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 construct Nomogram prediction model for pulmonary infection in patients after aortic dissection surgery, so as to provide reference for early screening of high-risk groups and carrying out preventive nursing measures.Methods:This was a retrospective case-control study. The case data of patients after aortic dissection surgery in the Second Affiliated Hospital of Anhui Medical University from January 2020 to October 2023 were selected by convenient sampling method and divided into pulmonary infection group and non-pulmonary infection group according to whether pulmonary infection occurred within one week after surgery. The risk factors of pulmonary infection after aortic dissection surgery were analyzed by Logistic regression and the Nomogram prediction model was constructed by R4.3.3.The model was evaluated by area under the receiver operating characteristic curve, calibration curve and decision curve analysis.Results:A total of 324 patients with aortic dissection were collected, and the incidence of postoperative pulmonary infection was 26.9%(87/324). There were 87 cases in pulmonary infection group, including 65 males and 22 females, with a median age of 58.0 years. There were 237 cases in non-pulmonary infection group, including 180 males and 57 females, with a median age of 60.0 years. Finally, operation time ( OR=1.015, 95% CI 1.007-1.022), intraoperative blood transfusion ( OR=1.001, 95% CI 1.000-1.022), mechanical ventilation time ( OR=7.624, 95% CI 2.679-21.692), postoperative invasive operation ( OR=6.310, 95% CI 1.545-25.778) and postoperative renal insufficiency ( OR=6.723, 95% CI 1.219-37.063) were independent risk factors for pulmonary infection after aortic dissection surgery. The area under the receiver operating characteristic curve of the model was 0.978, sensitivity of 93.7%, and specificity of 90.8%. The calibration curve showed good consistency, and the decision curve analysis curve showed good net benefit. Conclusions:Operation time, intraoperative blood transfusion, mechanical ventilation time, postoperative invasive operation and postoperative renal insufficiency are high-risk factors of pulmonary infection after aortic dissection surgery and the constructed predictive model has predictive value.

Post-translational modification of protein is a key mechanism for regulating cancer development and immune re-sponses,and has become an important target for cancer diagnosis and treatment.Among them,lactylation modification provides a new perspective for the precise prevention and control of tumors by affecting metabolism and epigenetics.Lactylation modification precisely regulates the functions of histone and non-histone,affecting tumor cell characteristics,microenvironment acidification,and immune cell functions,becoming a key hub connecting metabolic reprogramming and malignant phenotypes.This article reviews the regulatory roles of lactylation modification in tumor development,immune escape,clinical translation,and explores the potential of targeted inhi-bition of lactylation modification and its related pathways,as well as the inhibition of key enzyme activity.

Objective:To construct Nomogram prediction model for pulmonary infection in patients after aortic dissection surgery, so as to provide reference for early screening of high-risk groups and carrying out preventive nursing measures.Methods:This was a retrospective case-control study. The case data of patients after aortic dissection surgery in the Second Affiliated Hospital of Anhui Medical University from January 2020 to October 2023 were selected by convenient sampling method and divided into pulmonary infection group and non-pulmonary infection group according to whether pulmonary infection occurred within one week after surgery. The risk factors of pulmonary infection after aortic dissection surgery were analyzed by Logistic regression and the Nomogram prediction model was constructed by R4.3.3.The model was evaluated by area under the receiver operating characteristic curve, calibration curve and decision curve analysis.Results:A total of 324 patients with aortic dissection were collected, and the incidence of postoperative pulmonary infection was 26.9%(87/324). There were 87 cases in pulmonary infection group, including 65 males and 22 females, with a median age of 58.0 years. There were 237 cases in non-pulmonary infection group, including 180 males and 57 females, with a median age of 60.0 years. Finally, operation time ( OR=1.015, 95% CI 1.007-1.022), intraoperative blood transfusion ( OR=1.001, 95% CI 1.000-1.022), mechanical ventilation time ( OR=7.624, 95% CI 2.679-21.692), postoperative invasive operation ( OR=6.310, 95% CI 1.545-25.778) and postoperative renal insufficiency ( OR=6.723, 95% CI 1.219-37.063) were independent risk factors for pulmonary infection after aortic dissection surgery. The area under the receiver operating characteristic curve of the model was 0.978, sensitivity of 93.7%, and specificity of 90.8%. The calibration curve showed good consistency, and the decision curve analysis curve showed good net benefit. Conclusions:Operation time, intraoperative blood transfusion, mechanical ventilation time, postoperative invasive operation and postoperative renal insufficiency are high-risk factors of pulmonary infection after aortic dissection surgery and the constructed predictive model has predictive value.

