It is believed that dampness is a key pathological factor contributing to digestive dysfunction in chronic renal failure. Damp pathogens tend to be entangled with or transformed into other pathogenic factors， obstructing the flow of qi， disturbing the functions of the zang-fu organs， and impairing the spleen and stomach's transportation and transformation functions， ultimately leading to disease onset. Based on this understanding， the treatment principle emphasizes dispelling dampness， regulating qi， and harmonizing the five zang organs. For the syndrome of spleen and kidney qi deficiency with dampness， the self-formulated Buyuan Fengzang Decoction （补元封藏煎） is used to tonify the kidney， secure essence， strengthen the spleen， and eliminate dampness. For the syndrome of turbid dampness obstructing the lung and stomach， the self-formulated Sulian Xiexin Decoction （苏连泻心汤） is applied to open with acrid， descend with bitter， dry dampness， and discharge turbidity. For the syndrome of dampness stagnating and transforming into heat， with concurrent spleen deficiency and liver qi stagnation， the self-formulated Chailian Wendan Decoction （柴连温胆汤） is employed to soothe the liver， strengthen the spleen， and disperse the accumulation. For the syndrome of damp obstruction with qi stagnation and constrained yang， the self-formulated Caozhi Erchen Decoction （草知二陈汤） is used to resolve dampness， relieve constraint， raise yang， and promote the defensive qi.

Objective To explore the risk factors for postoperative bleeding after endoscopic resection of gastric gastrointestinal stromal tumor(gGIST)and to construct prediction models using 4 different machine learning algorithms for accurately predicting postoperative bleeding.Methods The clinical data of gGIST patients were collected,and the patients were randomly divided into a training cohort(n=502)and a validation cohort(n=130)at an 8:2 ratio.Synthetic minority over-sampling technique-nominal continuous was used for oversampling in the training cohort.Four prediction models were constructed using gradient boost machine(GBM),deep learning,generalized linear model and distributed random forest,separately;and in addition,the least absolute shrinkage and selection operator was used to screen variables and construct a traditional Logistic regression model.Model performance was evaluated by calculating the area under the receiver operating characteristic curve(AUC),sensitivity,specificity,accuracy,positive predictive value and negative predictive value.Interpretability analyses,including feature importance,SHapley additive exPlanation and force plot,were performed on the optimal model,and a practically applicable web application was developed.Results Among 632 patients,78(12.3%)experienced postoperative bleeding.In the validation cohort,GBM model performed best among 5 prediction models,with an AUC value of 0.889 and a 95%CI of 0.829-0.948,superior to the other 4 models.Variable importance analysis identified surgeon experience,operation time,intraoperative hemorrhage,tumor size as the factors affecting postoperative bleeding prediction.The SHapley additive exPlanation plot and force plot showed the distribution characteristics of variables in the binary classification prediction results and the effect of each variable on the prediction results.Conclusion GBM model has high predictive value for postoperative bleeding following endoscopic resection of gGIST,and the construction of the web application facilitates its clinical use.

The biological activity of chitinase in degrading chitin has garnered extensive attention,particularly for its potential applications in biological control.This study utilized four spore-form-ing Bacillus strains isolated from Dermacentor nuttalli ticks collected in the Hulunbuir region.Traditional bacterial culture methods were employed for isolation and identification,followed by 16S rRNA sequencing and phylogenetic analysis of the purified cultures.chitin-hydrolyzing strains were screened using colloidal chitin plates,and specific chitinase genes were detected via PCR.Fer-mentation was conducted at 37.0 ℃ for 4 d,and the supernatants were subjected to enzyme activity analysis using the DNS method.Four Gram-positive Bacillus strains were successfully isolated from tick tissue samples,they were identified as B.proteolyticus,B.paramycoides,B.thuringien-sis,and B.cereus,and renamed IMH/B-1,IMH/P-1,IMH/T-1,and IMH/C-1,respectively.PCR a-nalysis detected chitinase genes in B.proteolyticus and B.thuringiensis,while B.cereus and B.pa-ramycoides lacked these genes.However,three strains B.proteolyticus,B.thuringiensis,and B.ce-reus demonstrated significant(P＜0.01)chitin degradation activity on colloidal chitin.Enzyme ac-tivity assays revealed that chitinase activity ranged from 1.292 to 2.032 U/mL,with B.proteolytic-us exhibiting the highest activity 2.032 U/mL,followed by B.cereus 1.496 U/mL and B.thuring-iensis 1.324 U/mL.This study provides a foundation for further research and application of chiti-nase-producing Bacillus strains.

