To explore the correlation between MTHFR C677T gene polymorphism and hypertension, hyperhomocysteinemia(Hcy), and hyperlipidemia in the Tibetan population of Tibet.

Objective To study the five-year survival status and influencing factors of elderly patients with lung cancer complicated with chronic obstructive pulmonary disease (COPD). Methods A cohort study was conducted to follow up 450 patients with lung cancer and chronic obstructive pulmonary disease who were hospitalized in our hospital from January 2018 to December 2023. The endpoint of the follow-up was the end of a five-year period or death. The Life Tables method was used to calculate survival rates and plot survival curves. The Cox proportional hazards model was used to analyze the influencing factors of five-year survival. Results The results indicated that the overall five-year survival rate of patients was 4.89%, and it decreased year by year. Cox regression analysis showed that age, gender, family functioning, and psychological status significantly influenced patient survival rate (all P<0.05). Stratified analysis found that the smoking status, family functioning, and psychological status of male patients all had an impact on survival rate (all P<0.05), while the psychological status of female patients had a more significant impact on survival (P=0.008). Conclusion This study provides a scientific basis for comprehensive intervention of elderly lung cancer patients with COPD. It is recommended that clinical attention should be paid to psychological and family factors to improve patient prognosis.

Background: The genetic basis for hyperglycaemia in pregnancy remain unclear. This study aimed to uncover the genetic determinants of gestational diabetes mellitus (GDM) and investigate their applications. Methods: We performed a meta-analysis of genome-wide association studies (GWAS) for GDM in Chinese women (464 cases and 1,217 controls), followed by de novo replications in an independent Chinese cohort (564 cases and 572 controls) and in silico replication in European (12,332 cases and 131,109 controls) and multi-ethnic populations (5,485 cases and 347,856 controls). A polygenic risk score (PRS) was derived based on the identified variants. Results: Using the genome-wide scan and candidate gene approaches, we identified four susceptibility loci for GDM. These included three previously reported loci for GDM and type 2 diabetes mellitus (T2DM) at MTNR1B (rs7945617, odds ratio [OR], 1.64; 95% confidence interval [CI],1.38 to 1.96]), CDKAL1 (rs7754840, OR, 1.33; 95% CI, 1.13 to 1.58), and INS-IGF2-KCNQ1 (rs2237897, OR, 1.48; 95% CI, 1.23 to 1.79), as well as a novel genome-wide significant locus near TBR1-SLC4A10 (rs117781972, OR, 2.05; 95% CI, 1.61 to 2.62; Pmeta=7.6×10-9), which has not been previously reported in GWAS for T2DM or glycaemic traits. Moreover, we found that women with a high PRS (top quintile) had over threefold (95% CI, 2.30 to 4.09; Pmeta=3.1×10-14) and 71% (95% CI, 1.08 to 2.71; P=0.0220) higher risk for GDM and abnormal glucose tolerance post-pregnancy, respectively, compared to other individuals. Conclusion Our results indicate that the genetic architecture of glucose metabolism exhibits both similarities and differences between the pregnant and non-pregnant states. Integrating genetic information can facilitate identification of pregnant women at a higher risk of developing GDM or later diabetes.

Background: The genetic basis for hyperglycaemia in pregnancy remain unclear. This study aimed to uncover the genetic determinants of gestational diabetes mellitus (GDM) and investigate their applications. Methods: We performed a meta-analysis of genome-wide association studies (GWAS) for GDM in Chinese women (464 cases and 1,217 controls), followed by de novo replications in an independent Chinese cohort (564 cases and 572 controls) and in silico replication in European (12,332 cases and 131,109 controls) and multi-ethnic populations (5,485 cases and 347,856 controls). A polygenic risk score (PRS) was derived based on the identified variants. Results: Using the genome-wide scan and candidate gene approaches, we identified four susceptibility loci for GDM. These included three previously reported loci for GDM and type 2 diabetes mellitus (T2DM) at MTNR1B (rs7945617, odds ratio [OR], 1.64; 95% confidence interval [CI],1.38 to 1.96]), CDKAL1 (rs7754840, OR, 1.33; 95% CI, 1.13 to 1.58), and INS-IGF2-KCNQ1 (rs2237897, OR, 1.48; 95% CI, 1.23 to 1.79), as well as a novel genome-wide significant locus near TBR1-SLC4A10 (rs117781972, OR, 2.05; 95% CI, 1.61 to 2.62; Pmeta=7.6×10-9), which has not been previously reported in GWAS for T2DM or glycaemic traits. Moreover, we found that women with a high PRS (top quintile) had over threefold (95% CI, 2.30 to 4.09; Pmeta=3.1×10-14) and 71% (95% CI, 1.08 to 2.71; P=0.0220) higher risk for GDM and abnormal glucose tolerance post-pregnancy, respectively, compared to other individuals. Conclusion Our results indicate that the genetic architecture of glucose metabolism exhibits both similarities and differences between the pregnant and non-pregnant states. Integrating genetic information can facilitate identification of pregnant women at a higher risk of developing GDM or later diabetes.

