Objective: To investigate the current status of hyperopic reserve among preschool children and to determine the associations with genetic and behavioral factors, so as to provide evidence for early intervention strategies and the development of working protocols for myopia prevention with a forward shift of the prevention threshold. Methods: A total of 924 preschool children (3-6 years of age) from 6 kindergartens in Jiading District of Shanghai were enrolled by stratified random sampling from March to June 2024. Cycloplegic refraction using 1% atropine eye drops was performed to obtain accurate spherical equivalent (SE) values. Questionnaires were used to collect the genetic history and daily behavioral habits of the children. Refractive status was classified into three categories based on the SE:sufficient hyperopic reserve, relatively insufficient hyperopic reserve, and pre myopia. Results: The overall detection rate of insufficient hyperopic reserve among preschool children was 82.25%, consisting of 72.29% for relatively insufficient hyperopic reserve and 9.96% for pre myopia. Multivariate Logistic regression analysis showed that parental bilateral myopia was independently and positively associated with relatively insufficient hyperopic reserve ( OR =2.13) and pre myopia ( OR =4.35) compared to children with sufficient hyperopic reserve; electronic screen exposure time ≥2 h/d on school days was an independent and positive risk factor for pre myopia ( OR =2.83); all of the above associations were statistically significant (all P <0.05). No statistically significant association was noted between outdoor activity and hyperopic reserve (all P >0.05). Conclusions The insufficient hyperopic reserve detection rate is relatively high among preschool children in Jiading District, Shanghai. Early management of electronic device use with regular screening and monitoring for preschool children with a family history of myopia should be strengthened to shift the prevention threshold earlier and prevent childhood myopia.

Objective To investigate the mechanism by which SLC38A1 promotes hepatocellular carcinoma(HCC)progression through glutamine transport-mediated activation of the PI3K/AKT signaling pathway.Methods Bioinformatics analysis was employed to assess the correlation between SLC38A1 expression and clinicopathological features/prognosis in HCC patients,with functional enrichment analysis of SLC38A1 conducted.SLC38A1 was silenced or over expressed via transfection in Huh-7 cells,with glutamine inhibited using DRP-104 and the PI3K/AKT pathway suppressed using LY294002.Cell viability,proliferation,migration,invasion,and glutamine concentration were evaluated using CCK-8,EdU staining,scratch wound healing assay,Transwell chamber assay,and glutamine assay kits,respectively.PI3K/AKT pathway activity was assessed by Western blotting.Results SLC38A1 mRNA and protein levels were significantly higher in HCC tumor tissues than in adjacent normal tissues(P<0.05).HCC patients with high SLC38A1 expression exhibited significantly lower overall survival than those with low expression(P<0.05).SLC38A1 expression correlated significantly with pathologic T-stage(pT),N-stage(pN),M-stage(pM),survival status,and immune infiltration in HCC patients(P<0.05).SLC38A1 silencing markedly reduced glutamine uptake in HCC cells(P<0.001),suppressing cell viability,proliferation,migration,invasion,and PI3K/AKT pathway activity(P<0.001).Conversely,SLC38A1 overexpression promoted proliferation,migration,invasion,and PI3K/AKT activation(P<0.001).DRP-104-mediated glutamine inhibition suppressed HCC cell malignancy and PI3K/AKT signaling while abolishing the oncogenic effects of SLC38A1 overexpression(P<0.001).PI3K/AKT pathway inhibition blocked the pro-tumorigenic effects of SLC38A1 overexpression(P<0.001).Conclusion SLC38A1 promotes proliferation,migration,and invasion in HCC by activating the PI3K/AKT pathway through enhanced glutamine transport.

