ObjectiveTo evaluate the effects of drinking chlorinated water on the intestinal flora of rats and mice and to explore differences in the intestinal microecological responses of model animals to chlorine stimulation in chlorinated drinking water systems. MethodsSix 8-week-old male SD rats and six 8-week-old male C57BL/6J mice were acclimated for 5 days. Subsequently, the drinking water for both rats and mice was changed from pure water to water containing free chlorine at a concentration of 25.4–32.4 nmol/L. The intervention lasted for 8 weeks, during which all animals were fed the same diet. Fecal samples were collected before the intervention (week 0, as the control group) and on the last day of week 3 and week 8 (as the chlorinated water group), and microbial composition was analyzed by 16S rRNA sequencing, including α diversity (Chao1 and Shannon indices), β diversity [principal component analysis (PCA)], and linear discriminant analysis effect size (LEfSe) analysis. ResultsAfter 3 weeks of intervention, α diversity of the intestinal flora in both rats and mice in the chlorinated water group was significantly lower than that in the control group (P < 0.01). After 8 weeks of intervention, no significant difference in α diversity was observed between rats in the chlorinated water group and those in the control group, whereas α diversity in mice remained significantly lower than that in the control group (P < 0.01). β diversity analysis showed significant alterations in microbial structure in the chlorinated water group. LEfSe analysis indicated that, compared with the control group, the abundances of Bacteroides and Ruminococcus were significantly reduced in the chlorinated water group, and microbial disturbance was more pronounced in mice [linear discriminant analysis (LDA) > 4.0], with a greater decrease in microbial diversity and a larger number of differentially abundant genera. ConclusionConsumption of chlorinated water can alter the diversity and structure of the intestinal flora in both rats and mice, with mice being more sensitive to this exposure. These findings suggest that the potential effects of chlorine on gut microecology should be considered in drinking water management and model selection for animal experiments.

Dental implantology has developed rapidly for over half a century,since pure titanium(99.7％)dental cylindrical threaded implants were exploited and osseointegration was introduced in 1960s by Prof.Br?nemark.The long term retention rates of 10 years or more are over 95％.However,the biological complications jeopardize the long term effects of dental implant treatment seriously.The prevalence of dental implant biological complications varies greatly among different reports resulting from the disparities on the defini-tions of dental implant biological complications.After analyzing and summarizing the major opinions proposed internationally in recent years,the consensus for the definition of dental implant biological complications has been reached.Generally the dental implant biologi-cal implications can be classified into early stage(before restoration)biological complications and late stage(after restoration)biological complications.The early stage biological complications include acute and chronic infections,pain,soft tissue deficiency,and osseointegration failure,etc.The late stage complications include peri-implant diseases(peri-implant mucositis and peri-implantitis),soft tissue deficiency around implant,implant loosening and dropping off,etc.The various risk factors related to different dental implant biological complications,the strategies of the prevention and treatment for the dental implant biological complications have been discussed comprehensively,and the consensus has been reached.It is aimed to advocate the dentist to pay more attention to the early prevention of the biological implant complications,to promote more researches on the implant biological complications,and to help elevate the level of dental implantology in our country.

Objective:To develop an economical,simple,and accessible method for early identification of high-risk pregnant women with gestational diabetes mellitus(GDM),this study developed and evaluated multiple machine learning models,identified the optimal prediction model,and constructed a clinical decision support sys-tem(CDSS)based on this model.Methods:A total of 464 pregnant women who visited the Second Affiliated Hospital of Dalian Medical University from January 1,2023 to December 30,2024 were included,of which 386 were used to establish a prediction model(231 in the training set and 155 in the testing set),and the remaining 78 were used as a validation.Adopting the methods of double-point sequence correlation and chi-square test,four machine learning models were constructed after selecting feature variables:Logistic Regression,Random Forest,Support Vector Machine,and eXtreme Gradient Boosting(XGBoost).Preliminary judgment of the maximum weight mod-el,further comparison of the discriminative ability,calibration ability,and clinical practicality of each model to evalu-ate and select the optimal model,develop its CDSS,and verify the accuracy of the model.Results:①Correlation analysis identified predictors of GDM:age,pre-pregnancy body mass index(BMI),systolic/diastolic blood pres-sure,white blood cell count,hemoglobin,lymphocyte ratio,fasting plasma glucose,uric acid,direct bilirubin,chronic hypertension complicating pregnancy,and assisted reproductive technology conception.②XGBoost dominated the ensemble model and demonstrated the best performance in discrimination(AUC 0.931,95%CI 0.910-0.967),cali-bration,and clinical utility among the four models.③The CDSS achieved an accuracy of 78.2%,sensitivity of 64.7%,and specificity of 82.0%in the XGBoost model.Conclusions:The XGBoost model has the highest ability to predict GDM in the early stage.Developing its CDSS not only facilitates doctors to quickly assess GDM risk,but also is suitable for promotion to remote areas,where high-risk population screening can be achieved through re-mote data.

