Objective To investigate the therapeutic effect of Huangkui capsules on targeted drug-related proteinuria in patients with hepatocellular carcinoma (HCC). Methods A retrospective analysis was conducted on clinical data of HCC patients with targeted drug-related proteinuria from June 2023 to December 2024 at Zhongshan Hospital, Fudan University. According to the treatment plan, patients were divided into the conventional treatment group and the Huangkui combination treatment group (Huangkui capsules combined with conventional treatment), and the clinical efficacy between the two groups was compared. The logistic regression analysis was used to identify the main factors affecting treatment efficacy. Results The Huangkui combination treatment group (n＝29) showed a significantly higher overall effective rate (79.3% vs 42.3%, P＝0.005), and an earlier proteinuria improvement (median time: 3 months vs 6 months, P＝0.008) than the conventional treatment group (n＝26) . The multivariate logistic regression analysis showed angiotensin-converting enzyme inhibitor (ACEI) or angiotensin Ⅱ receptor blocker (ARB) using (OR＝0.190, 95%CI 0.045-0.808, P＝0.025), targeted drug adjustment (OR＝0.132, 95%CI 0.030-0.581, P＝0.007), and Huangkui capsules using (OR＝0.168, 95%CI 0.039-0.730, P＝0.017) were protective factors for treatment efficacy of targeted drug-related proteinuria. Conclusions On the basis of conventional treatment, additive treatment with Huangkui capsules can alleviate targeted drug-related proteinuria faster and more effectively in HCC patients.

Objective To construct a dynamic prediction model for JC polyomavirus (JCV) reactivation after kidney transplantation. Methods A retrospective analysis was conducted on the clinical data of 128 recipients who met the inclusion criteria and received kidney transplantation in Nanfang Hospital, Southern Medical University, from June 2021 to December 2024. Dynamic Cox regression and dynamic restricted mean survival time (RMST) regression based on the landmark method were adopted to establish the dynamic prediction models, and Monte Carlo cross-validation was used to evaluate model performance. Results The dynamic models could predict the incidence and onset time of JCV reactivation within the subsequent 3 months according to covariates at each landmark time point from the 1st to the 6th month after transplantation. Remuzzi score, body mass index and warm ischemia time were independent risk factors for JCV reactivation, while a history of urinary BK polyomavirus reactivation served as a protective factor. The median values of the area under the curve, C-index and Brier score of the dynamic Cox model were 0.873, 0.851 and 0.065 respectively, and the C-index of the dynamic RMST model was 0.829, all of which were superior to those of the static model. Conclusions The landmark-based dynamic models exhibit excellent predictive performance, which may integrate baseline data and post-transplant follow-up information to realize dynamic assessment of the risk and time window of JCV reactivation, thereby providing evidence for optimizing post-operative monitoring and individualized intervention strategies.

Objective To evaluate the effectiveness of a preventive workflow in participatory observation based on healthcare failure mode and effect analysis(HFMEA)in reducing intraoperative acquired pressure injury(IAPI)in patients undergoing spinal surgery,and to provide evidences for prospective nursing management.Methods A preventive workflow of participatory observation-based HFMEA was established,which included defining HFMEA topic,forming a multidisciplinary team,creating a process map(through participatory observation),conducting failure mode and hazard analysis(using a Hazard scoring matrix and decision tree analysis,and identifying the causes of key failure through Pareto chart analysis),implementing improvement actions,and tracking effectiveness.Ultimately,11 high-risk failure modes were identified for further improvement:poor design of form/information system,heavy workload and fast pace,lack of departmental regulations,risk factors of IAPI,and unclear duty and responsibility in a multidisciplinary team.A series measures for improvement were developed and implemented based on the identified key causes.A pre-and post-control study was carried out.A total of 180 patients who received spinal surgery between November and April 2024 and received traditional preventive methods were assigned to a control group.While further 218 patients who received spinal surgery between May and December 2023 were assigned to the trial group with the participatory observation on the basis of HFMEA prevention workflow.IAPI incidence rate and severity were compared between the two groups,as well as the completion rate of preoperative IAPI risk assessment in the trial group.Results The trial group demonstrated significantly lower IAPI incidence and severity compared with those in the control group(P<0.05).In the trial group,178 patients(98.92%)completed the preoperative IAPI risk assessment,and 172 patients(95.47%)completed the intraoperative assessment.Conclusion The participatory observation based HFMEA prevention workflow can effectively reduce an incidence and severity of IAPI in patients undergoing spinal surgery,ensure patient safety and thereby enhance the quality of nursing management.

