Objective: To present the plotting of trend analysis charts and the application of multi-rule trend analysis method for monitoring the quality of whole blood, washed red blood cells, and leukocyte-depleted suspended red blood cells. Methods: Samples of whole blood, washed red blood cells, and leukocyte-depleted suspended red blood cells were collected in accordance with relevant protocols and requirements. The results of blood quality testing were compiled. The trend line charts were plotted for the deviation rates of volume, hemoglobin (Hb) content, supernatant protein content, and leukocyte residual count. In addition, the trend line chart were plotted for hematocrit and post-storage hemolysis rate. Results: The trend analysis rules for the deviation rate of volume from the standard were: 1 categorized as out-of-control, (3 of 4) as warning, R as warning, 8 as alert, and 12 as alert. For Hb content deviation rate, the rules were: <0% categorized as out-of-control; <20% but ≥0% (for whole blood) or <10% but ≥0% (for washed red blood cells and leukocyte-depleted suspended red blood cells) as a warning. For supernatant protein content deviation rate, the rules were: ≥0.0% as out-of-control, and >-10% but <0.0% as a warning. For hematocrit, the rules were: 1 as out-of-control and (3 of 4) as warning. For leukocyte residual count deviation rate, the rules were: >0.0% as out-of-control, and >-10% but ≤0.0% as a warning. For post-storage hemolysis rate, the rule was: ≥0.8% out-of-control. Conclusion: The application of multi-rule trend analysis facilitates the timely identification of trend abnormalities and factors causing deviations in the collection and preparation processes of whole blood, washed red blood cells, and leukocyte-depleted suspended red blood cells, thereby providing reliable numerical evidence for their correction and adjustment.

Genetic transformation is a fundamental tool in molecular biology research of medicinal plants. Tailoring transgenic technologies to each distinct medicinal plant would necessitate a substantial investment of time and effort. Here, we present a simple hairy root transformation method that does not require sterile conditions, utilizing Agrobacterium rhizogenes strain K599 and the visible RUBY reporter system. Transgenic hairy roots were obtained for six tested medicinal plant species, roots or rhizomes of which have recognized medicinal value, spanning four botanical families and six genera (Platycodon grandiflorus, Atractylodes macrocephala, Scutellaria baicalensis, Codonopsis pilosula, Astragalus membranaceus, and Glycyrrhiza uralensis). Furthermore, two previously identified Glycyrrhiza uralensis UGTs that convert liquiritigenin into liquiritin in heterologous systems were studied in planta using the method. Our results indicate that overexpression of GuUGT1 but not GuUGT10 and Cas9-mediated knockout of GuUGT1 profoundly influenced the accumulation of liquiritin and isoliquiritin in licorice roots. Therefore, the method described here represents a simple, rapid and widely applicable hairy root transformation method that enables fast gene functional study in medicinal plants.

OBJECTIVE: In Pakistan, traditional medicines are an important component of the medical system, with numerous varieties and great demands. However, due to the scattered resources and the lack of systematic collection and collation, adulteration of traditional Pakistani medicine (TPM) is common, which severely affects the safety of their medicinal use and the import and export trades. Therefore, it is urgent to systematically organize and unify the management of TPM and establish a set of standards and operable methods for the identification of TPM. METHODS: We collected and organized the information on 128 TPMs with regard to their medicinal parts, efficacy, usage, and genetic material, based on Pakistan Hamdard Pharmacopoeia of Eastern Medicine: Pharmaceutical Codex. The genetic information of TPM is summarized from national center for biotechnology information (NCBI) and global pharmacopoeia genome database (GPGD). Furthermore, we utilized bioinformatics technology to supplement the chloroplast genome (cp-genome) data of 12 TPMs. To build the web server, we used the Linux + Apache + MySQL + PHP (LAMP) system and constructed the webpage on a PHP: Hypertext Preprocessor (PHP) model view controller (MVC) framework. RESULTS: We constructed a new genomic database, the traditional Pakistani medicine genomic database (TPMGD). This database comprises five entries, namely homepage, medicinal species, species identification, basic local alignment search tool (BLAST), and download. Currently, TPMGD contains basic profiles of 128 TPMs and genetic information of 102 TPMs, including 140 cytochrome c oxidase subunit I (COI) sequences and 119 mitochondrial genome sequences from Bombyx mori, 1 396 internal transcribed spacer 2 (ITS2) sequences and 1 074 intergenic region (psbA-trnH) sequences specific to 92 and 83 plant species, respectively. Additionally, TPMGD includes 199 cp-genome sequences of 82 TPMs. CONCLUSION TPMGD is a multifunctional database that integrates species description, functional information inquiry, genetic information storage, molecular identification of TPM, etc. The database not only provides convenience for TPM information queries but also establishes the scientific basis for the medication safety, species identification, and resource protection of TPM.