Objectives:To develop a risk prediction model for sudden cardiac arrest(CA)in children with congenital heart disease(CHD)after surgery and validate its predictive efficacy,providing a reference for the prevention of CA and risk stratification.Methods:Medical records were retrospectively analyzed from 5 029 children who were hospitalized in Fuwai Hospital,Chinese Academy of Medical Sciences from January 1,2020 to May 31,2022 and underwent CHD surgery.The patients were divided into two groups:those who experienced CA after surgery(n=33)and those who did not(n=4 996).A random forest model for predicting the risk of postoperative CA was established on the training dataset using R software,and the predictive effect of the model was evaluated on the validation dataset using indicators of predictive accuracy,sensitivity,specificity,positive predictive value,negative predictive value.Results:The incidence of CA in this center was 0.66%,survival rate is 72.73%.Using the random forest algorithm,the importance of risk factors for sudden CA after CHD surgery was ranked by variable importance scoring,with the following top 6 important predictive variables:blood pressure,lactate levels,heart rate,cardiac rhythm,arterial oxygen partial pressure,and blood oxygen saturation on the first day after surgery.The model established by the random forest algorithm on the training set was validated on the test set,yielding a predictive accuracy of 99.8%,specificity of 87.5%,sensitivity of 99.9%,kappa coefficient of 0.8225,positive predictive value of 99.9%,and negative predictive value of 77.8%.Conclusions:The established prediction model of sudden CA in children with CHD after surgery had good performance.It might help medical staffon decision making of early intervention,preventing the occurrence of CA,and improving the outcomes of children with high risk of CA post surgery.

Objective To explore the expert consensus on perinatal care management of infants with congenital heart disease(hereinafter referred to as"Consensus")in order to promote the standardization of integrated nursing.Methods The literature was systematically searched and several discussions were organized within the group to compile the first draft of the Consensus.From January to March 2024,20 experts in the clinical nursing,nursing management,clinical medicine and other fields of congenital heart disease were solicited through 2 rounds of Delphi,and 8 experts were invited to conduct a validation to revise the items to form the final Consensus.Results The recovery rates of the 2 rounds of questionnaires were 100％;the experts'authority coefficient was 0.89;the Kendall's W were 0.172,0.211,with statistical significance(P＜0.05).The Consensus included 8 first-level subjects,namely prenatal examination and consultation,postpartum screening,standardized referral,preoperative nursing,intraoperative nursing,postoperative nursing,other disease screening,health education and discharge follow-up.Conclusion The Consensus is scientific and rigorous,and it can provide a reference basis for clinical nursing staff to carry out the care and management of newborns with congenital heart disease.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Abstract Traditional Chinese medicine (TCM) has demonstrated unique advantages in the prevention and treatment of chronic diseases such as glycolipid metabolism disorder. However, its widespread application has been hindered by the unclear biological essence of TCM syndromes and therapeutic mechanisms. As an emerging interdisciplinary field, phenomics integrates multi-dimensional data including genome, transcriptome, proteome, metabolome, and microbiome. When combined with TCM's holistic philosophy, it forms TCM phenomics, providing novel approaches to reveal the biological connotation of TCM syndromes and the mechanisms of herbal medicine. Taking glycolipid metabolism disorder as an example, this paper explores the application of TCM phenomics in glycolipid metabolism disorder. By analyzing molecular characteristics of related syndromes, TCM phenomics identifies differentially expressed genes, metabolites, and gut microbiota biomarkers to elucidate the dynamic evolution patterns of syndromes. Simultaneously, it deciphers the multi-target regulatory networks of herbal formulas, demonstrating their therapeutic effects through mechanisms including modulation of insulin signaling pathways, improvement of gut microbiota imbalance, and suppression of inflammatory responses. Current challenges include the subjective nature of syndrome diagnosis, insufficient standardization of animal models, and lack of integrated multi-omics analysis. Future research should employ machine learning, multimodal data integration, and cross-omics longitudinal studies to establish quantitative diagnostic systems for syndromes, promote the integration of precision medicine in TCM and western medicine, and accelerate the modernization of TCM.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Objectives:To develop a risk prediction model for sudden cardiac arrest(CA)in children with congenital heart disease(CHD)after surgery and validate its predictive efficacy,providing a reference for the prevention of CA and risk stratification.Methods:Medical records were retrospectively analyzed from 5 029 children who were hospitalized in Fuwai Hospital,Chinese Academy of Medical Sciences from January 1,2020 to May 31,2022 and underwent CHD surgery.The patients were divided into two groups:those who experienced CA after surgery(n=33)and those who did not(n=4 996).A random forest model for predicting the risk of postoperative CA was established on the training dataset using R software,and the predictive effect of the model was evaluated on the validation dataset using indicators of predictive accuracy,sensitivity,specificity,positive predictive value,negative predictive value.Results:The incidence of CA in this center was 0.66%,survival rate is 72.73%.Using the random forest algorithm,the importance of risk factors for sudden CA after CHD surgery was ranked by variable importance scoring,with the following top 6 important predictive variables:blood pressure,lactate levels,heart rate,cardiac rhythm,arterial oxygen partial pressure,and blood oxygen saturation on the first day after surgery.The model established by the random forest algorithm on the training set was validated on the test set,yielding a predictive accuracy of 99.8%,specificity of 87.5%,sensitivity of 99.9%,kappa coefficient of 0.8225,positive predictive value of 99.9%,and negative predictive value of 77.8%.Conclusions:The established prediction model of sudden CA in children with CHD after surgery had good performance.It might help medical staffon decision making of early intervention,preventing the occurrence of CA,and improving the outcomes of children with high risk of CA post surgery.

