As the second part of this series, this article summarizes and synthesizes the key aspects of UK Serious Hazards of Transfusion (SHOT), SHOT’s continuous promotion of learning from excellent daily transfusion events over the past six years. This summary is based on an introduction to the holistic approach to improving patient safety—proactively learning from both failures and successes. The covered topics include an overview, definitions, case studies, implementation methods, safety culture, psychological safety in the workplace, civility in work, the use of neutral language, leading and lagging indicators, and compassionate governance. It is hoped that this article will assist domestic colleagues in understanding and studying the strategic significance of the transformation of transfusion safety governance in the UK, and inspire reflection on the strategic development direction of transfusion safety governance in China.

Objective: To analyze the current burden of autism spectrum disorder (ASD) among children and adolescents in China and globally, and to predict the disease burden from 2024 to 2035, providing a scientific basis for formulating relevant public health policies and intervention measures. Methods: Based on the Global Burden of Disease (GBD) database in 2023, the Joinpoint regression model was used to analyze the changing trends of the disease burden of ASD among children and adolescents in China and globally from 1990 to 2023, and the average annual percent change (AAPC) was calculated. An autoregressive integrated moving average (ARIMA) model was constructed to predict the disease burden trends of ASD among children and adolescents in China and globally from 2024 to 2035. Results: The prevalence and disability adjusted life years (DALYs) rate of ASD among children and adolescents in China increased from 452.69/100 000 and 86.67/100 000 in 1990 to 762.84/100 000 and 148.52/ 100 000 in 2023(AAPC=1.60%, 1.65%, both P <0.01). The prevalence and DALYs rate of ASD among children and adolescents globally increased from 648.49/100 000 and 123.47/100 000 to 862.44/100 000 and 167.16/100 000(AAPC=0.87%, 0.93%, both P <0.01). In 2023, the highest ASD prevalence and DALY rates occurred in children under 5 years old, with China reporting 848.14/100 000 and 166.69/100 000, both below the global averages of 928.80/100 000 and 181.34/100 000. Projections indicated that by 2035, the ASD prevalence and DALY rates in China would rise to 906.83/100 000 and 168.71/100 000, still below the global averages of 938.04/100 000 and 184.49/100 000. Conclusion The disease burden of ASD among children and adolescents in China and globally has generally increased from 1990 to 2023, with a higher risk of disease at younger ages.

Objective: To investigate the characteristics and potential risk factors associated with HIV infection among newly reported HIV positive male student cases in Jiangsu Province from 2023 to 2024, so as to provide evidence for targeted intervention strategies. Methods: Data were obtained from the China CDC Surveillance System on newly reported HIV positive male student cases from 2023 to 2024. A survey was conducted to collect information on demographic characteristics, knowledge of AIDS prevention, education and training history, HIV testing history, behavioral and substance use patterns, and other relevant factors prior to HIV diagnosis among 343 newly reported HIV positive male student cases in Jiangsu Province. Multivariate Logistic regression analysis was used to assess the risk factors of HIV infection among male students. Results: Among the HIV positive male student cases, homosexual behavior accounted for 93.88% of transmission routes, while 10.20% involved heterosexual contact, as well as 4.08% for two sexual hehaviors. Awareness of HIV prevention knowledge was 97.08%, and 66.76% had previously undergone HIV testing. Among the respondents, 10.50% had used rush poppers as enhancers, and 72.30% had received HIV prevention education within the past year. Among students cases with homosexual behavior, the median time from first homosexual contact to HIV diagnosis was ≤2 years, with 54.66% of cases falling into this category; the most common way of finding same sex partners was through social software, accounting for 88.20% of cases, while the proportion of those via "Blued" app reached 87.07% ; the proportion of using condoms every time during sexual activity in the past six months was 12.27%. Among the student cases with homosexual behavior, the results of multiple Logistic regression analysis showed that student cases aged 18 to 24 ( OR =4.52) and >24 ( OR = 19.23 ), without receiving education on HIV prevention in the past year ( OR =1.86), having consistent condom use ( OR =2.73) and not using condoms ( OR =2.12) during the last sexual activity were more likely to had the first same sex sexual activity for more than 3 years before being diagnosed as HIV positive cases (all P <0.05). Student cases who were uncertain about their partner s sexual identity ( OR =0.33), and who primarily identified same sex partners through "other" means ( OR = 0.23 ) were more likely to avoid HIV testing; in contrast, student cases with consistent condom use during the last homosexual encounter ( OR =7.20) was significantly associated with increased likelihood of HIV testing (all P <0.05). Conclusions Newly reported HIV positive male student cases in Jiangsu Province exhibit serious discrepancies between knowledge and practice regarding HIV prevention. Measures are needed to accelerate the optimization of campus based HIV prevention education content and delivery methods. Simultaneously, enhanced management of extracurricular male populations is essential to effectively control the spread of HIV.

