The Chinese Pharmacopoeia is the legal technical standard which should be followed during the research, production, use, and administration of drugs. At present, the new edition of the Chinese Pharmacopoeia is planned to be promulgated and implemented. This article summarizes and analyzes the main characteristics and the content of updates and amendments of the Chinese Pharmacopoeia 2025 Edition(Volume Ⅰ), to provide a reference for the correct understanding and accurate implementation the new edition of the pharmacopoeia.

Objective: To investigate the trends in incidence and mortality of lung cancer in Huangpu District, Shanghai Municipality from 2002 to 2019, so as to provide the evidence for formulating lung cancer prevention and control measures. Methods: Data of lung cancer incidence and mortality among residents in Huangpu District from 2002 to 2019 were collected through the Shanghai Cancer Registration and Reporting Management System. The crude incidence and mortality of lung cancer was calculated, and standardized by the data from the Chinese Fifth National Population Census in 2000 (Chinese-standardized rate) and the Segi's world standard population in 1960 (world-standardized rate). The trends in incidence and mortality of lung cancer among residents by age and gender were evaluated using annual percent change (APC). Results: A total of 12 965 cases of lung cancer were reported in Huangpu District from 2002 to 2019, and the crude incidence rate was 80.66/105, the Chinese-standardized incidence rate was 34.54/105, and the world-standardized incidence rate was 31.30/105, all showing upward trends (APC=4.588%, 2.933% and 3.247%, all P<0.05). A total of 10 102 deaths of lung cancer were reported, and the crude mortality rate was 62.30/105, showing an upward trend (APC=0.959%, P<0.05); the Chinese-standardized mortality was 25.93/105, and the world-standardized mortality was 22.05/105, both showing downward trends (APC=-1.282% and -1.263%, both P<0.05). The crude incidence and mortality rates of lung cancer in males were higher than those in females (101.39/105 vs. 60.52/105, 85.45/105 vs. 39.87/105, both P<0.05). The crude incidence and mortality rates of lung cancer showed upward trends with age (both P<0.05), reaching their peaks in the age groups of 80-<85 years (341.37/105) and 85 years or above (355.97/105), respectively. Conclusions The incidence of lung cancer showed an upward trend, while the mortality showed a downward trend in Huangpu District from 2002 to 2019. Elderly men were the high-risk group for lung cancer incidence and mortality.

This study aims to investigate the effects and mechanisms of the effective-compounds of Jinshui Huanxian formula (ECC-JHF) in improving pulmonary fibrosis. Animal experiments were approved by the Ethics Committee of the Animal Experiment Center of Henan University of Chinese Medicine (approval number: IACUC-202306012). The mouse model of pulmonary fibrosis was induced using bleomycin (BLM). Hematoxylin-eosin (H&E) staining was used to detect the histopathological changes of lung tissues. Masson staining was used to assess the degree of fibrosis in lung tissues. Immunofluorescence (IF) and real-time quantitative PCR (qPCR) were performed to measure the expression of collagen type I (COL I), α-smooth muscle actin (α-SMA), fibronectin (FN), interleukin (IL)-1β, IL-6, and tumor necrosis factor α (TNF-α) in lung tissues. Flow cytometry (FCM) was employed to detect the proportion of M1 and M2 macrophages in the bronchoalveolar lavage fluid (BALF) of mice. IF and qPCR were also used to detect the expression of lipase family member N (LIPN) in lung tissues. Free fatty acid assay kit was used to detect the level of free fatty acids in lung tissue. Bone marrow-derived macrophages (BMDMs) were treated with interleukin-4 (IL-4) to induce M2 polarization. FCM was used to measure the proportion of CD206⁺ M2 macrophages. IF was utilized to detect LIPN expression and lipid droplet decomposition. The results showed that in BLM-induced pulmonary fibrosis mice, ECC-JHF significantly attenuated BLM-induced alveolar inflammation and collagen deposition, inhibited fibroblast activation in lung tissues, and decreased the proportion of M2 macrophages in BALF. It also significantly suppressed LIPN expression and free fatty acid level in lung tissues. In the IL-4 induced BMDMs M2 polarization model, ECC-JHF significantly inhibited the proportion of CD206⁺ M2 macrophages, down-regulated the expression of LIPN, and blocked lipid droplet catabolism. These results suggest that ECC-JHF may alleviate bleomycin-induced pulmonary fibrosis by inhibiting lipid droplet decomposition and M2 macrophage polarization.

