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.

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.

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.

Antithrombotic agents, including antiplatelet agents and anticoagulants, are widely used in Korea because of the increasing incidence of cardiocerebrovascular disease and the aging population. The management of patients using antithrombotic agents during endoscopic procedures is an important clinical challenge. The clinical practice guidelines for this issue, developed by the Korean Society of Gastrointestinal Endoscopy, were published in 2020. However, new evidence on the use of dual antiplatelet therapy and direct anticoagulant management has emerged, and revised guidelines have been issued in the United States and Europe. Accordingly, the previous guidelines were revised. Cardiologists were part of the group that developed the guideline, and the recommendations went through a consensus-reaching process among international experts. This guideline presents 14 recommendations made based on the Grading of Recommendations, Assessment, Development, and Evaluation methodology and was reviewed by multidisciplinary experts. These guidelines provide useful information that can assist endoscopists in the management of patients receiving antithrombotic agents who require diagnostic and elective therapeutic endoscopy. It will be revised as necessary to cover changes in technology, evidence, or other aspects of clinical practice.

Objective To analyze the situation of life sciences and medical research involving human subjects conducted in a public hospital in Ningxia from 2020 to 2022 and explore the problems in the current management of such research and pro-vides suggestions for improvement.Methods Data were collected from research project application documents,task assign-ments,progress reports,final reports,and statistical tables of clinical drug trials conducted in the hospital from 2021 to 2022.Statistical analysis was performed on the life sciences and medical research involving human subjects.Results The number of research projects involving human subjects in the hospital increased annually from 2020 to 2022,accounting for more than 50%of the total projects.Over the three years,the sample size exceeded 100,000,and the number of projects with a cumulative collec-tion of more than 3,000 individuals showed an upward trend.However,the project registration rate was less than 10%.These types of research involve a large number of biological samples and information data,which have research value.However,re-searchers lack awareness of project registration,highlighting the need for greater attention from the hospital.Conclusion It is a trend to explore the medical value of this type of research in depth.The hospital should closely follow relevant national and re-gional policies,conduct dynamic monitoring and evaluation,and enhance supervision and management capabilities as a key factor in protecting biological samples and information data.This study also provides reference for other public hospitals in carrying out related regulatory work.