Objective:To conduct a scope review of relevant studies on the application of virtual reality (VR) technology in breast cancer patients, identifying the basic content of interventions, outcome indicators, and application effects, with the aim of providing a reference for clinical healthcare professionals applying this technology.Methods:Based on the research methodology for scope reviews, a computer search was conducted in Chinese and English databases, including China National Knowledge Infrastructure, Wanfang Database, VIP, China Biology Medicine disc, PubMed, Web of Science, Cochrane Library, and Embase, with the search period extending to August 31, 2023. A categorical analysis of the included literature was conducted.Results:A total of 15 articles were included, primarily discussing the effects of VR technology on breast cancer patients' physical health, psychological well-being, cognitive function, and quality of life. Intervention frequencies were mainly once or twice daily, or twice weekly, with intervention durations ranging from 10 to 90 minutes and intervention periods from 2 to 12 weeks. VR interventions were found to improve physical function, psychological health, and cognitive function to some extent, increase patient rehabilitation adherence and satisfaction, and improve quality of life.Conclusions:VR technology can be an effective tool to support the treatment of breast cancer patients. However, the design of intervention protocols needs improvement. Future large-sample, multi-center, long-term follow-up randomized controlled trials are needed to verify the application effects of VR technology for breast cancer patients and promote its clinical application.

Objective To develop a combined model integrating radiomics and 3D deep learning features for improving the predictive efficacy of overall survival in non-small cell lung cancer(NSCLC)patients undergoing radiotherapy,thereby providing a foundation for optimizing individualized radiotherapy strategies.Methods A retrospective analysis was conducted on 522 NSCLC patients from 3 centers.Radiomics features were extracted from the tumor region of interest on radiotherapy planning CT scans,and a 3D-SE-ResNet was constructed to extract deep learning features.Following feature extraction,features were selected via univariate Cox analysis and Lasso-Cox regression,and a combined model was established by fusing the two feature types through principal component analysis.The discriminative ability of the model was evaluated using the concordance index(C-index)and the area under the receiver operating characteristic curve(AUC),while the risk stratification efficacy was verified by Kaplan-Meier survival analysis.Results The predictive performance of deep learning features was significantly superior to that of radiomics features(C-index:0.73 vs 0.65).The combined model achieved the highest predictive performance in the training set,internal test set,and external test set(C-index:0.74,0.69,0.72 respectively),with higher AUC values for predicting 1-year,2-year,and 3-year OS than either single model.Kaplan-Meier analysis showed significant differences in survival between the high-and low-risk groups(Log-rank test,P<0.001),and calibration curves indicated good consistency between predicted and actual survival outcomes.Conclusion The combined model integrating radiomics and 3D deep learning features can accurately predict survival outcomes in NSCLC patients undergoing radiotherapy.The multi-center validation results support its potential application in prognosis stratification for individualized radiotherapy.

