Prescription optimization is a crucial aspect in the study of traditional Chinese medicine （TCM） compounds. In recent years， the introduction of mathematical methods， data mining techniques， and artificial neural networks has provided new tools for elucidating the compatibility rules of TCM compounds. The study of TCM compounds involves numerous variables， including the proportions of different herbs， the specific extraction parts of each ingredient， and the interactions among multiple components. These factors together create a complex nonlinear dose-effect relationship. In this context， it is essential to identify methods that suit the characteristics of TCM compounds and can leverage their advantages for effective application in new drug development. This paper provided a comprehensive review of the cutting-edge optimization experimental design methods applied in recent studies of TCM compound compatibilities. The key technical issues， such as the optimization of source material selection， dosage optimization of compatible herbs， and multi-objective optimization indicators， were discussed. Furthermore， the evaluation methods for component effects were summarized during the optimization process， so as to provide scientific and practical foundations for innovative research in TCM and the development of new drugs based on TCM compounds.

Objective To predict the lymph node metastasis status of patients with invasive pulmonary adenocarcinoma by constructing machine learning models based on primary tumor radiomics, peritumoral radiomics, and habitat radiomics, and to evaluate the predictive performance and generalization ability of different imaging features. Methods A retrospective analysis was performed on the clinical data of 1 263 patients with invasive pulmonary adenocarcinoma who underwent surgery at the Department of Thoracic Surgery, Jiangsu Province Hospital, from 2016 to 2019. Habitat regions were delineated by applying K-means clustering (average cluster number of 2) to the grayscale values of CT images. The peritumoral region was defined as a uniformly expanded area of 3 mm around the primary tumor. The primary tumor region was automatically segmented using V-net combined with manual correction and annotation. Subsequently, radiomics features were extracted based on these regions, and stacked machine learning models were constructed. Model performance was evaluated on the training, testing, and internal validation sets using the area under the receiver operating characteristic curve (AUC), F1 score, recall, and precision. Results After excluding patients who did not meet the screening criteria, a total of 651 patients were included. The training set consisted of 468 patients (181 males, 287 females) with an average age of (58.39±11.23) years, ranging from 29 to 78 years, the testing set included 140 patients (56 males, 84 females) with an average age of (58.81±10.70) years, ranging from 34 to 82 years, and the internal validation set comprised 43 patients (14 males, 29 females) with an average age of (60.16±10.68) years, ranging from 29 to 78 years. Although the habitat radiomics model did not show the optimal performance in the training set, it exhibited superior performance in the internal validation set, with an AUC of 0.952 [95%CI (0.87, 1.00)], an F1 score of 84.62%, and a precision-recall AUC of 0.892, outperforming the models based on the primary tumor and peritumoral regions. Conclusion The model constructed based on habitat radiomics demonstrated superior performance in the internal validation set, suggesting its potential for better generalization ability and clinical application in predicting lymph node metastasis status in pulmonary adenocarcinoma.

In protein engineering, while computational models are increasingly used to predict mutation effects, their evaluations primarily rely on high-throughput deep mutational scanning (DMS) experiments that use surrogate readouts, which may not adequately capture the complex biochemical properties of interest. Many proteins and their functions cannot be assessed through high-throughput methods due to technical limitations or the nature of the desired properties, and this is particularly true for the real industrial application scenario. Therefore, the desired testing datasets, will be small-size (∼10-100) experimental data for each protein, and involve as many proteins as possible and as many properties as possible, which is, however, lacking. Here, we present VenusMutHub, a comprehensive benchmark study using 905 small-scale experimental datasets curated from published literature and public databases, spanning 527 proteins across diverse functional properties including stability, activity, binding affinity, and selectivity. These datasets feature direct biochemical measurements rather than surrogate readouts, providing a more rigorous assessment of model performance in predicting mutations that affect specific molecular functions. We evaluate 23 computational models across various methodological paradigms, such as sequence-based, structure-informed and evolutionary approaches. This benchmark provides practical guidance for selecting appropriate prediction methods in protein engineering applications where accurate prediction of specific functional properties is crucial.

Ubiquitination and deubiquitination are common post-translational modifications of proteins. As the largest family of deubiquitinating enzymes, ubiquitin-specific proteases (USPs) play critical roles in various physiological and pathological processes. Dysregulation of their functions results in a series of physiological damages and pathological alterations, ultimately contributing to the occurrence and progression of malignant tumors, including pancreatic cancer. Currently, research on the functional mechanisms of deubiquitinating enzymes in pancreatic cancer is still limited. This review focused on the research progress of USPs family in the occurrence and development of pancreatic cancer, to elucidate their impacts on the malignant biological behaviors of pancreatic cancer and its underlying mechanisms. The therapeutic potential of USPs inhibitors in pancreatic cancer were also discussed.

