⁶⁰Co-γ ray irradiation has the unique advantages of high efficiency， strong penetration， operation at room temperature and no residue， which has been widely used in the sterilization of Chinese medicinal materials， decoction pieces， Chinese patent medicine. However， the irradiation effect may cause changes in the content of chemical components in Chinese materia medica or the emergence of new radiolysis products， leading to reduced efficacy and uncontrollable safety risks. This paper reviewed the relevant literature at home and abroad， summarized the effect of irradiation sterilization on various types of chemical compositions of Chinese medicinal materials and their preparations， and analyzed and explored the rule of change. The results showed that the content changes of various chemical components in Chinese materia medica after ⁶⁰Co-γ ray irradiation sterilization varied. The contents of most flavonoids， terpenoids， phenylpropanoids and quinones decreased after irradiation， and the degree of decrease increased with the elevated irradiation dose. The contents of lignans， alkaloids， isoflavones and some terpenoids did not change significantly before and after irradiation， while the content changes of triterpenoid saponins， dihydroflavonols， chalcones， sugars and glycosides after irradiation were not yet uniform. Therefore， it is recommended to pay attention to the compositional changes of irradiated Chinese medicines， strengthen the research on the standards of irradiated Chinese medicines， and standardize the irradiation and sterilization of Chinese medicines in order to promote the healthy and rational application of irradiated Chinese medicines.

[This corrects the article DOI: 10.1016/j.apsb.2022.03.002.].

OBJECTIVE: This study reports the first imported case of Lassa fever (LF) in China. Laboratory detection and molecular epidemiological analysis of the Lassa virus (LASV) from this case offer valuable insights for the prevention and control of LF. METHODS: Samples of cerebrospinal fluid (CSF), blood, urine, saliva, and environmental materials were collected from the patient and their close contacts for LASV nucleotide detection. Whole-genome sequencing was performed on positive samples to analyze the genetic characteristics of the virus. RESULTS: LASV was detected in the patient's CSF, blood, and urine, while all samples from close contacts and the environment tested negative. The virus belongs to the lineage IV strain and shares the highest homology with strains from Sierra Leone. The variability in the glycoprotein complex (GPC) among different strains ranged from 3.9% to 15.1%, higher than previously reported for the seven known lineages. Amino acid mutation analysis revealed multiple mutations within the GPC immunogenic epitopes, increasing strain diversity and potentially impacting immune response. CONCLUSION The case was confirmed through nucleotide detection, with no evidence of secondary transmission or viral spread. The LASV strain identified belongs to lineage IV, with broader GPC variability than previously reported. Mutations in the immune-related sites of GPC may affect immune responses, necessitating heightened vigilance regarding the virus.
Humans ; China/epidemiology* ; Genome, Viral ; Lassa Fever/virology* ; Lassa virus/classification* ; Molecular Epidemiology ; Phylogeny

ObjectiveTo explore whether miR-375 regulates the malignant characteristics of osteosarcoma (OS) by influencing the expression of MMP13. MethodsPlasmid DNAs and miRNAs were transfected into OS cells and HEK293 cells using Lipofectamine 3000 reagent. Real-time quantitative polymerase chain reaction was performed to measure the expression of miR-375 and MMP13 in OS patients and OS cells. Western blot was performed to analyze the MMP13 protein in the patients with OS and OS cells. The targeting relationship between miR-375 and MMP13 was analyzed by luciferase assay. Migration and invasion were analysed by heal wound and transwell assays, respectively. ResultsmiR-375 expression in OS tissues was lower than that in normal tissues. The expression of MMP13 was upregulated in OS tissues. MMP13 expression was negatively correlated withmiR-375 expression in patients with OS. Migration and invasion were significantly inhibited in OS cells with the miR-375 mimic compared with OS cells with the miRNA control. MMP13 partially reversed the inhibition of migration and invasion induced by miR-375 in the OS cells. ConclusionmiR-375 attenuates migration and invasion by downregulating the expression of MMP13 in OS cells.

Objective To analyze the factors affecting the prognosis of patients with knee osteoarthritis,and to construct a nomogram prediction model in conjunction with multi-dimensional clinical indicators.Methods The clinical data of 234 pa-tients with knee osteoarthritis who were treated in our hospital from January 2015 to June 2021 were retrospectively analyzed,including 126 males and 108 females;age more than 60 years old for 135 cases,age less than 60 years old for 99 cases.Lysholm knee function score was used to evaluate the prognosis of the patients,and the patients were divided into good progno-sis group for 155 patients and poor prognosis group for 79 patients according to the prognosis.The clinical data of the subjects in the experimental cohort were analyzed by single factor and multiple factors.The patients were divided into experimental co-hort and verification cohort,the results of the multiple factor analysis were visualized to obtain a nomogram prediction model,the receiver operating characteristic curve(ROC),calibration curve and decision curve were used to evaluate the model's dis-crimination,accuracy and clinical benefit rate.Results The results of multivariate analysis showed that smoking,pre-treatment K-L grades of Ⅲto Ⅳ,and high levels of interleukin 6(IL-6)and matrix metallo proteinase-3(MMP-3)were risk factors for the prognosis of patients with knee osteoarthritis.ROC test results showed that the area under the curve of the nomogram model in the experimental cohort and validation cohort was 0.806[95％CI(0.742,0.866)]and 0.786[(95％CI(0.678,0.893)],re-spectively.The results of the calibration curve showed that the Brier values of the experimental cohort and verification cohort were 0.151 points and 0.134 points,respectively.When the threshold probability value in the decision curve was set to 31％,the clinical benefit rates of the experimental cohort and validation cohort were 51％and 56％,respectively.Conclusion The prognostic model of patients with knee osteoarthritis constructed based on multi-dimensional clinical data has both theoretical and practical significance,and can provide a reference for taking targeted measures to improve the prognosis of patients.