Objective: To examine the association of joint effect of overweight and obesity with dyslipidemia on left ventricular hypertrophy (LVH) in children, so as to provide scientific evidence for the prevention of early cardiovascular damage in children. Methods: Data were obtained from the second followup crosssectional survey of Huantai Childhood Cardiovascular Health Cohort study in 2021, comprising 1 047 children aged 10-15 years with complete information. Based on overweight and obesity status and dyslipidemia status, all participants were divided into four groups:normal weight with normal lipid levels, normal weight with dyslipidemia, overweight and obesity with normal lipid levels, and overweight and obesity with dyslipidemia. Left ventricular mass index (LVMI) levels and prevalence of LVH across four groups were compared. Multivariate Logistic regression model was used to examine the association of joint effect of overweight and obesity with dyslipidemia on LVH in children. Results: There were significant differences in LVMI levels [(28.66±7.10, 29.63±4.71,31.49±5.86,32.65±4.80)g/m2.7] and prevalence of LVH (4.28%, 12.50%, 22.74%, 31.30%) across four groups (F/χ2=50.76, 90.92, P<0.05). After adjustment for confounding variables such as gender,age,screen time,sleep duration,fruit and vegetable intake,carbonated beverage consumption,physical activity and elevated blood pressure, compared to children with both normal weight and normal lipid levels, the risk of LVH in children with dyslipidemia alone increased (OR=3.27, 95%CI=1.57-6.82，P<0.05). Children with overweight and obesity alone also had a significantly increased risk of LVH (OR=6.33, 95%CI=3.76-10.66), and the highest risk was observed in those with both overweight and obesity with dyslipidemia (OR=9.66, 95%CI=5.35-17.43) (P<0.05). Conclusions The joint effect of overweight and obesity with dyslipidemia is positively correlated with LVH in children. To prevent LVH in children, both overweight and obesity with dyslipidemia should be paid attention to.

ObjectiveThe rapid advancement of bioanalytical technologies has heightened the demand for high-throughput, label-free, and real-time cellular analysis. Electrochemical impedance spectroscopy (EIS) operating in the GHz frequency range (GHz-EIS) has emerged as a promising tool for characterizing cell suspensions due to its ability to rapidly and non-invasively capture the dielectric properties of cells and their microenvironment. Although GHz-EIS enables rapid and label-free detection of cell suspensions, significant challenges remain in interpreting GHz impedance data for complex samples, limiting the broader application of this technique in cellular research. To address these challenges, this study presents a novel method that integrates GHz-EIS with deep learning algorithms, aiming to improve the precision of cell suspension concentration identification and quantification. This method provides a more efficient and accurate solution for the analysis of GHz impedance data. MethodsThe proposed method comprises two key components: dielectric property dataset construction and backpropagation (BP) neural network modeling. Yeast cell suspensions at varying concentrations were prepared and separately introduced into a coaxial sensor for impedance measurement. The dielectric properties of these suspensions were extracted using a GHz-EIS dielectric property extraction method applied to the measured impedance data. A dielectric properties dataset incorporating concentration labels was subsequently established and divided into training and testing subsets. A BP neural network model employing specific activation functions (ReLU and Leaky ReLU) was then designed. The model was trained and tested using the constructed dataset, and optimal model parameters were obtained through this process. This BP neural network enables automated extraction and analytical processing of dielectric properties, facilitating precise recognition of cell suspension concentrations through data-driven training. ResultsThrough comparative analysis with conventional centrifugal methods, the recognized concentration values of cell suspensions showed high consistency, with relative errors consistently below 5%. Notably, high-concentration samples exhibited even smaller deviations, further validating the precision and reliability of the proposed methodology. To benchmark the recognition performance against different algorithms, two typical approaches—support vector machines (SVM) and K-nearest neighbor (KNN)—were selected for comparison. The proposed method demonstrated superior performance in quantifying cell concentrations. Specifically, the BP neural network achieved a mean absolute percentage error (MAPE) of 2.06% and an R² value of 0.997 across the entire concentration range, demonstrating both high predictive accuracy and excellent model fit. ConclusionThis study demonstrates that the proposed method enables accurate and rapid determination of unknown sample concentrations. By combining GHz-EIS with BP neural network algorithms, efficient identification of cell concentrations is achieved, laying the foundation for the development of a convenient online cell analysis platform and showing significant application prospects. Compared to typical recognition approaches, the proposed method exhibits superior capabilities in recognizing cell suspension concentrations. Furthermore, this methodology not only accelerates research in cell biology and precision medicine but also paves the way for future EIS biosensors capable of intelligent, adaptive analysis in dynamic biological research.