Objective:To explore the diagnostic key points, treatment strategies, and prognosis of graft-versus-host disease (GVHD) after liver transplantation.Methods:The clinical data of 5 recipients diagnosed with GVHD after liver transplantation at the Liver Transplantation Center of the First Affiliated Hospital of Army Medical University from May 1999 to October 2024 were retrospectively analyzed. The causes, onset, diagnosis, treatment, and prognosis of GVHD after liver transplantation were summarized and analyzed. Literature was searched in CNKI, Wanfang, VIP, Chinese Medical Journal Full-text Database, PubMed, Web of Science, and Google Scholar using the Chinese keywords "移植物抗宿主病+肝移植", and the English keywords "graft versus host disease + liver transplantation". The search time ranged from January 1988 to January 2025. Inclusion criteria for the literature: (1) meeting the clinical or pathological diagnostic criteria of GVHD after liver transplantation; (2) recipient age >18 years; (3) case number ≥2. Exclusion criteria: incomplete clinical data such as incidence, mortality, and clinical manifestations of GVHD after liver transplantation. The retrieved literature was reviewed.Results:All 5 recipients were male. Among them, 4 cases underwent liver transplantation at this center. The incidence of GVHD after liver transplantation in this center was 0.46% (4/872). All 5 cases developed symptoms such as fever, rash, diarrhea, oral ulcers, and pancytopenia on the 19th (5-21) day after liver transplantation. One case had gastrointestinal bleeding. Two cases were diagnosed by skin pathological biopsy, and three cases were diagnosed based on clinical manifestations such as fever, rash, diarrhea, and bone marrow suppression. One case discontinued immunosuppressants, and four cases reduced the dosage of immunosuppressants. Four cases were treated with high-dose glucocorticoids, four with intravenous immunoglobulin (IVIG), three with ruxolitinib, and three with hematopoietic factors. All five cases received protective isolation, anti-infection, and symptomatic supportive treatment. Among the three recipients treated with ruxolitinib, body temperature returned to normal, rash gradually faded, oral ulcers gradually healed, blood cells returned to normal, and they were eventually discharged after recovery. The remaining two cases showed no symptom improvement and died of severe lung infection and multiple organ failure. Literature review A total of 34 articles were included. The incidence of GVHD after liver transplantation was 1.03% (279/27 018), and the onset time ranged from 7 to 1,865 days post-transplantation; 272 cases (97.49%) occurred within 1-8 weeks. The main clinical manifestations included fever (195 cases, 69.89%), rash (267 cases, 95.70%), diarrhea (173 cases, 62.01%), and bone marrow suppression (214 cases, 76.70%). Treatment mainly involved adjustment of immunosuppressants (201 cases, 72.04%), high-dose corticosteroids (215 cases, 77.06%), and IVIG pulse therapy (146 cases, 52.33%). In the end, 83 cases (29.75%) recovered and were discharged, while the mortality rate was 70.25% (196/279), with causes of death including infection, gastrointestinal bleeding, and multiple organ failure.Conclusions:GVHD after liver transplantation has a low incidence, high mortality, and poor prognosis. Diagnosis mainly relies on typical clinical manifestations and pathological results of tissue biopsy. Early administration of high-dose corticosteroids combined with IVIG pulse therapy, timely reduction or discontinuation of immunosuppressants, use of ruxolitinib, active infection management, and enhanced symptomatic and supportive care are effective strategies for treating GVHD after liver transplantation.