Objective To establish a mouse model of H1N1 influenza wind-heat syndrome by combining climate intervention with influenza virus nasal drops.Methods Seventy-two BALB/c mice were divided randomly into nine groups:a Control group,wind-heat(FR)groups(FR-3Day,FR-5Day),and Model groups(1LD-3Day,2LD-3Day,3LD-3Day,1LD-5Day,2LD-5Day,2LD-5Day,3LD-5Day)(n=8 mice per group).Mice in the Control group were housed in a normal environment,while mice in the FR and Model groups were kept in wind-heat conditions for 7 d.Mice in the Model groups received nasal PR8 influenza virus infection on the 8th day,and mice in the Control and FR heat groups received equal amounts of physiological saline nasal drops.After virus challenge,each group was housed in a normal environment and samples were taken on days 3 and 5.The appearance of the mice was observed and recorded and the lung index,routine blood parameters,lung tissue pathology,serum interleukin(IL)-6 levels,and virus titers were detected in each group based on their behavioral status,stools,and body temperature.Results After 7 d of wind-heat intervention,mice in the FR groups showed no significant abnormalities in terms of appearance,stools,body temperature,routine blood parameters,or lung tissue pathology compared with the Control group.The appearance,lung index,red blood cell count,hemoglobin,hematocrit,pathological result,and body temperature in the Model groups worsened progressively with increasing time and toxin dosage,while the neutrophil percentage,lymphocyte percentage,virus titer,and serum IL-6 levels peaked on day 3 after viral attack,for the same viral dose,and then decreased slightly on day 5.Conclusions PR8 nasal drops and 7 d of wind-heat climate intervention can be used to establish a mouse model of influenza wind-heat syndrome.

Objective To compare and find an optimal model for predicting the risk of DKD occurrence in patients with type 2 diabetes mellitus(T2DM).Methods A total of 2005 patients with T2DM were enrolled in this study from The Second Hospital of Shijiazhuang City during December 2017 to December 2022.All the subjects were divided into a training set(n=1403)and a validation set(n=602)according to the ratio of 3∶1 by simple random sampling.With the occurrence of DKD as the outcome variablein the training set,important feature variables were screened by LASSO regression.Six different machine learning models were established according to the feature variables,thenthe optimal model was determined by comparison,and anonlinerisk predictor for DKD occurrence was constructed in patients with T2DM.Results Taking the occurrence of DKD as the outcome variable in the training set,the results of LASSO regression analysis showed that the optimal value of the model was 10-fold cross validation lambda.1se=0.01662473,and 15 characteristic variables with nonzero coefficient were screened out to be related to the occurrence of DKD.The data included sex,age,family history of DM,DM duration,LDL-C,HbA1c,WBC,PDW,Scr,urine α1-microglobulin,urine β2-microglobulin,urine microalbumin,hypertension,hypokalemia,and DR.In the training set and validation set,the prediction performance of XGBoost model was better than that of other models(AUC=0.872,0.893,95%CI 0.853～0.891,0.865～0.921),the sensitivity was 0.779,0.863,and the specificity was 0.721,0.758,respectively.The F1 scores were 0.774 and 0.787.DCA analysis showed that the XGBoost model had a greater net benefit and threshold probability.According to the XGBoost model,the online predictor of DKD risk in T2DM patients was laid out,and two patients were selected for application,the results showed that the predictive value of the model was 0.185 in non-DKD patients,and the predictive value was 0.510 in DKD patients.Conclusions The XGBoost model is the best model for predicting the occurrence of DKD in T2DM patients,and an online predictor was successfully built.

AIM To compare the chemical constituents in traditional decoction and formula granule decoction of classical famous prescription Wendan Decoction.METHODS The HPLC fingerprints were established,after which the contents of adenosine,synephrine,liquiritin,naringin,hesperidin,6-gingerol and adenosine cyclophosphate were determined,cluster analysis,principal component analysis and multidimensional scaling analysis were adopted in the investigation of component differences,and the equivalent of formula granules was adjusted.RESULTS The similarities of HPLC fingerprints for 10 batches of traditional decoctions were higher than those of HPLC fingerprints for 9 batches of formula granule decoctions(P＜0.01).Adenosine,synephrine,liquiritin,hesperidin and cyclic adenosine monophosphate demonstrated higher contents in traditional decoctions than those in formula granule decoctions(P＜0.05),6-gingerol displayed lower content than that in the latter produced by manufacturers A,C(P＜0.05),which was higher than that in the latter produced by manufacturer B(P＜0.01).Various batches of traditional decoctions and formula granule decoctions could be obviously distinguished,adenosine,synephrine and hesperidin exhibited great influences on the classification of principal component analysis,and the quality of formula granule decoctions produced by manufacturer C was closer to that of traditional decoctions.After equivalent correction,the contents of various constituents in formula granule decoctions produced by manufacturers A,C showed no significant differences as compared with those in traditional decoction(P＞0.05).CONCLUSION The formula granules of Wendan Decoction from different manufacturers exist quality differences,so the preparation process and extraction process of this preparation should be optimized to improve quality,and equivalent ratio should be adjusted according to actual requirements to ensure its scientific and rational clinical application.