Background: The genetic basis for hyperglycaemia in pregnancy remain unclear. This study aimed to uncover the genetic determinants of gestational diabetes mellitus (GDM) and investigate their applications. Methods: We performed a meta-analysis of genome-wide association studies (GWAS) for GDM in Chinese women (464 cases and 1,217 controls), followed by de novo replications in an independent Chinese cohort (564 cases and 572 controls) and in silico replication in European (12,332 cases and 131,109 controls) and multi-ethnic populations (5,485 cases and 347,856 controls). A polygenic risk score (PRS) was derived based on the identified variants. Results: Using the genome-wide scan and candidate gene approaches, we identified four susceptibility loci for GDM. These included three previously reported loci for GDM and type 2 diabetes mellitus (T2DM) at MTNR1B (rs7945617, odds ratio [OR], 1.64; 95% confidence interval [CI],1.38 to 1.96]), CDKAL1 (rs7754840, OR, 1.33; 95% CI, 1.13 to 1.58), and INS-IGF2-KCNQ1 (rs2237897, OR, 1.48; 95% CI, 1.23 to 1.79), as well as a novel genome-wide significant locus near TBR1-SLC4A10 (rs117781972, OR, 2.05; 95% CI, 1.61 to 2.62; Pmeta=7.6×10-9), which has not been previously reported in GWAS for T2DM or glycaemic traits. Moreover, we found that women with a high PRS (top quintile) had over threefold (95% CI, 2.30 to 4.09; Pmeta=3.1×10-14) and 71% (95% CI, 1.08 to 2.71; P=0.0220) higher risk for GDM and abnormal glucose tolerance post-pregnancy, respectively, compared to other individuals. Conclusion Our results indicate that the genetic architecture of glucose metabolism exhibits both similarities and differences between the pregnant and non-pregnant states. Integrating genetic information can facilitate identification of pregnant women at a higher risk of developing GDM or later diabetes.

Background: The genetic basis for hyperglycaemia in pregnancy remain unclear. This study aimed to uncover the genetic determinants of gestational diabetes mellitus (GDM) and investigate their applications. Methods: We performed a meta-analysis of genome-wide association studies (GWAS) for GDM in Chinese women (464 cases and 1,217 controls), followed by de novo replications in an independent Chinese cohort (564 cases and 572 controls) and in silico replication in European (12,332 cases and 131,109 controls) and multi-ethnic populations (5,485 cases and 347,856 controls). A polygenic risk score (PRS) was derived based on the identified variants. Results: Using the genome-wide scan and candidate gene approaches, we identified four susceptibility loci for GDM. These included three previously reported loci for GDM and type 2 diabetes mellitus (T2DM) at MTNR1B (rs7945617, odds ratio [OR], 1.64; 95% confidence interval [CI],1.38 to 1.96]), CDKAL1 (rs7754840, OR, 1.33; 95% CI, 1.13 to 1.58), and INS-IGF2-KCNQ1 (rs2237897, OR, 1.48; 95% CI, 1.23 to 1.79), as well as a novel genome-wide significant locus near TBR1-SLC4A10 (rs117781972, OR, 2.05; 95% CI, 1.61 to 2.62; Pmeta=7.6×10-9), which has not been previously reported in GWAS for T2DM or glycaemic traits. Moreover, we found that women with a high PRS (top quintile) had over threefold (95% CI, 2.30 to 4.09; Pmeta=3.1×10-14) and 71% (95% CI, 1.08 to 2.71; P=0.0220) higher risk for GDM and abnormal glucose tolerance post-pregnancy, respectively, compared to other individuals. Conclusion Our results indicate that the genetic architecture of glucose metabolism exhibits both similarities and differences between the pregnant and non-pregnant states. Integrating genetic information can facilitate identification of pregnant women at a higher risk of developing GDM or later diabetes.

