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.

The incidence of thyroid cancer has been increasing,and early diagnosis and treatment are crucial for improving patient prognosis.With the advancement of artificial intelligence(AI)technology,significant progress has been made in its application in the diagnosis and treatment of thyroid cancer.AI technology has notably enhanced the diagnostic accuracy of thyroid cancer.By optimizing imaging examinations such as ultrasound and CT scans,it can more precisely identify malignant features of thyroid nodules.In fine-needle aspiration biopsy,the integration of AI with genetic testing technologies has improved both the accuracy and efficiency of diagnosis.In terms of treatment,AI assists in intraoperative functional preservation,reducing the risk of surgical trauma.For instance,it can accurately identify the locations of the recurrent laryngeal nerve and parathyroid glands.Additionally,AI is capable of predicting the efficacy of 131I treatment and the risk of complications,thereby guiding postoperative follow-up and management.The core strength of AI technology lies in its powerful data processing and analytical capabilities,enabling it to uncover latent patterns within data and provide a scientific basis for treatment decision-making.Looking ahead,with continuous technological advancements,AI is expected to propel the diagnosis and treatment of thyroid cancer towards greater intelligence and precision.However,challenges such as data privacy and algorithm transparency need to be addressed.This article provides a review of the research progress of AI technology in the fields of diagnosis,treatment,and prognosis prediction of thyroid cancer,explores the current strengths and weaknesses of AI technology,and looks forward to its future development directions while acknowledging challenges like data privacy and algorithm transparency.

Objective This study focuses on a tertiary public hospital in Ningxia to explore the practical application of discipline-specific research performance evaluation.To establish a performance evaluation index system tailored to the characteris-tics of tertiary public hospitals in underdeveloped regions and propose strategies for improving research performance evaluation through empirical research,thereby promoting high-quality hospital development.Methods Guided by the performance evalua-tion indicators for tertiary public hospitals and the accreditation standards for tertiary hospitals,and aligned with the hospital's o-verall work plan,a multi-dimensional and multi-level evaluation method was adopted.Following the SMART principles(Specific,Measurable,Achievable,Relevant,Time-bound)and differentiated scoring principles,research performance evaluation indica-tors were summarized,screened,and weighted.An empirical analysis of the research status of 20 disciplines from 2021 to 2024 was conducted to establish and continuously optimize a research performance evaluation index system suited to the hospital's needs.Results A research performance evaluation index system for public hospital disciplines was finalized,comprising 5 first-level indicators,14 second-level indicators,and 40 third-level indicators(22 quantitative and 18 qualitative).This system standardized the management of discipline-specific research and effectively promoted steady growth,structural adjustment,and development in hospital research.Conclusion Constructing a scientific,standardized,and operable research performance eval-uation index system is of significant importance for enhancing the research level of disciplines in public hospitals and strengthening discipline construction.

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 This study focuses on a tertiary public hospital in Ningxia to explore the practical application of discipline-specific research performance evaluation.To establish a performance evaluation index system tailored to the characteris-tics of tertiary public hospitals in underdeveloped regions and propose strategies for improving research performance evaluation through empirical research,thereby promoting high-quality hospital development.Methods Guided by the performance evalua-tion indicators for tertiary public hospitals and the accreditation standards for tertiary hospitals,and aligned with the hospital's o-verall work plan,a multi-dimensional and multi-level evaluation method was adopted.Following the SMART principles(Specific,Measurable,Achievable,Relevant,Time-bound)and differentiated scoring principles,research performance evaluation indica-tors were summarized,screened,and weighted.An empirical analysis of the research status of 20 disciplines from 2021 to 2024 was conducted to establish and continuously optimize a research performance evaluation index system suited to the hospital's needs.Results A research performance evaluation index system for public hospital disciplines was finalized,comprising 5 first-level indicators,14 second-level indicators,and 40 third-level indicators(22 quantitative and 18 qualitative).This system standardized the management of discipline-specific research and effectively promoted steady growth,structural adjustment,and development in hospital research.Conclusion Constructing a scientific,standardized,and operable research performance eval-uation index system is of significant importance for enhancing the research level of disciplines in public hospitals and strengthening discipline construction.

