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.

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:To explore the diagnostic value of shear wave elastography(SWE)for clinically significant prostate cancer(csPCa).Methods:We retrospectively analyzed the clinical data of 359 cases with suspected prostate cancer(PCa)in Baoji Central Hospital from June 2017 to July 2023.All the patients underwent the following examinations in the order of serum prostate-spe-cific antigen(PSA)testing,transrectal ultrasonography(TRUS),measurement of the stiffness of the entire prostate gland by SWE,and TRUS-guided prostate puncture biopsy.The stiffness of the entire prostate gland was defined as the average of Young's modulus at both sides of the base,middle,and apex of the prostate,including the maximum Young's modulus(Emax),mean Young's modulus(Emean),and minimum Young's modulus(Emin).We analyzed the correlation of the parameters of the stiffness of the entire prostate gland with the pathological results,focusing on their diagnostic performance for csPCa.Results:Of the 359 cases,189 were diag-nosed by pathological puncture biopsy as BPH,26 as non-csPCa,and 144 as csPCa.The PSA level,Emax,Emean and Emin were significantly higher in the csPCa than those in the BPH and non-csPCa groups(all P＜0.01),but showed no statistically significant difference between the BPH and non-csPCa groups(all P＞0.05).The area under the receiver operating characteristic curve(AUC),optimal cut-off value,sensitivity,specificity,positive predictive value(PPV),negative predictive value(NPV)and accura-cy of Emax in the diagnosis of csPCa were 0.852,143.92 kPa,72.22％,84.65％,75.91％,81.98％and 79.67％;those of Emean were 0.868,82.42 kPa,67.36％,91.16％,83.62％,80.66％and 81.62％;and those of Emin were 0.682,32.73 kPa,47.22％,89.30％,73.91％,71.54％and 72.14％,respectively.In the non-csPCa group,Emax,Emean and Emin were found be-low the optimal cut-off value in 73.08％(19/26),92.31％(24/26)and 88.46％(23/26),respectively.Conclusion:The stiff-ness of the entire prostate gland measured by SWE contributes to the diagnosis of csPCa,reduces unnecessary detection of non-csPCa,and provides some reference for its active surveillance.

Objective:To analyze the preferences of medical staff in TCM hospitals in Beijing regarding the achievements transformation of hospital traditional Chinese medicine preparations,and to provide a reference for formulating incentive policies.Methods:233 medical staff from five TCM hospitals in Beijing were taken as the research objects,and surveyed with a questionnaire designed based on the discrete choice experiment(DCE).Mixed logit models and latent class models were then used to analyze their transformation preferences.Results:The mixed Logit model revealed that seven key attributes significantly influenced medical staff's preferences for the achievements transformation of traditional Chinese medicine preparations(P<0.05).Latent class analysis identified three distinct preference groups among respondents:an organization-dependent group(27.0%),a pro-transformation group(61.4%),and a conservative group(11.6%).Conclusions:Medical Staff preferred transformation conditions that increased monthly income;utilized"human use+re-experimentation";involved the hospital's achievements transformation department as the entity;were funded by the hospital;offered a 70%profit share;enabled promotion three years earlier,and assigned patents to the hospital.The study recommends implementing diverse incentive measures and developing differentiated strategies tailored to the distinct Medical Staff categories to facilitate the transformation of hospital traditional Chinese medicine preparations into new drugs.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

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: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.

This study was aimed at understanding the prevalence and drug resistance status of Salmonella of waterfowl ori-gin in the Guangdong region in the past decade,to guide prevention and control efforts.The drug-sensitive paper slide method was used to conduct drug susceptibility testing on 314 waterfowl-originating Salmonella strains isolated from 238 waterfowl farms in the Guangdong region from 2013 to 2023.The isolated Salmonella strains were most resistant to penicillin,amoxicil-lin,cefradine,and cefazolin in the β-lactam group;sulphadoxine dimethylpyrimidine in the sulphonamide group;and tetracy-cline in the tetracycline group.The resistance rates ranged from 73.57％to 89.49％.The highest sensitivity was observed to amikacin,gentamicin,and kanamycin in the aminoglycoside group,and norfloxacin in the quinolone group,with susceptibility rates all exceeding 50％.The 280 strains of Salmonella showed multi-drug resistance to six classes of antimicrobial drugs and high resistance(as much as 60.83％)to five drug classes.Correlation analysis revealed the highest correlations for florfenicol with gentamicin,and for amoxicillin with penicillin(r=0.650 for both),followed by gentamicin with kanamycin(r=0.620).Salmonella resistance in waterfowl in Guangdong Province was generally severe and showed a complex pattern of drug resist-ance.Detection of waterfowl pathogens should be strengthened to prevent the spread of drug-resistant bacteria and support ra-tional use of antibiotics.This work provides a reference for Salmonella prevention and control in waterfowl farms.