Objective: To establish a prediction model for high-risk cardiovascular disease (CVD) among residents aged 35 to 75 years, so as to provide the basis for improving CVD prevention and control measures. Methods: Permanent residents aged 35 to 75 years were selected from Dongcheng District, Beijing Municipality using the stratified random sampling method from 2018 to 2023. Demographic information, lifestyle, waist circumference and blood biochemical indicators were collected through questionnaire surveys, physical examinations and laboratory tests. Influencing factors for high-risk CVD among residents aged 35 to 75 years were identified using a multivariable logistic regression model, and a prediction model for high-risk CVD was established. The predictive effect was evaluated using the receiver operating characteristic (ROC) curve. Results: A total of 6 968 individuals were surveyed, including 2 821 males (40.49%) and 4 147 females (59.51%), and had a mean age of (59.92±9.33) years. There were 1 155 high-risk CVD population, with a detection rate of 16.58%. Multivariable logistic regression analysis showed that gender, age, smoking, central obesity, systolic blood pressure, fasting blood glucose, triglyceride and low-density lipoprotein cholesterol were influencing factors for high-risk CVD among residents aged 35 to 75 years (all P<0.05). The area under the ROC curve of the established prediction model was 0.849 (95%CI: 0.834-0.863), with a sensitivity of 0.693 and a specificity of 0.863, indicating good discrimination. Conclusion The model constructed by eight factors including demographic characteristics, lifestyle and blood biochemical indicators has good predictive value for high-risk CVD among residents aged 35 to 75 years.

Polypoidal choroidal vasculopathy(PCV)is one of the important subtypes of neovascular age-related macular degeneration(nARMD), which causes severe vision loss. It is necessary to distinguish PCV from other nARMD subtypes to guide the clinical treatment plans and predict disease outcomes. In recent years, artificial intelligence(AI)has been widely used in the diagnosis and research of ophthalmic diseases. By utilizing machine learning or deep learning combined with examination images in disease classification, lesion segmentation, and quantitative assessment, etc. This article reviews the recent applications of AI in the differential diagnosis of PCV through various examination images, the segmentation and quantification of biomarkers, as well as the prediction of genotype, response to anti-vascular endothelial growth factor(VEGF)therapy, and the short-term risk of vitreous hemorrhage. It summarizes the difficulties and challenges in clinical practice of AI and looks forward to the advantages and development trends of AI in PCV applications in the future. The article aims to provide more information for further research and application, thereby improving the diagnostic rate of PCV, optimizing treatment plans, and improving patients' visual prognosis.

Polypoidal choroidal vasculopathy(PCV)is one of the important subtypes of neovascular age-related macular degeneration(nARMD), which causes severe vision loss. It is necessary to distinguish PCV from other nARMD subtypes to guide the clinical treatment plans and predict disease outcomes. In recent years, artificial intelligence(AI)has been widely used in the diagnosis and research of ophthalmic diseases. By utilizing machine learning or deep learning combined with examination images in disease classification, lesion segmentation, and quantitative assessment, etc. This article reviews the recent applications of AI in the differential diagnosis of PCV through various examination images, the segmentation and quantification of biomarkers, as well as the prediction of genotype, response to anti-vascular endothelial growth factor(VEGF)therapy, and the short-term risk of vitreous hemorrhage. It summarizes the difficulties and challenges in clinical practice of AI and looks forward to the advantages and development trends of AI in PCV applications in the future. The article aims to provide more information for further research and application, thereby improving the diagnostic rate of PCV, optimizing treatment plans, and improving patients' visual prognosis.

ObjectiveTo characterize the seasonal patterns of influenza in Kunming City, Yunnan Province before, during, and after the COVID-19 pandemic, and provide scientific evidence for optimizing influenza prevention and control strategies. MethodsInfluenza-like illness (ILI) and etiological surveillance data for influenza from the 14^th week of 2010 to the 13^th week of 2024 in Kunming City of Yunnan Province were collected. Harmonic regression models were constructed to analyze the epidemic characteristics and seasonal patterns of influenza before (2010/2011‒2019/2020 influenza seasons), during (2020/2021‒2022/2023 influenza seasons), and after (2023/2024 influenza season) the COVID-19 pandemic. ResultsBefore the COVID-19 pandemic, influenza in Kunming City mainly exhibited an annual cyclic pattern without a significant semi-annual periodicity, peaking from December to February of the next year, with an epidemic duration of 20‒30 weeks. During the pandemic, influenza seasonality shifted, with an increase in semi-annual periodicity and an approximate one month delay in annual peaks. However, after the pandemic, the annual amplitude of influenza increased compared with that before the pandemic, and the epidemic duration extended by about one month. Although the annual peak largely reverted to the pre-pandemic levels, the annual peaks for different influenza subtypes/lineages had not fully recovered. ConclusionInfluenza seasonality in Kunming City underwent substantial alterations following the COVID-19 pandemic and has not yet fully reverted to pre-pandemic levels. Continuous surveillance on different subtypes/lineages of influenza viruses remains essential, and prevention and control strategies should be adjusted and optimized in a timely manner based on current epidemic trends.

