AIM:To investigate the current status and influencing factors of knowledge-attitude-practice in myopia prevention and control among children and adolescents in Ningbo City, thereby providing a scientific basis for formulating targeted prevention strategies.METHODS: Children and adolescents aged 6-12 years old were selected from the medical-school collaborative myopia prevention network in Ningbo City between August 2024 and May 2025 using stratified cluster sampling. Information on myopia prevention knowledge(15 items)and practice(9 items)was collected through questionnaire surveys. Logistic regression models were used to analyze factors influencing myopia occurrence in children and adolescents.RESULTS: A total of 664 children and adolescents aged 6-12 years were enrolled in this study. Participants were divided by age into three groups: 6-7 years old(n=221), 8-9 years old(n=221), and 10-12 years old(n=222). Of the 664 questionnaires distributed, 637 valid questionnaires were returned(201 from the 6-7 age group, 235 from the 8-9 age group, and 201 from the 10-12 age group), yielding an effective response rate of 95.9%. Based on myopia screening results, the non-myopic group comprised 203 participants(31.9%), including 100 males and 103 females, with a mean age of 8.82±1.98 years old. The myopic group comprised 434 participants(68.1%), including 213 males and 221 females, with a mean age of 9.10±1.95 years old. The myopia prevalence rates in the 6-7, 8-9, and 10-12 age groups were 37.8%(76/201), 71.9%(169/235), and 94.0%(189/201), respectively(P<0.001). Regarding the knowledge and practice of myopia prevention, the overall awareness rate in the non-myopic group(59.7%±9.7%)was significantly higher than that in the myopic group(48.7%±8.5%; P<0.001). Additionally, the non-myopic group scored higher on the key practice of “regular eye examinations”(4.27±0.96)compared to the myopic group(4.10±1.05; P<0.05). Logistic regression analysis indicated that age was the primary risk factor for myopia occurrence.CONCLUSION: Age is the dominant factor in the onset of myopia, and there is a phenomenon of “knowledge-practice gap”; the traditional health education model has limitations, and a precise prevention and control system based on developmental patterns should be established.

Objective: To investigate the status of condom use and its influencing factors among men who have sex with men (MSM), so as to provide a basis for improving condom utilization rates and AIDS prevention and control in this population. Methods: From May to October 2024, a snowball sampling method was employed to recruit MSM in Songjiang District, Shanghai Municipality. Self-administered questionnaires were used to collect data on demographic characteristics, AIDS-related knowledge, sexual behaviors, pre-exposure prophylaxis (PrEP) and post-exposure prophylaxis (PEP), and condom use in the past six months. Multivariable logistic regression model was used to analyze the influencing factors for consistent condom use. Results: A total of 921 MSM were surveyed, with a median age of 29.00 (interquartile range, 9.00) years. Among them, 697 (75.68%) were aware of AIDS-related knowledge, 826 (89.69%) expressed willingness to use PrEP, and 835 (90.66%) were willing to use PEP. Additionally, 787 (85.45%) MSM reported their age at first homosexual intercourse as ≥18 years, while 519 (56.35%) reported consistent condom use in the past six months. Multivariable logistic regression analysis revealed that MSM who were aware of AIDS-related knowledge (OR=0.582, 95% CI: 0.423-0.801), willing to use PrEP (OR =0.611, 95% CI: 0.385-0.969), and whose age at first homosexual intercourse was <18 years (OR=0.480, 95% CI: 0.330-0.700) were less likely to consistent use condoms. Conclusion The proportion of consistent condom use among the MSM remains relatively low, which is primarily associated with AIDS-related knowledge, willingness to use PrEP, and the age at first homosexual intercourse.

