This paper systematically expounds the core challenges and development vision facing transfusion medicine and discipline construction in China under the strategic goals of achieving "Chinese modernization" and "Healthy China". This article puts forward for the first time the "blood transfusion view" of Chinese blood transfusion practitioners and elaborates on key challenges in the field, including the disciplinary system, talent pool, service balance, technical bottlenecks and standardization in emerging fields. To address the challenges, it is urgently necessary to establish the disciplinary status of transfusion medicine, improve talent cultivation, enhance the integration of scientific research and technology, optimize transfusion assessment, promote autologous blood transfusion, advance the localization of evidence-based medicine, and expand the management functions of biological therapy. The ultimate goal is to build a modern blood transfusion system led by science and technology and with Chinese characteristics to safeguard people's health.

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 investigate the current knowledge status of transfusion medicine among pediatricians/postgraduates and the reliability of large language models (LLMs) for assisted learning, and to assess changes in pediatricians' transfusion medicine knowledge before and after the implementation of the "Pediatric Transfusion Guideline" (hereafter referred to as the "Guideline"). Methods: In January 2022 (prior to the implementation of the "Guideline"), a questionnaire was developed based on the "Guideline" content and distributed to pediatricians. Subsequently, in July 2025 (after the implementation of the "Guideline"), the "Pediatric Transfusion Medicine Knowledge Questionnaire" was designed based on the content of the January 2022 questionnaire. This questionnaire survey was conducted on pediatricians/postgraduates and LLMs. We analyzed the level of transfusion medicine knowledge among pediatricians/postgraduates and the reliability of LLMs for assisted learning, and compared the accuracy of pediatricians' responses before and after "Guideline" implementation. Results: The survey results after the implementation of the "Guidelines" revealed that pediatricians/postgraduates achieved response accuracy rates exceeding 80% on the topic of "Patient Blood Management". However, response accuracy rates were below 30% for topics including "Types and Indications of Blood Components/Products" and "E-valuation of Transfusion Efficacy". The pediatricians' accuracy rates for related questions before and after the implementation of the "Guidelines" were 14.7%-68.9% and 3%-38%, respectively, and the comparison of accuracy rates for each question showed significant differences (P<0.001). The accuracy rates of the LLMs on the questionnaire were all below 90%. Among them, Doubao (81.1%) and Kimi (86.4%) achieved relatively higher accuracy rates, while Tencent Yuanbao (Hunyuan) had the lowest accuracy rate at only 59.5%. Conclusion: The implementation of the "Guideline" may have improved pediatricians' knowledge level of pediatric transfusion medicine. However, their knowledge level of pediatric transfusion remains low, and LLMs cannot yet provide absolutely reliable guidance. Systematic training in pediatric transfusion medicine is urgently needed.

OBJECTIVES: Multiple myeloma (MM) is a hematologic malignancy characterized by clonal proliferation of plasma cells and remains incurable. Patients with primary refractory multiple myeloma (PRMM) show poor response to initial induction therapy. This study aims to develop a machine learning-based model to predict treatment response in newly diagnosed multiple myeloma (NDMM) patients, in order to optimize therapeutic strategies. METHODS: NDMM and post-treatment MM patients hospitalized in the Department of Hematology, Third Xiangya Hospital, Central South University, between August 2022 and July 2023 were enrolled. Post-treatment MM patients were categorized into PRMM patients and treatment-responsive MM (TRMM) patients based on therapeutic efficacy. Serum metabolites were detected and analyzed via metabolomics. Based on the metabolomics analysis results and combined with transcriptomic sequencing data of NDMM patients from databases, differentially expressed amino acid metabolism-related genes (AAMGs) among post-treatment NDMM patients with varying therapeutic outcomes were screened. Using bioinformatics analyses and machine learning algorithms, a predictive model for treatment response in NDMM was constructed and used to identify patients at risk for PRMM. RESULTS: A total of 61 patients were included: 22 NDMM, 23 TRMM, and 16 PRMM patients. Significant differences in metabolite levels were observed among the 3 groups, with differential metabolites mainly enriched in amino acid metabolism pathways. Follow-up data were available for 16 of the 22 NDMM patients, including 12 treatment responders (ND_TR group) and 4 with PRMM (ND_PR group). A total of 23 differential metabolites were identified between these 2 groups: 6 metabolites (e.g., tryptophan) were upregulated and 17 (e.g., citric acid) were downregulated in the ND_TR group. Transcriptomic data from 108 TRMM and 77 PRMM patients were analyzed to identify differentially expressed AAMGs, which were then used to construct a prediction model. The area under the receiver operating characteristic curve (AUC) for the model exceeded 0.8, and AUC values in 3 external validation cohorts were all above 0.7. CONCLUSIONS This study delineated the metabolic alterations in MM patients with different treatment response, suggesting that dysregulated amino acid metabolism may be associated with poor treatment response in PRMM. By integrating metabolomics and transcriptomics, a machine learning-based predictive model was successfully established to forecast treatment response in NDMM patients.
Humans ; Multiple Myeloma/drug therapy* ; Machine Learning ; Male ; Female ; Metabolomics/methods* ; Middle Aged ; Aged ; Treatment Outcome ; Transcriptome ; Computational Biology ; Adult ; Multiomics

