In order to provide a reference basis for the development of relevant compound preparations， this article takes a comprehensive analysis of the usage and dosage of famous classical formulas in Han dynasty from various perspectives， and gives corresponding countermeasures on this basis. Through the comprehensive analysis of the classification and statistics of Zhongjing's medication characteristics， decoction methods， administration and dosage， and combining conversion methods of weights and measures by ancient medical practitioners， along with the dosage and administration of the listed Han dynasty famous classical formulas， it was found that the "Jiangxi method" served as a general guideline for administration according to Zhongjing's original text. This method allowed for flexible dosing based on the conversion of the ancient measurements to modern equivalents［13.8 g per Liang（两）］， ensuring the safe and effective medication of these formulas. After combing， it is found that although the dosage of single medicine is large in famous classical formulas from Han dynasty， the administration is flexible. The crude drug amount per administration serves as the foundational dose， with the frequency of administration adjusted flexibly according to the condition. This dosing approach becomes the key for the rational development of compound formulations of famous classical formulas. Based on the conclusions of the study， it is recommended that when developing compound formulations of famous classical formulas in Han dynasty， the original administration method and dosage should be respected. The original crude drug amount per administration should be considered as the daily foundational dose， with the frequency of administration described within a range（1 to N times per day， where N is the maximum number of administrations as per the original text）. The specific frequency of administration can be adjusted flexibly by clinical practitioners based on the individual condition. This approach should also be adopted in toxicological studies， where the dosage per administration serves as the basis for toxicity research， and the toxicity profile at the maximum administration frequency should be observed， providing guidance on the clinical safety range. Corresponding drug labels should provide information within a range to indicate toxicological risk intervals.

The pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a serious impact on global public health and the economy. SARS-CoV-2 infiltrates host cells via its surface spike protein, which binds to angiotensin-converting enzyme 2 on the host cell membrane. As a result, small molecules targeting spike protein have emerged as a hotspot in anti-SARS-CoV-2 drug research. Activity screening is an important step in seeking small molecule drugs. Therefore, this article aims to review the biological activity evaluation methods of small molecule inhibitors targeting SARS-CoV-2 spike protein, with the goal of laying the foundation for the discovery of new anti-SARS-CoV-2 drugs.

Objective To analyze the literature on artificial intelligence in forensic research from 2012 to 2022 in the Web of Science Core Collection Database,to explore research hotspots and developmen-tal trends.Methods A total of 736 articles on artificial intelligence in forensic medicine in the Web of Science Core Collection Database from 2012 to 2022 were visualized and analyzed through the litera-ture measuring tool CiteSpace.The authors,institution,country(region),title,journal,keywords,cited references and other information of relevant literatures were analyzed.Results A total of 736 articles published in 220 journals by 355 authors from 289 institutions in 69 countries(regions)were identi-fied,with the number of articles published showing an increasing trend year by year.Among them,the United States had the highest number of publications and China ranked the second.Academy of Forensic Science had the highest number of publications among the institutions.Forensic Science Inter-national,Journal of Forensic Sciences,International Journal of Legal Medicine ranked high in publica-tion and citation frequency.Through the analysis of keywords,it was found that the research hotspots of artificial intelligence in the forensic field mainly focused on the use of artificial intelligence technol-ogy for sex and age estimation,cause of death analysis,postmortem interval estimation,individual identification and so on.Conclusion It is necessary to pay attention to international and institutional cooperation and to strengthen the cross-disciplinary research.Exploring the combination of advanced ar-tificial intelligence technologies with forensic research will be a hotspot and direction for future re-search.

Objective To explore the therapeutic mechanism of Modified Lugen Formula(Phragmitis Rhizoma,Cicadae Periostracum,Batryticatus Bombyx,Lonicerae Japonicae Flos,Glycyrrhiza,Menthae Haplocalycis Herba,Notopterygii Rhizoma et Radix,Puerariae Lobatae Radix,Bupleuri Radix)in treating influenza from the virus-host interaction interface.Methods The phytocompounds were first collected from the HERB database,and then potential active compounds were screened out by Lipinski's rules of five.The targets of active compounds were further predicted through the SwissTargetPrediction platform.Differentially expressed genes(DEGs)were determined from the human H1N1 influenza dataset GSE90732 available in the Gene Expression Omnibus database(GEO).H1N1-Homo sapiens-related protein-protein interactions(PPIs)were gathered from the Pathogen-Host Interaction Search Tool(PHISTO).The above mentioned bioinformatic datasets were integrated.Then a PPI network and a Formula-virus-host interaction network were constructed using Cytoscape.Functional enrichment analyses were performed by using R software.Finally,molecular docking was carried out to evaluate the binding activities between the key compounds and targets.Results A total of 1 252 active compounds,1 415 targets,951 influenza-related DEGs,and 10 142 H1N1-Homo sapiens-related PPIs were obtained.There were 72 intersection targets between the Modified Lugen Formula and influenza.Functional enrichment analyses showed that these targets are closely related to host defense and programmed cell death.The network topological analysis showed that active compounds in the Modified Lugen Formula,such as oleanolic acid,γ-undecalactone,and longispinogenin,regulate viral proteins M2,NA,NS1,and HA and/or the host factors HSP90AA1,NRAS,and ITGB1,thus exert therapeutic effect.Molecular docking results confirmed that these compounds had a good binding ability with the targets.Conclusion Multiple active ingredients in Modified Lugen Formula directly target influenza virus proteins and/or host factors,thereby play an anti-influenza role in multiple dimensions,including inhibiting virus replication,regulating host defense and cell death.This study provides a theoretical basis for further experimental analysis of the action mechanism of the Modified Lugen Formula in treating influenza.

