Transfusion safety monitoring and learning provide a mechanism for identifying transfusion risks, enabling medical institutions to implement intervention measures, to reduce transfusion risks, and ultimately to improve patient safety. Recently, in response to ongoing challenges in transfusion safety, the UK Serious Hazards of Transfusion (SHOT) integrated and transformed the recommendations scattered in previous annual reports, and released the Standards for Transfusion Safety. This document specifies standards and requirements/criterion in eight key areas, including transfusion safety, transfusion information technology and equipment, supporting staff to work safely, staff education and training, safety culture, patients as safety partners, haemovigilance and risk management, and governance. The issue and implementation of the Standards marks a significant transformation in the UK's blood transfusion safety governance strategy. Understanding the content and background of the Standards will be beneficial for contemplating and exploring the future direction of China's blood transfusion safety governance strategy.

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 develop a 24 hour physical activity index for school aged children, and to analyze the association of 24 hour physical activity index with screening myopia and obesity, so as to provide a more effective assessment tool for student health risk screening. Methods: From April to June 2024, a total of 451 students in Grades 3-4 from two monitored primary schools in Shanghai were selected using stratified cluster random sampling method. Data on eight core indicators, including daily moderate to vigorous physical activity time, total physical activity time, outdoor activity time, screen time, sleep duration, sleep efficiency, social jetlag and daytime sleepiness, were collected through questionnaires and accelerometer monitoring. Each indicator was standardized and synthesized into a 0-80 point school aged children s 24 hour physical activity index. Spearman correlation analysis and t-test were used to assess consistency between questionnaire and accelerometer derived indices. Multivariate Logistic regression was applied to analyze the association of strength of the composite index with single behavior indicators in screening myopia and overweight/obesity. Results: The compliance rates were higher for moderate to vigorous physical activity time and screen time (50.8%, 98.7%), while the compliance rate for outdoor activity time was only 42.6%, and that for sufficient sleep duration was merely 10.2%. There was no significant difference between the total scores derived from the questionnaire and accelerometer methods (45.13±5.83, 45.05±6.87, t=0.29, P >0.05), but they showed a strong positive correlation ( r=0.58, P <0.01). Multivariate Logistic regression revealed that adjusting for individual behaviors such as grade, gender and both parents being myopic, among single behavior indicators of 24 hour physical activity index, only insufficient outdoor activity time was significantly associated with screening myopia among school aged children ( OR=1.50, 95%CI =1.01-2.21); the detection risk of screening myopia and obesity in the low index group were higher than those in the high index group ( OR=2.47, 95%CI =1.02-5.96; OR=16.63,95%CI = 5.99- 46.20) (all P <0.05). Conclusion The developed 24 hour physical activity index for school aged children demonstrates good measurement accuracy and shows stronger associations with screening myopia and obesity than single behavior indicator.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

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 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.

BACKGROUND:During healing process of chronic wounds,excessive production of reactive oxygen species can impair the function of L929 fibroblasts,thereby delaying wound repair.Therefore,protecting fibroblasts from oxidative stress is important to promote wound healing. OBJECTIVE:To assess the protective effects of carboxymethyl chitosan-oxidized chondroitin sulfate/platelet-rich plasma(CMC-OCS/PRP)hydrogel on L929 cells under H2O2 stimulation. METHODS:CMC-OCS/PRP hydrogels were prepared,and the micromorphology,degradation performance,scavenging ability of H2O2 and hydroxyl radical and biocompatibility of the hydrogels were characterized.L929 cells with good growth state were taken and cultured in five groups.The control group was cultured conventionally.H2O2 was added to the H2O2 group.Carboxymethyl chitosan-oxidized chondroitin sulfate hydrogel extract+H2O2 was added to the CMC-OCS group.Platelet-rich plasma gel extract+H2O2 was added to the PRP group.The CMC-OCS/PRP group was treated with carboxymethyl chitosan-oxidized chondroitin sulfate/platelet-rich plasma hydrogel extract+H2O2.Each group was treated with hydrogel extract for 6 hours,and then H2O2 for 24 hours.After culture,the levels of active oxygen and malondialdehyde,apoptosis and expression of collagen fiber I protein were detected.In the presence of H2O2,the above hydrogel extracts were directly or indirectly co-cultured with L929 fibroblasts for 36 hours,respectively.Migration ability of the cells was detected by scratch test and Transwell chamber test. RESULTS AND CONCLUSION:(1)CMC-OCS/PRP hydrogels had uniform and interrelated porous structure and good degradation ability,could effectively remove H2O2 and hydroxyl radicals in vitro,and had good biocompatibility.(2)Compared with the control group,the apoptosis rate,reactive oxygen species,and malondialdehyde levels were increased(P<0.05);the spread area of cells was decreased(P<0.05),and the expression of collagen fiber I protein had no significant changes(P>0.05)in the H2O2 group.Compared with the H2O2 group,reactive oxygen species level was decreased in the CMC-OCS group(P<0.05),malondialdehyde level was decreased(P<0.05),and cell spread area was increased(P<0.05)in the PRP group,CMC-OCS group,and CMC-OCS/PRP group;apoptosis rate was decreased in the CMC-OCS/PRP group(P<0.05),and collagen fiber I protein expression was increased in the PRP group,CMC-OCS group,and CMC-OCS/PRP group(P<0.05).(3)Compared with the control group,the number of cell migration was decreased(P<0.05),and the migration area had no significant change(P>0.05)in the H2O2 group.Compared with the H2O2 group,the number and area of cell migration were increased in the PRP group,CMC-OCS group,and CMC-OCS/PRP group(P<0.05),and the increase was most significant in the CMC-OCS/PRP group.(4)Under oxidative stress,CMC-OCS/PRP hydrogel can improve the migration ability of fibroblasts,resist cell apoptosis,and preserve cell extension function.