Background and objective Cystic lung cancer,a special type of lung cancer,has been paid more and more attention.The most common pathological type of cystic lung cancer is adenocarcinoma.The invasiveness of cystic lung adenocarcinoma is vital for the selection of clinical treatment and prognosis.The aim of this study is to analyze the multiple clinical features of cystic lung adenocarcinoma,explore the independent risk factors of its invasiveness,and establish a risk pre-diction model.Methods A total of 129 cases of cystic lung adenocarcinoma admitted to the Department of Thoracic Surgery of the First Affiliated Hospital of Nanjing Medical University from January 2021 to July 2022 were retrospectively analyzed and divided into pre-invasive group[atypical adenomatous hyperplasia(AAH),adenocarcinoma in situ(AIS)and minimally invasive adenocarcinoma(MIA)]and invasive group[invasive adenocarcinoma(IAC)]according to pathological findings.There were 47 cases in the pre-invasive group,including 19 males and 28 females,with an average age of(51.23±14.96)years.There were 82 cases in the invasive group,including 60 males and 22 females,with an average age of(61.27±11.74)years.Mul-tiple clinical features of the two groups were collected,including baseline data,imaging data and tumor markers.Univariate analysis,LASSO regression and multivariate Logistic regression analysis were used to screen out the independent risk factors of the invasiveness of cystic lung adenocarcinoma,and the risk prediction model was established.Results In univariate analysis,age,gender,smoking history,history of emphysema,neuron-specific enolase(NSE),number of cystic airspaces,lesion di-ameter,cystic cavity diameter,nodule diameter,solid components diameter,cyst wall nodule,smoothness of cyst wall,shape of cystic airspace,lobulation,short burr sign,pleural retraction,vascular penetration and bronchial penetration were statisti-cally different between the pre-invasive group and invasive groups(P<0.05).The above variables were processed by LASSO regression dimensionality reduction and screened as follows:age,gender,smoking history,NSE,number of cystic airspaces,lesion diameter,cystic cavity diameter,cyst wall nodule,smoothness of cyst wall and lobulation.Then the above variables were included in multivariate Logistic regression analysis.Cyst wall nodule(P=0.035)and lobulation(P=0.001)were found to be independent risk factors for the invasiveness of cystic lung adenocarcinoma(P<0.05).The prediction model was established as follows:P=e^x/(1+e^x),x=-7.927+1.476* cyst wall nodule+2.407* lobulation,and area under the curve(AUC)was 0.950.Conclusion Cyst wall nodule and lobulation are independent risk factors for the invasiveness of cystic lung adenocarcinoma,which have certain guiding significance for the prediction of the invasiveness of cystic lung adenocarcinoma.

[Objective]To investigate the effect and mechanism of Shuxuetong and its main component hirudin on the apoptosis of cerebellar granule neurons(CGNs)in Sprague-Dawley(SD)rats.[Methods]CGNs incubated in vitro for 7 days were divided into survival control group or 25 K group(cultured in medium containing 25 mmol/L KCL)and apopto-sis group or 5 K group(cultured in medium containing 5 mmol/L KCL).CGNs were separately treated with proportionally diluted and different concentrations of Shuxuetong(1/50,1/40,1/30,1/20 and 1/10)and the corresponding different con-centrations of hirudin(2,2.5,3.34,5 and 10 U/mL).Hoechst staining was performed to analyze the apoptosis.Western blot was used to detect the expression levels of Cleaved Caspase-3,Bim and VEGF.[Results]Hoechst staining showed that 5 K group had a higher apoptosis rate than 25 K group.In 25 K group,there was no significant change in the apoptosis rate between neurons treated with different concentrations of Shuxuetong and hirudin,but significant changes was found in 5 K group and the higher the concentration,the lower the apoptosis rate.Western blot results revealed that,compared with control neurons in 5 K group,Shuxuetong injection and hirudin treatments resulted in a decrease of Cleaved Caspase-3 and Bim expression,but an increase of VEGF protein.[Conclusions]Shuxuetong and its main component hirudin inhibits the apoptosis of CGNs through suppressing proapoptotic BH3-only protein Bim.