Objective To explore theoretical application of health communication effect evaluation in order to provide support for health communication.Methods Using health communication,health activities,health science popularization,effect evaluation as precise keywords,literature collected by databases from built to May 27,2024 was searched,and literature was screened by removing duplicate literature and reading title and abstract of literature.A total of 121 literatures were selected as research samples.Results Most of literatures did not adopt theoretical framework,and only 33 literatures had theoretical applications,mainly in fields of public health and communication.The most widely used literatures were theory of knowing and believing,Lasswell's 5W mode of communication and theory of persuasion,which were dominated by Western theories and lacked application and innovation of localized theories.Conclusion The future health communication effect evaluation should use more theoretical framework to support research,learn from and integrate theories of different disciplines,and explore localization theory research path.

Dental implantology has developed rapidly for over half a century,since pure titanium(99.7％)dental cylindrical threaded implants were exploited and osseointegration was introduced in 1960s by Prof.Br?nemark.The long term retention rates of 10 years or more are over 95％.However,the biological complications jeopardize the long term effects of dental implant treatment seriously.The prevalence of dental implant biological complications varies greatly among different reports resulting from the disparities on the defini-tions of dental implant biological complications.After analyzing and summarizing the major opinions proposed internationally in recent years,the consensus for the definition of dental implant biological complications has been reached.Generally the dental implant biologi-cal implications can be classified into early stage(before restoration)biological complications and late stage(after restoration)biological complications.The early stage biological complications include acute and chronic infections,pain,soft tissue deficiency,and osseointegration failure,etc.The late stage complications include peri-implant diseases(peri-implant mucositis and peri-implantitis),soft tissue deficiency around implant,implant loosening and dropping off,etc.The various risk factors related to different dental implant biological complications,the strategies of the prevention and treatment for the dental implant biological complications have been discussed comprehensively,and the consensus has been reached.It is aimed to advocate the dentist to pay more attention to the early prevention of the biological implant complications,to promote more researches on the implant biological complications,and to help elevate the level of dental implantology in our country.

Objective:To develop an economical,simple,and accessible method for early identification of high-risk pregnant women with gestational diabetes mellitus(GDM),this study developed and evaluated multiple machine learning models,identified the optimal prediction model,and constructed a clinical decision support sys-tem(CDSS)based on this model.Methods:A total of 464 pregnant women who visited the Second Affiliated Hospital of Dalian Medical University from January 1,2023 to December 30,2024 were included,of which 386 were used to establish a prediction model(231 in the training set and 155 in the testing set),and the remaining 78 were used as a validation.Adopting the methods of double-point sequence correlation and chi-square test,four machine learning models were constructed after selecting feature variables:Logistic Regression,Random Forest,Support Vector Machine,and eXtreme Gradient Boosting(XGBoost).Preliminary judgment of the maximum weight mod-el,further comparison of the discriminative ability,calibration ability,and clinical practicality of each model to evalu-ate and select the optimal model,develop its CDSS,and verify the accuracy of the model.Results:①Correlation analysis identified predictors of GDM:age,pre-pregnancy body mass index(BMI),systolic/diastolic blood pres-sure,white blood cell count,hemoglobin,lymphocyte ratio,fasting plasma glucose,uric acid,direct bilirubin,chronic hypertension complicating pregnancy,and assisted reproductive technology conception.②XGBoost dominated the ensemble model and demonstrated the best performance in discrimination(AUC 0.931,95%CI 0.910-0.967),cali-bration,and clinical utility among the four models.③The CDSS achieved an accuracy of 78.2%,sensitivity of 64.7%,and specificity of 82.0%in the XGBoost model.Conclusions:The XGBoost model has the highest ability to predict GDM in the early stage.Developing its CDSS not only facilitates doctors to quickly assess GDM risk,but also is suitable for promotion to remote areas,where high-risk population screening can be achieved through re-mote data.

