Objective: To investigate the current status of benefit finding among young and middle-aged patients with type 2 diabetes mellitus (T2DM) and analyze its influencing factors, so as to provide a reference for improving the level of benefit finding in this population. Methods: From November 2022 to May 2023, young and middle-aged patients with T2DM aged 18-59 years hospitalized in the endocrinology departments of 2 tertiary hospitals in Hengyang City, Hunan Province were selected as survey subjects by a convenience sampling method. Basic demographic information was collected using a general questionnaire survey. Benefit finding, resourcefulness, and stigma were evaluated using the Benefit Finding Scale, the Chinese Version of the Resourcefulness Scale, and the Type 2 Diabetes Stigma Assessment Scale, respectively. A multiple linear regression model was used to analyze the influencing factors of benefit finding among young and middle-aged patients with T2DM. Results: A total of 305 young and middle-aged patients with T2DM were investigated, including 222 males (72.79%) and 83 females (27.21%). There were 231 cases aged 45-59 years, accounting for 75.74%. The scores for benefit finding, resourcefulness, and stigma were (42.86±6.06), (75.12±11.30), and (41.20±10.10), respectively. Multiple linear regression analysis showed that young and middle-aged patients with T2DM who were male (β′=0.088), aged 18-<45 years (β′=0.083), absence of diabetes complications (β′=0.124), and had higher resourcefulness scores (β′=0.679) had higher levels of benefit finding, while patients with higher stigma scores (β′=-0.097) had lower levels of benefit finding. Conclusion The level of benefit finding among young and middle-aged patients with T2DM was moderate, and was related to gender, age, diabetes complications, resourcefulness, and stigma.

Transformer models have emerged as pivotal tools within the realm of drug discovery, distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes. Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data, these models showcase remarkable efficacy across various tasks, including new drug design and drug target identification. The adaptability of pre-trained transformer-based models renders them indispensable assets for driving data-centric advancements in drug discovery, chemistry, and biology, furnishing a robust framework that expedites innovation and discovery within these domains. Beyond their technical prowess, the success of transformer-based models in drug discovery, chemistry, and biology extends to their interdisciplinary potential, seamlessly combining biological, physical, chemical, and pharmacological insights to bridge gaps across diverse disciplines. This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields. In our review, we elucidate the myriad applications of transformers in drug discovery, as well as chemistry and biology, spanning from protein design and protein engineering, to molecular dynamics (MD), drug target identification, transformer-enabled drug virtual screening (VS), drug lead optimization, drug addiction, small data set challenges, chemical and biological image analysis, chemical language understanding, and single cell data. Finally, we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.

Transformer models have emerged as pivotal tools within the realm of drug discovery,distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes.Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data,these models showcase remarkable efficacy across various tasks,including new drug design and drug target identification.The adaptability of pre-trained trans-former-based models renders them indispensable assets for driving data-centric advancements in drug discovery,chemistry,and biology,furnishing a robust framework that expedites innovation and dis-covery within these domains.Beyond their technical prowess,the success of transformer-based models in drug discovery,chemistry,and biology extends to their interdisciplinary potential,seamlessly combining biological,physical,chemical,and pharmacological insights to bridge gaps across diverse disciplines.This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields.In our review,we elucidate the myriad applications of transformers in drug discovery,as well as chemistry and biology,spanning from protein design and protein engineering,to molecular dynamics(MD),drug target iden-tification,transformer-enabled drug virtual screening(VS),drug lead optimization,drug addiction,small data set challenges,chemical and biological image analysis,chemical language understanding,and single cell data.Finally,we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.

Objective To systematically evaluate the effectiveness and acceptability of antidepressant drugs in the treatment of postpartum depression(PPD).Methods The PubMed,Cochrane Library,Embase,Web of Science,China National Knowledge Infrastructure(CNKI),Wanfang Database,VIP Journals of Chinese Scienc were searched,and Chinese Biomedical Literature Service System(SinoMed)database until November 2023.Screen randomized controlled trials(RCTs)of antidepressant drugs for the treatment of PPD.The treatment group was given antidepressant drugs,and the control group was given placebo or another antidepressant drug.Meta-analysis of effectiveness and acceptability is performed using Stata 17.0 software.Results A total of 27 RCTs with a total of 2 202 patients were included.The results of meta-analysis showed:The top three efficacy relative to placebo were mirtazapine[odds ratio(OR)=2.25,95％confidence interval(CI)=(1.20-3.30),P＜0.05],nortriptyline[OR=1.50,95％CI=(0.55-2.44),P＞0.05],venlafaxine[OR=1.35,95％CI=(0.13-2.56),P＞0.05].Acceptability is compared with placebo in the top three Chinese herbal medicine[OR=0.47,95％CI=(-0.72-1.66),P＞0.05],nortriptyline[OR=-0.08,95％CI=(-1.16-1.33),P＞0.05],venlafaxine[OR=-0.12,95％CI=(-1.47-1.24),P＞0.05].Conclusion Nortriptyline,venlafaxine,trazodone,and duloxetine are effective in treating PPD without obvious adverse drug reactions.

