Esophageal cancer is a prevalent malignant tumor of the digestive tract in China, and radical surgery remains the cornerstone of its comprehensive treatment. However, multifactorial challenges such as postoperative gastrointestinal tract reconstruction, traumatic stress, and tumor-related metabolic disturbances render esophageal cancer patients highly susceptible to malnutrition. Perioperative nutritional support therapy plays a crucial role in enhancing surgical safety, improving clinical outcomes, and elevating patients' quality of life by regulating metabolic homeostasis, preserving organ function, and optimizing the immune microenvironment. This article reviews the mechanisms underlying malnutrition in esophageal cancer, methods for nutritional status assessment, and precision intervention pathways based on multi-omics evaluations. The aim is to strengthen clinicians' awareness of standardized perioperative nutritional management for esophageal cancer patients and promote its clinical implementation, thereby facilitating postoperative recovery and improving long-term quality of life.

BACKGROUND: The role of inflammation in the development of gestational diabetes mellitus (GDM) has recently become a focus of research. The systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI), novel indices, reflect the body's chronic immune-inflammatory state. This study aimed to investigate the associations between the SII or SIRI and GDM. METHODS: A prospective birth cohort study was conducted at Beijing Obstetrics and Gynecology Hospital from February 2018 to December 2020, recruiting participants in their first trimester of pregnancy. Baseline SII and SIRI values were derived from routine clinical blood results, calculated as follows: SII = neutrophil (Neut) count × platelet (PLT) count/lymphocyte (Lymph) count, SIRI = Neut count × monocyte (Mono) count/Lymph count, with participants being grouped by quartiles of their SII or SIRI values. Participants were followed up for GDM with a 75-g, 2-h oral glucose tolerance test (OGTT) at 24-28 weeks of gestation using the glucose thresholds of the International Association of Diabetes and Pregnancy Study Groups (IADPSG). Logistic regression was used to analyze the odds ratios (ORs) (95% confidence intervals [CIs]) for the the associations between SII, SIRI, and the risk of GDM. RESULTS: Among the 28,124 women included in the study, the average age was 31.8 ± 3.8 years, and 15.76% (4432/28,124) developed GDM. Higher SII and SIRI quartiles were correlated with increased GDM rates, with rates ranging from 12.26% (862/7031) in the lowest quartile to 20.10% (1413/7031) in the highest quartile for the SII ( Ptrend <0.001) and 11.92-19.31% for the SIRI ( Ptrend <0.001). The ORs (95% CIs) of the second, third, and fourth SII quartiles were 1.09 (0.98-1.21), 1.21 (1.09-1.34), and 1.39 (1.26-1.54), respectively. The SIRI findings paralleled the SII outcomes. For the second through fourth quartiles, the ORs (95% CIs) were 1.24 (1.12-1.38), 1.41 (1.27-1.57), and 1.64 (1.48-1.82), respectively. These associations were maintained in subgroup and sensitivity analyses. CONCLUSION The SII and SIRI are potential independent risk factors contributing to the onset of GDM.
Humans ; Female ; Pregnancy ; Diabetes, Gestational/immunology* ; Prospective Studies ; Adult ; Inflammation/immunology* ; Glucose Tolerance Test ; Birth Cohort

Objective:To provide evidence for medical institutions to explore the standardized management in Investigator Initiated Trials (IIT) through understanding the problems and providing suggestions of IIT management in the past 10 years.Methods:The publication year, region, content, existing problems, and suggestions were abstracted and analyzed by Bibliometric analysis.Results:58 studies were included in the analysis from 12 regions, and the top three regions were Shanghai, Beijing, and Guangdong Province. There were 10 items of IIT management, including management status, ethical management, process management, information management, contract management, approval management, human genetic resource management, researcher management, multi-center management, and methodology system management. The problems and suggestions of each item of IIT management were summarized respectively.Conclusions:The imperfect supervision system of IIT has brought great difficulties to the management department. Medical institutions should clarify management points and risk points from the item of IIT management and take targeted measures to actively promote high-quality IIT research.

