Objective:To investigate the risk factors of skin adverse events associated with immune checkpoint inhibitor (ICI) therapy in patients with urologic neoplasms, and establish a predictive model.Methods:A single-center retrospective case-control study enrolled 91 advanced urologic neoplasms patients who received ICI therapy at the Department of Urology, Beijing Friendship Hospital, Capital Medical University from January 2020 to June 2025. Patients were divided into the skin lesion group ( n=44) and the control group ( n=47). Patients in the skin lesion group experienced related skin adverse events during ICI treatment, while patients in the control group did not experience such events during ICI treatment. The general data and laboratory indicators were compared between the two groups. The normally distributed measurement data were expressed as mean±standard deviation ( ± s), and the independent sample t-test was used for comparison between groups; the non-normally distributed measurement data were expressed as the median (interquartile range) [ M ( Q1, Q3)], and the Kruskal-Wallis test was used for comparison between groups. The count data were expressed as the number of cases and percentages, and the Chi-test was used for comparison between groups. First, a univariate analysis was conducted on the influencing factors of skin adverse events in patients with urologic neoplasms after ICI treatment. Then, the indicators with statistically significant differences in the univariate analysis were further included in the multivariate Logistic regression model to screen the independent risk factors for predicting skin adverse events. The R software was used to incorporate the factors with significant differences from multivariate analysis into the prediction model and construct a Nomogram. The calibration curve was utilized to evaluate the consistency between predicted values and actual observed results. Meanwhile, the discrimination of the model was verified by the receiver operating characteristic (ROC) curve and the area under the curve (AUC), so as to comprehensively verify the reliability and clinical application value of the prediction model. Results:The results of univariate analysis showed that there were statistically significant differences between the skin lesion group and the control group in terms of the proportion of other immune responses, serum albumin level, absolute eosinophil count, and C-reactive protein (CRP) levels ( P<0.05). These factors were included in multivariate Logistic regression, which identified elevated absolute eosinophil count and elevated CRP as the independent risk factors for related skin adverse events in patients with urologic neoplasms after ICI treatment. A predictive nomogram was built based on these factors. The calibration curve showed high consistency between predicted and actual probabilities, and ROC analysis confirmed the combined model had high predictive value (AUC=0.883, P<0.001). Conclusions:Elevated absolute eosinophil count and elevated CRP level are independent predictors of immune-related skin adverse events in urologic neoplasms patients after ICI treatment. The prediction model constructed based on these two factors facilitates early clinical screening and identification of high-risk patients.

Kidney stones are a disease with high incidence, high recurrence rate and heavy economic burden. Current clinical treatments clear formed stones but fail to target the pathophysiological core of recurrence. Beyond systemic risk factors like obesity and diabetes, diet-driven gut dysbiosis is a key potential mediator of stone recurrence. Moreover, the gut-kidney syndrome and gut-kidney axis theories reveal the bidirectional regulation between intestine and kidney, among these, the gut microbiota-metabolite axis as the core functional carrier of the gut-kidney axis has a regulatory mechanism key to resolving recurrence. Currently, 16S rRNA sequencing and metagenomic sequencing have identified gut microbiota differences between stone and non-stone groups: kidney stone patients show reduced alpha diversity, imbalanced Firmicutes/ Bacteroidetes ratio, significantly higher abundance of harmful bacteria (e.g., Escherichia- Shigella), and lower abundance of oxalate-degrading bacteria ( Oxalobacter formigenes, Lactobacillus) and short-chain fatty acid (SCFA)-producing bacteria ( Faecalibacterium). Notably, gut dysbiosis persists even after surgery.Meanwhile, targeted and non-targeted metabolomics show significant abnormalities in intestinal and urinary metabolites (e.g., SCFA tryptophan derivatives) in stone patients, along with downregulated pathways including purine metabolism, caffeine metabolism and aromatic amino acid metabolism. These abnormalities promote stone formation by disrupting the intestinal barrier, worsening renal inflammation and triggering renal oxidative stress. This article reviews the aforementioned gut microbiota, metabolites and related regulatory pathways with significant inter-group differences. Combined with advances in genomics and metabolomics, it aims to provide theoretical references for further clarifying the mechanism of gut microbiota-metabolite axis regulates kidney stone formation and recurrence, and for developing microecological interventions.