The incidence of thyroid cancer has been increasing,and early diagnosis and treatment are crucial for improving patient prognosis.With the advancement of artificial intelligence(AI)technology,significant progress has been made in its application in the diagnosis and treatment of thyroid cancer.AI technology has notably enhanced the diagnostic accuracy of thyroid cancer.By optimizing imaging examinations such as ultrasound and CT scans,it can more precisely identify malignant features of thyroid nodules.In fine-needle aspiration biopsy,the integration of AI with genetic testing technologies has improved both the accuracy and efficiency of diagnosis.In terms of treatment,AI assists in intraoperative functional preservation,reducing the risk of surgical trauma.For instance,it can accurately identify the locations of the recurrent laryngeal nerve and parathyroid glands.Additionally,AI is capable of predicting the efficacy of 131I treatment and the risk of complications,thereby guiding postoperative follow-up and management.The core strength of AI technology lies in its powerful data processing and analytical capabilities,enabling it to uncover latent patterns within data and provide a scientific basis for treatment decision-making.Looking ahead,with continuous technological advancements,AI is expected to propel the diagnosis and treatment of thyroid cancer towards greater intelligence and precision.However,challenges such as data privacy and algorithm transparency need to be addressed.This article provides a review of the research progress of AI technology in the fields of diagnosis,treatment,and prognosis prediction of thyroid cancer,explores the current strengths and weaknesses of AI technology,and looks forward to its future development directions while acknowledging challenges like data privacy and algorithm transparency.

Regardless of the underlying etiology, renal fibrosis is the final histological outcome of progressive kidney disease. Unilateral ureteral obstruction (UUO) is an ideal and reproducible experimental rodent model of renal fibrosis, which is characterized by tubulointerstitial inflammatory responses, accumulation of extracellular matrix, tubular dilatation and atrophy, and fibrosis. The magnitude of UUO-induced renal fibrosis is experimentally manipulated by the species chosen, animal age, and the severity and duration of the obstruction, while relief of the obstruction allows the animal to recover from fibrosis. The pathogenesis of renal fibrosis is complex and multifactorial and is orchestrated by activation of renin-angiotensin system (RAS), oxidative stress, inflammatory response, transforming growth factor beta 1-Smad pathway, activated myofibroblasts, cell death (apoptosis, autophagy, ferroptosis, and necroptosis), destruction of intracellular organelles, and signaling pathway. The current therapeutic approaches have limited efficacy. Inhibition of RAS and use of antioxidants and antidiabetic drugs, such as inhibitors of sodium-glucose cotransporter 2 and dipeptidyl peptidase-4, have recently gained attention as therapeutic strategies to prevent renal scarring. This literature review highlights the state of the art regarding the molecular mechanisms relevant to the management of renal fibrosis caused by UUO.

Regardless of the underlying etiology, renal fibrosis is the final histological outcome of progressive kidney disease. Unilateral ureteral obstruction (UUO) is an ideal and reproducible experimental rodent model of renal fibrosis, which is characterized by tubulointerstitial inflammatory responses, accumulation of extracellular matrix, tubular dilatation and atrophy, and fibrosis. The magnitude of UUO-induced renal fibrosis is experimentally manipulated by the species chosen, animal age, and the severity and duration of the obstruction, while relief of the obstruction allows the animal to recover from fibrosis. The pathogenesis of renal fibrosis is complex and multifactorial and is orchestrated by activation of renin-angiotensin system (RAS), oxidative stress, inflammatory response, transforming growth factor beta 1-Smad pathway, activated myofibroblasts, cell death (apoptosis, autophagy, ferroptosis, and necroptosis), destruction of intracellular organelles, and signaling pathway. The current therapeutic approaches have limited efficacy. Inhibition of RAS and use of antioxidants and antidiabetic drugs, such as inhibitors of sodium-glucose cotransporter 2 and dipeptidyl peptidase-4, have recently gained attention as therapeutic strategies to prevent renal scarring. This literature review highlights the state of the art regarding the molecular mechanisms relevant to the management of renal fibrosis caused by UUO.