To improve the quality evaluation system of Hibisci Mutabilis Folium, this study established high performance liquid chromatography(HPLC) fingerprints of Hibisci Mutabilis Folium and evaluated the quality differences of medicinal materials from different places of production by chemometrics. Furthermore, a content measurement method of differential components was established based on quantitative analysis of multi-components by single-marker(QAMS). The fingerprints of 17 batches of Hibisci Mutabilis Folium from different places of production were constructed, with a total of 19 common peaks marked and seven components confirmed. The similarity between the sample fingerprints and the reference fingerprints ranged from 0.890 to 0.974. By utilizing principal component analysis(PCA), hierarchical cluster analysis(HCA), and orthogonal partial least squares-discriminant analysis(OPLS-DA), the chemical patterns of fingerprints were identified. Five components that could be used to evaluate the quality differences of Hibisci Mutabilis Folium were screened, namely peak 6(quercetin 3-O-β-robinobioside), peak 7(rutin), peak 9(kaempferol-3-O-β-robinobioside), peak 10(kaempferol-3-O-rutinoside), and peak 14(tiliroside). The relative correction factors of isoquercitrin, kaempferol-3-O-β-robinobioside, kaempferol-3-O-rutinoside, kaempferol-3-O-β-D-glucoside, and tiliroside were measured with rutin as the internal reference. The QAMS method was established for the content measurement of six flavonoids, and the results showed there was no significant difference compared to the results obtained by an external standard method. In summary, the HPLC fingerprints and QAMS method established in the study, demonstrating stability and accuracy, can provide a reference for the overall quality evaluation of Hibisci Mutabilis Folium.
Chromatography, High Pressure Liquid/methods* ; Drugs, Chinese Herbal/chemistry* ; Quality Control ; Principal Component Analysis

OBJECTIVE: Recombination events are common and serve as the primary driving force of diverse human adenovirus (HAdV), particularly in children with acute respiratory tract infections (ARIs). Therefore, continual monitoring of these events is essential for effective viral surveillance and control. METHODS: Respiratory specimens were collected from children with ARIs between January 2022 and December 2023. The penton base, hexon, and fiber genes were amplified from HAdV-positive specimens and sequenced to determine the virus type. In cases with inconsistent typing results, genes were cloned into the pGEM-T vector to detect recombination events. Metagenomic next-generation sequencing (mNGS) was performed to characterize the recombinant HAdV genomes. RESULTS: Among 6,771 specimens, 277 (4.09%, 277/6,771) were positvie for HAdV, of which 157 (56.68%, 157/277) were successfully typed, with HAdV-B3 being the dominant type (91.08%, 143/157), and 14 (5.05%, 14/277) exhibited inconsistent typing results, six of which belonged to species B. The penton base genes of these six specimens were classified as HAdV-B7, whereas their hexon and fiber genes were classified as HAdV-B3, resulting in a recombinant genotype designated P7H3F3, which closely resembled HAdV-B114. Additionally, a partial gene encoding L1 52/55 kD was identified, which originated from HAdV-B16. CONCLUSION A novel recombinant, P7H3F3, was identified, containing sequences derived from HAdV-B3 and HAdV-B7, which is similar to HAdV-B114, along with additional sequences from HAdV-B16.
Humans ; Adenoviruses, Human/isolation & purification* ; Respiratory Tract Infections/epidemiology* ; Child, Preschool ; Child ; Recombination, Genetic ; Male ; Beijing/epidemiology* ; Infant ; Female ; Phylogeny ; Adenovirus Infections, Human/epidemiology* ; Acute Disease ; Genome, Viral