Objective: To develop a RHCE genotyping assay based on kompetitive allele-specific PCR (KASP) and assess its clinical accuracy for RhCE blood group determination. Methods: KASP primers were designed to interrogate three RHCE loci: the 109 bp insertion/deletion in intron 2, c. 307T>C, and c. 676C>G. A total of 1 194 RhD-positive inpatients from Chengdu were typed by both KASP genotyping and manual tube serology. Discordant samples (n=10) were retested by both methods and further resolved by Sanger sequencing. An additional 377 cases were tested for the c. 48C>G locus to evaluate the predictive accuracy of individual loci and combined locus testing for RhC antigen. Results: Genotyping concordance with serology was 100.0% for both the c. 676C>G locus (RhE/Rhe) and the c. 307T>C locus (Rhc). For RhC prediction using the 109 bp insertion, overall accuracy was 99.7% (1 191/1 194); the 3 discordant cases were confirmed by Sanger sequencing to be false negatives attributable to 109 bp deletion in intron 2. Testing the c. 48C>G allele for RhC prediction yielded 7 false positives, with an accuracy of 98.1% (370/377). RhC antigen status was determined by combining the 109 bp insertion and the c. 48C allele. After excluding 10 samples with inconsistent results between the two loci, the accuracy reached 100% in the remaining 367 samples. When both loci were applied in combination, accuracy reached 100% in the 367 cases with concordant results. Among the 1 194 patients, CCee (45.8%) and CcEe (31.7%) were the most common RhCE phenotypes. The e antigen had the highest positivity rate (92.2%), and the Ce haplotype was the most frequent (66.9%). Conclusion: The KASP-based RHCE genotyping method achieves high accuracy for clinical RhCE typing. Combining the 109 bp insertion/deletion with the c. 48C allele significantly improves RhC antigen prediction compared with either locus alone. This method was applied to RhCE genotyping of 1 194 RhD-positive inpatients in Chengdu, providing local RhCE phenotype and haplotype distribution data to support RhCE-matched transfusion practice.

To compare the performance differences between the multidisciplinary team (MDT) model and the conventional diagnostic and treatment model for lung cancer, and to explore a high-quality development pathway for optimizing lung cancer diagnostic and treatment resources.

Lung cancer ranks first in morbidity and mortality among malignant tumors in China. Low-dose computed tomography (LDCT), as the primary screening method, faces increasingly prominent challenges of false positives and overdiagnosis associated with its high sensitivity. In recent years, the integration of artificial intelligence (AI) and three-dimensional reconstruction technology has provided a novel approach to addressing these challenges. This review highlights how AI and three-dimensional reconstruction contribute to improving image quality, enabling precise identification of pulmonary nodules and intelligent risk stratification, optimizing dynamic follow-up strategies, and assisting in preoperative planning, thereby driving the transformation of the LDCT screening paradigm. Meanwhile, current technical limitations and future directions are also discussed.

Objective: Blood donor selection criteria provide the core framework for assessing whether a prospective donor is suitable to donate blood, with the dual aim of protecting donor health and recipient safety while supporting a sufficient and accessible blood supply. Over the past century, with the establishment of blood banking systems and the progressive identification of transfusion-transmissible pathogens, health questionnaires, physical assessment and laboratory testing have become integral parts of the donor selection process, contributing to the safe, reliable and effective operation of blood services. In recent decades, advances in science and technology, together with a deeper understanding of human physiology, disease risk and transfusion safety, have made it necessary to systematically re-examine the scientific basis, necessity and appropriateness of existing donor selection criteria. As an introductory commentary to this series on whole-blood interdonation intervals and the upper age limit for blood donation, this article discusses the rationale underlying the establishment and dynamic adjustment of donor selection criteria. Drawing on the relevant expert consensus statements in this series and an overview of multicenter real-world studies from China, it explores the balance between donor safety and blood supply sufficiency and proposes future directions for optimizing donor selection criteria in China through local adaptation of policies, evidence-informed decision-making, and pilot evaluation.

