Objective: To investigate the current status of personnel structure and service development of blood transfusion departments/blood banks in medical institutions across China, and to provide evidence for discipline construction. Methods: A questionnaire survey was conducted among 3 117 medical institutions in 31 provinces via the Chinese Medical Doctor Association-Blood Transfusion Physician Branch. Data on department naming, staff composition, professional titles, background, and services were analyzed descriptively. Results: Overall, 71.9% of departments were named "Blood Transfusion Department", while 26.4% were still "Laboratory/Blood Bank". Technicians accounted for 86%, physicians only 9%, and nurses 3%. The average number of physicians per tertiary hospital was 0.92, compared to 0.12 in secondary hospitals, with marked regional disparities. Among physicians, 44.5% had a laboratory background and 43.9% clinical background; 3.3% of physicians switched to technician due to promotion barriers. Basic serological tests (ABO/RhD:99.4%; antibody screening:96.2%) were widely available, but advanced tests (antibody identification:18.7%; platelet antibody: 18.2%) remained insufficient. Overall, 70.5% of departments offered therapeutic procedures, mainly autotransfusion (24.5%) and platelet-rich plasma (PRP) collection (20.4%); plasma exchange was only 15.9%, and 29.5% performed no therapeutic services. Conclusion: Transfusion departments in China are still in the early stage of transitioning from traditional medical technology departments to clinical disciplines. The development of the discipline is constrained by administrative disciplinary positioning, talent construction and internal service capacity. Meanwhile, emerging services including transfusion consultations and specialized outpatient clinics have gained initial momentum.

ObjectiveTo develop and evaluate an O-RADSbased multiparametric model integratingcontrast-enhanced US （CEUS） and the z-score standardized risk of ovarian malignancy algorithm （ROMA z-score） to improve the diagnostic accuracy for differentiating benign， borderline， and malignant ovarian epithelial tumors （OETs）. MethodsThis retrospective study included 129 patients with pathologically confirmed OETs between May 2018 and December 2024. Clinical variables （age， menopausal status， BMI， HE4， CA125） and sonographic features （conventional US and CEUS） were collected. Tumors were classified as benign， borderline， or malignant according to pathological findings. A random forest algorithm was used to contruct the predictive model. Receiver operating characteristic （ROC） curves were generated tocompare the diagnostic performance of O-RADS， O-RADS+CEUS， O-RADS+ROMA z-score， and O-RADS+ CEUS+ROMA z-score. ResultsAmong the 129 patients， 63 had benign tumors， 25 had borderline tumors， and 41 had malignant tumors. Compared with O-RADS alone， the combined O-RADS+CEUS+ROMA z-score model demonstrated superior diagnostic performance， with an AUC of 0.87 （95% CI： 0.79-0.95） vs. 0.64 （95% CI： 0.51-0.76）， P＜0.001， for differentiating benign from borderline tumors， and 0.90 （95% CI： 0.83-0.97） vs. 0.64 （95% CI： 0.50-0.78）， P＜0.001， for differentiating borderline from malignant tumors. The random forest analysis identified HE4， CA125 and CEUS features as the most important predictors，contributing 11.02， 8.82， 18.1 points， respectively， to the total importance score of 51.94. In the test cohort， the model achieved an overall diagnostic accuracy of 72.97%， with an AUC of 0.89（95%CI：0.77-1.00）， sensitivity of 67.06%， and specificity of 85.01%. ConclusionsO-RADS+CEUS+ROMA z-score multiparametric model demonstrated the highest diagnostic performance for in differentiating benign from borderline and borderline from malignant OETs. HE4， CA125 and CEUS features were identified as key predictive risk factors， supporting the role of this model in improving preoperative risk stratification of OETs.