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

The construction method and simulation parameter settings for the discrete element model of Jianwei Xiaoshi Granules, as the primary material of Jianwei Xiaoshi Tablets, are not yet clear. The accuracy of the simulation model significantly influences the dynamic response characteristics between granules. Therefore, it is necessary to calibrate the parameters to improve the accuracy of the simulation parameters. Using the repose angle of Jianwei Xiaoshi Granules as the response value, the response surface methodology was employed to optimize and calibrate the discrete element parameters. Physical experiments were conducted to determine the physical properties of Jianwei Xiaoshi Granules. Based on the Hertz-Mindlin with Johnson-Kendall-Roberts(JKR) V2 model and virtual simulation methods, a repose angle determination model was constructed in EDEM software. The repose angle was measured using image analysis and numerical fitting methods. The Plackett-Burman experiment was used to screen the initial parameters for significance in the discrete element simulation. The significant parameters were then subjected to a steepest ascent experiment to determine the optimal parameter range. Furthermore, based on the Box-Behnken experiment, a second-order regression equation between significant parameters and repose angle was established, with the repose angle of 37.64° in the physical experiment as the target value. The regression equation was optimized and solved. The significance screening experiment revealed that the granule-granule static friction coefficient, granule-granule rolling friction, and granule-steel plate rolling friction of Jianwei Xiaoshi Granules significantly influenced the simulated repose angle. The optimal parameter combination was found to be 0.330, 0.222, and 0.229. The simulation results with this optimal parameter combination showed that there was no significant difference between the simulated repose angle and the repose angle obtained in the physical experiment, with a relative error of 0.05%, which further validated the reliability of the calibrated discrete element parameters for Jianwei Xiaoshi Granules.
Drugs, Chinese Herbal/chemistry* ; Calibration ; Computer Simulation

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

Evolution and natural selection have endowed animal venoms, including scorpion venoms, with a wide range of pharmacological properties. Consequently, scorpions, their venoms, and/or their body parts have been used since time immemorial in traditional medicines, especially in Africa and Asia. With respect to their pharmacological potential, bioactive peptides from scorpion venoms have become an important source of scientific research. With the rapid increase in the characterization of various components from scorpion venoms, a large number of peptides are identified with an aim of combating a myriad of emerging global health problems. Moreover, some scorpion venom-derived peptides have been established as potential scaffolds helpful for drug development. In this review, we summarize the promising scorpion venoms-derived peptides as drug candidates. Accordingly, we highlight the data and knowledge needed for continuous characterization and development of additional natural peptides from scorpion venoms, as potential drugs that can treat related diseases.
Animals ; Scorpion Venoms/pharmacology* ; Peptides/pharmacology* ; Scorpions ; Drug Development ; Medicine, Traditional

Objective Constructing a risk prediction model of IgA nephropathy proteinuria treated by traditional Chinese medicine based on random survival forest model,Screening prognostic risk factors of IgA nephropathy proteinuria.Methods Collecting retrospectively clinical data of 129 cases diagnosed with IgA nephropathy,randomly divided them into training set(60%)and test set(40%).The risk prediction model of IgA nephropathy proteinuria was constructed in the training set with the random survival forest model,and the prognostic risk factors were screened by VIMP method.The accuracy of risk prediction model was validated in the test set with time-dependent ROC curve(tdROC).Results According to the result of VIMP,the prognostic risk factors for IgA nephropathy proteinuria are in the order of eGFR,hypertension,traditional Chinese medicine,24 hUPRO>1 g,genomo sclerosis ratio,Lee grading,fat,hyperlipidemia,hypertrophymia,hyparmane ledmia,Anemia,age and gender.The eGFR was negatively and non-linearly associated with the risk rate of developing persistent proteinuria.Glomerulosclerosis ratio greater than 0.3 is approximately linearly and positively associated with the risk rate of persistent proteinuria.Conclusion Random survival forest model has good predictive performance in the risk prediction model of IgA nephropathy proteinuria treated by traditional Chinese medicine.This risk model can determine the result of IgA nephropathy treated by traditional Chinese medicine,and which is helpful for clinical follow-up monitoring and formulation of individualized treatment plans.