Purpose/Significance By using data mining methods,the medication rules and prescription characteristics of traditional Chinese medicine(TCM)for treating hypertension with preserved ejection fraction heart failure are discussed.Method/Process The da-tabase is established based on the TCMprescription information of hypertensive patients with preserved ejection fraction heart failure ad-mitted to the Cardiovascular Disease Department of the First Affiliated Hospital of Heilongjiang University of Chinese Medicine from Janu-ary 2019 to July 2022.SPSS Statistics and Moderler software are used to analyze the frequency,taste,meridian tropism,efficacy,associ-ation rules,clustering and factor analysis of the prescriptions to explore medication rules.Result/Conclusion TCMin the treatment of hy-pertension with preserved ejection fraction heart failure is mainly to replenish qi and spleen,nourish the heart and calm the mind,relieve phlegm,relieve cough and asthma,and pay attention to promoting blood circulation and removing blood stasis,promoting water infiltra-tion and dampness,which can reflect the overall concept of TCMand the characteristics of diagnosis and treatment based on syndrome dif-ferentiation and provide references for clinical medication.

Post-stroke depression is a common complication after a stroke. Based on the theory of ＂deficiency of heart qi leading to sadness＂recorded in Huangdi Neijing, our team proposes that deficiency of heart qi is the basic pathogenesis of post-stroke depression, with sadness being its main manifestation. Therefore, we advocate treating post-stroke depression from the perspective of the heart, and clinically, it can be divided into four stages: heart-liver stage, heart-lung stage, heart-spleen stage, and heart-kidney stage. Treating post-stroke depression with the principle of regulating qi and nourishing the heart, patients in the heart-liver and heart-lung stages are mainly middle-aged and young adults, with a pathological nature of qi stagnation and mild deficiency of heart qi, and therefore, the method of promoting the flow of meridians and regulating qi should be adopted. In contrast, patients in the heart-spleen and heart-kidney stages are mainly middle-aged and elderly, with a pathological nature of qi deficiency and severe deficiency of heart qi, and therefore, nourishment and tonification should be prioritized. In response to the above four stages, our team has achieved good clinical efficacy by administering Xiaoyao Powder, Baihe Dihuang Decoction, Ganmai Dazao Decoction and Jingui Shenqi Pill respectively. This study discusses the approach to treating post-stroke depression from the perspective of a "deficiency of heart qi leading to sadness" by examining three aspects: the theoretical basis of Chinese and Western medicine to treat post-stroke depression from the heart, the basic pathogenesis and staging of post-stroke depression, and the treatment principle of regulating qi and nourishing the heart. The study also briefly analyzes the clinical experience of the medications used for this condition to provide a reference for the treatment of post-stroke depression.

Surgical robot is the national strategic diagnostic and therapeutic equipment research focus,get a number of scientific research institutes,colleges and universities and enterprises pay extensive attention to the design and development of a variety of surgical robots,and registration declaration.This article explores the critical technologies of surgical robots and key areas for optimizing their performance,including kinematic positioning errors,pose errors,feedback model errors,image recognition positioning errors,path planning,and safety aspects.The findings not only provide a scientific basis for future standardization research on surgical robots but also offer significant theoretical and practical references for the research,manufacturing,and registration processes in the medical robotics industry.

It is regarded that the general qi is the combination of spleen-kidney qi and heart-lung yang qi in the chest， which is filled between the muscles and is able to maintain the normal shape of the muscles. Accordingly， it is proposed that the core pathogenesis of amyotrophic lateral sclerosis （ALS） is the deficiency sinking of general qi， with "deficiency" as the root， and "toxin" and "wind-heat" as the key factors for the rapid progression of the disease. The development process of ALS was classified into the initial onset stage， the increasingly progressive stage， and the rapid exacerbation stage， and differentiated and treated by deficiency， toxin， and wind-heat. Based on clinical experience， the wind medicinals were preferred. We formed self-prescribed Juxian Decoction （举陷汤） and modified from the perspective of "deficiency， toxicity， wind-heat" based on the wind medicinals of Huangqi （Radix Astragali）， Shengma （Actaea cimicifuga L.）， Chaihu （Bupleurum spp.） and so on. For the initial onset stage， Yiqi Shengji Juxian Decoction （益气生肌举陷汤）， Wenyang Liyan Juxian Decoction （温阳利咽举陷汤） could be used； for the increasingly progressive stage， Jiedu Juxian Decoction （解毒举陷汤） could be used； for the rapid exacerbation stage， Yinqiao Shufen Juxian Decoction （银翘疏风举陷汤）， Yangxue Xifeng Juxian Decoction （养血熄风举陷汤） could be used.