OBJECTIVE: Pneumoconiosis, a lung disease caused by irreversible fibrosis, represents a significant public health burden. This study investigates the causal relationships between gut microbiota, gene methylation, gene expression, protein levels, and pneumoconiosis using a multi-omics approach and Mendelian randomization (MR). METHODS: We analyzed gut microbiota data from MiBioGen and Esteban et al. to assess their potential causal effects on pneumoconiosis subtypes (asbestosis, silicosis, and inorganic pneumoconiosis) using conventional and summary-data-based MR (SMR). Gene methylation and expression data from Genotype-Tissue Expression and eQTLGen, along with protein level data from deCODE and UK Biobank Pharma Proteomics Project, were examined in relation to pneumoconiosis data from FinnGen. To validate our findings, we assessed self-measured gut flora from a pneumoconiosis cohort and performed fine mapping, drug prediction, molecular docking, and Phenome-Wide Association Studies to explore relevant phenotypes of key genes. RESULTS: Three core gut microorganisms were identified: Romboutsia ( OR = 0.249) as a protective factor against silicosis, Pasteurellaceae ( OR = 3.207) and Haemophilus parainfluenzae ( OR = 2.343) as risk factors for inorganic pneumoconiosis. Additionally, mapping and quantitative trait loci analyses revealed that the genes VIM, STX8, and MIF were significantly associated with pneumoconiosis risk. CONCLUSIONS This multi-omics study highlights the associations between gut microbiota and key genes ( VIM, STX8, MIF) with pneumoconiosis, offering insights into potential therapeutic targets and personalized treatment strategies.
Humans ; Male ; East Asian People/genetics* ; Europe ; Gastrointestinal Microbiome ; Lung ; Macrophage Migration-Inhibitory Factors/metabolism* ; Mendelian Randomization Analysis ; Multiomics ; Pneumoconiosis/microbiology* ; Intramolecular Oxidoreductases

Objective:This study aims to explore the job preferences of county-level Centers for Disease Control and Prevention(CDC)personnel and to provide a basis for the development of effective incentive mechanisms.Methods:This study used a combination of stratified sampling and latent class sampling to investigate 1 809 respondents from 56 county-level CDCs in Shandong Province,Hubei Province,and Guizhou Province.Data were analysed using a mixed logit model and a latent class model,and willingness to pay was calculated.Results:The results of the mixed logit model showed that,all attributes and their levels had a significant influence(P<0.05),with establishment being the most important motivating factor(β=2.249).In the latent class model,respondents were divided into three categories.The main differences between the three classes were the choice of exit options and differences in preferences for job attributes.Conclusion:County-level CDC personnel preferred jobs with higher incomes,very good benefit levels,establishment,low workload,better recognition and respect from the public,more opportunities for career advancement,and abundant training opportunities.It is recommended that the total number of establishment should be rationally controlled and dynamically adjusted to balance the differences between working conditions within and outside the establishment;that a comprehensive approach should be adopted to improve both hygiene and motivation factors;and that different incentives should be adopted for different categories of CDC staff. Those who are willing to make a change should be provided with more opportunities for training and career advancement.

Objective To evaluate the effects of fasting and high-fat diet on the pharmacokinetics of rabeprazole sodium enteric-coated tablets in healthy Chinese subjects.Methods A single-center,randomized,open,two-agent,two-sequence,four-cycle,fully repeated crossover,single-dose trial design was used in this study,healthy subjects were assigned to receive single dose of rabeprazole sodium enteric-coated tablets 0.1 g in either fasting or high-fat diet state,and blood samples were taken at different time points,respectively.The concentrations of rabeprazole sodium enteric-coated in plasma were determined by liquid chromatography-tandem mass spectrometry(LC-MS/MS),the model method of the non-compartmental was used to calculate the pharmacokinetic parameters by Phoenix WinNonlin 8.2.Results The main pharmacokinetic parameters of rabeprazole sodium enteric-coated tablets in fasting state and high-fat diet state were as follows:Cmax were(339.63±156.47)and(318.86±132.13)ng·mL-1;t1/2 were(2.34±0.68)and(3.60±2.40)h;AUC0_t were(556.62±251.65)and(528.50±201.78)ng·mL-1·h;AUC0-∞ were(563.39±255.69)and(535.15±203.24)ng·mL-1·h;tmax were 3.65 and 6.99 h.After high-fat diet,the Cmax and AUC of rapeprazole sodium after high-fat and high-calorie diet decreased,Cmax decreased by 6.12％,AUC0-t decreased by 5.05％,AUC0-∞ decreased by 5.01％,andtmaxwas delayed by about 3.34 h.Cmax,AUC0-t and AUC0-∞ 90％confidence interval were 73.13％-115.10％,83.22％-112.28％and 83.40％-112.13％,respectively.Neither was between 85.00％-125.00％.Conclusion High-fat diet affects the absorption rate and degree of rabeprazole sodium enteric-coated,so it is suitable to be administered on an empty stomach.