Growth arrest DNA damage-inducible protein 45 β (GADD45B) has been reported to be a regulatory factor for active DNA demethylation and is implicated in the modulation of synaptic plasticity and chronic stress-related psychopathological processes. However, its precise role and mechanism of action in stress susceptibility remain elusive. In this study, we found a significant reduction in GADD45B expression specifically in the ventral, but not the dorsal hippocampal CA1 (dCA1) of stress-susceptible mice. Furthermore, we demonstrated that GADD45B negatively regulates susceptibility to social stress and NMDA receptor-dependent long-term potentiation (LTP) in the ventral hippocampal CA1 (vCA1). Importantly, through pharmacological inhibition using the NMDA receptor antagonist MK801, we provided further evidence supporting the hypothesis that GADD45B potentially modulates susceptibility to social stress by influencing NMDA receptor-mediated LTP. Collectively, these results suggested that modulation of NMDA receptor-mediated synaptic plasticity is a pivotal mechanism underlying the regulation of susceptibility to social stress by GADD45B.
Animals ; Receptors, N-Methyl-D-Aspartate/antagonists & inhibitors* ; CA1 Region, Hippocampal/drug effects* ; Male ; Stress, Psychological/physiopathology* ; Mice ; Neuronal Plasticity/drug effects* ; Long-Term Potentiation/drug effects* ; Mice, Inbred C57BL ; Antigens, Differentiation/metabolism* ; Dizocilpine Maleate/pharmacology* ; Excitatory Amino Acid Antagonists/pharmacology* ; GADD45 Proteins

Accurate timing of myelination is crucial for the proper functioning of the central nervous system. Here, we identified a de novo heterozygous mutation in TMEM63A (c.1894G>A; p. Ala632Thr) in a 7-year-old boy exhibiting hypomyelination. A Ca²⁺ influx assay suggested that this is a loss-of-function mutation. To explore how TMEM63A deficiency causes hypomyelination, we generated Tmem63a knockout mice. Genetic deletion of TMEM63A resulted in hypomyelination at postnatal day 14 (P14) arising from impaired differentiation of oligodendrocyte precursor cells (OPCs). Notably, the myelin dysplasia was transient, returning to normal levels by P28. Primary cultures of Tmem63a^-/- OPCs presented delayed differentiation. Lentivirus-based expression of TMEM63A but not TMEM63A_A632T rescued the differentiation of Tmem63a^-/- OPCs in vitro and myelination in Tmem63a^-/- mice. These data thus support the conclusion that the mutation in TMEM63A is the pathogenesis of the hypomyelination in the patient. Our study further demonstrated that TMEM63A-mediated Ca²⁺ influx plays critical roles in the early development of myelin and oligodendrocyte differentiation.
Animals ; Cell Differentiation/physiology* ; Oligodendroglia/metabolism* ; Mice, Knockout ; Mice ; Male ; Myelin Sheath/metabolism* ; Humans ; Child ; Cells, Cultured ; Oligodendrocyte Precursor Cells/metabolism*

Nipah virus (NiV) and related viruses form a distinct henipavirus genus within the Paramyxoviridae family. NiV continues to spillover into the humans causing deadly outbreaks with increasing human-bat interaction. NiV encodes the large protein (L) and phosphoprotein (P) to form the viral RNA polymerase machinery. Their sequences show limited homologies to those of non-henipavirus paramyxoviruses. We report two cryo-electron microscopy (cryo-EM) structures of the Nipah virus (NiV) polymerase L-P complex, expressed and purified in either its full-length or truncated form. The structures resolve the RNA-dependent RNA polymerase (RdRp) and polyribonucleotidyl transferase (PRNTase) domains of the L protein, as well as a tetrameric P protein bundle bound to the L-RdRp domain. L-protein C-terminal regions are unresolved, indicating flexibility. Two PRNTase domain zinc-binding sites, conserved in most Mononegavirales, are confirmed essential for NiV polymerase activity. The structures further reveal anchoring of the P protein bundle and P protein X domain (XD) linkers on L, via an interaction pattern distinct among Paramyxoviridae. These interactions facilitate binding of a P protein XD linker in the nucleotide entry channel and distinct positioning of other XD linkers. We show that the disruption of the L-P interactions reduces NiV polymerase activity. The reported structures should facilitate rational antiviral-drug discovery and provide a guide for the functional study of NiV polymerase.
Nipah Virus/chemistry* ; Cryoelectron Microscopy ; Viral Proteins/genetics* ; RNA-Dependent RNA Polymerase/genetics* ; Phosphoproteins/genetics* ; Humans ; Models, Molecular ; Protein Binding

Objective: Through analyzing the current handling capabilities for complex cases in blood transfusion compatibility detection and the application status of artificial intelligence-assisted (AI-assisted) technologies, this study explores the establishment of an effective AI-augmented protocol for managing challenging blood transfusion compatibility detection. Methods: This research systematically analyzes, designs, and explores an AI-augmented operational workflow for resolving challenging blood transfusion compatibility detection cases. Through three representative case studies, we evaluate its effectiveness, accuracy, and efficiency in addressing real-world diagnostic challenges. Results: The AI-augmented operational model demonstrates significant efficacy in resolving complex blood transfusion compatibility challenges, including complex blood typing, antibody specificity identification, challenging cross-matching, and transfusion strategy formulation. Conclusion: AI-augmented technologies demonstrate immense potential in resolving complex blood transfusion compatibility detections. By enabling intelligent, automated, precise, and standardized solutions, they significantly enhance diagnostic accuracy and operational efficiency, which is critical for ensuring transfusion safety and advancing personalized transfusion medicine. This study delineates both the advantages and existing limitations of AI implementation, explores future developmental trajectories, and provides a theoretical framework and practical implementation pathways for deeper integration of AI in transfusion medicine.

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.

Real-world study (RWS), based on multi-source data from real medical environments, is gradually becoming an important supplement to traditional randomized controlled trials, and its application in the field of transfusion medicine is becoming increasingly widespread. This article systematically reviews the definition and methodological system of RWS, examines its application cases in clinical blood transfusion research, and discusses the advantages, limitations, and future research directions of RWS, aiming to provide a reference for evidence-based research in blood transfusion medicine.