ObjectiveTo develop a predictive model and application for spontaneous passage of common bile duct stones using automated machine learning algorithms given the complexity of treatment decision-making for patients with common bile duct stones， and to reduce unnecessary endoscopic retrograde cholangiopancreatography （ERCP） procedures. MethodsA retrospective analysis was performed for the data of 835 patients who were scheduled for ERCP after a confirmed diagnosis of common bile duct stones based on imaging techniques in Changshu First People’s Hospital （dataset 1） and Changshu Traditional Chinese Medicine Hospital （dataset 2）. The dataset 1 was used for the training and internal validation of the machine learning model and the development of an application， and the dataset 2 was used for external testing. A total of 22 potential predictive variables were included for the establishment and internal validation of the LASSO regression model and various automated machine learning models. The area under the receiver operating characteristic curve （AUC）， sensitivity， specificity， and accuracy were used to assess the performance of models and identify the best model. Feature importance plots， force plots， and SHAP plots were used to interpret the model. The Python Dash library and the best model were used to develop a web application， and external testing was conducted using the dataset 2. The Kolmogorov-Smirnov test was used to examine whether the data were normally distributed， and the Mann-Whitney U test was used for comparison between two groups， while the chi-square test or the Fisher’s exact test was used for comparison of categorical data between groups. ResultsAmong the 835 patients included in the study， 152 （18.20%） experienced spontaneous stone passage. The LASSO model achieved an AUC of 0.875 in the training set （n=588） and 0.864 in the validation set （n=171）， and the top five predictive factors in terms of importance were solitary common bile duct stones， non-dilated common bile duct， diameter of common bile duct stones， a reduction in serum alkaline phosphatase （ALP）， and a reduction in gamma-glutamyl transpeptidase （GGT）. A total of 55 models were established using automated machine learning， among which the gradient boosting machine （GBM） model had the best performance， with an AUC of 0.891 （95% confidence interval： 0.859‍ ‍—‍ ‍0.927）， outperforming the extreme randomized tree mode， the deep learning model， the generalized linear model， and the distributed random forest model. The GBM model had an accuracy of 0.855， a sensitivity of 0.846， and a specificity of 0.857 in the test set （n=76）. The variable importance analysis showed that five factors had important influence on the prediction of spontaneous stone passage， i.e.， were solitary common bile duct stones， non-dilated common bile duct， a stone diameter of <8 mm， a reduction in serum ALP， and a reduction in GGT. The SHAP analysis of the GBM model showed a significant increase in the probability of spontaneous stone passage in patients with solitary common bile duct stones， non-dilated common bile duct， a stone diameter of <8 mm， and a reduction in serum ALP or GGT. ConclusionThe GBM model and application developed using automated machine learning algorithms exhibit excellent predictive performance and user-friendliness in predicting spontaneous stone passage in patients with common bile duct stones. This application can help avoid unnecessary ERCP procedures， thereby reducing surgical risks and healthcare costs.

Acute lung injury （ALI） is a common clinical inflammatory respiratory emergency with high morbidity and mortality， for which there is no effective and safe therapeutic drug. Nuclear factor-κB （NF-κB）， as a classic inflammatory signaling pathway， can interact with upstream and downstream regulatory factors such as Toll-like receptor 4 （TLR4）， mitogen- activated protein kinase （MAPK）， nucleotide-binding domain leucine-rich repeat and pyrin domain-containing receptor 3 （NLRP3）， high mobility group box-1 protein 1 （HMGB1）， to jointly affect ALI. This review summarizes the latest research findings in recent years regarding the treatment of ALI through traditional Chinese medicine （TCM） interventions targeting NF-κB signaling pathways. It has been found that a variety of TCM monomers （danshensu methyl ester， salidroside total glycosides， berberine， Codonopsis pilosula polysaccharides， ursolic acid， chrysophanol， and polyphenols from longan seed kernels， etc.） and compound formulas （Resolving-dampness and defeating-toxins formula， Jinyin qingre oral liquid， Xuebijing injection， Combined treatment of lung and intestine， Huangqi baihe decoction， etc.） can modulate NF-κB signaling pathway， and can prevent and control ALI by inhibiting inflammation， improving oxidative stress， reducing apoptosis and modulating the intestinal flora in a multi-pathway manner.

