Objective: To report the antibody identification, blood management during pregnancy and the monitoring process of fetal hemolytic disease of fetus and newborn (HDFN) in a pregnant woman with a history of blood transfusion and pregnancy who developed anti-Jr . Methods: Saline tube technique and anti-human globulin technique were used for maternal blood typing, unexpected antibody screening and identification, as well as for determining antibody titer and IgG subclasses. PCR-SSP was employed for genotyping of 18 blood group systems. Next-generation sequencing (NGS) was utilized for gene sequencing of 38 blood group systems. Sanger sequencing was applied to verify rare blood group mutations detected by NGS and to investigate the corresponding rare blood group genes in family members. Blood preparation was achieved through anemia management in prenatal clinics and autologous blood collection during pregnancy. The newborn underwent the three primary tests for HDFN and plasma IgG subclass testing. Results: The pregnant woman's blood type was B, RhD positive, with a positive unexpected antibody screen, and the antibody identification pattern was consistent with a high-frequency antigen antibody. Gene sequencing revealed a homozygous ABCG2 c.376C>T mutation in the woman, resulting in the Jr(a-) phenotype, and anti-Jr antibody was present in her plasma. No compatible Jr(a-) blood was found among family members. The maternal anti-Jr IgG titer remained stable at 256 during pregnancy, with no detectable IgG1 or IgG3 subclasses against the Jr antigen. A total of 800 mL of autologous blood was collected in two stages during pregnancy. The newborn was B, RhD positive, Jr(a+), with a positive unexpected antibody screen (anti-Jr ). IgG subclass typing detected no IgG1 or IgG3. The direct antiglobulin test was positive, while the acid elution test was negative. Conclusion: The combination of serology and blood group genetic analysis provides a diagnostic basis for identifying antibodies to high-frequency antigens. Managing perinatal anemia and implementing staged autologous blood storage can secure blood supply for the perioperative period. IgG antibody subclass typing offers a reference for clinical assessment and prevention of HDFN.

Abstract The prevention and control of myopia in Chinese children and adolescents has become a major public health issue. While maintaining increased outdoor activity as a cornerstone intervention, there is an urgent need to explore new complementary approaches that can be effectively implemented in both indoor and outdoor settings. In recent years, environmental spatial frequency has gained increasing attention as one of the key environmental factors influencing the development and progression of myopia. Both animal studies and human research have confirmed that indoor environments lacking mid to high spatial frequency components, often characterized as "visually impoverished", can promote axial elongation and myopia through mechanisms such as disruption of retinal neural signaling, impaired accommodative function, and altered expression of related molecules. Based on the scientific consensus, it is recommended that "enriching of environmental spatial frequency" should be integrated into the myopia prevention and control framework. Following the principles of schoolled organization, family cooperation, community involvement, and student participation, specific measures are put forward in three areas：optimizing school visual settings, improving home spatial environments, and promoting healthy visual behavior. The aim is to create "visually friendly" indoor environments as an important supplement to outdoor activity, thereby providing a novel perspective and strategy for comprehensively advancing myopia prevention and control among children and adolescents.

In order to investigate the chemical constituents of glycosides in Aconitum tanguticum (Maxim.) Stapf, column chromatographic techniques such as silica gel, ODS, Sephadex LH-20, and semi-preparative high performance liquid chromatography were used to afford eight glycosides from the n-butanol fraction of the 85% ethanol extract of Aconitum tanguticum. Based on the physicochemical properties and spectral data, these compounds were identified as N-4-O-(β-D-glucopyranosyl)-phenethylbenzamide (1), N-(2'-β-D-glucopyranosyl-5'-methoxysalicyl)-4-hydroxy-3-methoxyanthranilic acid methyl ester (2), N-(2'-β-D-glucopyranosyl-5'-hydroxysalicyl)-4-hydroxy-3-methoxyanthranilic acid methyl ester (3), salidroside (4), benzyl primeveroside (5), phenethanol-β-D-xylose-(1''→6')-β-D-glucopyranoside (6), 4-dihydroxyphenethoxy-8-O-β-D-[6-O-(4-O-β-D-glucopyranosyl)-feruloyl]-glucopyranoside (7), phenethanol-α-L-arabinopyranosyl-(1''→6')-β-D-glucopyranoside (8). Among them, compounds 1 and 2 were new compounds, and compounds 5,6,8 were isolated from Aconitum tanguticum for the first time.