The “Technical Guideline for Epidemic Prevention and Control Disinfection in Large-Scale Events”(hereinafter referred to as “the Guideline”), organized and compiled by the National Disease Control and Prevention Administration, was officially released in April 2024. This guideline aims to ensure the effective implementation of large-scale group activities, mitigate the impact of infectious disease outbreaks on such events, and maintain hygiene and safety standards at event venues. During the compilation process, data were systematically collected in alignment with epidemic prevention requirements and disinfection principles, incorporating research findings from domestic and international disinfection practices. Information was gathered through field investigations, expert consultations in epidemiology and disinfection, and roundtable discussions with representatives from organizations responsible for disinfection operations at large-scale events, thereby ensuring the scientific rigor and practical applicability of the content. The Guideline provides comprehensive technical disinfection guidance for relevant authorities and event organizers, addressing critical aspects such as disinfection protocols, operational principles, emergency response strategies, and technical specifications. By standardizing hygiene assurance measures for large-scale events, including considerations of participant demographics, venue characteristics, and event scale, the guideline establishes a framework to proactively minimize the risk of infectious disease transmission.

Post-translational modification of protein is a key mechanism for regulating cancer development and immune re-sponses,and has become an important target for cancer diagnosis and treatment.Among them,lactylation modification provides a new perspective for the precise prevention and control of tumors by affecting metabolism and epigenetics.Lactylation modification precisely regulates the functions of histone and non-histone,affecting tumor cell characteristics,microenvironment acidification,and immune cell functions,becoming a key hub connecting metabolic reprogramming and malignant phenotypes.This article reviews the regulatory roles of lactylation modification in tumor development,immune escape,clinical translation,and explores the potential of targeted inhi-bition of lactylation modification and its related pathways,as well as the inhibition of key enzyme activity.

Objective To investigate the value of constructing a risk prediction model of brain metastasis in lung cancer based on clinical-CT imaging.Methods The clinical and CT imaging data of 208 patients with lung cancer confirmed by surgical pathology or puncture biopsy were analyzed retrospectively,including 98 patients in the metastasis group and 110 patients in the non-metastasis group.Univariable and binary logistic regression analyses were performed between the two groups,and the clinical,CT imaging,and clinical-CT imaging models were constructed according to the selected independent risk factors.Prediction model performance was eval-uated with receiver operating characteristic(ROC)curve,calibration curve and decision curve analysis(DCA).Results Multivariate analysis showed that T stage,pathological type,radiotherapy and chemotherapy,surgery,long diameter(LD),short diameter(SD),minimum CT value(CTmin)were the independent risk factors for predicting brain metastasis in lung cancer(P<0.05).The area under the curve(AUC)of clinical,CT imaging and clinical-CT imaging models were 0.925,0.764,0.941,respectively.DeLong test analysis showed that the AUC of clinical-CT imaging model,clinical model and CT imaging model was statistical difference(Z=2.093,5.777,all P<0.05).The calibration curve suggested a good fit of the clinical-CT imaging model.The DCA suggested that the clinical-CT imaging model demonstrates good clinical benefits.Conclusion The clinical-CT imaging model can effectively predict the occurrence of brain metastasis in lung cancer,which is helpful to guide the development of accurate diagnosis and treatment plan.

