Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

To summarize the clinical experience of Professor LIU Shangyi in treating pruritus vulvae. It is believed that women have the physiological characteristics of liver and kidney as the root， and their pubic area is easily attacked by wind-dampness pathogenic qi， so the core mechanism of pruritus vulvae is proposed as wind-dampness accumulation and deficiency of liver and kidney. The core treatment method is to dispel wind-dampness and nourish the liver and kidneys， and modify the Danggui Decoction （当归饮子） to form a self-prescribed Yinyang Formula （阴痒方） as the basic prescription to treat pruritus vulvaen.

The underlying cause of low response rates to existing immunotherapies is that tumor cells dominate tumor immune escape through surface antigen deficiency and inducing tumor immunosuppressive microenvironment (TIME). Here, we proposed an in situ tumor cell engineering strategy to disrupt tumor immune escape at the root by restoring tumor cell MHC-I/tumor-specific antigen complex (MHC-I/TSA) expression to promote T-cell recognition and by silencing tumor cell CD55 to increase the ICOSL⁺ B-cell proportion and reverse the TIME. A doxorubicin (DOX) and dual-gene plasmid (MAC pDNA, encoding both MHC-I/ASMTNMELM and CD55-shRNA) coloaded drug delivery system (LCPN@ACD) with tumor targeting and charge/size dual-conversion properties was prepared. LCPN@ACD-induced ICD promoted DC maturation and enhanced T-cell activation and infiltration. LCPN@ACD enabled effective expression of MHC-I/TSA on tumor cells, increasing the ability of tumor cell recognition and killing. LCPN@ACD downregulated tumor cell CD55 expression, increased the proportion of ICOSL⁺ B cells and CTLs, and reversed the TIME, thus greatly improving the efficacy of αPD-1 and CAR-T therapies. The application of this in situ tumor cell engineering strategy eliminated the source of tumor immune escape, providing new ideas for solving the challenges of clinical immunotherapy.

OBJECTIVE: To investigate the association between ABO blood types and gestational diabetes mellitus (GDM) risk. METHODS: A prospective birth cohort study was conducted. ABO blood types were determined using the slide method. GDM diagnosis was based on a 75-g, 2-h oral glucose tolerance test (OGTT) according to the criteria of the International Association of Diabetes and Pregnancy Study Groups. Logistic regression was applied to calculate the odds ratios ( ORs) and 95% confidence intervals ( CIs) between ABO blood types and GDM risk. RESULTS: A total of 30,740 pregnant women with a mean age of 31.81 years were enrolled in this study. The ABO blood types distribution was: type O (30.99%), type A (26.58%), type B (32.20%), and type AB (10.23%). GDM was identified in 14.44% of participants. Using blood type O as a reference, GDM risk was not significantly higher for types A ( OR = 1.05) or B ( OR = 1.04). However, women with type AB had a 19% increased risk of GDM ( OR = 1.19, 95% CI = 1.05-1.34; P < 0.05), even after adjusting for various factors. This increased risk for type AB was consistent across subgroup and sensitivity analyses. CONCLUSION The ABO blood types may influence GDM risk, with type AB associated with a higher risk. Incorporating it-either as a single risk factor or in combination with other known factors-could help identify individuals at risk for GDM before or during early pregnancy.
Humans ; Female ; Pregnancy ; Diabetes, Gestational/etiology* ; ABO Blood-Group System ; Adult ; Prospective Studies ; Risk Factors ; Young Adult

Objective A new occipital bone removal technology was applied to improve the success rate of brain removal.Methods The skull was sawed based on the traditional brain removal technology,and part of the occipital bone was removed downward centered in external occipital protuberance to the foramen magnum,then exposed the telencephalon,cerebellum and posterior medulla oblongata.After that,removed the tentorium cerebelli and cut down the medulla oblongata and the related cranial nerves at the skull base,then removed the brain tissues.Results The removed brain tissues had structurally intact telencephalon,cerebellum and brain stem,clear vessels in the cerebral sulci,and relatively intact optic chiasm,olfactory tracts and vertebro-basilar arteries.Conclusion Brain removal through a fenestration on the external occipital protuberance can effectively preserve the integrity of brain specimens,and improve the success rate of brain removal,which is of great significance for central nervous system teaching and improvement of human brain tissue repositories.

Hypertension is a systemic chronic vascular disease. From the perspective of Traditional Chinese Syndromes, hypertension belongs to the category of liver fire, vertigo, liver yang, headache and so on. Chinese medicine treatment of hypertension has gradually become a hot research topic, and using Chinese herbal medicine to reduce blood pressure has also achieved good results. In recent years, researches on anti-hypotension of Bidens pilosa L. has gradually increased. The related research of Bidens pilosa L., including the ancient literature, modern research, functional components and mechanism were mainly summarized, the application of Bidens pilosa L. in lowering blood pressure were anticipated, with a view to provide reference for the further development and utilization of Bidens pilosa L. in treatment of hypertension.