Transfusion and Anemia Expertise Initiative-Control/Avoidance of Bleeding developed a strategy for platelet and plasma infusion management in critically ill children based on systematic reviews and consensus meetings of international multidisciplinary experts. One good practice statement and six expert consensus statements were proposed for plasma and platelet transfusions in critically ill children following severe trauma, traumatic brain injury, and/or intracranial hemorrhage. This article introduces the specific methods and basis for the formation of recommendations in this part of the guide.

[Objective]To explore the effects of astragalin(AST)on autophagy and apoptosis of astrocytes in the L4-5 dorsal horn of the spinal cord in mice with inflammatory pain induced by complete Freund's adjuvant(CFA).[Methods]Twenty-four male C57BL/6 mice,aged six months,were randomly assigned to four groups:control group,saline group,CFA model group,and CFA+AST group,six mice in each group.The inflammatory pain model was established by injection of 10 μL CFA into the right lateral malleolus fossa.The saline group were injected with an equal amount of normal saline at the same site.The inflammatory pain mice in CFA+AST group were further treated with AST(60 mg/kg)intraperitoneally once a day for 21 consecutive days.Multiplex immunofluorescence staining was used to detect the coexpression of autophagy-related factors including ATG 12 and Beclin-1,apoptosis-related factors including Cleaved-Caspase3 and Caspase9,and the astrocyte marker such as GFAP in the L4-5 spinal dorsal horn of the mice in each group.Western blot was used to examine the protein expression levels of autophagy-related proteins(ATG12,Beclin-1)and apoptosis-related proteins(Caspase 3,Caspase 9)in the L4-5 spinal dorsal horn of mice.[Results]Immunofluorescent staining showed that in the L4-5 dorsal horn of the spinal cord,the fluorescence intensity of ATG12(P<0.000 1)and Beclin-1(P<0.000 1)was significantly increased,while that of Cleaved-Caspase 3(P<0.001)and Caspase 9(P<0.000 1)was decreased in the CFA+AST group when compared to the CFA model group.Furthermore,AST could inhibit the activation of astrocytes.Western blot further confirmed that AST significantly upregulated the expression of ATG12(P<0.000 1)and Beclin-1(P<0.000 1)in the L4-5 spinal cord of CFA mice,and downregulated the expression of Caspase 3(P<0.01)and Caspase 9(P<0.001).[Conclusions]AST promotes autophagy of astrocytes and inhibits their apoptosis in the L4-5 spinal dorsal horn of CFA mice.

To guide transfusion practice in critically ill children who often need plasma and platelet transfusions, the Transfusion and Anemia Expertise Initiative-Control/Avoidance of Bleeding (TAXI-CAB) developed Recommendations and Expert Consensus for Plasma and Platelet Transfusion Practice in Critically Ill Children. This guideline addresses 53 recommendations related to plasma and platelet transfusion in critically ill children with 8 kinds of diseases, laboratory testing, selection/treatment of plasma and platelet components, and research priorities. This paper introduces the specific methods and results of the recommendation formation of the guideline.

Based on systematic review and consensus meetings of international multidisciplinary experts, the Transfusion and Anemia Expert Initiative—Control/Avoidance of Bleeding (TAXI-CAB) project team developed management strategies for platelet and plasma transfusion in critically ill children. This consensus presents five expert consensus statements and two recommendations addressing two key questions: 1) What Laboratory Tests and Physiologic Triggers Should Guide the Decision to Administer a Platelet or Plasma Transfusion in Critically ill Children? 2) What Product Attributes Are Optimal to Guide Specific Product Selection? This consensus provides guidance for decision-making regarding plasma and platelet transfusion in critically ill children in two aspects: relevant laboratory testing indicators and additional special properties of blood components. This article explains the rationale behind the recommendations in this part of the guideline, aiming to emphasize the need for clinicians to develop transfusion strategies based on multidimensional assessment, while calling for enhanced interdisciplinary collaboration and evidence-based research to optimize blood management in critically ill children, reducing the risk of over-transfusion and improving treatment outcomes. Furthermore, there remains an urgent need for further research to explore laboratory indicators associated with bleeding risk to guide transfusion therapy.