AIM To investigate the effects of Qifu Yixin Granules on cardiac inflammation in a rat model of heart failure.METHODS The rats were induced into chronic heart failure(CHF)models by 6-week intraperitoneal injection of doxorubicin followed by the random assignment of the successful rat models into the model group,the captopril group(22.5 mg/kg),and the low-dose,medium-dose,and high-dose Qifu Yixin Granules groups(2.84,5.67,11.34 g/kg),in contrast to the normal rats of the blank group.The rats had their body weight monitored;their cardiac function assessed by echocardiography;their serum levels of NT-proBNP,TNF-α,IL-6,IL-1 and CRP measured by ELISA;their cardiac morphological alterations observed by HE and Masson staining;their cardiac protein expressions of TLR4,MyD88 and NF-κB detected by immunohistochemistry and Western blot;and their cardiac mRNA expressions of TLR4,MyD88 and NF-κB measured by RT-qPCR.RESULTS Compared to the blank group,the model group exhibited significantly reduced body weight,LVEF and LVFS(P＜0.01),alongside significantly elevated LVEDD,LVESD,and serum concentrations of NT-proBNP,TNF-α,IL-6,IL-1 and CRP(P＜0.01).Additionally,the model group displayed greater myocardial inflammatory cell aggregation,increased collagen deposition(P＜0.01);and upregulated myocardial protein and mRNA expressions of TLR4,MyD88 and NF-κB(P＜0.01).Compared to the model group,the groups intervened with captopril or medium/high dose Qifu Yixin Granules demonstrated significantly increased body weight,LVEF and LVFS(P＜0.05,P＜0.01);significantly reduced LVEDD,LVESD,and serum levels of the aforementioned indicators(P＜0.05,P＜0.01);mitigated inflammation and collagen deposition(P＜0.05,P＜0.01);and downregulated myocardial protein and mRNA expressions of TLR4,MyD88 and NF-κB(P＜0.05,P＜0.01).CONCLUSION Qifu Yixin Granules attenuate cardiac inflammation and improve cardiac function in doxorubicin-induced CHF rats;this therapeutic effect is mediated by inhibiting the activation of the TLR4/MyD88/NF-κB signaling pathway.

Objective To propose a multiple regression-variational auto-encoders(MR-VAE)model to realize precise and non-invasive prediction of intra-abdominal pressure(IAP)in critically ill patients.Methods At first,a dataset was constructed by retrospectively analysing baseline characteristics and clinical indicators of 100 critically ill patients admitted to the Intensive Care Unit of Daping Hospital of Army Medical University between 30 August 2019 and 30 March 2021.Then,a MR-VAE prediction model was developed by integrating a feedforward neural network for supervised regression onto a variational autoencoder(VAE)framework and incorporating multiple regression strategies to mitigate feature interference.Finally,the MR-VAE model had its performance evaluated by its comparison with five classical models including support vector machines(SVM),convolutional neural networks(CNN),Scikit-learn integrated model(SIM),multi-layer perceptron(MLP)and K-nearest neighbors(KNN),and its prediction accuracy verified by testing the data of 10 randomly selected patients.Results The MR-VAE model behaved the best when compared with the five classical models,with a mean squared error(MSE)of 0.207,a root mean square error(RMSE)of 0.454,a mean absolute error(MAE)of 0.361,a median absolute deviation(MAD)of 0.243,an explained variance score(EVS)of 0.814 and a R2of 0.823,which also outperformed the five models in fitting performance,convergence and final loss.In random sample testing,the MR-VAE model exhibited high consistency between predicted and actual values.Conclusion The MR-VAE model proposed can accurately predict IAP,which has great potential in reducing the repeated measurements of IAP in critically ill patients and providing new ideas for the early diagnosis and treatment of IAH.