Objective:To analyze the coupling coordination relationship and spatial correlation among the service demand,resource allocation and utilization efficiency of Traditional Chinese Medicine(TCM),aiming to provide theoretical support and optimization strategies for achieving the coordinated operation of the TCM systems in various provinces and promoting the coordinated development of TCM in different provinces.Methods:The data were collected from the China Health and Family Planning Statistical Yearbook(2013-2017)and the China Health Statistics Yearbook(2018-2023),the entropy method was employed to determine the weight of each evaluation index within the subsystems.A coupling coordination degree model and spatial econometric model were applied to assess the coupling coordination values and spatial correlations of the TCM system across various provinces in China.Results:In 2022,the national average coupling coordination degree was 0.603,with values of 0.648,0.577,and 0.563 for the eastern,central,and western regions,respectively.The western region had the highest number of provinces classified as"disordered type".A spatial clustering effect of the coupling coordination degree across 30 provinces.Conclusions:While the allocation of TCM resources has shown steady improvement,the demand for TCM services and utilization efficiency have exhibited a declining trend.The coupling coordination degree follows a decreasing gradient from east to west and exhibits significant spatial effects,a regional collaborative development mechanism for TCM should be established.

Objective:To construct a risk classification model for adverse events of medical consumables,so as to achieve automatic evaluation for risk level of such events,and enhance the capability for risk management of adverse events of medical consumables,and ensure the safety of medical apparatuses.Methods:The data of adverse events of medical consumables of 370 cases of 148 types that were reported by Shandong Provincial Third Hospital from 2020 to 2023 were selected,and they were divided into high-risk and low-risk types.Eight key factors of them,which included the number of cases,injury level,type of registration certificate,with source and without source,high-value and low-value,domestic and imports,product classification,and risk levels,were counted to form a dataset.K-nearest neighbor(KNN),support vector machine(SVM)and decision tree algorithms in machine learning were used to construct a risk classification model for adverse events of medical consumables.The data of 12 adverse events of medical consumables of 5 types of our hospital,which were newly reported in 2024,were integrated for their parameters.Then,the accuracy rate and prediction performance of the model were further analyzed.Results:By comparing the KNN,SVM and decision tree algorithm models,the effect of SVM algorithm model was better,and its accuracy rate was 90.54%,and its area under curve(AUC)value of the receiver operating characteristic(ROC)curve was 0.944,and its Kolmogorov-Smirnov(KS)test value was 0.808.The model had favorable predictive performance.The results,that invoked SVM algorithm model to conduct verification of actual prediction for 12 adverse events of medical consumables of 5 types,indicated it was same between predictive outcomes and risk levels of manual evaluation.Conclusion:The risk classification model of adverse events of medical consumables has established an operational model for assessing the risk level of such events,which can assist monitoring personnel for adverse event of medical apparatuses to quickly and accurately find risk signals of adverse events of medical consumables,and improve the monitoring capability of them for these adverse events.

Objective To observe the value of machine learning(ML)models based on ultrasound radiomics for preoperatively distinguishing atypical parathyroid tumor(APT)/parathyroid carcinoma(PC)and parathyroid adenoma(PA).Methods Totally 330 primary hyperparathyroidism patients who underwent surgical treatments were retrospectively enrolled and categorized into APT/PC group(n=78)and PA group(n=252)according to surgical pathology and clinical follow-up results,also divided into training set(n=231)and test set(n=99)at the ratio of 7∶3.Based on preoperative ultrasound,545 radiomics features were extracted,and recursive feature elimination(RFE),Kruskal-Wallis or analysis of variance methods were used to screen the features,respectively.Support vector machine(SVM),linear discriminant analysis(LDA),least absolute shrinkage and selection operator logistic regression(LRLASSO),also random forest(RF)and decision tree(DT)algorithms were adopted to construct ML models for differentiating APT/PC and PA,respectively.Then the models were trained in training set,their performance were verified in test set,and a 5-fold cross-validation was adopted to screen out the better combinations.Results Compared with Kruskal-Wallis and analysis of variance methods,the distinguishing efficacy of SVM,LDA,LRLASSO,RF and DT models constructed based on features screened out using RFE method in training set(area under the curve[AUC]=0.870,0.878,0.850,0.847,1.000)and test set(AUC=0.856,0.842,0.827,0.847 and 0.704)were all relatively higher.In test set,the AUC of SVM,LDA,LRLASSO and RF models constructed based on the features screened out using RFE method(included 25,23,17 and 23 features)were all higher than that of DT model(8 features)(all P＜0.001).No significant difference of AUC was found between SVM,LRLASSO or RF models and LDA model(all P＞0.05).The AUC of SVM and RF models were higher than that of LRLASSO model(both P＜0.05),while of SVM and RF models were not significantly different(P＞0.05),indicating that SVM,LDA and RF models were better ones.Conclusion SVM,LDA,LRLASSO,RF and DT models based on ultrasound radiomics could effectively distinguish APT/PC and PA preoperatively,among which SVM,LDA and RF models had better diagnostic efficacy.