Intelligent prescription recommendation has become an important research direction in traditional Chinese medicine (TCM), offering new opportunities to support clinical decision-making and promote the modernization of TCM practice. With the rapid development of artificial intelligence (AI), a variety of computational approaches have been proposed to learn prescription patterns from clinical data and generate personalized treatment recommendations. However, despite increasing research activity, systematic and comprehensive reviews of AI-driven methods for TCM prescription recommendation remain limited. In this study, we present a comprehensive review of computational approaches for herbal prescription recommendation (HPR) in TCM. Existing methods are systematically categorized into several major paradigms, including traditional machine learning methods, topic model methods, sequential generative methods, deep learning and graph-based methods, and large language model–based frameworks. In addition to summarizing methodological developments, we also review commonly used public datasets and evaluation metrics in this field. Furthermore, representative models with publicly available implementations are experimentally evaluated on multiple benchmark datasets to provide a comparative analysis of their performance on the HPR task. Finally, we discuss the key challenges that hinder the practical deployment of intelligent prescription recommendation systems, including data heterogeneity, limited interpretability, and insufficient integration of TCM domain knowledge. Future research directions are outlined to facilitate the development of more reliable, interpretable, and clinically applicable AI-assisted HPR systems for TCM.

Chemical constituents of n-butanol fraction of ethanol extract from the leaves of Cyclocarya paliurus (Batalin) Iljinskaja were studied. Ten compounds were purified by silica gel, MCI, ODS, Sephadex LH-20 column chromatography and semi-preparative high-performance liquid chromatography. Based on the physicochemical properties and spectroscopic data, these compounds were identified as (4S)-4,8-dihydroxy-α-tetralone-5-[6′-(3′′,5′′-dimethoxy-(E)-p-coumaroyl)]-β-D-glucopyranoside (1), (4S)-4,8-dihydroxy-α-tetralone-5-[6′-(E)-feruloyl]-β-D-glucopyranoside (2), (1S,3R,4S,5R)-4,5-di-O-caffeoyl quinic acid methyl ester (3), (1R,3R,4S,5R)-3,4-di-O-caffeoyl quinic acid methyl ester (4), (3R,5R)-3,4,5-tri-O-caffeoylquinic acid methyl ester (5), (1R,3R,4S,5R)-3-O-caffeoyl-5-O-p-coumaroyl quinic acid methyl ester (6), (1R,3R,4S,5R)-3-O-p-coumaroy-4-O-caffeoyl quinic acid methyl ester (7), (1R,3R,4S,5R)-3-O-caffeoyl-4-O-p-coumaroy quinic acid methyl ester (8), (3R,5R)-3,5-di-O-caffeoyl quinic acid methyl ester (9) and (1S,3R,4R,5R)-4-O-caffeoyl-5-O-feruloyl quinic acid methyl ester (10). Among them, compound 1 was a new compound, and compounds 3–10 were isolated from the genus Cyclocarya for the first time.

Abstract The prevention and control of myopia in children and adolescents has become a national health strategy in China, and its prevention and management are transitioning from experience driven approaches to precise and intelligent strategies. Understanding the role of artificial intelligence (AI) in myopia prevention and control can provide a foundation for a comprehensive myopia prevention and control system. AI, with its powerful capabilities in data analysis and pattern recognition, shows significant potential in myopia risk prediction, screening and diagnosis, intervention support and behavior monitoring in children and adolescents. The article systematically summarizes the scientific evidence and consensus on the application of AI for myopia prevention and control in children and adolescents. From the perspective of public health practice, it proposes hierarchical expert recommendations for multiple scenarios including schools, families, communities and medical institutions, as well as policy and research with development. Furthermore, it discusses current challenges and future directions, aiming to provide a reference for building an intelligent, precise and fully covered comprehensive myopia prevention and control system in children and adolescents.

Objective: To conduct a scoping review on the types, construction methods and predictive performance of health literacy prediction models based on machine learning methods, so as to provide the reference for the improvement and application of such models. Methods: Publications on health literacy prediction models conducted using machine learning methods were retrieved from CNKI, Wanfang Data, VIP, PubMed and Web of Science from inception to May 1, 2024. The quality of literature was assessed using the Prediction Model Risk of Bias ASsessment Tool. Basic characteristics, modeling methods, data sources, missing value handling, predictors and predictive performance were reviewed. Results: A total of 524 publications were retrieved, and 22 publications between 2007 and 2024 were finally enrolled. Totally 48 health literacy prediction models were involved, and 25 had a high risk of bias (52.08%), with major issues focusing on missing value handling, predictor selection and model evaluation methods. Modeling methods included regression models, tree-based machine learning methods, support vector machines and neural network models. Predictors primarily encompassed factors at four aspects: individual, interpersonal, organizational and society/policy aspects, with age, educational level, economic status, health status and internet use appearing frequently. Internal validation was conducted in 14 publications, and external validation was conducted in 4 publications. Forty-two models reported the areas under the receiver operating characteristic curve, which ranged from 0.52 to 0.983, indicating good discrimination. Conclusion Health literacy prediction models based on machine learning methods perform well, but have deficiencies in risk of bias, data processing and validation.