Objective To discuss the application value of CT and DSA multimodal image fusion technology in endovascular interventional therapy for arterial occlusive lesions of lower extremities and to evaluate its efficacy and safety so as to provide a scientific basis for clinical decision-making.Methods A total of 283 lower limbs with arterial complete occlusive lesions,who received treatment at Affiliated Baiyun Hospital of Guizhou Medical University hospital from January 2020 to December 2023,were selected for this study.The 283 diseased lower limbs were randomly divided into study group(n=142)and control group(n=141).In the study group the endovascular interventional therapy assisted by CT and DSA multimodal image fusion technology was adopted,while in the control group the traditional DSA-guided endovascular interventional therapy was employed.The imaging parameters,surgical success rates,X-ray exposure doses,time spent for operation,incidence of postoperative complications,changes of ankle-brachial index(ABI),primary patency rate,assisted primary patency rate,and secondary patency rate were compared between the two groups.Results The surgical success rate in the study group was 96.47％,which was significantly higher than 87.94％in the control group(P＜0.05).The mean time spent for operation in the study group was(125.42±23.74)minutes,which was shorter than(147.81±29.33)minutes in the control group.The mean X-ray exposure dose in the study group was(2 856.34±427.82)mGy·cm2,which was lower than(3 674.53±512.60)mGy·cm2 in the control group.The incidence of postoperative complications in the study group was 4.23％,which was significantly lower than 12.57％in the control group(P＜0.05).The ABI values of the affected limbs in the study group and control group increased from preoperative(0.65±0.15)and(0.60±0.18)respectively to postoperative(1.09±0.32)and(0.90±0.28)respectively.The postoperative ABI value in the study group was higher than that in the control group(P＜0.05).The postoperative 12-month primary patency rate,assisted primary patency rate and secondary patency rate in the study group were 78.17％,85.92％and 90.14％respectively,which were better than 67.38％,75.89％and 80.85％respectively in the control group.Conclusion For arterial occlusive lesions of lower extremities,endovascular interventional therapy with the help of CT and DSA multimodal image fusion technology has high surgical success rates,low incidence of complications,and satisfactory revascularization rate.This technology provides new idea and method for the treatment of arterial occlusive lesions of lower extremities with high clinical safety.Therefore,this technology is worthy of clinical promotion and application.

Objective:To investigate the effect of CT-derived fractional flow reserve (CT-FFR) measurement sites on the values and the diagnostic performance, and to determine the optimal measurement site for CT-FFR using invasive FFR as the reference standard.Methods:This study was part of the CT-FFR CHINA clinical trial. Patients with suspected coronary artery disease who were scheduled for invasive coronary angiography (ICA) were prospectively recruited from five clinical centers across the country from November 2018 to March 2020. Each enrolled patient underwent coronary CT angiography (CCTA), CT-FFR, ICA, and invasive pressure wire-based FFR assessments sequentially within one week. Four groups of CT-FFR values were obtained on each enrolled target vessels according to different CT-FFR measurement locations: 1, 2, 3 cm distal to the target lesion, and terminal vessel groups. Spearman and Bland-Altman analyses were used to explore the correlation and consistency of CT-FFR values and FFR values at different measurement sites. The measurement deviation of CT-FFR was also compared. Diagnostic accuracy and performance of CT-FFR, including sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC), in discriminating myocardial ischemia were analyzed across all measurement site groups on a per-vessel level, using FFR as the reference standard.Results:A total of 289 patients with 345 target lesion vessels were included. According to CCTA, there were 51 target vessels (14.8%) with<50% stenosis, 106 vessels (30.7%) with 50%-69% stenosis, and 188 vessels (54.5%) with stenosis≥70%. At per-vessel level, CT-FFR and FFR values at each measurement position group were highly positively correlated: 1 cm distal to target lesion group, r=0.734 ( P<0.001); 2 cm distal to target lesion group, r=0.732 ( P<0.001); 3 cm distal to target lesion group, r=0.737 ( P<0.001); terminal vessel group was 0.719 ( P<0.001). At per-vessel level, CT-FFR and FFR values of all measurement sites were in good agreement (Bland-Altman analysis results): 1 cm distal to target lesion group, 0.014 (95% LoA 0.002-0.026); 2 cm distal to target lesion group, 0.026 (95% LoA 0.015-0.038); 3 cm distal to target lesion group, 0.040 (95% LoA 0.039-0.051); terminal vessel group, 0.075 (95% LoA 0.064-0.087). And at per-vessel level, the accuracy of diagnosing myocardial ischemia with CT-FFR at 1 cm was highest [84.6% (95% CI 80.4%-88.3%)], and the lowest accuracy in the terminal vessel group [67.0% (95% CI 61.7%-72.0%)]. However, there was no significant difference in the diagnostic accuracy of CT-FFR at 1 cm, 2 cm [80.6% (95% CI 76.1%-84.6%)] and 3 cm [77.5% (95% CI 72.6%-81.7%)]. AUC of CT-FFR at 1 cm distal to the lesion were both highest for global level and moderately stenosis (50%-69%) lesions [0.85 (95% CI 0.81-0.89), 0.84 (95% CI 0.77-0.90)]. And the differences were statistically significant among the four measurement location groups (all P<0.05). Conclusions:The deviation of CT-FFR increases with measurement site distance distal to target lesions. One centimeter distal to the target lesion is the optimal measurement site, and the CT-FFR value here shows the highest diagnostic performance for myocardial ischemic lesions, especially for moderate stenosis.