After ischemic stroke,intracranial cells experience stress due to ischemic and hypoxic injury,leading to a series of aseptic immune response processes.The oxidative stress process in microglias triggers the activation of the NLRP3 inflammasome,which promotes the release of inflammatory factors such as IL-1β and IL-18,contributing to the inflammatory reaction caused by isch-emic stroke.In addition,NLRP3 inflammasome is involved in the polarization,pyroptosis and autophagy of microglias,regulating the prognosis of ischemic stroke.This review summarizes the specific mechanisms of NLRP3 inflammasome in regulating microglial status and its involvement in ischemia-reperfusion injury.It also discusses the associated treatment strategies,identifies the current research focus and blanks,and provides some guidance and ideas for future research.

Objective:To investigate the characteristics of the endometrial microbiota in patients with varying degrees of intrauterine adhesion (IUA).Methods:This single-center cross-sectional observational study enrolled 115 patients with IUA who were treated at the Hysteroscopic Center of Fuxing Hospital, Capital Medical University, from May 2022 to October 2023. After quality control and data preprocessing, 81 samples met the inclusion criteria for analysis. Patients were grouped according to an established IUA scoring and grading system into mild IUA ( n=38) and moderate-to-severe IUA ( n=43). Endometrial tissue was collected under sterile conditions. Bacterial genomic DNA was extracted, the 16S rRNA V3-V4 region was amplified, and sequencing was performed on an Illumina platform. Differences in endometrial microbiota diversity and composition were compared between the two groups. Results:Patients with varying degrees of IUA exhibited comparable species richness, evenness and diversity of endometrial microbiota. At the phylum level, the endometrial microbiota across all subjects was predominantly composed of Proteobacteria, Firmicutes, Cyanobacteriota, Bacteroidota, and Actinobacteriota, with Proteobacteria (32.29%) and Firmicutes (23.82%) showing the highest mean relative abundances. At the genus level, Ralstonia (16.67%), Lactobacillus (13.45%), and Streptococcus (7.07%) were the most abundant genera. Group comparisons showed that the abundance of Ralstonia was higher in the mild IUA group, whereas Lactobacillus, Vibrio and Pseudoalteromonas were more abundant in the moderate-to-severe IUA group; however, these differences did not reach statistical significance (all P>0.05). LEfSe analysis further indicated that Lactobacillus, Vibrio, Pseudoalteromonas, Aeromonas, Ureaplasma and Acetobacterium were relatively enriched in the moderate-to-severe IUA group, while Geobacillus, Stomatobaculum and Fusicatenibacter were more abundant in the mild IUA group. Conclusion:The composition of the endometrial microbiota differs among patients with varying IUA severity. IUA progression may be associated with alterations in the endometrial microbiota; however, causal relationships and underlying mechanisms require further investigation.

Objective:To investigate the clinical value of dynamic AI ultrasonic intelligent assisted diagnosis system for preoperative evaluation of thyroid nodules with diameter ≤1.0 cm.Methods:From Apr. 1, 2023, to Dec. 30, 2023, 742 thyroid nodules with diameter ≤1.0 cm were removed from 532 patients with thyroid nodule disease who received surgical treatment in the Department of Thyroid (hernia) of the First Medical Center of the Chinese People’s Liberation Army General Hospital. Among them, 423 were d≤0.5 cm. 319 cases (235 males and 507 females) with 0.5

The deep integration of artificial intelligence (AI) and multimodal data in the medical field presents vast application prospects, with its implementation in robotic surgery still in the early stages. Surgical robots assist surgeons in decision-making and operation through quantifiable data and visualized imaging, where data serves as the key driver of innovation for AI in robotic surgery. AI is pushing the boundaries of robotic autonomy, enhancing the surgical experience and improving both the quality and efficiency of procedures. This paper focuses on artificial intelligence surgery, especially the key applications of AI in robotic gastrointestinal surgery, systematically reviewing recent advances in surgical scene enhancement, surgical phase recognition, instrument tracking, intraoperative force feedback, and autonomous manipulation. Furthermore, it discusses major challenges including the scarcity of high-quality data, limited interpretability of algorithms, and the need for real-time performance. Although fully autonomous robotic surgery remains a long-term goal, the pathway toward progressive implementation is becoming increasingly clear.