Objective:To assess the predictive value of aspartate aminotransferase-to-alanine amino-transferase ratio (DRR) on overall survival of patients with pancreatic ductal adenocarcinoma (PDAC) who underwent radical pancreaticoduodenectomy.Methods:A retrospective analysis was performed on the clinical data of 137 patients who underwent radical pancreaticoduodenectomy and were diagnosed with PDAC postoperatively at the Chinese PLA General Hospital from January 2015 to December 2020. There were 97 male and 40 female patients, with an average age of (58±10) years old. The patients were grouped according to the optimal survival risk cutoff value of DRR, and the differences in key clinical and pathological indicators between the groups were compared. Kaplan-Meier method was used for survival analysis, and log-rank test was used for comparison of survival rates. Multivariate Cox analysis was performed to evaluate the prognostic factors affecting survival.Results:The 137 PDAC patients were divided into two groups based on the optimal cutoff value of DRR, namely 1.1: DRR≥1.1 was defined as the high-DRR group ( n=29), and DRR<1.1 was defined as the low-DRR group ( n=108). The cumulative survival rate of the low-DRR group was better than that of the high-DRR group, and the difference was statistically significant ( P=0.003). The results of the multivariate Cox regression analysis showed that DRR≥1.1 ( HR=2.485, 95% CI: 1.449-4.261, P=0.001), preoperative biliary drainage ( HR=1.845, 95% CI: 1.030-3.306, P=0.039), lymph node metastasis N2 stage ( HR=2.240, 95% CI: 1.123-4.470, P=0.022), high tumor differentiation ( HR=2.001, 95% CI: 1.279-3.129, P=0.002), and intravascular cancer emboli ( HR=2.240, 95% CI: 1.123-4.470, P=0.022) were risk factors for poor overall survival in PDAC patients who underwent radical pancreaticoduodenectomy. Conclusion:DRR has predictive value for overall survival after surgery in PDAC patients undergoing radical pancreatoduodenectomy. A DRR of 1.1 or greater is a risk factor for poor overall survival after surgery in PDAC patients.

Intraductal papillary mucinous neoplasm (IPMN) is one of the precancerous lesions of the pancreas. Currently there is controversial over the management and follow-up strategy of IPMN, including the timing of surgery. The core problem lies in the accurate preoperative assessment of the nature of the lesions and the risk of malignant transformation. Cumulation of high-quality evidence and development of efficient evaluation methods are vital for the establishment of standardized decision-making system and the improvement of clinical benefits to patients. This review aims to summarize the consensus and controversies on surgical evaluation standards in the latest guidelines and representative literatures, and to look forward to the development direction of IPMN diagnosis and treatment decisions in combination with the progress of related evaluation techniques.

Desmoplastic stroma of pancreatic ductal adenocarcinoma plays an important role in tumor progression and treatment resistance. Stroma-targeted therapies are therefore promising for clinical application and extensive related researches are undergoing. In this article, recent advances in stromal targeting strategies were reviewed from three perspectives: cancer-associated fibroblasts, extracellular matrix and angiogenesis, and an outlook for the future of this strategy was also provided.

Objective:To study the risk factors for early recurrence of patients undergoing radical pancreaticoduodenectomy (PD) for pancreatic ductal adenocarcinoma (PDAC) and construct a normogram model.Methods:Patients undergoing open radical PD for PDAC at Faculty of Hepato-Pancreato-Biliary Surgery, Chinese PLA General Hospital from January 2014 to December 2021 were retrospectively screened. A total of 213 patients were enrolled, including 145 males and 68 females, aged (58.4±9.8) years. Patients were divided into the early recurrence group ( n=59, recurrence within 6 months after surgery) and a control group ( n=154, no recurrence within 6 months after surgery). Using minimum absolute value convergence and selection operator regression (LASSO) and multi-factor logistic regression analysis, we screened out the best predictor of early recurrence after PD for PDAC, and then established a nomogram model. The effectiveness of the model was validated by receiver operating characteristic (ROC) curve, calibration curves, and decision analysis curves. Results:Multivariate logistic regression analysis showed that patients with obstructive jaundice, vascular invasion, massive intraoperative bleeding, high-risk tumors (poorly differentiated or undifferentiated), high carbohydrate antigen 19-9 to total bilirubin ratio, and high fibrinogen and neutrophil to lymphocyte ratio scores had a higher risk of early postoperative recurrence. Based on the indexes above, a nomogram prediction model was constructed. The area under the ROC curve was 0.797 (95% CI: 0.726-0.854). Validation of the calibration curve exhibited good concordance between the predicted probability and ideal probability, decision curve analysis showed that the net benefits of the groupings established according to the model were all greater than 0 within the high risk threshold of 0.08 to 1.00. Conclusion:The nomogram for predicting early recurrence after PD for PDAC has a good efficiency, which could be helpful to screen out the high-risk patients for adjuvant or neoadjuvant therapy.