Objective : To investigate the protective effect of dimethyl fumarate(DMF) on maternal intrahepatic cholestasis(ICP) during pregnancy induced by di(2-ethylhexyl) phthalate(DEHP) exposure and its mechanism. Methods : Thirty-two 8-week-old female institute of cancer research(ICR) mice were randomly divided into 4 groups: Ctrl group, DEHP group, DMF group and DEHP+DMF group. DEHP and DEHP+DMF groups were treated with DEHP(200 mg/kg) by gavage every morning at 9:00 a.m. DMF and DEHP+DMF groups were treated with DMF(150 mg/kg) from day 13 to day 16 of gestation by gavage. After completion of gavage on day 16 of pregnancy, maternal blood, maternal liver, placenta, and amniotic fluid were collected from pregnant mice after a six-hour abrosia. The body weight of the mother rats and the body weight of the fetus rats were sorted and analyzed; the levels of total bile acid(TBA), alkaline phosphatase(ALP), aspartate aminotransferase/alanine aminotransferase(AST/ALT) in serum and TBA in liver, amniotic fluid and placenta were detected by biochemical analyzer; HE staining was used to observe the pathological changes of liver tissue; Quantitative reverse transcription PCR(RT-qPCR) was used to detect the expression levels of tumor necrosis factor-α(TNF-α), interleukin(IL)-6, IL-1, IL-18 and NOD-like receptor thermal protein domain associated protein 3(NLRP3) in the liver; Western blot was used to detect the expression of the nuclear factor KappaB(NF-κB) and NLRP3. Results : Compared with the control group, the body weight of the DEHP-treated dams and pups decreased(P<0.05); the levels of TBA, ALP, AST/ALT in the serum of dams and the levels of TBA in the liver, amniotic fluid, and placenta of dams increased(P<0.05); the histopathological results showed that liver tissue was damaged, bile ducts were deformed, and there was inflammatory cell infiltration around them; the levels of inflammation-related factors TNF-α, IL-6, IL-1, IL-18 and NLRP3 transcription in maternal liver increased(P<0.05); the expression of NF-κB and NLRP3 protein in maternal liver significantly increased( P<0. 05). Compared with the DEHP group,the body weight of both dams and fetuses significantly increased in DEHP + DMF group( P<0. 05); the levels of TBA,ALP,AST/ALT in the serum of dams and amniotic fluid of fetuses decreased( P<0. 05); the degree of liver lesions was improved; the transcription levels of inflammation-related factors TNF-α,IL-6,IL-1,IL-18 and NLRP3 in maternal liver decreased( P<0. 05); the expression of NF-κB and NLRP3 protein in maternal liver significantly decreased( P<0. 05). Conclusion DMF can effectively protect the DEHP exposure to lead to female ICP,and its mechanism may be through inhibiting the NF-κB/NLRP3 pathway and reducing liver inflammation.

OBJECTIVES: To study the clinical manifestations and genetic characteristics of children with maturity-onset diabetes of the young type 2 (MODY2), aiming to enhance the recognition of MODY2 in clinical practice. METHODS: A retrospective analysis was conducted on the clinical data of 13 children diagnosed with MODY2 at the Department of Pediatrics of Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology from August 2017 to July 2023. RESULTS: All 13 MODY2 children had a positive family history of diabetes and were found to have mild fasting hyperglycemia [(6.4±0.5) mmol/L] during health examinations or due to infectious diseases. In the oral glucose tolerance test, two cases met the diagnostic criteria for diabetes with fasting blood glucose, while the others exhibited impaired fasting glucose or impaired glucose tolerance. The one-hour post-glucose load (1-hPG) fluctuated between 8.31 and 13.06 mmol/L, meeting the diagnostic criteria for diabetes recommended by the International Diabetes Federation. All 13 MODY2 children had heterozygous variants in the glucokinase (GCK) gene, with Cases 6 (GCK c.1047C>A, p.Y349X), 11 (GCK c.1146_1147ins GCAGAGCGTGTCTACGCGCGCTGCGCACATGTGC, p.S383Alafs*87), and 13 (GCK c.784_785insC, p.D262Alafs*13) presenting variants that had not been previously reported. CONCLUSIONS This study enriches the spectrum of genetic variations associated with MODY2. Clinically, children with a family history of diabetes, incidental findings of mild fasting hyperglycemia, and negative diabetes-related antibodies should be considered for the possibility of MODY2.
Humans ; Diabetes Mellitus, Type 2/diagnosis* ; Male ; Female ; Child ; Retrospective Studies ; Glucokinase/genetics* ; Adolescent ; Child, Preschool ; Glucose Tolerance Test

