Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.
Humans ; Schizophrenia/pathology* ; Diffusion Tensor Imaging/methods* ; Male ; Female ; Adult ; Brain/metabolism* ; Young Adult ; Middle Aged ; White Matter/pathology* ; Gene Expression ; Nerve Net/diagnostic imaging* ; Graph Neural Networks

Objective To investigate the value of patient data-based real-time quality control(PBRTQC)in internal quality control(IQC)for blood cells analysis based on the data from patients.Methods The data of patients'blood cells,including white blood cell count(WBC),hemoglobin(Hb),red blood cell count(RBC),hematocrit(HCT),mean corpuscular volume(MCV),mean cor-puscular hemoglobin(MCH),mean corpuscular hemoglobin concentration(MCHC),and platelet(PLT)were collected from August 1,2023 to February 26,2024,and the extracted patient data were analyzed on the AI-based real-time quality control intelligent moni-toring platform.The corresponding IQC data for this period were reviewed,and the results of PBRTQC and IQC were compared and an-alyzed.The causes of the emerging warning or alarm prompts were checked and analyzed to explore the application value of PBRTQC in the IQC process of blood cell analysis.Results It is found that when the quality control product was unstable due to overlong opening time of the reagent or improper storage conditions,and the performance changes of the operating system during the detection process,the PBRTQC intelligent monitoring platform was able to issue risk warning or alarm prompt in advance.PBRTQC may have certain limi-tations,such as the error of red blood cell count,which need to be identified.Conclusion PBRTQC is superior to IQC in blood cell analysis and may play a complementary role in IQC.Meanwhile,it is necessary to exclude the possibility that PBRTQC is significantly influenced by the patient population in medical laboratories.

Objective To investigate the value of patient data-based real-time quality control(PBRTQC)in internal quality control(IQC)for blood cells analysis based on the data from patients.Methods The data of patients'blood cells,including white blood cell count(WBC),hemoglobin(Hb),red blood cell count(RBC),hematocrit(HCT),mean corpuscular volume(MCV),mean cor-puscular hemoglobin(MCH),mean corpuscular hemoglobin concentration(MCHC),and platelet(PLT)were collected from August 1,2023 to February 26,2024,and the extracted patient data were analyzed on the AI-based real-time quality control intelligent moni-toring platform.The corresponding IQC data for this period were reviewed,and the results of PBRTQC and IQC were compared and an-alyzed.The causes of the emerging warning or alarm prompts were checked and analyzed to explore the application value of PBRTQC in the IQC process of blood cell analysis.Results It is found that when the quality control product was unstable due to overlong opening time of the reagent or improper storage conditions,and the performance changes of the operating system during the detection process,the PBRTQC intelligent monitoring platform was able to issue risk warning or alarm prompt in advance.PBRTQC may have certain limi-tations,such as the error of red blood cell count,which need to be identified.Conclusion PBRTQC is superior to IQC in blood cell analysis and may play a complementary role in IQC.Meanwhile,it is necessary to exclude the possibility that PBRTQC is significantly influenced by the patient population in medical laboratories.

Objective:To construct A novel scoring model of immune signatures for colon cancer based on machine learning,which improve the survival prediction and immune therapy.Methods:Screening immune signatures from 1 301 immune-related genes(IRG)by the combined strategy of Lasso+bootstrap+multi Cox to calculate IRG scores of colon cancer patients from TCGA databases,and comprehensive the differences on function,prognostic status and immune therapy between high IRG scores group and IRG scores group.Results:Groups based on IRG scores were significantly different on the prognostic status of colon cancer patients,which were validated by other independent datasets.The IRG scores also could assess the effect of immune therapy of colon cancer.Conclusion:This study provides ideas for immune therapy and researches of colon cancer based on immune genes,and IRG scores can be used to assess the prognosis of colon cancer patients.

There were 3 000 Zhuangs,1 102 Miaos, and 1 283 Yaos in Baise City of Guangxi,who were enrolled in this population sampling stratfying survey. Height, weight, blood pressure, fasting blood glucose, and lipids were determined, and compared with those of 2 000 Hans of the same town. The prevalence of hypertension and metablic syndrome in Zhuang inhabitants was high, so were the disorders of glycemia and lipidemia, while in Miao and Yao minorities, the prevalences were comparatively lower. The awareness, treatment, and control of hypertension in these minorities were insufficient.