Objective: To analyze the results of national external quality assessment (EQA) for transfusion compatibility test from 2018 to 2023, with the aim of providing references for improving laboratory testing quality and ensuring the safety of clinical blood transfusion. Methods: Three EQA programs were conducted annually, each distributing 22 quality assessment samples. Participating transfusion laboratories were required to complete testing within specified deadlines and to submit results along with documentation of testing methodologies, reagents, and equipment used. National Center for Clinical Laboratories (NCCL) conducted statistical analysis of laboratory results, evaluated testing outcomes and related circumstances, and provided feedback to participating laboratories. EQA data from transfusion laboratories across China from 2018 to 2023 were collected and systematically analyzed. Results: From 2018 to 2023, the qualification rates for all five items (ABO forward typing, ABO reverse typing, Rh blood group typing, antibody screening, and cross-matching) were 67.59%, 77.11%, 77.38%, 72.78%, 79.96%, and 85.16%, respectively. The mean qualification rates for ABO forward typing, ABO reverse typing, RhD blood group typing, antibody screening, and cross-matching over the past six years were 96.25%±0.59%, 90.45%±4.52%, 96.05%±0.71%, 90.88%±2.86%, and 88.34%±3.48%, respectively. The qualification rates in 2019, 2020, 2022, and 2023 all showed a stable trend of "blood stations>tertiary hospitals>secondary hospitals". The mean qualification rate of laboratories in secondary hospitals from 2018 to 2023 was significantly lower than those of laboratories in tertiary hospitals and blood stations (P<0.05), while no significant difference was observed between laboratories in tertiary hospitals and blood stations (P>0.05). The micro column agglutination method was the most widely used in all five tests. In the four test items, namely ABO forward typing, ABO reverse typing, antibody screening, and cross-matching, there was a statistically significant difference in the qualification rate of micro column agglutination method compared to other methods (P<0.05). There was a statistical difference in the qualification rate between manual and automated detection using micro column agglutination method in the cross-matching tests (P<0.05), whereas no significant difference was noted for the other test items (P>0.05). Conclusion: From 2018 to 2023, the number of laboratories participating in EQA activities has been increasing year by year, and the qualification rate has shown an overall upward trend. The type of laboratory is a key factor affecting the qualification rate, and the testing capabilities of some laboratories still need to be improved. The micro column agglutination method is widely used in transfusion compatibility tests. The established EQA program effectively monitors quality issues in laboratories, drives continuous improvement, and ensures sustained enhancement of testing standards to safeguard clinical blood safety.

Objective:To analyze the results of national external quality assessment (EQA) for transfusion compatibility test in 2023, and provide reference for quality management of clinical transfusion compatibility testing.Methods:The EQA of clinical transfusion compatibility testing by NCCL was performed 3 times in 2023 among included laboratories. The panel consisting of 22 samples was distributed to 4 186 laboratories across 31 provinces (Including 2 961 tertiary hospital laboratories, 1 085 secondary hospital laboratories, 23 primary hospital laboratories, 106 blood station laboratories and 11 independent clinical laboratories). Each panel contains 11 red blood cell and 11 plasma samples per 1.5 ml/tube. Each participant laboratory of the EQA program was required to carry out the detection and return results in expected time. Statistical analysis and evaluation on the reported results were conducted by NCCL from the aspects of regional distribution, laboratory grading, testing methodology, reagent and testing system usage.Results:The qualification rates of EQA for five items including ABO positive typing, ABO reverse typing, RhD blood type, antibody screening, and cross matching were 96.68%, 95.10%, 96.46%, 95.32%, and 91.04%, respectively. The EQA qualification rate of tertiary hospital laboratories was 87.77% (2 599/2 961), which was significantly higher than the 77.79% (844/1 085) of secondary hospital laboratories. There were significant differences in the qualification rate of participating laboratories among different regions. The utilization rates of micro column agglutination method in ABO positive typing, ABO reverse typing, RhD blood type, antibody screening, and cross matching were 80.81% (10 080/12 474), 75.06% (9 337/12 440), 81.38% (10 118/12 433), 89.59% (11 104/12 394) and 76.25% (9 495/12 453), respectively. The qualification rate of micro column agglutination method was significantly higher than that of saline slide method in ABO positive typing detection ( P<0.05). The qualification rate of micro column agglutination method was significantly higher than that of the polyamine method and anti-human globulin test tube method in antibody screening ( P<0.05). There were statistically significant differences in qualification rate of 7 reagents in ABO reverse typing, antibody screening and cross matching ( P<0.05). There was no statistically significant difference in the qualification rate between the two detection systems for other reagents, except for the ABO reverse typing where the qualification rate of reagent 1 in a single system was higher than that in a mixed system ( P<0.05). Conclusion:The testing capabilities of clinical laboratories in different regions and different type varied significantly in China. Micro column agglutination method was the most popular selection in transfusion compatibility testing. The regents used in these laboratories showed good performance. However, the detection efficiency of some reagents still need to be improved. EQA could be used to evaluate, monitor, and improve the quality of testing.

