher proportion of age,cranial CT cisternal grading of Ⅰ to Ⅱ,D-D levels,and GCS scores＜6(P＜0.05).There were no statistically significant differences in C-reactive protein,prothrombin time,activated partial thromboplastin time,international normalized ratio,total cholesterol,triglycerides,high-density lipoprotein cholesterol,and low-density lipoprotein cholesterol levels between patients with poor and good prognosis(P＞0.05).Cranial CT cisternal grading,D-D levels,and GCS scores were influencing factors for short-term postoperative poor prognosis in patients with severe traumatic brain injury(P＜0.05).The area under the curve for poor prognosis by three indicators in combination was 0.941(95％CI,0.906 to 0.975),which was higher than the area under the curve for the individual predictions of cranial CT cisternal grad-ing,D-D levels,and GCS scores(P＜0.05).Conclusion The influencing factors for short-term postoperative prognosis in patients with severe traumatic brain injury include cranial CT cisternal grading,D-D levels,and GCS scores.The model based on these three indicators has certain appli-cation value in predicting patient prognosis.

Objective To develop and validate a model to predict the risk of radiation-induced lung injury (RILI) and assess its clinical feasibility. Methods Clinical data from 125 patients with non-small cell lung cancer (NSCLC) were included in the study. The patients were divided into training group (88 cases) and validation group (38 cases). Key predictive factors were identified using univariate and multivariate logistic regression analyses combined with least absolute shrinkage and selection operator (LASSO) regression. A predictive model was constructed and evaluated using a nomogram, receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis. Results The key variables identified by the model were tumor volume (P = 0.017), Eastern Cooperative Oncology Group performance status score (P = 0.035), 95% of the minimum dose to the target volume (P = 0.028), percentage of bilateral lung volume receiving 20 Gy of radiation (P < 0.001), and neutrophil-to-lymphocyte ratio (P = 0.021). The ROC curve showed that the areas under the curve (AUC) for the model in the training and validation groups were 0.987 and 0.992, respectively, indicating good predictive ability. The calibration curve and decision curve further confirmed the accuracy and clinical practicability of the model. Conclusion The predictive model proposed in this study can accurately assess the risk of developing RILI in patients with NSCLC who have undergone radiotherapy, demonstrating its potential value in clinical practice.

BACKGROUND:Disruption of biological rhythms(circadian rhythms)is a typical problem associated with aging.Maintaining the normal function of biological rhythms may be a promising anti-aging strategy.Expression of nuclear factor erthroid 2-related factor 2(Nrf2)is biologically regulated.The glycogen synthase kinase 3(GSK3)system represents a"regulatory valve"that controls subtle oscillations in Nrf2 levels.Circadian changes in the transcript levels of antioxidant genes can influence the response of organisms to oxidative stress.However,the specific molecular mechanism of GSK3/Nrf2 in regulating organismal aging is still puzzling. OBJECTIVE:To search for the general pattern of GSK3/Nrf2-regulated biological rhythms in organismal aging by reviewing the literature in this field. METHODS:The bibliographic method was used to search,review and screen the relevant literature using the keywords of"glycogen synthase kinase 3,nuclear factor erthroid 2-related factor 2,biorhythms and aging"to lay a theoretical foundation for the analysis of the whole paper.Comparative analysis method,through reading and analyzing the obtained literature,was performed to compare the similarities and differences between the literature,thereby providing reasonable theoretical support for the argument.Further comparative analysis of the literature was conducted to clarify the relationship between the relevant indicators as well as the ideas for analysis throughout the text. RESULTS AND CONCLUSION:GSK3 can indirectly regulate Nrf2 expression through the regulation of rhythm genes.GSK3 and Nrf2 are components of anti-aging programs and are associated with biological rhythms.In addition,GSK3/Nrf2 is involved in several metabolic pathways,including those associated with age-related diseases(type 2 diabetes and cancer)and neurodegenerative diseases.

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 analyze the association between inflammation-related dietary patterns and the risk for cognitive impairment in older adults aged ≥65 years in longevity areas in China by using reduced rank regression (RRR) analysis.Methods:This study used cross-sectional data from the 2021 Healthy Aging and Biomarkers Cohort Study, including the information about study participants' demographic characteristics, lifestyles, daily life activities, and disease histories. Dietary intake was obtained by using a simplified food frequency questionnaire. Cognitive impairment was evaluated based on the Mini-Mental State Examination Scale combined with years of education. Fasting venous blood samples were collected to detect inflammatory markers, especially high-sensitivity C-reactive protein (hs-CRP) and the platelet-to-lymphocyte ratio (PLR). RRR analysis was used to obtain inflammation-related dietary patterns using hs-CRP and PLR as response variables. Multivariate logistic regression model was used to analyze the association between dietary pattern score and the risk for cognitive impairment. Restricted cubic spline was used to explore the dose response relationship, and mediation analysis was used to quantify the mediating effects of hs-CRP and PLR.Results:Two dietary patterns were identified with RRR. The primary pattern was characterized by higher intakes of flour, red meat, and dairy products, and lower intake of fresh vegetables, explaining 6.84% of the variance in food intake and 0.50% of the variance in inflammatory markers. Compared with the T1 group, the T3 group had significantly higher risk for cognitive impairment ( OR=1.242, 95% CI: 1.034-1.491). Each one standard deviation increase in the dietary pattern score was associated with an 8.7% increase in the risk for cognitive impairment ( OR=1.087, 95% CI: 1.008-1.172), with a significant linear trend (overall-model P<0.001, non-linear P=0.295). Mediation analysis indicated that hs-CRP mediated 6.2% of the association between the dietary pattern and the risk for cognitive impairment. Conclusion:The inflammation- related dietary pattern characterized by higher consumption of flour, red meat, and dairy products and lower consumption of fresh vegetables is associated with an increased risk for cognitive impairment in older adults, and hs-CRP partially mediates this association.