Objective To develop a combined model integrating radiomics and 3D deep learning features for improving the predictive efficacy of overall survival in non-small cell lung cancer(NSCLC)patients undergoing radiotherapy,thereby providing a foundation for optimizing individualized radiotherapy strategies.Methods A retrospective analysis was conducted on 522 NSCLC patients from 3 centers.Radiomics features were extracted from the tumor region of interest on radiotherapy planning CT scans,and a 3D-SE-ResNet was constructed to extract deep learning features.Following feature extraction,features were selected via univariate Cox analysis and Lasso-Cox regression,and a combined model was established by fusing the two feature types through principal component analysis.The discriminative ability of the model was evaluated using the concordance index(C-index)and the area under the receiver operating characteristic curve(AUC),while the risk stratification efficacy was verified by Kaplan-Meier survival analysis.Results The predictive performance of deep learning features was significantly superior to that of radiomics features(C-index:0.73 vs 0.65).The combined model achieved the highest predictive performance in the training set,internal test set,and external test set(C-index:0.74,0.69,0.72 respectively),with higher AUC values for predicting 1-year,2-year,and 3-year OS than either single model.Kaplan-Meier analysis showed significant differences in survival between the high-and low-risk groups(Log-rank test,P<0.001),and calibration curves indicated good consistency between predicted and actual survival outcomes.Conclusion The combined model integrating radiomics and 3D deep learning features can accurately predict survival outcomes in NSCLC patients undergoing radiotherapy.The multi-center validation results support its potential application in prognosis stratification for individualized radiotherapy.

The biological activity of chitinase in degrading chitin has garnered extensive attention,particularly for its potential applications in biological control.This study utilized four spore-form-ing Bacillus strains isolated from Dermacentor nuttalli ticks collected in the Hulunbuir region.Traditional bacterial culture methods were employed for isolation and identification,followed by 16S rRNA sequencing and phylogenetic analysis of the purified cultures.chitin-hydrolyzing strains were screened using colloidal chitin plates,and specific chitinase genes were detected via PCR.Fer-mentation was conducted at 37.0 ℃ for 4 d,and the supernatants were subjected to enzyme activity analysis using the DNS method.Four Gram-positive Bacillus strains were successfully isolated from tick tissue samples,they were identified as B.proteolyticus,B.paramycoides,B.thuringien-sis,and B.cereus,and renamed IMH/B-1,IMH/P-1,IMH/T-1,and IMH/C-1,respectively.PCR a-nalysis detected chitinase genes in B.proteolyticus and B.thuringiensis,while B.cereus and B.pa-ramycoides lacked these genes.However,three strains B.proteolyticus,B.thuringiensis,and B.ce-reus demonstrated significant(P＜0.01)chitin degradation activity on colloidal chitin.Enzyme ac-tivity assays revealed that chitinase activity ranged from 1.292 to 2.032 U/mL,with B.proteolytic-us exhibiting the highest activity 2.032 U/mL,followed by B.cereus 1.496 U/mL and B.thuring-iensis 1.324 U/mL.This study provides a foundation for further research and application of chiti-nase-producing Bacillus strains.

Objective:To conduct a scope review of relevant studies on the application of virtual reality (VR) technology in breast cancer patients, identifying the basic content of interventions, outcome indicators, and application effects, with the aim of providing a reference for clinical healthcare professionals applying this technology.Methods:Based on the research methodology for scope reviews, a computer search was conducted in Chinese and English databases, including China National Knowledge Infrastructure, Wanfang Database, VIP, China Biology Medicine disc, PubMed, Web of Science, Cochrane Library, and Embase, with the search period extending to August 31, 2023. A categorical analysis of the included literature was conducted.Results:A total of 15 articles were included, primarily discussing the effects of VR technology on breast cancer patients' physical health, psychological well-being, cognitive function, and quality of life. Intervention frequencies were mainly once or twice daily, or twice weekly, with intervention durations ranging from 10 to 90 minutes and intervention periods from 2 to 12 weeks. VR interventions were found to improve physical function, psychological health, and cognitive function to some extent, increase patient rehabilitation adherence and satisfaction, and improve quality of life.Conclusions:VR technology can be an effective tool to support the treatment of breast cancer patients. However, the design of intervention protocols needs improvement. Future large-sample, multi-center, long-term follow-up randomized controlled trials are needed to verify the application effects of VR technology for breast cancer patients and promote its clinical application.