Objective To develop a predictive model for postoperative mortality risk in patients with acute aortic dissection(AAD)using the Extreme Gradient Boosting(XGBoost)algorithm combined with Shapley Additive Explanation(SHAP),and to establish a prediction website to serve as a diagnostic and therapeutic support platform for clinicians and patients.Methods A retrospective cohort study design was adopted.Data from 782 AAD patients who underwent surgical treatment at the Fourth Hospital of Hebei Medical University from January 2013 to December 2023 were collected,including basic information and initial serum biomarker test results.Patients were randomly divided into training and test sets at a 7:3 ratio.An external validation set consisting of 313 AAD patients admitted to the Second Hospital of Hebei Medical University from January 2020 to December 2023 was also established for further model validation.Variables were screened using LASSO regression,and an XGBoost machine learning model was constructed and interpreted using SHAP.The predictive performance of the model was evaluated using receiver operating characteristic(ROC)curve analysis.Using the Shiny package,the XGBoost model was deployed to shinyapps.io to create a prediction website for postoperative mortality risk in AAD patients.One patient was selected by simple random sampling from the test set and the external validation set respectively for the prediction example on the Shiny webpage.Results The XGBoost model demonstrated high predictive performance for postoperative mortality in AAD patients,with area under the ROC curve(AUC)values of 0.928(95%CI 0.901-0.956)in the training set,0.919(95%CI 0.891-0.949)in the test set,and 0.941(95%CI 0.915-0.967)in the external validation set.SHAP values indicated the following order of variable importance in the model(from highest to lowest):"lactate dehydrogenase""blood chlorine""multiple organ injury""carbon dioxide combining power""prothrombin time""α-hydroxybutyric acid""creatine kinase isoenzyme""Stanford classification""combined use of bedside blood purification""gender""acute kidney injury""gastrointestinal bleeding""brain injury"and"shock".A risk prediction website for adverse postoperative outcomes in AAD patients was developed using XGBoost-SHAP method(https://dun-dunxiaolu.shinyapps.io/document/)and validated with examples.One randomly selected patient from each of the test and external validation sets was applied:the predicted mortality risk value for patient 1(who died postoperatively)was 0.9539,and that for patient 2(who survived postoperatively)was 0.0206.Conclusions The XGBoost-SHAP model demonstrates high accuracy in predicting postoperative mortality risk for AAD patients.The online prediction tool established based on this model enhances the identification efficiency of high-risk postoperative mortality patients.

ObjectiveTo explore the scientific connotation of fried charcoal survivability of Lonicerae Japonicae Flos（LJF） by analyzing the correlation between the color change and the intrinsic components during the processing of LJF Carbonisata（LJFC）， and taking pH， charcoal adsorption and microscopic characteristics as indexes. MethodLJFC samples with different degrees of processing were prepared according to the stir-frying time of 0.0， 1.5， 3.0， 4.5， 6.0， 7.5， 9.0， 10.5 min（numbered S1-S8）， and the contents of gallic acid， chlorogenic acid， cryptochlorogenic acid， rutin， luteoloside， isochlorogenic acid A and isochlorogenic acid C were determined by high performance liquid chromatography（HPLC）， and the L^*（brightness）， a^*（red-greenness） and b^*（yellow-blueness） of LJFC samples with different degrees of processing were determined by spectrophotometer， and the correlation analysis and principal component analysis（PCA） between the contents of seven representative components and the color of the samples were carried out by SPSS 26. 0 and SIMCA-P 14.1. Then pH， adsorption force and characteristic structure of different samples of LJFC were detected and the processing pattern of LJFC was analyzed. ResultThe results of quantitative analysis revealed that the contents of luteoloside， rutin， chlorogenic acid and isochlorogenic acid A gradually decreased， and the contents of cryptochlorogenic acid， isochlorogenic acid C and gallic acid firstly increased and then decreased. The L^* and b^* of the sample powders decreased， and a^* showed a trend of increasing and then decreasing. The L^* and b^* were positively correlated with the contents of chlorogenic acid， rutin， luteoloside， isochlorogenic acid A， b^* was positively correlated with the content of gallic acid， and a^* was positively correlated with the contents of cryptochlorogenic acid and isochlorogenic acid C. PCA revealed that samples could be clearly divided into 3 groups， S1-S2 as one group， S3-S5 as one group， and S6-S8 as one group， with S3 having the highest score. The results of regression analysis showed that only isochlorogenic acid C could be used to predict the contents of components by colorimetric values combined with regression equations. Physicochemical analysis showed that pH of LJFC increased with the increase of degree of charcoal stir-frying， while adsorption force showed a tendency of increasing and then decreasing， with the highest adsorption force in the S5 sample， and the non-glandular hairs， calcium oxalate clusters and pollen grains had a varying degree of decreasing with the deepening of processing degree， and the microstructures of S6-S8 samples were obviously charred with pollen grains almost invisible. ConclusionThe changes in chemical composition and color characteristics of LJFC during the processing have certain correlations， combined with the changes in physicochemical properties， S5 sample is found to be the optimal processed products， which can provide a reference for the processing standardization and quality evaluation of LJFC， and enrich the scientific connotation of fried charcoal survivability of LJF.