Objective To investigate the classification effect of deep neural networks in irregular time series data,and to predict the recurrence risk of 362 patients with diffuse large B-cell lymphoma(DLBCL)in a hospital in Shanxi from 2014 to 2020.Methods A total of 362 diagnosed DLBCL patients who achieved complete remission after initial chemotherapy were collected retrospectively,and the recurrence risk was predicted within the next two years.First,LASSO regression was used to screen the variables.Then a deep neural network model of irregular time series data based on GRU-ODE-Bayes was constructed and compared with some traditional models and other deep neural network models.Results Among all the models under study,the traditional models do not perform as well as the deep neural network models in classification.The GRU-ODE-Bayes model was the best,with AUC of 0.85,sensitivity of 0.84,specificity of 0.71,and G-means of 0.77.Conclusion Compared with other models,the GRU-ODE-Bayes model can predict the recurrence of DLBCL patients more accurately.It could benefit the individualized treatment for patients and decision-making for physicians.

Objective:To explore the predictive value of preoperative pelvic floor electromyography(EMG)parameters for the risk of urinary incontinence after prostate cancer surgery.Methods:This study retrospectively analyzed the medical records of 271 patients who underwent radical prostatectomy in the urology department of Peking University First Hospital from January 2020 to October 2022.The data included patient age,body mass index(BMI),international prostate symptom score(IPSS),prostate-specific antigen(PSA)levels,Gleason score,type of surgery,urethral reconstruction,lymph node dis-section,nerve preservation,catheterization duration,D'Amico risk classification,American Society of Anesthesiologists(ASA)score,Charlson comorbidity index,postoperative duration,prostate volume,and pelvic floor EMG parameters(pre-resting mean,fast muscle mean,and slow muscle mean scores).Independent risk factors affecting early postoperative urinary incontinence were identified through multiva-riate Logistic regression analysis.The predictive efficacy of pelvic floor EMG results was evaluated by cal-culating the area under the receiver operating characteristic(ROC)curve,and the optimal threshold for early postoperative urinary incontinence was determined based on the Youden index and clinical signifi-cance.Results:The study included 271 prostate cancer patients,with an 81.9％rate of voluntary urinary control post-surgery.The median score for fast pelvic floor muscles was 23.5(18.2,31.6),and for slow muscles,it was 12.5(9.6,17.3).Among the patients,179(66.1％)did not preserve nerves,and 110(40.6％)underwent urethral reconstruction.Advanced age and low fast muscle scores were identified as independent risk factors for urinary incontinence.Patients aged ≤60 had 5.482 times the voluntary urinary control rate compared with those aged ≥70(95％CI:1.532-19.617,P＜0.05).There was a significant correlation between fast muscle scores and urinary incontinence recovery(OR=1.209,95％CI:1.132-1.291,P＜0.05).When the optimal threshold for preoperative fast muscle score was set at 18.5,the ROC sensitivity and specificity were 80.6％and 61.2％,respectively.Con-clusion:Preoperative pelvic floor EMG parameters show good predictive accuracy and clinical applicabili-ty for the risk of urinary incontinence after prostate cancer surgery.These parameters can be used for ear-ly identification of urinary incontinence risk,with age and fast muscle scores being important predictors.