Recent advances in large models demonstrate significant prospects for transforming the field of medical imaging. These models, including large language models, large visual models, and multimodal large models, offer unprecedented capabilities in processing and interpreting complex medical data across various imaging modalities. By leveraging self-supervised pretraining on vast unlabeled datasets, cross-modal representation learning, and domain-specific medical knowledge adaptation through fine-tuning, large models can achieve higher diagnostic accuracy and more efficient workflows for key clinical tasks. This review summarizes the concepts, methods, and progress of large models in medical imaging, highlighting their potential in precision medicine. The article first outlines the integration of multimodal data under large model technologies, approaches for training large models with medical datasets, and the need for robust evaluation metrics. It then explores how large models can revolutionize applications in critical tasks such as image segmentation, disease diagnosis, personalized treatment strategies, and real-time interactive systems, thus pushing the boundaries of traditional imaging analysis. Despite their potential, the practical implementation of large models in medical imaging faces notable challenges, including the scarcity of high-quality medical data, the need for optimized perception of imaging phenotypes, safety considerations, and seamless integration with existing clinical workflows and equipment. As research progresses, the development of more efficient, interpretable, and generalizable models will be critical to ensuring their reliable deployment across diverse clinical environments. This review aims to provide insights into the current state of the field and provide directions for future research to facilitate the broader adoption of large models in clinical practice.
Humans ; Diagnostic Imaging/methods* ; Precision Medicine/methods* ; Image Processing, Computer-Assisted/methods*

This study aimed to explore the pharmacological mechanism of Yourenji Capsules(YRJ) in improving osteoporosis by combining network pharmacology and proteomics technologies. The SD rats were randomly divided into a blank control group and a 700 mg·kg~(-1) YRJ group. The rats were subjected to gavage administration with the corresponding drugs, and the blank serum, drug-containing serum, and YRJ samples were compared using ultra performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry(UPLC-Q-TOF-MS/MS) to analyze the main components absorbed into blood. Network pharmacology analysis was conducted based on the YRJ components absorbed into blood to obtain related targets of the components and target genes involved in osteoporosis, and Venn diagrams were used to identify the intersection of drug action targets and disease targets. The STRING database was used for protein-protein interaction(PPI) network analysis of potential target proteins to construct a PPI network. Gene Ontology(GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment were performed using Enrichr to investigate the potential mechanism of action of YRJ. Ovariectomy(OVX) was performed to establish a rat model of osteoporosis, and the rats were divided into a sham group, a model group, and a 700 mg·kg~(-1) YRJ group. The rats were given the corresponding drugs by gavage. The femurs of the rats were subjected to label-free proteomics analysis to detect differentially expressed proteins, and GO functional enrichment and KEGG pathway enrichment analyses were performed on the differentially expressed proteins. With the help of network pharmacology and proteomics results, the mechanism by which YRJ improves osteoporosis was predicted. The analysis of the YRJ components absorbed into blood revealed 23 bioactive components of YRJ, and network pharmacology results indicated that key targets involved include tumor necrosis factor(TNF), tumor protein p53(TP53), protein kinase(AKT1), and matrix metalloproteinase 9(MMP9). These targets are mainly involved in osteoclast differentiation, estrogen signaling pathways, and nuclear factor-kappa B(NF-κB) signaling pathways. Additionally, the proteomics analysis highlighted important pathways such as peroxisome proliferator-activated receptor(PPAR) signaling pathways, mitogen-activated protein kinase(MAPK) signaling pathways, and β-alanine metabolism. The combined approaches of network pharmacology and proteomics have revealed that the mechanism by which YRJ improves osteoporosis may be closely related to the regulation of inflammation, osteoblast, and osteoclast metabolic pathways. The main pathways involved include the NF-κB signaling pathways, MAPK signaling pathways, and PPAR signaling pathways, among others.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Osteoporosis/metabolism* ; Proteomics ; Rats ; Rats, Sprague-Dawley ; Network Pharmacology ; Female ; Protein Interaction Maps/drug effects* ; Capsules ; Humans ; Signal Transduction/drug effects*