Objective To propose a multiple regression-variational auto-encoders(MR-VAE)model to realize precise and non-invasive prediction of intra-abdominal pressure(IAP)in critically ill patients.Methods At first,a dataset was constructed by retrospectively analysing baseline characteristics and clinical indicators of 100 critically ill patients admitted to the Intensive Care Unit of Daping Hospital of Army Medical University between 30 August 2019 and 30 March 2021.Then,a MR-VAE prediction model was developed by integrating a feedforward neural network for supervised regression onto a variational autoencoder(VAE)framework and incorporating multiple regression strategies to mitigate feature interference.Finally,the MR-VAE model had its performance evaluated by its comparison with five classical models including support vector machines(SVM),convolutional neural networks(CNN),Scikit-learn integrated model(SIM),multi-layer perceptron(MLP)and K-nearest neighbors(KNN),and its prediction accuracy verified by testing the data of 10 randomly selected patients.Results The MR-VAE model behaved the best when compared with the five classical models,with a mean squared error(MSE)of 0.207,a root mean square error(RMSE)of 0.454,a mean absolute error(MAE)of 0.361,a median absolute deviation(MAD)of 0.243,an explained variance score(EVS)of 0.814 and a R2of 0.823,which also outperformed the five models in fitting performance,convergence and final loss.In random sample testing,the MR-VAE model exhibited high consistency between predicted and actual values.Conclusion The MR-VAE model proposed can accurately predict IAP,which has great potential in reducing the repeated measurements of IAP in critically ill patients and providing new ideas for the early diagnosis and treatment of IAH.

AIM To investigate the effects of Qifu Yixin Granules on cardiac inflammation in a rat model of heart failure.METHODS The rats were induced into chronic heart failure(CHF)models by 6-week intraperitoneal injection of doxorubicin followed by the random assignment of the successful rat models into the model group,the captopril group(22.5 mg/kg),and the low-dose,medium-dose,and high-dose Qifu Yixin Granules groups(2.84,5.67,11.34 g/kg),in contrast to the normal rats of the blank group.The rats had their body weight monitored;their cardiac function assessed by echocardiography;their serum levels of NT-proBNP,TNF-α,IL-6,IL-1 and CRP measured by ELISA;their cardiac morphological alterations observed by HE and Masson staining;their cardiac protein expressions of TLR4,MyD88 and NF-κB detected by immunohistochemistry and Western blot;and their cardiac mRNA expressions of TLR4,MyD88 and NF-κB measured by RT-qPCR.RESULTS Compared to the blank group,the model group exhibited significantly reduced body weight,LVEF and LVFS(P＜0.01),alongside significantly elevated LVEDD,LVESD,and serum concentrations of NT-proBNP,TNF-α,IL-6,IL-1 and CRP(P＜0.01).Additionally,the model group displayed greater myocardial inflammatory cell aggregation,increased collagen deposition(P＜0.01);and upregulated myocardial protein and mRNA expressions of TLR4,MyD88 and NF-κB(P＜0.01).Compared to the model group,the groups intervened with captopril or medium/high dose Qifu Yixin Granules demonstrated significantly increased body weight,LVEF and LVFS(P＜0.05,P＜0.01);significantly reduced LVEDD,LVESD,and serum levels of the aforementioned indicators(P＜0.05,P＜0.01);mitigated inflammation and collagen deposition(P＜0.05,P＜0.01);and downregulated myocardial protein and mRNA expressions of TLR4,MyD88 and NF-κB(P＜0.05,P＜0.01).CONCLUSION Qifu Yixin Granules attenuate cardiac inflammation and improve cardiac function in doxorubicin-induced CHF rats;this therapeutic effect is mediated by inhibiting the activation of the TLR4/MyD88/NF-κB signaling pathway.

In recent years, with the increasing societal focus on drug quality and safety, quality issues have become a major challenge faced by the pharmaceutical industry, directly impacting consumer health and market trust. By combining multispectral imaging technology with machine learning, it is possible to achieve rapid, non-destructive, and precise detection of traditional Chinese medicine(TCM) preparations, thereby revolutionizing traditional detection methods and developing more convenient and automated solutions. This paper provides a comprehensive review of the current applications of rapid, non-destructive detection techniques based on machine learning algorithms in the field of TCM preparations. It analyzed the principles and advantages of commonly used rapid, non-destructive detection techniques, offering a reference for the application and promotion of these technologies in TCM preparation detection. Additionally, this paper explored various data preprocessing techniques, operational processes, and machine learning algorithms to enhance data utilization efficiency. Finally, it focused on the challenges of applying machine learning in TCM preparation detection and offered corresponding recommendations, providing guidance for the future integration of machine learning with rapid, non-destructive detection techniques in practical production.
Machine Learning ; Drugs, Chinese Herbal/analysis* ; Medicine, Chinese Traditional/methods* ; Humans ; Quality Control