IgA nephropathy (IgAN) is the most common primary glomerulonephritis. Mesangial hyperplasia and deposition of IgA immune complex are typical pathological changes of IgAN. Animal model is an important tool to explore the pathogenesis, diagnosis and treatment plan, and evaluate the safety and efficacy of drugs. At present, there are many kinds of IgAN animal models, including humanized animal models and non-humanized animal models, and there are great differences in the modeling principle, method, time and pathological changes. The humanized animal model is closer to the pathogenesis of IgAN in humans and has gradually become a research hotspot. In this paper, common animal models of IgAN in detail from the aspects of modeling method and time, characteristics and significance of each model, and the research progress of anthropomorphic animal models are reviewed to provide reference and inspiration for the basic research of IgAN.

Dyslipidemia refers to the increased level of TG and total cholesterol in plasma, and also generally refers to other forms of dyslipidemia, which is characterized by non-obvious symptoms in the early stage of the disease, and the first episode is cardiovascular disease. The prevalence of dyslipidemia varies with country, region and population, and the prevalence of dyslipidemia in China is in increase, which is influenced by various risk factors. This paper summarizes the prevalence and risk factors of dyslipidemia and introduces the emerging risk factors of dyslipidemia, such as HIV infection, systemic lupus erythematosus, sarcopenia and environmental problem.

Due to its high sensitivity and non-destructive nature, Raman spectroscopy has become an essential analytical tool in biopharmaceutical analysis and drug development. Despite of the computational demands, data requirements, or ethical considerations, artificial intelligence (AI) and particularly deep learning algorithms has further advanced Raman spectroscopy by enhancing data processing, feature extraction, and model optimization, which not only improves the accuracy and efficiency of Raman spectroscopy detection, but also greatly expands its range of application. AI-guided Raman spectroscopy has numerous applications in biomedicine, including characterizing drug structures, analyzing drug forms, controlling drug quality, identifying components, and studying drug-biomolecule interactions. AI-guided Raman spectroscopy has also revolutionized biomedical research and clinical diagnostics, particularly in disease early diagnosis and treatment optimization. Therefore, AI methods are crucial to advancing Raman spectroscopy in biopharmaceutical research and clinical diagnostics, offering new perspectives and tools for disease treatment and pharmaceutical process control. In summary, integrating AI and Raman spectroscopy in biomedicine has significantly improved analytical capabilities, offering innovative approaches for research and clinical applications.

Dyslipidemia refers to the increased level of TG and total cholesterol in plasma, and also generally refers to other forms of dyslipidemia, which is characterized by non-obvious symptoms in the early stage of the disease, and the first episode is cardiovascular disease. The prevalence of dyslipidemia varies with country, region and population, and the prevalence of dyslipidemia in China is in increase, which is influenced by various risk factors. This paper summarizes the prevalence and risk factors of dyslipidemia and introduces the emerging risk factors of dyslipidemia, such as HIV infection, systemic lupus erythematosus, sarcopenia and environmental problem.

Objective:To systematically evaluate and integrate qualitative research from nurses on perceptions and feelings of elder self-neglect, with the aim of providing a reference for early identification, prevention, and intervention of elder self-neglect by nursing staff.Methods:Qualitative research of nurses' perceptions and feelings of elder self-neglect was systematically searched in the Cochrane Library, PubMed, Embase, Web of Science, China National Knowledge Infrastructure, WanFang Data, VIP, and China Biology Medicine disc by computer. The search period was from database establishment to March 31, 2024. The quality of the literature was evaluated according to the quality evaluation criteria for qualitative research of the Australia Joanna Briggs Institute Evidence-Based Health Care Center. The aggregate integration approach was used to integrate the findings of the study.Results:A total of eight papers were included to distill 30 findings, and similar findings were generalized and integrated to form 11 categories and synthesized into three integrated findings, including nurses' perception of elder self-neglect, manifestations of elder self-neglect from nurses' perspectives, and influencing factors of elder self-neglect from nurses' perspectives.Conclusions:Nurses should understand the manifestations and influencing factors of elder self-neglect, improve own professionalism, and help the elderly establish positive coping mechanisms to promote healthy aging.