OBJECTIVE: To explore the current status and changing trends of the disease burden of traumatic brain injury (TBI) in China from 1990 to 2023, and to quantitatively assess the impact of different influencing factors on this disease burden, thereby providing references for the prevention of TBI. METHODS: Based on the 2023 Global Burden of Disease Study (GBD), indicators including incidence and years lived with disability (YLDs) were used to analyze the status and changing trends of TBI disease burden in China from 1990 to 2023. Additionally, the decomposition method established by Gupta was adopted to quantify the effects of population growth, population aging, age-specific incidence rate, and disease severity on YLDs. RESULTS: From 1990 to 2023, the age-standardized incidence rate and YLDs rate of TBI in China showed an overall upward trend, with a significant downward trend between 2015 and 2020, followed by a resumption of upward trend after 2020. The disease burden of TBI in males was higher than that in females, with a larger increase amplitude. The elderly population had higher TBI incidence rate and YLDs rate, also with a larger upward amplitude. Falls were the main cause of TBI in China, and the changing trend of the disease burden caused by falls was consistent with the overall trend of TBI disease burden; meanwhile, the elderly population bore a relatively high disease burden from falls. Taking 1990 as the baseline, the growth rates of YLDs in males and females in 2023 were 101.54% and 101.40%, respectively. For males, the proportions of YLDs growth attributed to population growth, population aging, age-specific incidence rate, and disease severity were 26.91%, 49.62%, 37.74%, and -12.73%, respectively; for females, the corresponding proportions were 28.85%, 57.69%, 27.65%, and -12.79%. CONCLUSION From 1990 to 2023, population aging had a significant impact on the disease burden of TBI in China. Strengthening the prevention and control of falls and paying close attention to males and the elderly population should be the key focuses of TBI prevention and control work in China in the future.
Humans ; Brain Injuries, Traumatic/epidemiology* ; China/epidemiology* ; Male ; Female ; Incidence ; Middle Aged ; Aged ; Adult ; Cost of Illness ; Adolescent ; Young Adult ; Persons with Disabilities/statistics & numerical data* ; Child ; Global Burden of Disease ; Disability-Adjusted Life Years ; Child, Preschool ; Infant ; Aged, 80 and over

The patient, assigned female at birth and aged 1 year and 7 months, presented with clinical manifestations of 46,XY disorders of sex development. The external genitalia exhibited a severely undermasculinized phenotype. Laboratory tests and gonadal biopsy indicated poor Leydig cell function and good Sertoli cell function. Genetic testing revealed compound heterozygous mutations of c.867-2A>C and c.547G>A (p.G183R) in the LHCGR gene. The patient was ultimately diagnosed with type II Leydig cell hypoplasia. Type II Leydig cell hypoplasia presents a broad spectrum of clinical phenotypes, characterized by a lack of parallel function between Leydig cells and Sertoli cells, and significant individual variability in spermatogenesis and gender assignment. This condition should be considered when there is poor Leydig cell function but good development of Wolffian duct derivatives.
Female ; Humans ; Infant ; Disorder of Sex Development, 46,XY/genetics* ; Leydig Cells/pathology* ; Mutation ; Receptors, LH/genetics* ; Testis/abnormalities*

Type 2 diabetes (T2D) is an independent risk factor for cognitive impairment. The dysregulation of hypoxia inducible factor (HIF) signaling in T2D patients results in impaired adaptive responses to hypoxia, thereby accelerating the progression of complications. However, limited knowledge is available regarding its precise function in diabetes-associated cognitive impairment (DACI). Here, elevated HIF-1α levels were observed in brain endothelial cells (ECs) of db/db mice. Functionally, brain ECs-specific knockdown of H if1 a significantly ameliorated T2D-induced memory loss and neuronal damage. Glycolysis in brain ECs was inhibited in this process, as indicated by RNA-seq, leading to decreased hippocampal lactate production through reduced LDHA expression. Notably, T2D patients showed increased cerebrospinal fluid lactate levels, which were strongly associated with their cognitive dysfunction. Intrahippocampal injection of lactate accelerated cognitive dysfunction and impaired adult hippocampal neurogenesis (AHN) in db/db mice. Conversely, reducing hippocampal lactate levels through the intrahippocampal injection of oxamate delayed the onset of memory deficits. Furthermore, asiatic acid was discovered to protect db/db mice from cognitive impairment by decreasing brain endothelial HIF-1α expression and subsequently reducing hippocampal lactate-induced AHN damage. Overall, this study elucidates the inhibiting role played by endothelial HIF-1α-driven lactate in AHN and highlights a potential tactic of targeting HIF-1α in brain ECs for treating cognitive impairment.