Objective:To investigate the correlation between digital breast tomosynthesis(DBT)image features,pathological features,and the results of breast-conserving surgery(BCS)in early stage breast cancer.Methods:We retrospectively analyzed 422 cases of BCS resulting in successful breast preservation and 211 BCS cases that were followed by mastectomy.All of the patients underwent surgery at Tianjin Medical University Cancer Institute&Hospital between January 2019 and December 2023.Preoperative DBT images and clinicopathological features were assessed.A univariate Logistic regression analysis was performed to screen out the characteristics associated with BCS surgic-al results,after which multivariate Logistic regression analysis was performed to screen out the characteristics.Results:Univariate Logistic regression analysis showed that age(P=0.020),architectural distortion(P<0.001),breast composition(extremely dense,P=0.001),and mo-lecular subtype(Her2,P=0.001)were statistically different between the two groups.Multivariate Logistic regression analysis showed that ar-chitectural distortion(P<0.001),breast composition(extremely dense breast,P=0.003),and molecular subtype(Her2,P<0.001)differed signi-ficantly between the two groups.Conclusions:Breast composition,architectural distortion,and molecular subtype correlated with BCS res-ults.Extremely dense breast composition,architectural distortion,and Her2 subtype are associated with a higher possibility of conversion to mastectomy.These factors serve as effective predictive indicators of BCS results and thus aid clinicians in deciding the appropriate surgical strategies in the treatment of breast cancer.

Objective:To construct and validate a machine learning model for preoperative prediction of perineural invasion (PNI) status in intrahepatic cholangiocarcinoma (ICC).Methods:Clincial data of 329 patients, including 245 admitted to Zhengzhou University People's Hospital from January 2018 to June 2023 and 84 admitted to the Affiliated Cancer Hospital of Zhengzhou University from January 2013 to January 2020 were retrospectively analyzed. Patients were divided into a training set ( n=231) and a validation set ( n=98). Clinicopathological data including age, gender, hepatitis B virus (HBV) infection status were collected. Predictive variables were determined using least absolute shrinkage and selection operator (LASSO) regression analysis. Six machine learning algorithms including random forest (RF), logistic regression, and linear kernel-based support vector machine were selected to construct the preoperative prediction model for PNI in ICC. Performance metrics of the model were calculated using a confusion matrix, and the final model was selected. The model performance was evaluated in the validation set. Calibration curves were plotted to evaluate the final model, and a Pareto chart was used to visualize the importance of predictive variables. Results:LASSO regression identified nine predictive variables included in the prediction model, including carbohydrate antigen 19-9 (CA19-9), HBV infection status, alkaline phosphatase, alanine aminotransferase, prothrombin time, total bilirubin, albumin, neutrophil times gamma-glutamyl transferase to lymphocyte ratio, and tumor burden score. Among the trained six models, the area under the curve (AUC) of the RF model was 0.909, with a sensitivity of 0.842 and an accuracy of 0.870. Compared with the AUC of the RF model, the AUCs of the other 5 models were lower (all P<0.05). The AUC of the RF model for predicting PNI in ICC in validation set was 0.736. Calibration curves showed good fit of the RF model's prediction of PNI in ICC in both training and validation sets. The Pareto chart showed that CA19-9 was the most important predictive variable in the model, followed by HBV infection status. Conclusion:The machine learning model based on the RF algorithm has a high accuracy in preoperative prediction of PNI status in ICC.

Plastics are one of the most important polymers with huge global demand. However, the downsides of this polymer are that it is difficult to degrade, which causes huge pollution. The environmental-friendly bio-degradable plastics therefore could be an alternative and eventually fulfill the ever-growing demand from every aspect of the society. One of the building blocks of bio-degradable plastics is dicarboxylic acids, which have excellent biodegradability and numerous industrial applications. More importantly, dicarboxylic acid can be biologically synthesized. Herein, this review discusses the recent advance on the biosynthesis routes and metabolic engineering strategies of some of the typical dicarboxylic acids, in hope that it will help to provide inspiration to further efforts on the biosynthesis of dicarboxylic acids.
Biodegradable Plastics ; Dicarboxylic Acids ; Polymers/metabolism* ; Biodegradation, Environmental ; Metabolic Engineering