AIM:To analyze differential proteins associated with the pathogenesis of dry eye(DE)using bioinformatics methods, in order to reveal their potential molecular mechanisms.METHODS: Articles published in PubMed and EMBASE databases from the inception of the database to August 31, 2023, that used proteomic methods to detect protein expression in clinical samples of dry eye were searched. Differential proteins were selected and further analyzed using the STRING database and Cytoscape software for hub gene screening and module analysis. Protein-protein interaction(PPI)analysis, gene ontology(GO)functional annotation, and Kyoto encyclopedia of genes and genomes(KEGG)pathway enrichment analysis were performed.RESULTS: A total of 21 articles were included, identifying 74 differentially expressed proteins. The most frequently occurring differential proteins were calgranulin A(SA1008), lipocalin-1(LCN1), lysozyme C(LYZ), mammaglobin-B(SCGB2A1), proline-rich protein 4(PRR4), transferrin(TF), and calgranulinB(S100A9). The top 10 hub genes were serum albumin(ALB), tumor necrosis factor(TNF), interleukin 6(IL6), IL1B, IL8, matrix metalloproteinase 9(MMP9), alpha-1-antitrypsin(SERPINA1), IL10, complement component 3(C3), and lactotransferrin(LTF). Module analysis suggested MMP9 and PRR4 as seed genes. KEGG analysis showed that differential proteins were mainly enriched in the IL17 signaling pathway(61.9%).CONCLUSION: The results reveal potential molecular targets and pathways for DE and confirm the association between the pathogenesis of DE and inflammation. Further in-depth research is needed to confirm the significance of these biomarkers in clinical practice.

Objective: To analyze the demographic characteristics of patients with red blood cell transfusion reactions, the usage of red blood cell preparations, and the differences in the composition ratio of adverse reactions based on multi-center data from the Haemovigilance Network, in order to reveal the clinical characteristics of red blood cell transfusion and its underlying issues. Methods: Clinical data of patients who experienced transfusion reactions after red blood cell transfusion in the Haemovigilance Network from 2018 to 2023 were collected. The demographic characteristics of patients who experienced transfusion reactions with different types of red blood cell preparations, the utilization of these preparations, and the differences of the composition ratios of transfusion reactions were analyzed. Count data were expressed as numbers (n) or percentages (%), and comparisons between groups were performed using the Chi-square test. Results: Red blood cell transfusion reactions were more common in females (53.56%), with the majority of patients aged 50-69 years (35.54%). The Han polulation accounted for the vast majority of patients (92.77%), and patients in the hematology and obstetrics/gynecology departments had a relatively high proportion of transfusion reactions (13.26% and 14.26%, respectively). Leukocyte-reduced red blood cells and suspended red blood cells were the most common types of transfusion reactions reported among red blood cell preparations. Allergic reactions and non-hemolytic febrile reactions were the most common transfusion reactions, and there were significant differences in the composition ratios of allergic reactions (χ =869.89, P<0.05) and non-hemolytic febrile reactions (χ =812.75, P<0.05) across various types of red blood cell preparations. Conclusion: There are differences in the demographic characteristics and composition ratio of transfusion reactions among different red blood cell preparations. The management of red blood cell transfusion reactions should be tailored to patient characteristics and conditions, and the selection and use of blood products should be optimized to reduce or avoid the occurrence of transfusion reactions, such as considering the use of washed red blood cells for patients with a history of transfusion allergies or those prone to allergies.

Artificial intelligence (AI) has advantages such as improving work performance, enhancing time efficiency, and reducing work costs, and has been widely applied in various industries, including healthcare. A comprehensive evaluation of medical AI is significant for optimizing its application in the medical field. However, a panoramic evaluation system for medical AI is currently lacking. Therefore, this review aims to provide a panoramic overview of the research progress in evaluating current medical AI from model performance, social acceptance, and medical ethics considerations. It summarizes the evaluation methods of medical AI in different studies, with the expectation of providing references for the panoramic evaluation and optimization of medical AI.

Recently, the number of artificial intelligence methods used to develop clinical risk prediction models has rapidly increased. To ensure the value of clinical prediction model research, researchers must report the research content transparently, completely, and accurately. Updated Guidance for Reporting Clinical Prediction Models that Use Regression or Machine Learning Methods (TRIPOD+AI) was released in 2024 and covers a checklist of 27 major items. It aims to promote the complete reporting of global clinical prediction model research and facilitate research evaluation, model evaluation, and model implementation. This article interprets and compares aspects such as the formulation process, checklist content, applicable scenarios, and advantages of TRIPOD+AI, as well as the original Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) checklist. It also analyzes an example of predicting the depression of elderly patients using artificial intelligence methods, providing references for researchers to standardize the reporting of clinical prediction models.