AIM:To investigate the effects of macrophage galactose-type lectin 1(Mgl1)gene deletion on he-matopoietic stem/progenitor cells(HSPCs)under steady-state conditions and inflammation.METHODS:Mice were di-vided into a control group(wild-type)and an experimental group(Mgl1 gene-deleted).Flow cytometry was used to ana-lyze the proportions of various hematopoietic cell lineages in the peripheral blood and bone marrow of both groups,assess-ing the impact of Mgl1 gene deletion on steady-state hematopoiesis(n=3～4).Transplantation and colony-forming assays were utilized to evaluate the effects of Mgl1 gene deletion onthe repopulation capacity and colony-forming ability of HSPCs(n=5).The LPS-induced inflammation model was employed to examine the effects of Mgl1 gene deletion on the inflamma-tory response of HSPCs both in vitro and in vivo(n=5～8).Western blot and RT-qPCR were conducted to analyze the alter-ations in signaling pathways regulated by Mgl1 in the inflammatory response of HSPCs(n=3).RESULTS:(1)Mgl1 gene deletion had no significant effecton steady-state hematopoiesis(P>0.05).(2)Mgl1 gene deletion promoted inflam-mation-induced cell differentiation of HSPCs(P<0.01).(3)Mgl1 gene deletion accelerated the exhaustion of HSPCs un-der prolonged inflammatory conditions(P<0.01).(4)Mgl1 was found to regulate the inflammatory response of HSPCs via the NF-κB signaling pathway.CONCLUSION:Mgl1 gene deletion enhances the inflammatory response of HSPCs via the NF-κB signaling pathway.

Objective To systematically evaluate the effectiveness and acceptability of antidepressant drugs in the treatment of postpartum depression(PPD).Methods The PubMed,Cochrane Library,Embase,Web of Science,China National Knowledge Infrastructure(CNKI),Wanfang Database,VIP Journals of Chinese Scienc were searched,and Chinese Biomedical Literature Service System(SinoMed)database until November 2023.Screen randomized controlled trials(RCTs)of antidepressant drugs for the treatment of PPD.The treatment group was given antidepressant drugs,and the control group was given placebo or another antidepressant drug.Meta-analysis of effectiveness and acceptability is performed using Stata 17.0 software.Results A total of 27 RCTs with a total of 2 202 patients were included.The results of meta-analysis showed:The top three efficacy relative to placebo were mirtazapine[odds ratio(OR)=2.25,95％confidence interval(CI)=(1.20-3.30),P＜0.05],nortriptyline[OR=1.50,95％CI=(0.55-2.44),P＞0.05],venlafaxine[OR=1.35,95％CI=(0.13-2.56),P＞0.05].Acceptability is compared with placebo in the top three Chinese herbal medicine[OR=0.47,95％CI=(-0.72-1.66),P＞0.05],nortriptyline[OR=-0.08,95％CI=(-1.16-1.33),P＞0.05],venlafaxine[OR=-0.12,95％CI=(-1.47-1.24),P＞0.05].Conclusion Nortriptyline,venlafaxine,trazodone,and duloxetine are effective in treating PPD without obvious adverse drug reactions.

Purpose: Chronic low-grade inflammation is linked to the biology of aging; however, evidence supporting a causal relationship between periodontitis—a dysbiotic biofilminitiated inflammatory disease—and accelerated aging remains limited. This study investigated the causality between periodontitis and biological aging and identified potentially shared genomic loci, genes, and pathways. Methods: We conducted a 2-sample Mendelian randomization (MR) analysis to explore the causality of periodontitis on age acceleration measures (DNAm PhenoAge acceleration, GrimAge acceleration, Hannum age acceleration, and intrinsic epigenetic age acceleration) using a dataset from genome-wide association studies of European ancestry populations.Independent genetic variants associated with each trait were used as instrumental variables.The inverse variance-weighted (IVW) method served as the primary MR approach, supplemented by sensitivity testing. We also performed additional statistical genetic analyses to identify pleiotropic loci, shared functional genes, and potential biological pathways, integrating large-scale expression quantitative trait loci data from blood samples. Results: The MR analysis indicated a causal relationship between periodontitis and DNAm PhenoAge acceleration (IVW β=0.308; 95% confidence interval, 0.056–0.561; P=0.017), a finding corroborated by sensitivity analyses. There was a significant genetic overlap between periodontitis and age acceleration. Pleiotropic analysis revealed 24 shared SNPs associated with 242 genes, predominantly involved in immune functions and pathways related to cellular processes. Further integration analysis showed that 91 of these pleiotropic genes were causally linked to both conditions, with C6orf183 identified as a potential mediator. Conclusions This study presents compelling genetic evidence supporting a causal relationship between periodontitis and accelerated aging. Further research is required to validate these findings and investigate the underlying mechanisms.