Traumatic fractures and stress fractures are common orthopedic diseases,and there is great potential in researching bone turnover,repair,and promotion of fracture healing.Basic medical experiments often use animal models of long bone fractures in limbs to study the mechanisms of various interventions on fracture healing.Fracture healing is a complex process influenced by multiple factors and involves multiple molecules and pathways.Therefore,to explore the mechanisms more deeply,accelerate the translation of results,and improve the clinical efficacy,it is particularly important to choose the appropriate animal fracture modeling methods in experimental research.Based on this,this paper conducts a literature review of animal species and modeling methods commonly used for long bone fracture models in experimental research.It summarizes five methods:bone defect method,physical impact method,mechanical bending method,open osteotomy method,and drilling method.A side-by-side comparison of their advantages,disadvantages,and scope of application is made,aiming to provide suitable fracture models for studyingthe mechanisms of fracture healing interventions.

Objective - - To analyze the prevalence and influencing factors of multi site work related musculoskeletal disorders （ ） Methods WMSDs in surgeons. A total of 102 surgeons from four hospitals were selected as study subjects by convenient sampling method. The Chinese version of Musculoskeletal Disorders Questionnaire was used to investigate the prevalence of ， Results WMSDs in the past one year the related individuals and occupational factors. The total prevalence of WMSDs among （ ）， （ ） （ ） surgeons was 54.9%. The top three sites were neck 48.0% lower back 35.3% and shoulder 32.4% . The prevalence of （ vs ，P ） WMSDs in multiple sites was higher than that in a single site 43.1% 11.8% <0.01 . Multivariate logistic regression ， ， analysis showed that surgeons who smoked were tired at work and had a bent back had a higher risk of developing WMSDs ［ （ - ）， （ - ）， （ - ）， P ］ odds ratios and 95% confidence intervals were 3.66 1.41 9.46 8.33 2.15 32.20 and 18.74 2.14 166.77 all <0.01 Conclusion - after excluding the influence of confounding factors. The prevalence rate of multi site WMSDs among surgeons is ， high and the influencing factors include bad living habits and occupational factors such as working load and working posture.

Arabidopsis thaliana is an important and long-established model species for plant molec-ular biology,genetics,epigenetics,and genomics.However,the latest version of reference genome still contains a significant number of missing segments.Here,we reported a high-quality and almost complete Col-0 genome assembly with two gaps(named Col-XJTU)by combining the Oxford Nanopore Technologies ultra-long reads,Pacific Biosciences high-fidelity long reads,and Hi-C data.The total genome assembly size is 133,725,193 bp,introducing 14.6 Mb of novel sequences compared to the TAIR1 0.1 reference genome.All five chromosomes of the Col-XJTU assembly are highly accurate with consensus quality(QV)scores＞60(ranging from 62 to 68),which are higher than those of the TAIR10.1 reference(ranging from 45 to 52).We completely resolved chro-mosome(Chr)3 and Chr5 in a telomere-to-telomere manner.Chr4 was completely resolved except the nucleolar organizing regions,which comprise long repetitive DNA fragments.The Chr1 cen-tromere(CEN1),reportedly around 9 Mb in length,is particularly challenging to assemble due to the presence of tens of thousands of CEN180 satellite repeats.Using the cutting-edge sequencing data and novel computational approaches,we assembled a 3.8-Mb-long CEN1 and a 3.5-Mb-long CEN2.We also investigated the structure and epigenetics of centromeres.Four clusters of CEN180 monomers were detected,and the centromere-specific histone H3-like protein(CENH3)exhibited a strong preference for CEN 180 Cluster 3.Moreover,we observed hypomethylation patterns in CENH3-enriched regions.We believe that this high-quality genome assembly,Col-XJTU,would serve as a valuable reference to better understand the global pattern of centromeric polymorphisms,as well as the genetic and epigenetic features in plants.

