Objective: To elucidate the pharmacological material basis of Danggui Buxue Decoction for its "blood-tonifying" effect through spectrum-effect relationship analysis.
Methods: The chemical constituents of Danggui Buxue Decoction were qualitatively analyzed using liquid chromatography-quadrupole-orbitrap mass spectrometry (LC-QE/MS). An ultra-performance liquid chromatography (UPLC) fingerprint of Danggui Buxue Decoction was established, and the common peaks of 30 batches of self-made Danggui Buxue Decoction were quantitatively analyzed. A zebrafish anemia model induced by phenylhydrazine was established, and the spectrum-effect relationship analysis method was applied to correlate the pharmacological data of 30 batches of Danggui Buxue Decoction with the fingerprint data to screen for its pharmacological material basis.
Results: A total of 90 prototype components were identified in the qualitative analysis of Danggui Buxue Decoction. The UPLC fingerprint of Danggui Buxue Decoction and the zebrafish anemia model were successfully established. The spectrum-effect relationship analysis of the pharmacological data and fingerprint data of 30 batches of Danggui Buxue Decoction revealed that the pharmacological materials of Danggui Buxue Decoction included 4-guanidinobutanoic acid (from Astragalus), isoleucine (from wine-prepared Angelica), lauric acid (from wine-prepare Angelica), ferulic acid (from both wine-prepared Angelica and Astragalus), 6-methoxy-2H-chromen-2-one (from both wine-prepared Angelica and Astragalus), sophoraisoflavone glucoside (from both wine-prepared Angelica and Astragalus), and an unknown peak 8.
Conclusion: This study screened the pharmacological material basis of Danggui Buxue Decoction through qualitative and quantitative analysis of its prototype chemical constituents combined with spectrum-effect relationship analysis, providing scientific support for the clinical application and secondary development of this classic prescription.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Accurate identification of endogenous and exogenous substances in food,particularly in competition supplies,is crucial for ensuring food safety and fair competition,as well as for protecting the legitimate rights and professional reputations of athletes.Testosterone(T)and dehydroepiandrosterone(DHEA)are important steroid hormones that can stimulate protein synthesis,increase the number and volume of muscle cells,and promote muscle growth and recovery.Both are often illegally used in the animal husbandry industry to promote animal growth and improve meat quality.However,current research in this area remains limited,and identification technologies require further investigation.This study focused on the techniques for identifying endogenous and exogenous hormones including T and DHEA in beef.A Soxhlet extraction method was established,reducing the pretreatment cycle to 110 min while achieving high extraction efficiency,with recovery rates of 102.5%for T and 91.9%for DHEA,respectively.Based on this,a gas chromatography-combustion-isotope ratio mass spectrometry(GC-C-IRMS)method was developed for analyzing carbon isotopes in T and DHEA,eliminating the need for derivatization.By adding reference materials to the extract,simultaneous measurement of reference materials and target analytes was achieved.The measurement of caffeine reference material,T and DHEA was completed within 40 min,with a measurement repeatability of 0.02‰.Theδ13C values of T and DHEA in standard substances,which may serve as exogenous additives,were determined using elemental analysis-isotope ratio mass spectrometry(EA-IRMS).The results indicated an average δ13C value of-29.44‰±0.81‰(k=1)for 10 T standards and-30.86‰±0.87‰(k=1)for 14 kinds of DHEA standards.This approach effectively distinguished between endogenous sources and exogenous addition of these two hormones in beef,thereby providing vital technical support for the assurance and supervision of food safety.

Forensic medicine has been designated as a first-level discipline,presenting new opportunities and challenges for the development of forensic medicine.Since the 1980s,the establishment of foren-sic medicine discipline and the cultivation of high-level forensic talents have become hot topics in the development of forensic medicine in China.Since the 13th Five-Year Plan,the forensic team of Shanxi Medical University has been aiming at the forefront,proposing the development goals of"Five First-class"and the discipline development path"Six Major Achievements".It has selected benchmark disci-plines,identified gaps in disciplinary development,unified thoughts,formulated completion timelines,concentrated superior resources,assigned tasks to individuals,and created an"Organized Fill-in-the-Blank Format"forensic medicine discipline construction model with the characteristics of the new era.The construction model of forensic medicine has achieved good results in the goals,discipline frame-work,scientific research,talent cultivation,discipline team and platform construction,forming a rela-tively complete discipline construction and management system,and accumulating valuable experience for the construction of first-level discipline and high-level talent cultivation of forensic medicine.

Creutzfeldt-Jakob disease(CJD)is a rapidly progressive and fatal neurodegenerative disorder caused by prions,with certain infectivity and iatrogenic transmission risks.With the rapid progress and application of new dia-gnostic biomarkers and detection methods,as well as the construction and improvement of surveillance and reporting systems,the detection of CJD in patients domestically and internationally has shown an increasing trend year by year.Due to its long incubation period and heterogeneity of early symptoms,early identification and diagnosis of the disease is difficult,increasing the risk of transmission within medical institutions.Currently,there is a lack of con-sensus on the infection prevention and control of CJD.In order to timely identify and diagnose CJD as well as effec-tively block its transmission in medical institutions,this consensus summarizes 15 clinical concerns and formulates 24 specific recommendations based on the latest domestic and international research findings and clinical evidence,as well as combines with clinical practice,aiming to standardize healthcare-associated infection prevention and control measures for CJD and reduce its transmission risk in medical institutions.