Antiviral drug research and development is an important research direction in the current and future biomedical field. The research and development of antiviral drugs not only requires the application of new strategies and new technologies, but also requires the complementary advantages and close cooperation of project teams. Based on the latest progress in this field and the author's drug research practice, this paper summarizes the underlying logic, innovative thinking and research paradigm of antiviral medicinal chemistry.

Objective To evaluate the bioequivalence of metronidazole tablet and reference formulation in Chinese healthy subjects.Methods A single-dose,two-cycle,randomized,open,self-crossover trial was designed with 48 healthy subjects randomly assigned to fasting or postprandial group.For each group,a single oral dose of metronidazole tablet(200 mg)or a reference preparation(200 mg)per cycle were enrolled.The concentration of metronidazole in plasma was measured by high performance liquid chromatography tandem mass spectrometry(HPLC-MS/MS).The non-compartmental model was applied to calculate the pharmacokinetic parameters for bioequivalence analysis via SAS 9.3 software.Results The main pharmacokinetic parameters of test and reference metronidazole tablets in the fasting group were as follows,the Cmax were(4 855.00±1 383.97)and(4 799.13±1 195.32)ng·h·mL-1;the AUC0-t were(54 834.68±12 697.88)and(55 931.35±11 935.28)ng·h·mL-1;the AUC0-∞ were(56 778.09±13 937.76)and(57 922.83±13 260.54)ng·h·mL-1;the Tmax were respectively 1.17 and 1.00 h;t1/2 were(8.99±1.76)and(9.11±1.73)h,respectively.The ratio of the geometric mean and its 90％confidence intervals(CI)of Cmax,AUC0-t and AUC0-∞ were all within the equivalent interval of 80.00％-125.00％.As for postprandial conditions,the main pharmacokinetic parameters of test and reference metronidazole tablets were as follows,the Cmax were(4 057.08±655.08)and(4 044.17±773.98)ng·h·mL-1;the AUC0-t were(55 956.42±12 228.12)and(55 121.04±11 784.55)ng·h·mL-1;the AUC0-∞ were(58 212.83±13 820.00)and(57 350.38±13 229.46)ng·h·mL-1;the Tmax were 2.50 and 2.25 h;the t1/2 were(9.37±1.68)and(9.37±1.79)h,respectively.The ratio of the geometric mean and 90％CI of Cmax,AUC0-t and AUC0-∞ were all within the equivalent interval of 80.00％-125.00％.Conclusion The two preparations were bioequivalent to Chinese healthy adult volunteers under both fasting and fed conditions.

Objective　To assess the willingness of students with latent tuberculosis infection (LTBI) in Jiangsu Province to undergo preventive treatment and identify factors influencing their decision, aiming to provide insights for tuberculosis prevention and control strategies in school. Methods The physical examination information of tuberculosis latent infection cases was collected from screenings of new school enrollment and contacts of tuberculosis patients in 6 cities of Jiangsu Province from December 2022 to December 2023. Data on past medical history and understanding of preventive treatment were gathered through an online questionnaire survey on the website of Juanxing, and the influencing factors related to the willingness to take preventive medication were analyzed by logistic regression analysis model. Results In December 2022 to December 2023, a total of 13 school tuberculosis outbreaks occurred in 6 cities, and 1 661 contacts were screened, among which 162 cases met the criteria for prophylactic medication, 96 cases were included in the study by filling in the questionnaire. A total of 22 600 new students from 56 schools participated in the TB screening upon enrollment, of which 358 tested positive for the tuberculin skin test alone, meeting the criteria for preventive medication, and 251 of them completed the willingness survey. Finally, 347 students who met the criteria for preventive treatment were included in the study, with 164 expressing to accept preventive treatment representing a treatment acceptance rate of 47.3%. The results of multivariate analysis showed that university (OR=17.950, 95%CI: 3.078-104.686, P=0.001) and contact with the source of school tuberculosis epidemic (OR=19.542, 95%CI: 6.289-60.726, P<0.001) were associated with increased willingness to receive preventive treatment, while unclear whether to pay for the drugs themselves (OR=0.349, 95%CI:0.133-0.916, P=0.032) was associated with decreased willingness to receive preventive treatment. Compared with Huai'an City, the willingness to receive preventive treatment was significantly lower among students from Nantong City (OR=0.005, 95%CI:0.000-0.063, P<0.001), Nanjing City (OR=0.022, 95%CI: 0.003-0.703, P<0.001) and Lianyungang City (OR=0.074, 95%CI:0.008-0.703, P=0.023). Conclusions The acceptance rate of preventive treatment among LTBI students in Jiangsu Province is not high and is affected by multiple factors. Health education and medication mobilization for preventive medication are essential.