The classification of stoma skin barriers varies based on their specific features. The curvature design of convex skin barriers provides a secure and effective seal for patients with flat, retracted stomas or peristomal skin folds. The secure sealing ability of convex skin barriers is attributed to several critical structural components. Although convex skin barriers offer many clinical advantages, there is currently no unified standard for measuring their characteristics, resulting in confusion among healthcare professionals when selecting stoma care products. To address this issue, the 2021 International Stoma Care Expert Meeting proposed the Expert Consensus on Characteristics of Convex Skin Barriers and Clinical Application, which clearly defines five essential properties and clinical application guidelines for convex barriers. However, as most consensus contributors are from Europe and North America, its applicability in Chinese healthcare settings may be limited. Therefore, this paper provides a detailed interpretation of the five characteristics and clinical application statements of convex skin barriers, aiming to offer practical guidance to clinical nurses in selecting appropriate convex products and managing stoma-related complications.

Objective To construct a quality control model for cervical liquid-based thin cell smears using two different artificial intelligence(AI)techniques and to compare the total use of the two methods to improve the level of quality control of cervical liquid-based thin cell smears through the assistance of hybrid AI.Methods In this study,105 cervical liquid-based thin cell smear samples were used.Convolutional neural network(CNN)algorithm and Transformer network algorithm were used as specific AI algorithms in the AI model.The labeled features included the number of cells in the slice,excessive red blood cells,excessive inflammatory cells,and air bubbles.The smear samples were pre-processed and digitized by smear,followed by image segmentation and feature extraction.Using the labeled feature data,machine learning models were trained and optimized.Statistical AI and physician QC results were analyzed by calculating KAPPA index,sensitivity,specificity,area under the curve(AUC),and other indexes for AI QC results.Results CNN algorithm QC results in normal smear,inflammatory background and bloody background were significantly different from the expert review QC results(P＜0.001).Transformer algorithm QC results were similar to the expert review results,with no statistical difference(P＞0.05).General practitioner QC results were statistically different from the expert review QC results in normal smear detection rate and bloody background(P＜0.001).CNN algorithm Kappa value was 0.567,which had medium consistency with expert review results.Transformer algorithm Kappa value was 0.890,with the best consistency with expert review results.General practitioner Kappa value was 0.675,which had better consistency with expert review results.Using the expert review results as a reference standard,the predictive efficacy of the Transformer algorithm and the general practitioners' QC results was evaluated,and the predictive efficacy of the Transformer algorithm was higher than that of the general practitioners in detecting hemorrhagic backgrounds and normal smears(inflammatory backgrounds:AUC=1.000;normal smears:AUC=0.768)(hemorrhagic backgrounds:AUC=0.849;normal smears:AUC=0.849;normal smear:AUC=0.500).Conclusion In this study,we found that the Transformer algorithm was effective in improving the quality control of cervical liquid-based thin-layer cell smears by assisting doctors to perform smear quality control scoring and improving the efficiency and accuracy of smear sample quality control.It can be used as a new quality control method for cervical cancer cytological screening and has potential clinical applications.

The ability of cells to sense and adapt to metabolic changes and environmental variations is essential for their functions. Recent advances in synthetic biology have uncovered increasing mechanisms through which cells detect changes in metabolism and environmental conditions, leading to broader applications. However, a systematic review on the regulation of cellular metabolism and environmental adaption is currently lacking. This article presents a comprehensive overview of this field from three perspectives. First, it introduces key transmembrane and sensor proteins involved in the cellular perception of metabolic and environmental changes. Next, it summarizes the adaptive regulation mechanisms that natural cells employ when confronted with intracellular and extracellular metabolic changes. Finally, the review explores the application scenarios based on cellular adaptive regulation in three aspects: dynamic control, rational metabolic engineering, and adaptive evolution and makes an outlook on the future development directions in this field. This review not only provides a comprehensive perspective on the mechanisms by which cells sense metabolic and environmental variations, but also lays a theoretical foundation for further innovations in the field of synthetic biology. With the continuous advancement of future technologies, a deeper understanding of cellular adaptive regulation mechanisms holds great potential to drive the development and application of novel biomanufacturing platforms.
Adaptation, Physiological ; Synthetic Biology ; Metabolic Engineering/methods* ; Environment ; Bacteria/genetics*