Creutzfeldt-Jakob disease(CJD)is a rapidly progressive and fatal neurodegenerative disorder caused by prions,with certain infectivity and iatrogenic transmission risks.With the rapid progress and application of new dia-gnostic biomarkers and detection methods,as well as the construction and improvement of surveillance and reporting systems,the detection of CJD in patients domestically and internationally has shown an increasing trend year by year.Due to its long incubation period and heterogeneity of early symptoms,early identification and diagnosis of the disease is difficult,increasing the risk of transmission within medical institutions.Currently,there is a lack of con-sensus on the infection prevention and control of CJD.In order to timely identify and diagnose CJD as well as effec-tively block its transmission in medical institutions,this consensus summarizes 15 clinical concerns and formulates 24 specific recommendations based on the latest domestic and international research findings and clinical evidence,as well as combines with clinical practice,aiming to standardize healthcare-associated infection prevention and control measures for CJD and reduce its transmission risk in medical institutions.

OBJECTIVE To explore the risk factors for hospital-associated infections(HAI)in dead patients so as to provide bases for development of prevention strategies for the hospital-associated infections.METHODS Totally 560 patients who died due to non-SARS-CoV-2 infections and hospitalized in Gansu Provincial People's Hospital for more than 48 hours in 2019 and 2023 were retrospectively analyzed,70 of whom had HAI and were assigned as the HAI group,and the rest of 490 patients were assigned as the non-HAI group.The incidence of HAI,major causes of death,risk factors and economic burden were observed.RESULTS The incidence of HAI was 12.50％a-mong the dead patients,the lower respiratory tract infection was the predominant type of infection,and the re-spiratory failure was the primary and direct cause of death in the HAI group;the total treatment cost of the HAI group was higher than that of the non-HAI group(P＜0.05).Univariate analysis showed that there were signifi-cant differences in the length of hospital stay,multidrug-resistant organisms infections,combined use of antibiot-ics,surgery,hemodialysis/peritoneal dialysis,invasive procedures(drainage,puncture,intubation,tracheoto-my),use of equipment like ventilator or urinary catheter,central venous catheter indwelling,blood transfusion,and use of immunosuppressors/glucocorticoids between the HAI group and the non-HAI group(P＜0.05),while there were no significant differences in the sex,age,underlying diseases,modified early warning score(MEWS)and major diagnosis and chemoradiotherapy between the two groups.Multivariate analysis indicated that the length of hospital stay more than 14 days,history of surgery and combined use of antibiotics were the risk factors for HAI in the dead patients(P＜0.05).CONCLUSION It is necessary for the hospital to identify the high-risk pa-tients as early as possible,pay close attention to the lower respiratory tract infection,reduce the risk of HAI by shortening the length of hospital stay,standardizing the invasive procedures and reasonably using antibiotics so as to improve the prognosis of the patients.

Objective This study aimed to investigate the antimicrobial resistance profiles of bacterial strains isolated from pediatric intensive care units(PICU)in China for better antimicrobial therapy.Methods Clinical isolates were collected from 17 institutions,including tertiary care children's hospitals and pediatric department of tertiary general hospitals in China from January 1,2020 to December 31,2022.Antimicrobial susceptibility testing was carried out according to a unified protocol using Kirby-Bauer method or automated systems.Results were interpreted according to the breakpoints released by the Clinical and Laboratory Standards Institute(CLSI)in 2020.Results A total of 10 688 isolates were collected,including gram-positive organisms(39.2％)and gram-negative organisms(60.8％).The top three organisms were S.aureus(13.6％,1 453/10 688),A.baumannii(10.0％,1 067/10 688),and coagulase-negative Staphylococcus(9.9％,1 058/10 688).Multi-drug resistant organisms(MDROs)were very common in children.The prevalence of methicillin-resistant Staphylococcus aureus(MRSA),carbapenem-resistant Enterobacterales(CRE),carbapenem-resistant E.coli,carbapenem-resistant K.pneumoniae(CRKP),carbapenem-resistant A.baumannii(CRAB),and carbapenem-resistant P.aeruginosa(CRPA)was 41.1％,19.4％,8.8％,30.9％,67.4％,and 28.8％,respectively.Overall,more than 50％of Enterobacteriales isolates were resistant to cephalosporins,while nearly 25％of Enterobacteriales isolates were resistant to carbapenems.MDROs were highly resistant to commonly used antibiotics.More than 80％of CRE and CRAB strains were resistant to all beta-lactam antibiotics.CRE and CRAB showed low resistance rates to tigecycline and polymyxin.CRPA showed lower resistance rates to piperacillin,beta-lactamase inhibitor combinations than the resistance rates to third and fourth generation cephalosporins.All of the Staphylococcus and Enterococcus isolates were susceptible to vancomycin and tigecycline.None of PRSP strains isolated from meningitis and nonmeningitis samples were resistant to rifampicin,vancomycin,or linezolid.The prevalence of β-lactamase-negative ampicillin-resistant(BLNAR)strains was 43.3％in Haemophilus influenzae.Conclusions MDROs were prevalent in PICU.It is necessary to establish an effective multidisciplinary team(MDT)to control the antimicrobial resistance.