Objective The critical genes associated with exhausted CD8+T cells were screened and validated by mapping the single-cell transcriptome profile of high-grade serous ovarian cancer(HGSOC).Methods The specific subtypes of T cells in the tumor microenvironment were analyzed using the single-cell sequencing data from the early stage of laboratory(SRA database:PRJNA756768)and integrating 5 HGSOC sequencing from the database,and the differentiation trajectory of T cell subsets was ex-plored through pseudotime analysis.Differential gene enrichment was used to determine immunosuppressed CD8+IL-2Low and CD8+IFN-γLow T cell subsets and differential genes,and candidate molecules closely related to exhausted CD8+T cells were screened based on patient prognosis.Flow cytometry was used to analyze the expression of PHLDA1 on CD8+T cells,CD4+T cells and Treg cells dur-ing the activation to exhaustion process of T cells in human PBMCs.ELISA was used to detect the levels of IFN-γ and IL-2 secreted by CD8+T cells in PHLDA1High and PHLDA1Low.Finally,flow cytometry was used to analyze the association between PHLDA1 and ex-hausted markers PD-1 and TIM-3.Results The results showed that T cells were grouped in three ways:(1)IL-2High and IL-2Low;(2)IFN-γHigh and IFN-γLow;and(3)exhausted and cytotoxic CD8+T cells.Subsequently,the intersection of its differentially expressed genes was taken,and the key gene PHLDA1 was ultimately screened.Flow cytometry analysis suggested that during the process of T cell activation to exhaustion,the expression of PHLDA1 continued to increase on CD8+T cells,CD4+T cells and Treg cells;The ELISA results showed that the levels of IFN-γ and IL-2 secreted by CD8+PHLDA1High T cells were significantly lower than those of CD8+PHLDA1Low T cells.Meanwhile,the CD8+PHLDA1High T cell subset could simultaneously cover the exhausted T cell types of CD8+TIM-3+and CD8+PD-1+.Conclusion Based on single-cell sequencing data,this study identified PHLDA1 as a key molecule responsi-ble for CD8+T cell exhaustion in OC,providing new insights for immunotherapy of OC.

Attenuated psychosis syndrome(APS)is a clinical disorder associated with a high risk of psychosis.Patients during this period typically exhibit multidimensional neurocognitive and social cognitive impairment.Cognitive impairment in APS patients is associated with structural and functional abnormalities in the frontal,temporal and subcortical brain regions.At present,drug therapy,psychotherapy,nutritional therapy and computer cognitive training are mainly used to improve the cognitive function of APS patients.In the future,we could comprehensively utilize brain imaging,electrophysiology,and molecular imaging to deeply explore the neuropathological mechanism of APS cognitive impairment,and combine computer,virtual reality,and artificial intelligence to develop new cognitive function intervention procedures,in order to achieve APS precise prevention and treatment.

Objective:The cluster analysis method was applied to evaluate the scientific research performance evaluation index system of departments in an obstetrics and gynecology hospital, and analyze the weaknesses of scientific research work of various departments, to provide a basis for the improvement of scientific research strength of departments.Methods:On the basis of the scientific research performance evaluation index system of departments in the obstetrics and gynecology hospital, the indicators and weights of the system were optimized through expert consultation, and the scientific research values from 2019 to 2021 were brought into the optimized scientific research performance evaluation index system to calculate the scientific research scores of each department, and then the cluster analysis method was applied to evaluate the index system.Results:Before and after the optimization of the scientific research performance evaluation index system of departments, the conformity with the discipline classification was 76.00% and 96.67% respectively ( P=0.039). In total of 30 departments were clustered into 4 categories: excellent (7), good (7), medium (4), and concerned (12). The average score of the total scientific research performance evaluation indicators of the 4 categories of departments was 15.022. The highest average score was for papers and monographs, and the lowest was for awards. Conclusions:This study applied the cluster analysis method to evaluate the scientific research performance evaluation index system of departments in the obstetrics and gynecology hospital and replaced quantitative indicators with quality indicators. It will optimize and improve the hospital hierarchical management methods, provide data support and classified guidance for the scientific research development of different categories of departments, and provide a reference basis for hospitals to formulate scientific research management policies such as achievement transformation, award, industry standard guidelines, etc.