Although there is guidance from different regulatory agencies, there are opportunities to bring greater consistency and stronger applicability to address the practical issues of establishing and operating a data monitoring committee (DMC) for clinical studies of Chinese medicine. We names it as a Chinese Medicine Data Monitoring Committee (CMDMC). A panel composed of clinical and statistical experts shared their experience and thoughts on the important aspects of CMDMCs. Subsequently, a community standard on CMDMCs (T/CACM 1323-2019) was issued by the China Association of Chinese Medicine on September 12, 2019. This paper summarizes the key content of this standard to help the sponsors of clinical studies establish and operate CMDMCs, which will further develop the scientific integrity and quality of clinical studies.

Objective: Osteoporosis has become the biggest cause of non-fatal health issue. Currently, the limitations of traditional anti-osteoporosis drugs such as long-term ill-effects and drug resistance, have raised concerns toward complementary and alternative therapies, particularly herbal medicines and their natural active compounds. Thus, this study aimed to provide an integrative analysis of active chemicals, drug targets and interacting pathways of the herbs for osteoporosis treatment. Methods: Here, we introduced a systematic pharmacology model, combining the absorption, distribution, metabolism, and excretion (ADME) screening model, drug targeting and network pharmacology, to probe into the therapeutic mechanisms of herbs in osteoporosis. Results: We obtained 86 natural compounds with favorable pharmacokinetic profiles and their 58 targets from seven osteoporosis-related herbs. Network analysis revealed that they probably synergistically work through multiple mechanisms, such as suppressing inflammatory response, maintaining bone metabolism or improving organism immunity, to benefit patients with osteoporosis. Furthermore, experimental results showed that all the five compounds (calycosin, asperosaponin VI, hederagenin, betulinic acid and luteolin) enhanced osteoblast proliferation and differentiation in vitro, which corroborated the validity of this system pharmacology approach. Notably, gentisin and aureusidin among the identified compounds were first predicted to be associated with osteoporosis. Conclusion: Herbs and their natural compounds, being characterized as the classical combination therapies, might be engaged in multiple mechanisms to coordinately improve the osteoporosis symptoms. This work may contribute to offer novel strategies and clues for the therapy and drug discovery of osteoporosis and other complex diseases.

Objective:To evaluate the effect of repetitive transcranial magnetic stimulation (rTMS) on cognitive dysfunction after traumatic brain injury (TBI). Methods:Randomized controlled trials (RCTs) of rTMS for treating traumatic brain injury (TBI) patients with cognitive dysfunction were retrieved from the databases of PubMed, Embase, Web of Science, Cochrane Library, CNKI, Wanfang Database, VIP and SinoMed from the establishment of these databases to June, 2021. Two researchers independently screened the articles, extracted the data, and evaluated the quality. Meta-analysis was performed using RevMan 5.4. Results:A total of ten RCTs with 368 patients were included. Subgroup analysis showed that rTMS could improve the single cognitive function (SMD = 1.00, 95%CI 0.04 to 1.96, P = 0.04) but for overall cognitive function (SMD = 0.47, 95%CI -0.05 to 0.98, P = 0.08). rTMS was well tolerated, that the adverse reactions such as dizziness and mild headache were not significantly different from the control group (RR = 1.67, 95%CI 0.98 to 2.86, P = 0.06). Conclusions:It is still uncertain in the effectiveness of rTMS on cognitive dysfunction after TBI, but it is well tolerated.