Nutritional support is essential for improving survival and prognosis in critical care medicine.However,traditional nutritional assessment methods have limitations such as strong subjectivity,dependence on clinical experience,lack of real-time data support,especially in complex environments such as intensive care unit(ICU).In recent years,the development of artificial intelligence(AI)technology has provided new opportunities for precise nutrition management.AI can analyze a large amount of clinical data through machine learning algorithms,monitor the physiological state of patients in real time,and dynamically adjust nutritional regimens to optimize the nutritional support effect of critically ill patients.At present,the application of AI in critical nutrition assessment mainly focuses on nutritional risk screening and tolerance assessment,but it is still in the preliminary exploration stage.The application of machine learning,deep learning and data mining technologies in the medical field provides more objective and efficient tools for nutritional assessment,such as personalized intervention by analyzing multi-dimensional data of patients(eating habits,physiological indicators,disease history,etc.).This paper analyzes the research prospect of AI in nutritional support treatment evaluation of critically ill patients,the application status,potential advantages and challenges of AI technology in nutritional assessment,focuses on the application of AI in data analysis,personalized nutrition plan formulation and clinical decision support,and discusses how to improve the effectiveness and safety of nutritional treatment by integrating multiple data sources,thus providing direction and ideas for future research.And through in-depth understanding of the application potential of AI,clinical medical staff can provide more accurate and scientific evaluation basis for nutritional support treatment of critically ill patients,and finally improve the clinical treatment effect of patients.However,widespread adoption of AI still faces challenges such as data privacy,ethical norms,algorithmic bias,and interdisciplinary collaboration.In the future,it is necessary to further optimize algorithm models,strengthen multidisciplinary cooperation(such as the collaboration of clinicians,nutritionists,and data engineers),and solve issues such as data standardization,cost-effectiveness,and patient privacy protection to achieve more accurate and personalized nutrition management strategies,ultimately improving the clinical outcomes of critically ill patients.

Respiratory viral infections such as influenza and respiratory syncytial virus infections continue to rapidly increase in patients worldwide.Host immune responses to respiratory viruses play a key role in the pathogenesis and clinical manifestations of the disease.Respiratory viruses not only activate antiviral immune responses,but also may lead to uncontrolled inflammatory re-sponses,characterized by significant release of pro-inflammatory cytokines in severely infected patients,resulting in lymphopenia,lymphocyte dysfunction,and abnormalities in immune cells such as neutrophils and macrophages.These respiratory virus-induced im-mune abnormalities may lead to microbial infection,septic shock,and severe multiorgan dysfunction.Therefore,clarifying the immu-nopathogenic mechanisms of patients with respiratory viral infections can guide clinical treatment and patient prognosis;in addition,rational regulation of the immune response of respiratory viruses in the host,including enhancing antiviral immunity while suppressing systemic inflammation,may be the key to successful treatment.This review mainly discusses the immunomodulation and related clini-cal treatment strategies for respiratory viral infections to help develop new therapeutic strategies for respiratory viral infections and pa-tient prognosis.

Respiratory viral infections such as influenza and respiratory syncytial virus infections continue to rapidly increase in patients worldwide.Host immune responses to respiratory viruses play a key role in the pathogenesis and clinical manifestations of the disease.Respiratory viruses not only activate antiviral immune responses,but also may lead to uncontrolled inflammatory re-sponses,characterized by significant release of pro-inflammatory cytokines in severely infected patients,resulting in lymphopenia,lymphocyte dysfunction,and abnormalities in immune cells such as neutrophils and macrophages.These respiratory virus-induced im-mune abnormalities may lead to microbial infection,septic shock,and severe multiorgan dysfunction.Therefore,clarifying the immu-nopathogenic mechanisms of patients with respiratory viral infections can guide clinical treatment and patient prognosis;in addition,rational regulation of the immune response of respiratory viruses in the host,including enhancing antiviral immunity while suppressing systemic inflammation,may be the key to successful treatment.This review mainly discusses the immunomodulation and related clini-cal treatment strategies for respiratory viral infections to help develop new therapeutic strategies for respiratory viral infections and pa-tient prognosis.

Nutritional support is essential for improving survival and prognosis in critical care medicine.However,traditional nutritional assessment methods have limitations such as strong subjectivity,dependence on clinical experience,lack of real-time data support,especially in complex environments such as intensive care unit(ICU).In recent years,the development of artificial intelligence(AI)technology has provided new opportunities for precise nutrition management.AI can analyze a large amount of clinical data through machine learning algorithms,monitor the physiological state of patients in real time,and dynamically adjust nutritional regimens to optimize the nutritional support effect of critically ill patients.At present,the application of AI in critical nutrition assessment mainly focuses on nutritional risk screening and tolerance assessment,but it is still in the preliminary exploration stage.The application of machine learning,deep learning and data mining technologies in the medical field provides more objective and efficient tools for nutritional assessment,such as personalized intervention by analyzing multi-dimensional data of patients(eating habits,physiological indicators,disease history,etc.).This paper analyzes the research prospect of AI in nutritional support treatment evaluation of critically ill patients,the application status,potential advantages and challenges of AI technology in nutritional assessment,focuses on the application of AI in data analysis,personalized nutrition plan formulation and clinical decision support,and discusses how to improve the effectiveness and safety of nutritional treatment by integrating multiple data sources,thus providing direction and ideas for future research.And through in-depth understanding of the application potential of AI,clinical medical staff can provide more accurate and scientific evaluation basis for nutritional support treatment of critically ill patients,and finally improve the clinical treatment effect of patients.However,widespread adoption of AI still faces challenges such as data privacy,ethical norms,algorithmic bias,and interdisciplinary collaboration.In the future,it is necessary to further optimize algorithm models,strengthen multidisciplinary cooperation(such as the collaboration of clinicians,nutritionists,and data engineers),and solve issues such as data standardization,cost-effectiveness,and patient privacy protection to achieve more accurate and personalized nutrition management strategies,ultimately improving the clinical outcomes of critically ill patients.