Objective The aim of this study was to draw single-cell transcriptome profiles of high-grade serous ovarian cancer(HGSOC),borderline ovarian cancer(OC),and normal ovaries in order to identify biomarkers that can diagnose and predict the prognosis of OC.Methods The differentially expressed genes between HGSOC,borderline OC,and normal ovarian tissues were ana-lyzed using single-cell data sequenced(SRA database:PRJNA756768).The cell subsets associated with tumor progression were screened by functional enrichment,cell communication between different subsets was analyzed by Cellchat,and cell differentiation traj-ectories were explored by pseudotime analysis to finally determine the subsets most relevant to tumor progression.Combined with OC transcriptome data of OC from the Cancer Genome Atlas(TCGA)with patient prognosis,biomarkers for diagnosing and predicting sur-vival of OC patients were ultimately screened.Results After using t-distribution stochastic neighbor embedding(t-SNE)for di-mensionality reduction,nine cell subpopulations were obtained:endothelial cells,myeloid cells,fibroblasts,T cells,stromal cells,B cells,and 3 epithelial cell subpopulations(C1,C4,and C7).Further analysis revealed that copy number variation(CNV)in the C4 group had the highest score in HGSOC,higher than those of borderline OC and normal ovaries,and was negatively correlated with prognosis.DMKN was a key marker gene in this group.Transcriptome analysis of OC in the TCGA database showed a close correlation between DMKN and poor prognosis(P=0.026),and the diagnostic efficacy of DMKN for OC was significant(A UC=0.906).Con-clusion This study is based on single-cell sequencing data to screen for DMKN,which can effectively diagnose and predict the prognosis of OC.This study provides new ideas for the diagnosis and prognosis prediction of OC.

NUF2 is responsible for the attachment of kinetochore-microtubules and proper chromosome segregation during mitosis. NUF2 is highly expressed in hepatocellular carcinoma, pancreatic cancer, esophageal cancer, non-small cell lung cancer, breast cancer and other tumor tissues and cells, and can be used as prognostic markers. Further clarifying the relationship between NUF2 and tumor prognosis can provide help for the application of NUF2 in prognostic assessment of cancers.

Objective:Delphi method and weighted average method are used to establish the evaluation index system of basic and clinical scientific research projects respectively, to provide support for screening high-quality projects, to avoid the disadvantages of " one size fits all" , and promote the transformation of project management from quantity and restatement to quality and results, in order to release the innovative vitality of talents and produce more high-quality results.Methods:Using the methods of literature review and expert interview, form the questionnaire of evaluation indicators for the conclusion of basic and clinical scientific research projects, then carry out two rounds of expert consultation with Delphi method, evaluate the authority of experts with authority coefficient, form the second round of questionnaire after analysis and discussion according to the results from the first round of expert consultation, screen and determine the evaluation indicators again in the same way. After two rounds of discussion, the evaluation index system of basic and clinical scientific research projects was developed, and the weighted average method and product method were used to calculate the weight and combination weight.Results:After two rounds of consultation, the evaluation index system for the conclusion of basic scientific research projects was finally established, with 2 first-class indicators, 12 second-class indicators and 33 third-class indicators, and the evaluation index system for the conclusion of clinical scientific research projects had 2 first-class indicators, 10 second-class indicators and 33 third-class indicators. In the second round, the authority coefficient of basic and clinical experts was 0.8, indicating that the authority of experts in this study was high. 29 basic and clinical projects concluded from 2018 to 2020 were selected to verify the index system.Conclusions:The index system formed in this study will be used to classify and evaluate the conclusion evaluation of basic scientific research projects and clinical scientific research projects. In the next step, the weight of the index system will be adjusted to make it more realistic and highlight high-quality achievements, guide the project leader to pay more attention to quality and results, and conduct follow-up evaluation at different stages.

Objective:To explore the competence characters of hospital young scientists and build a competency model.Methods:The model characters were screened by literature review, behavioral event interview, expert consultation and questionnaire survey. The competency characters were extracted by exploratory factor analysis.Results:The competency characters model includes 5 factors and 34 characters: comprehensive accomplishment, practical ability, personal trait, professional skill and knowledge quality. The Cronbach's α coefficient of the model was 0.980 and the Cronbach's α coefficient of the 5 factors ranged from 0.832 to 0.964, with a split-half reliability of 0.922. The content validity index of the scale was 0.977, and the content validity index of each entry ranged from 0.857 to 1.000. The value of KMO was 0.944.Conclusions:The competency model of hospital young scientists has high stability, and provides reference for young scientists' ability and quality, which will provide the basis for optimizing the training system and training strategy of hospital scientists.