ObjectiveTo investigate the protective effect and mechanism of Shaoyaotang （SYD） on the lipopolysaccharide （LPS）-induced damage of tight junction proteins in the human colorectal adenocarcinoma （Caco-2） cell model of inflammation via the Ras homolog gene family member A （RhoA）/Rho-associated coiled-coil forming protein kinase （ROCK） pathway. MethodsCaco-2 cells were grouped as follows： Blank， model （LPS， 10 mg·L^-1）， SYD-containing serum （10%， 15%， and 20%）， and inhibitor （Fasudil， 25 μmol·L^-1）. After 24 hours of intervention， the cell viability in each group was examined by the cell-counting kit 8 （CCK-8） method. Enzyme-linked immunosorbent assay was employed to determine the levels of endothelin-1 （ET-1）， tumor necrosis factor-α （TNF-α）， interleukin-1β （IL-1β）， and interleukin-6 （IL-6）. Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） and Western blot were employed to determine the mRNA and protein levels， respectively， of RhoA， ROCK2， claudin-5， and zonula occludens-1 （ZO-1） in cells of each group. ResultsCompared with the blank group， the model group showcased a marked reduction in the cell viability （P<0.01）， elevations in the levels of ET-1， TNF-α， IL-1β， and IL-6 （P<0.01）， declines in both mRNA and protein levels of ZO-1 and claudin-5 （P<0.01）， and rises in mRNA and protein levels of RhoA and ROCK2 （P<0.01）. Compared with the model group， the Shaoyaotang-containing serum （10%， 15%， and 20%） groups had enhanced cell viability （P<0.01）， lowered levels of ET-1， TNF-α， IL-1β， and IL-6 （P<0.01）， up-regulated mRNA and protein levels of ZO-1 and claudin-5 （P<0.05， P<0.01）， and down-regulated mRNA and protein levels of RhoA and ROCK2 （P<0.01）. Moreover， the inhibitor group and the 15% and 20% Shaoyaotang-containing serum groups had lower levels of ET-1， TNF-α， IL-1β， and IL-6 （P<0.05， P<0.01）， higher mRNA and protein levels of ZO-1 and claudin-5 （P<0.05， P<0.01）， and lower mRNA and protein levels of RhoA and ROCK2 （P<0.05， P<0.01） than the 10% Shaoyaotang-containing serum group. ConclusionThe Shaoyaotang-containing serum can lower the levels of LPS-induced increases in levels of inflammatory cytokines and endothelin to ameliorate the damage of tight junction proteins of the Caco-2 cell model of inflammation by regulating the expression of proteins in the RhoA/ROCK pathway.

ObjectiveTo investigate the protective effect and mechanism of Shaoyaotang （SYD） on the lipopolysaccharide （LPS）-induced damage of tight junction proteins in the human colorectal adenocarcinoma （Caco-2） cell model of inflammation via the Ras homolog gene family member A （RhoA）/Rho-associated coiled-coil forming protein kinase （ROCK） pathway. MethodsCaco-2 cells were grouped as follows： Blank， model （LPS， 10 mg·L^-1）， SYD-containing serum （10%， 15%， and 20%）， and inhibitor （Fasudil， 25 μmol·L^-1）. After 24 hours of intervention， the cell viability in each group was examined by the cell-counting kit 8 （CCK-8） method. Enzyme-linked immunosorbent assay was employed to determine the levels of endothelin-1 （ET-1）， tumor necrosis factor-α （TNF-α）， interleukin-1β （IL-1β）， and interleukin-6 （IL-6）. Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） and Western blot were employed to determine the mRNA and protein levels， respectively， of RhoA， ROCK2， claudin-5， and zonula occludens-1 （ZO-1） in cells of each group. ResultsCompared with the blank group， the model group showcased a marked reduction in the cell viability （P<0.01）， elevations in the levels of ET-1， TNF-α， IL-1β， and IL-6 （P<0.01）， declines in both mRNA and protein levels of ZO-1 and claudin-5 （P<0.01）， and rises in mRNA and protein levels of RhoA and ROCK2 （P<0.01）. Compared with the model group， the Shaoyaotang-containing serum （10%， 15%， and 20%） groups had enhanced cell viability （P<0.01）， lowered levels of ET-1， TNF-α， IL-1β， and IL-6 （P<0.01）， up-regulated mRNA and protein levels of ZO-1 and claudin-5 （P<0.05， P<0.01）， and down-regulated mRNA and protein levels of RhoA and ROCK2 （P<0.01）. Moreover， the inhibitor group and the 15% and 20% Shaoyaotang-containing serum groups had lower levels of ET-1， TNF-α， IL-1β， and IL-6 （P<0.05， P<0.01）， higher mRNA and protein levels of ZO-1 and claudin-5 （P<0.05， P<0.01）， and lower mRNA and protein levels of RhoA and ROCK2 （P<0.05， P<0.01） than the 10% Shaoyaotang-containing serum group. ConclusionThe Shaoyaotang-containing serum can lower the levels of LPS-induced increases in levels of inflammatory cytokines and endothelin to ameliorate the damage of tight junction proteins of the Caco-2 cell model of inflammation by regulating the expression of proteins in the RhoA/ROCK pathway.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.