By systematically combing ancient and modern literature， this paper examined Tribuli Fructus and Astragali Complanati Semen（ACS） used in the famous classical formulas from the aspects of name， origin， production area， harvesting and processing， clinical efficacy， so as to provide a basis for the development of famous classical formulas containing such medicinal materials. The results showed that the names of Tribuli Fructus in the past dynasties were mostly derived from its morphology， and there were nicknames such as Baijili， Cijili and Dujili. The name of ACS in the past dynasties were mostly originated from its production areas， and there were nicknames such as Baijili， Shayuan Jili and Tongjili. Because both of them had the name of Baijili， confusion began to appear in the Song dynasty. In ancient and modern times， the main origin of Tribuli Fructus were Tribulus terrestris， and ancient literature recorded the genuine producing areas of Tribuli Fructus was Dali in Shaanxi and Tianshui in Gansu， but today it is mainly cultivated in Anhui and Shandong. The fruit is the medicinal part， harvested in autumn throughout history. There is no description of the quality of Tribuli Fructus in ancient times， and the plump， firm texture， grayish-white color is the best in modern times. Traditional processing methods for Tribuli Fructus included stir-frying and wine processing， while modern commonly used is purified， fried and salt-processed. The ancient records of Tribuli Fructus were spicy， bitter， and warm in nature， with modern research adding that it is slightly toxic. The main effects of ancient and modern times include treating wind disorders， improving vision， promoting muscle growth， and treating vitiligo. The mainstream base of ACS used throughout history is Astragalus complanatus. Ancient texts indicated ACS primarily originated from Shaanxi province. Today， the finest varieties come from Tongguan and Dali in Shaanxi. The medicinal part is the seed， traditionally harvested in autumn. Modern harvesting occurs in late autumn or early winter， followed by sun-drying. Ancient texts valued seeds with a fragrant aroma as superior， while modern standards prioritize plump， uniform and free of impurities. Traditional processing methods for ACS included frying until blackened and wine-frying， while modern practice commonly employs purification methods. In terms of medicinal properties， the ancient and modern records are sweet and warm in nature. Due to originally classified under Tribuli Fructus， its effects were thus regarded as equivalent to those of Tribuli Fructus， serving as the medicine for treating wind disorders， additional functions included tonifying the kidneys and treating vitiligo. The present record of its efficacy is to tonify the kidney and promote Yang， solidify sperm and reduce urine， nourish the liver and brighten the eye， etc. Based on the textual research results， it is suggested that when developing the famous classical formulas of Tribuli Fructus medicinal materials， we should pay attention to the specific reference object of Baijili， T. terrestris and A. complanatus should be identified and selected， and the processing method should be in accordance with the requirements of the formulas.

Objective To investigate the value of constructing a risk prediction model of brain metastasis in lung cancer based on clinical-CT imaging.Methods The clinical and CT imaging data of 208 patients with lung cancer confirmed by surgical pathology or puncture biopsy were analyzed retrospectively,including 98 patients in the metastasis group and 110 patients in the non-metastasis group.Univariable and binary logistic regression analyses were performed between the two groups,and the clinical,CT imaging,and clinical-CT imaging models were constructed according to the selected independent risk factors.Prediction model performance was eval-uated with receiver operating characteristic(ROC)curve,calibration curve and decision curve analysis(DCA).Results Multivariate analysis showed that T stage,pathological type,radiotherapy and chemotherapy,surgery,long diameter(LD),short diameter(SD),minimum CT value(CTmin)were the independent risk factors for predicting brain metastasis in lung cancer(P<0.05).The area under the curve(AUC)of clinical,CT imaging and clinical-CT imaging models were 0.925,0.764,0.941,respectively.DeLong test analysis showed that the AUC of clinical-CT imaging model,clinical model and CT imaging model was statistical difference(Z=2.093,5.777,all P<0.05).The calibration curve suggested a good fit of the clinical-CT imaging model.The DCA suggested that the clinical-CT imaging model demonstrates good clinical benefits.Conclusion The clinical-CT imaging model can effectively predict the occurrence of brain metastasis in lung cancer,which is helpful to guide the development of accurate diagnosis and treatment plan.