Objective:To analyze the spatiotemporal correlation between the surveillance data of influenza in students reported by medical institutions and school absenteeism due to illness, and evaluate the application of Bayesian Structural Time Series model (BSTS) in the prediction of school influenza epidemic.Methods:A total of 13 schools in Dapeng new district of Shenzhen were selected. The incidence data of influenza in schools in Shenzhen from January 1, 2015 to December 31, 2019 were collected from China Disease Control and Prevention Information System and the illness related school absentence data during this period were collected from Shenzhen Student Health Surveillance System, and the spatiotemporal correlation between the data from two systems was analyzed and compared. BSTS was used to make long-term predictions of the monthly incidence of influenza in students in 2019 and short-term predictions of the weekly incidence of influenza in week 1-8 and week 45-52 of 2019 by using the data from two systems.Results:There was a temporal correlation between the data from China Disease Control and Prevention Information System and the data from Shenzhen Student Health Surveillance System ( r=0.93, P<0.001), and the lag of the former one was 1 day ( r=0.73, P<0.001). Influenza outbreaks were randomly distributed in different schools in Shenzhen, and there was no spatial correlation. The root mean square error ( RMSE) and mean absolute error ( MAE) were 0.35 and 0.28, respectively, in the long-term prediction, and the RMSE was 0.33 and 0.34, and the MAE was 0.26 and 0.28, respectively, in the short-term predictions of week 1-8 and week 45-52 of 2019, respectively, showing good prediction accuracy and fitting effect. Conclusion:By analyzing the data from China Disease Control and Prevention Information System and Shenzhen Student Health Surveillance System with BSTS, the dynamics of the school influenza epidemic can be accurately predicted, and effective technical support can be provided for the early warning and prevention and control of influenza epidemic.

Objective:To understand the changes in the prevelance smoking in people aged ≥15 years in Baoji, and provide evidence for the improvement of tobacco control strategies.Methods:Data were from the sampling survey of chronic diseases and their risk factors conducted in Baoji at an interval of five years from 2013 to 2023. The survey used multi-stage cluster random sampling method to select local people aged ≥15 years, and the information about their tobacco use were collected by face-to-face interview. Descriptive epidemiological methods were used to analyze the prevalence of smoking, and χ2 test was used to analyze the change trend. Results:The smoking rate in people aged ≥15 years in Baoji decreased from 2013 to 2023, and the standardized smoking rate decreased by 13.6% in 2023 compared with 2013. The standardized smoking cessation rate increased by 13.4% in 2018 compared with 2013, and the standardized smoking cessation rate decreased by 7.3% in 2023 compared with 2018. The standardized passive smoking rate decreased by 15.1% in 2018 compared with 2013, and the standardized passive smoking rate increased by 8.8% in 2023 compared with 2018. The average daily smoking amount increased by 3.7 cigarettes in 2018 compared with 2013, and the average daily smoking amount decreased by 3.9 cigarettes in 2023 compared with 2018.Conclusion:Progress has been made in tobacco control in Baoji, but problems still exist in tobacco control, to which close attention needs to be paid.

Objective To establish a reliable prediction model for radiation pneumonitis(RP)based on multi-regional radiomics analysis of localizable CT images.Methods A retrospective analysis was conducted on 185 patients who received radiotherapy from January 2021 to June 2023 in the Department of Radiotherapy,Xuzhou Cancer Hospital.Patients were classified as having RP or not based on imaging combined with clinical diagnosis.Three regions of interest(ROI)were defined in the localizable CT images:Lung,Lung-PTV and PTV,and their radiomics features were extracted.After feature screening using methods such as Mann-Whitney Utest,recursive feature elimination,and Lasso,a prediction model was established using support vector machine classification algorithm.The model performance was validated using 6 evaluation metrics:the area under the receiver operating characteristic curve(AUC),accuracy,specificity,sensitivity,positive predictive value,and negative predictive value.Results The prediction model consisted of 7 radiomics features.The clinical model of target-to-lung ratio,PTV model,Lung model,and Lung-PTV model achieved AUC values of 0.535,0.801,0.672,and 0.706 in the test set,respectively.The AUC value and accuracy of PTV model reached 0.843 and 0.775 in the training set,while 0.801 and 0.750 in the test set.PTV model was superior to Lung model,Lung-PTV model,and clinical model in predictive performance.The AUC values of the combined PTV+(Lung-PTV)model in the training and test sets were 0.867 and 0.806,respectively,higher than those of PTV model and Lung-PTV model.Conclusion The predictive ability of the prediction models constructed from radiomics features in different ROI for symptomatic RP varies.The radiomics prediction model using PTV as ROI exhibits superior predictive performance,and the combined multi-regional radiomics model can further improve the predictive ability for RP.