AIM:To investigate the effects of macrophage galactose-type lectin 1(Mgl1)gene deletion on he-matopoietic stem/progenitor cells(HSPCs)under steady-state conditions and inflammation.METHODS:Mice were di-vided into a control group(wild-type)and an experimental group(Mgl1 gene-deleted).Flow cytometry was used to ana-lyze the proportions of various hematopoietic cell lineages in the peripheral blood and bone marrow of both groups,assess-ing the impact of Mgl1 gene deletion on steady-state hematopoiesis(n=3～4).Transplantation and colony-forming assays were utilized to evaluate the effects of Mgl1 gene deletion onthe repopulation capacity and colony-forming ability of HSPCs(n=5).The LPS-induced inflammation model was employed to examine the effects of Mgl1 gene deletion on the inflamma-tory response of HSPCs both in vitro and in vivo(n=5～8).Western blot and RT-qPCR were conducted to analyze the alter-ations in signaling pathways regulated by Mgl1 in the inflammatory response of HSPCs(n=3).RESULTS:(1)Mgl1 gene deletion had no significant effecton steady-state hematopoiesis(P>0.05).(2)Mgl1 gene deletion promoted inflam-mation-induced cell differentiation of HSPCs(P<0.01).(3)Mgl1 gene deletion accelerated the exhaustion of HSPCs un-der prolonged inflammatory conditions(P<0.01).(4)Mgl1 was found to regulate the inflammatory response of HSPCs via the NF-κB signaling pathway.CONCLUSION:Mgl1 gene deletion enhances the inflammatory response of HSPCs via the NF-κB signaling pathway.

Purpose::To identify the risk factors for training-related lower extremity muscle injuries in young males by a non-invasive method of body composition analysis.Methods::A total of 282 healthy young male volunteers aged 18 -20 years participated in this cohort study. Injury location, degree, and injury rate were adjusted by a questionnaire based on the overuse injury assessment methods used in epidemiological studies of sports injuries. The occurrence of training injuries is monitored and diagnosed by physicians and treated accordingly. The body composition was measured using the BodyStat QuadScan 4000 multifrequency Bio-impedance system at 5, 50, 100 and 200 kHz to obtain 4 impedance values. The Shapiro-Wilk test was used to check whether the data conformed to a normal distribution. Data of normal distribution were shown as mean ± SD and analyzed by t-test, while those of non-normal distribution were shown as median (Q 1, Q 3) and analyzed by Wilcoxon rank sum test. The receiver operator characteristic curve and logistic regression analysis were performed to investigate risk factors for developing training-related lower extremity injuries and accuracy. Results::Among the 282 subjects, 78 (27.7%) developed training injuries. Lower extremity training injuries revealed the highest incidence, accounting for 23.4% (66 cases). These patients showed higher percentages of lean body mass ( p = 0.001), total body water (TBW, p=0.006), extracellular water ( p=0.020) and intracellular water ( p=0.010) as well as a larger ratio of basal metabolic rate/total weight ( p=0.006), compared with those without lower extremity muscle injuries. On the contrary, the percentage of body fat ( p=0.001) and body fat mass index ( p=0.002) were lower. Logistic regression analysis showed that TBW percentage > 65.35% ( p=0.050, odds ratio =3.114) and 3rd space water > 0.95% ( p=0.045, odds ratio =2.342) were independent risk factors for lower extremity muscle injuries. Conclusion::TBW percentage and 3rd space water measured with bio-impedance method are potential risk factors for predicting the incidence of lower extremity muscle injuries in young males following training.

Objective To build a data integration and sharing platform for healthcare consortiums to improve homogenized management and overall medical service of the regions.Methods The platform was developed with cloud architecture based on Hadoop,which used Java as the main development language and Apache HDFS and Apache MapReduce as the main underlying technologies.There were four core modules involved in the platform for data integration and governance,data resource warehousing,data service support and security docking center.Results The platform developed could be used for data integration and governance of healthcare consortiums,which provided application services for management,clinical diagnosis and treatment and scientific research to make full use of the data of healthcare consortiums.Conclusion The platform developed realizes data resource integration in healthcare consortiums,and contributes to data value release and information sharing and interconnection.[Chinese Medical Equipment Journal,2024,45(4):1-6]