Objective:To investigate the value of phase-resolved functional lung MRI (PREFUL-MRI) in identifying pulmonary perfusion and ventilation defects in patients with restrictive ventilatory dysfunction (RVD).Methods:This study was a cross-sectional study. Participants were prospectively enrolled from March 2023 to July 2024 at Beijing Chaoyang Hospital, Capital Medical University, where they underwent pulmonary function testing [indicators including first second forced expiratory volume (FEV 1), forced vital capacity(FVC), FVC percent predicted(FVC%pred), FEV 1 percent predicted(FEV 1%pred),et al] and low-dose CT examinations. Based on pulmonary function results, all participants were divided into an RVD group and a control group. The RVD group was further subdivided into a mild RVD subgroup (FEV 1%pred≥70%) and a moderate-to-severe RVD subgroup (FEV 1%pred<70%). All participants underwent PREFUL-MRI examination. Quantitative lung perfusion parameters were measured, including perfusion quantified (Q Quantified) and perfusion defect percentage (QDP). Ventilation parameters were also acquired, including regional ventilation (RVent), flow-volume loop correlation metric (FVL-CM), and ventilation defect percentages (VDP RVent and VDP FVL-CM). Comparisons of continuous variables between groups were performed using the Mann-Whitney U test, while categorical variables were compared using the χ2 test or Fisher′s exact test. Spearman correlation analysis was used to assess the relationships between pulmonary function parameters and lung perfusion and ventilation parameters. Variables showing statistically significant differences between the RVD and control groups, or between the mild and moderate-to-severe RVD subgroups in univariate analyses, were included in multivariate binary logistic regression analysis to identify independent risk factors for the occurrence of RVD and moderate-to-severe RVD, and predictive models were constructed. The predictive performance of the models was evaluated using receiver operating characteristic curves and the area under the curve (AUC). Results:A total of 37 patients were included in the RVD group (23 males and 14 females), with a median age of 59 (52, 75)years. Among them, 18 had mild RVD and 19 had moderate-to-severe RVD. The control group consisted of 49 individuals (39 males and 10 females), with a median age of 55(46.5, 60) years. The RVD group had lower FEV 1%pred, FVC%pred, and Q Quantified, and higher RVent and QDP compared to the control group ( P<0.05). The moderate-to-severe RVD subgroup had lower FEV 1%pred, FVC%pred, and Q Quantified, and higher RVent and QDP than the mild RVD subgroup ( P<0.05). In the RVD group, FEV 1%pred and FVC%pred showed significant positive correlations with RVent and Q Quantified, and significant negative correlations with QDP ( P<0.05). The FEV 1/FVC ratio was positively correlated with RVent and FVL-CM, and negatively correlated with VDP RVent, VDP FVL-CM, and QDP ( P<0.05). Logistic regression analysis revealed that Q Quantified ( OR=0.97, 95% CI 0.94-0.99, P=0.005) and QDP ( OR=1.23, 95% CI 1.07-1.41, P=0.003) were independent predictors of RVD, and the AUC of the predictive model was 0.823. QDP ( OR=1.23, 95% CI 1.07-1.41, P=0.003) was identified as an independent predictor of moderate-to-severe RVD, with the model achieving an AUC of 0.825. Conclusions:PREFUL-MRI facilitates the noninvasive assessment of pulmonary perfusion abnormalities in RVD. The quantitative perfusion parameter serves as a valuable biomarker for detecting RVD and evaluating its severity, offering a novel tool for both pathophysiological research and clinical assessment.