Objective:To construct a diabetes foot prediction model for adult patients with type 2 diabetes based on retrospective cohort study using data from a regional health data platform.Methods:Using Yinzhou Health Information Platform of Ningbo, adult patients with newly diagnosed type 2 diabetes from January 1, 2015 to December 31, 2022 were included in this study and divided randomly the train and test sets according to the ratio of 7∶3. LASSO regression model and bidirectional stepwise regression model were used to identify risk factors, and model comparisons were conducted with net reclassification index, integrated discrimination improvement and concordance index. Univariate and multivariate Cox proportional hazard regression models were constructed, and a nomogram plot was drawn. Area under the curve (AUC) was calculated as a discriminant evaluation indicator for model validation test its calibration ability, and calibration curves were drawn to test its calibration ability.Results:No significant difference existed between LASSO regression model and bidirectional stepwise regression model, but the better bidirectional stepwise regression model was selected as the final model. The risk factors included age of onset, gender, hemoglobin A1c, estimated glomerular filtration rate, taking angiotensin receptor blocker and smoking history. AUC values (95% CI) of risk outcome prediction at year 5 and 7 were 0.700 (0.650-0.749) and 0.715(0.668-0.762) for the train set and 0.738 (0.667-0.801) and 0.723 (0.663-0.783) for the test set, respectively. The calibration curves were close to the ideal curve, and the model discrimination and calibration powers were both good. Conclusions:This study established a convenient prediction model for diabetic foot and classified the risk levels. The model has strong interpretability, good discrimination power, and satisfactory calibration and can be used to predict the incidence of diabetes foot in adult patients with type 2 diabetes to provide a basis for self-assessment and clinical prediction of diabetic foot disease risk.

Objective:To develop a prediction model for the risk of diabetic retinopathy (DR) in patients with newly diagnosed type 2 diabetes mellitus (T2DM).Methods:Patients with new diagnosis of T2DM recorded in Yinzhou Regional Health Information Platform between January 1, 2015 and December 31, 2022 were included in the study. The predictor variables were selected by using Lasso-Cox proportional hazards regression model. Cox proportional hazards regression models were used to establish the prediction model for the risk of DR. Bootstrap method (500 resamples) was used for internal validation, and the performance of the model was assessed by C-index, the receiver operating characteristic curve and area under the curve (AUC), and calibration curve.Results:The predictor variables included in the final model were age of T2DM onset, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, estimated glomerular filtration rate, and history of lipid-lowering agent and angiotensin converting enzyme inhibitor uses. The C-index of the final model was 0.622, and the mean corrected C-index was 0.623 (95% CI: 0.607-0.634). The AUC values for predicting the risk of DR after 3, 5, and 7 years were 0.631, 0.620, and 0.624, respectively, with a high degree of overlap of the calibration curves with the ideal curves. Conclusion:In this study, a simple and practical risk prediction model for DR risk prediction was developed, which could be used as a reference for individualized DR screening and intervention in newly diagnosed T2DM patients.

Objective:To construct a risk prediction model for diabetes kidney disease (DKD).Methods:Patients newly diagnosed with type 2 diabetes mellitus (T2DM) between January 1, 2015, and December 31, 2022, were selected as study subjects from the Yinzhou Regional Health Information Platform in Ningbo City. The Lasso method was used to screen the risk factors, and the DKD risk prediction model was established using Cox proportional hazard regression models. Bootstrap 500 resampling was applied for internal validation.Results:The study included 49 706 subjects, with an median ( Q1, Q3) age of 60.00 (50.00, 68.00) years old, and 55% were male. A total of 4 405 subjects eventually developed DKD. Age at first diagnosis of T2DM, BMI, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, past medical history (hyperuricemia, rheumatic diseases), triglycerides, and estimated glomerular filtration rate were included in the final model. The final model's C-index was 0.653, with an average of 0.654 after Bootstrap correction. The final model's area under the receiver operating characteristic curve for predicting 4-year, 5-year, and 6-year was 0.657, 0.659, and 0.664, respectively. The calibration curve was closely aligned with the ideal curve. Conclusions:This study constructed a DKD risk prediction model for newly diagnosed T2DM patients based on real-world data that is simple, easy to use, and highly practical. It provides a reliable basis for screening high-risk groups for DKD.