Identification of critical material attributes(CMAs) is a key issue in the quality control of large-scale TCM products like Jianwei Xiaoshi Tablets. This study focuses on the granules of Jianwei Xiaoshi Tablets, using tablet tensile strength as the primary quality attribute. A method for identifying the CMAs and a design space for the granules were established, along with a predictive model for the granule CMAs based on Fourier transform near-infrared spectroscopy(FT-NIR). First, granules of Jianwei Xiaoshi Tablets with different properties were prepared using a partial factorial design method from the design of experiments(DOE). The powder properties of the granules were measured. An orthogonal partial least squares(OPLS) model was established to correlate the powder properties with tensile strength. Based on the characteristics of the comprehensive variables extracted by OPLS, the independent variables with the greatest explanatory power for tensile strength were identified. FT-NIR technology was then employed to establish a predictive model for the granule CMAs. The final CMAs identified were hygroscopicity, moisture content, D_(50), collapse angle, mass flow rate, and tapped density. The coefficients of determination of the prediction set(R■) and relative percentage deviation(RPD) of the prediction set for flowability, D_(50), and moisture content were 0.891, 0.994, and 0.998; and 2.97, 12.4, and 20.7, respectively. The established OPLS model clearly identified the impact of various factors on tensile strength, demonstrating good fit results. The model exhibited high prediction accuracy and can be used for the rapid and accurate determination of CMAs in granules of Jianwei Xiaoshi Tablets.
Drugs, Chinese Herbal/chemistry* ; Tablets/chemistry* ; Tensile Strength ; Quality Control ; Spectroscopy, Fourier Transform Infrared ; Spectroscopy, Near-Infrared

Objective China is among the 30 countries with a high burden of tuberculosis(TB)worldwide,and TB remains a public health concern.Kashgar Prefecture in the southern Xinjiang Autonomous Region is considered as one of the highest TB burden regions in China.However,molecular epidemiological studies of Kashgar are lacking. Methods A population-based retrospective study was conducted using whole-genome sequencing(WGS)to determine the characteristics of drug resistance and the transmission patterns. Results A total of 1,668 isolates collected in 2020 were classified into lineages 2(46.0%),3(27.5%),and 4(26.5%).The drug resistance rates revealed by WGS showed that the top three drugs in terms of the resistance rate were isoniazid(7.4%,124/1,668),streptomycin(6.0%,100/1,668),and rifampicin(3.3%,55/1,668).The rate of rifampicin resistance was 1.8%(23/1,290)in the new cases and 9.4%(32/340)in the previously treated cases.Known resistance mutations were detected more frequently in lineage 2 strains than in lineage 3 or 4 strains,respectively:18.6%vs.8.7 or 9%,P<0.001.The estimated proportion of recent transmissions was 25.9%(432/1,668).Multivariate logistic analyses indicated that sex,age,occupation,lineage,and drug resistance were the risk factors for recent transmission.Despite the low rate of drug resistance,drug-resistant strains had a higher risk of recent transmission than the susceptible strains(adjusted odds ratio,1.414;95%CI,1.023-1.954;P = 0.036).Among all patients with drug-resistant tuberculosis(DR-TB),78.4%(171/218)were attributed to the transmission of DR-TB strains. Conclusion Our results suggest that drug-resistant strains are more transmissible than susceptible strains and that transmission is the major driving force of the current DR-TB epidemic in Kashgar.