Development of a clinical prediction model for diabetic kidney disease with glucose and lipid metabolism disorders based on machine learning and bioinformatics technology.

OBJECTIVE: In this study, we investigated the internal relationship between the pathogenesis of diabetic kidney disease (DKD) and abnormal glucose and lipid metabolism to identify potential biomarkers for diagnosis and treatment and investigated the role of the immune microenvironment of glucose and lipid metabolism disorders in the occurrence and progression of DKD. MATERIALS AND METHODS: The chip datasets GSE104948 and GSE96804 from the Gene Expression Common Database (GEO) were merged using the "lima" and "sva" software packages in R Software (4.2.3), and the merged dataset was used as the validation set. The intersection between the differential genes of DKD and the glucose and lipid metabolism genes in the MSigDB database was identified, and a nomogram of the incidence risk of DKD was built using three machine learning methods, namely LASSO regression, support vector machine (SVM), and random forest (RF), to validate the accuracy of the prediction model. Immune scores were conducted using the unsupervised clustering method, and patients were divided into two subgroups. The two subgroups were screened for differential genes for enrichment analysis. The differential genes of patients diagnosed with DKD were clustered into two gene subgroups for co-expression analysis. In this study, we utilized the Cytoscape software to construct a network of interactions among key genes. RESULTS: Using machine learning, a diagnostic model was developed with G6PC and HSD17B14 as key factors. Enrichment analysis and immune scoring demonstrated that the development of DKD was related to the imbalance in the microenvironment brought about by glucose lipid metabolism disorders. CONCLUSIONS: G6PC and HSD17B14 may be potential biomarkers for DKD, and the established predictive model is more helpful in predicting the incidence of DKD.

[Analysis of clinical epidemiological characteristics of respiratory syncytial virus in children in a hospital of pediatric in Hubei Province from 2020 to 2023].

Objective: To explore the clinical epidemiological characteristics of respiratory syncytial virus in children in Hubei Province from 2020 to 2023. Method: A single-center and cross-sectional study was used to analyze the clinical data of 3 271 children with respiratory syncytial virus infection in Wuhan Children's Hospital affiliated to Huazhong University of Science and Technology from July 1, 2020 to June 30, 2023. Nonparametric rank sum test and χ2 test were used for comparative analysis. Results: From July 1, 2020 to June 30, 2023, a total of 25 583 children were included in the analysis, of which 3 271(12.8%) children infected RSV. The detection rate was 16.3% in 2020-2021, 14.7% in 2021-2022 and 9.1% in 2022-2023. The detection rate decreased year by year (χ2=222.054, P<0.05). From 2020 to 2023, there was an anti-seasonal epidemic of RSV in spring and autumn. The detection rate of RSV in infants under 1 year old was the highest, but the median ages of RSV positive children increased (H=140.575, P<0.05). Pneumonia was the main clinical manifestation of RSV respiratory tract infection. Conclusion: The epidemiological characteristics of RSV in children in Hubei Province were different from those before. From 2020 to 2023, the detection rate of RSV decreased year by year. Besides winter, the prevalence of RSV could also be seen in spring and autumn. The median age of children infected with RSV increased after the epidemic. Pneumonia was the main clinical manifestation after RSV infection.

[Diagnostic value of high frequency oscillation in localization of type â ¡ focal cortical dysplasia epilepsy].

Retrospective analysis was conducted on 9 patients with type Ⅱ focal cortical dysplasia (FCD) who underwent stereo-electroencephalography (SEEG) implantation in the Department of Neurosurgery of the First Affiliated Hospital of Fujian Medical University from November 2020 to February 2023. The onset area, onset time, and frequency of high-frequency oscillations (HFO) were analyzed and the correlation of HFOs with interictal, preictal, and ictal periods. SEEG recordings of 80-500 Hz HFOs were observed in both interictal and ictal periods in 9 patients, with 6 patients exhibiting fast ripples (FR) in the range of 250-500 Hz. Surgical resection of the seizure onset area and FR-generating electrodes was performed, and postoperative follow-up for over 2 years indicated Engel I in 5 cases. 6 patients showed continuous discharge during the preictal period, and the distribution index of continuous discharge was positively correlated with seizure frequency. HFOs in the range of 80-500 Hz were present in all four seizure onset patterns during the ictal period. The onset area and FR-emitting electrode were surgically removed in 6 patients with continuous discharge and overlapping HFOs during the preictal period, with 5 cases of Engel I. Type Ⅱ FCD discharges exhibited complexity, high discharge indices, and a close association with HFOs. Compared with the spike wave, the electrode range of HF is more limited, and the incidence of HF before attack is significantly increased, which is closely correlated with the onset area. The simultaneous occurrence of HFO and the spike waves has higher diagnostic value than the individual occurrence, effectively enhancing surgical efficacy.