Objective:To systematically evaluate predictive models for stress urinary incontinence (SUI) in pregnant and postpartum women, providing a reference for model development, application, and promotion.Methods:A comprehensive literature search was conducted in PubMed, Embase, Web of Science, Cochrane Library, China National Knowledge Infrastructure, Wanfang Database, and China Biology Medicine disc for studies on SUI predictive models in pregnant and postpartum women. The search period was from database inception to September 30, 2024. Two researchers independently screened the literature and extracted data according to inclusion and exclusion criteria. The risk of bias in the predictive models was assessed using the prediction model risk of bias assessment tool.Results:A total of 23 studies were included, covering 31 predictive models for SUI, with a combined sample size of 14 473 women. Among them, six models focused on predicting SUI in pregnant women, while 25 models were developed for postpartum SUI. The predictive factors identified in these models were categorized into nine groups, including: general information for pregnant and postpartum women, delivery data, neonatal data, past history, abortion history, lifestyle data, pelvic floor muscle screening results, 2D and 3D ultrasound data, and serological indicators. Among these, age, mode of delivery, parity, body mass index, history of SUI, and neonatal weight were widely recognized as key predictive factors. External validation was performed in five studies. Five studies showed good applicability and low bias risk, except for one study that had limitations in both bias risk and applicability, and the remaining studies exhibited a high risk of bias but demonstrated good applicability.Conclusions:The methodological quality of SUI predictive models for pregnant and postpartum women needs further improvement. External validation remains insufficient. Future model development should be based on large-sample, prospective studies, incorporating appropriate predictive factors and stratifying SUI risk in different populations to enhance clinical applicability.

Objective:To search, evaluate, and summarize evidence regarding strategies to promote pelvic floor muscle contraction (PFMC) function in postpartum women, providing a basis for clinical practice.Methods:A comprehensive search was conducted in computer decision support systems, guideline websites, relevant professional association websites, and English and Chinese databases for evidence related to strategies to promote PFMC function in postpartum women. The sources included guidelines, expert consensus, evidence summaries, systematic reviews, and original studies, with the search period from June 2014 to January 2025. Two researchers independently assessed the quality of the included articles and extracted data for the evidence summary.Results:A total of 24 articles were included: nine guidelines, five expert consensus, three evidence summaries, two systematic reviews, and five original studies. The evidence was summarized across four domains: screening and assessment, team building, intervention strategies, and outcome evaluation, resulting in 25 key pieces of evidence.Conclusions:This study summarizes the best evidence for strategies to promote PFMC function in postpartum women, providing scientific and rigorous evidence for clinical practice. It supports the development of effective training programs to enhance postpartum women's quality of life.

Objective:To systematically evaluate predictive models for stress urinary incontinence (SUI) in pregnant and postpartum women, providing a reference for model development, application, and promotion.Methods:A comprehensive literature search was conducted in PubMed, Embase, Web of Science, Cochrane Library, China National Knowledge Infrastructure, Wanfang Database, and China Biology Medicine disc for studies on SUI predictive models in pregnant and postpartum women. The search period was from database inception to September 30, 2024. Two researchers independently screened the literature and extracted data according to inclusion and exclusion criteria. The risk of bias in the predictive models was assessed using the prediction model risk of bias assessment tool.Results:A total of 23 studies were included, covering 31 predictive models for SUI, with a combined sample size of 14 473 women. Among them, six models focused on predicting SUI in pregnant women, while 25 models were developed for postpartum SUI. The predictive factors identified in these models were categorized into nine groups, including: general information for pregnant and postpartum women, delivery data, neonatal data, past history, abortion history, lifestyle data, pelvic floor muscle screening results, 2D and 3D ultrasound data, and serological indicators. Among these, age, mode of delivery, parity, body mass index, history of SUI, and neonatal weight were widely recognized as key predictive factors. External validation was performed in five studies. Five studies showed good applicability and low bias risk, except for one study that had limitations in both bias risk and applicability, and the remaining studies exhibited a high risk of bias but demonstrated good applicability.Conclusions:The methodological quality of SUI predictive models for pregnant and postpartum women needs further improvement. External validation remains insufficient. Future model development should be based on large-sample, prospective studies, incorporating appropriate predictive factors and stratifying SUI risk in different populations to enhance clinical applicability.

Objective:To search, evaluate, and summarize evidence regarding strategies to promote pelvic floor muscle contraction (PFMC) function in postpartum women, providing a basis for clinical practice.Methods:A comprehensive search was conducted in computer decision support systems, guideline websites, relevant professional association websites, and English and Chinese databases for evidence related to strategies to promote PFMC function in postpartum women. The sources included guidelines, expert consensus, evidence summaries, systematic reviews, and original studies, with the search period from June 2014 to January 2025. Two researchers independently assessed the quality of the included articles and extracted data for the evidence summary.Results:A total of 24 articles were included: nine guidelines, five expert consensus, three evidence summaries, two systematic reviews, and five original studies. The evidence was summarized across four domains: screening and assessment, team building, intervention strategies, and outcome evaluation, resulting in 25 key pieces of evidence.Conclusions:This study summarizes the best evidence for strategies to promote PFMC function in postpartum women, providing scientific and rigorous evidence for clinical practice. It supports the development of effective training programs to enhance postpartum women's quality of life.