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.

ObjectiveTo explore the pharmacological mechanism of Tanreqing gel （TRQG） in the treatment of acne by regulating the Toll-like receptor 2 （TLR2）/myeloid differentiation 88 （MyD88）/nuclear factor-κB （NF-κB） signaling pathway. MethodsThe minimum inhibitory concentration （MIC） of Tanreqing injection （TRQ） against Cutibacterium acnes was determined through the microdilution broth method， and drug resistance induction experiments were conducted. A mouse model of acne was established through inoculation of C. acnes on the back. The effects of TRQG on skin structure， pustule formation， collagen repair， and inflammatory response were evaluated by dermoscopy， histopathology， two-photon excitation fluorescence microscopy （TPEF）， and optical coherence tomography （OCT）. Differentially expressed genes （DEGs） were identified through transcriptome sequencing and then subjected to KEGG pathway enrichment analysis. In the RAW264.7 cell model， the optimal dosage of TRQG was determined through cell viability assays. Subsequently， the expression of key proteins in the TLR2/MyD88/NF-κB pathway and downstream inflammatory factors ［tumor necrosis factor-α （TNF-α）， interleukin （IL）-1β， and IL-6］ was determined by Western blot and real-time PCR. ResultsThe MIC of TRQ against C. acnes was 4 125 mg·L^-1. Although TRQ had weaker antibacterial activity than erythromycin （ERY） and fusidic acid （FA）， it was less likely to induce bacterial resistance. In vivo experiments showed that TRQG improved the skin structure， promoted collagen repair （P<0.01）， inhibited pustule formation （P<0.01）， alleviated follicular keratinization and sebaceous gland hyperplasia， and reduced inflammatory responses in the mouse model of acne compared with erythromycin gel （ERYG）. Transcriptome analysis suggested that the TLR/MyD88/NF-κB signaling pathway was a key enriched pathway. Further experimental validation indicated that TRQG inhibited the expression of TLR2， MyD88， TNF receptor-associated factor 6 （TRAF6）， phosphorylated （p）-transforming growth factor-β-activated kinase 1 （TAK1）， p-inhibitor of NF-κB subunit α （IκBα）， and p-NF-κB in this pathway （P<0.01）. Meanwhile， Real-time PCR results confirmed that TRQG reduced the mRNA levels of TNF-α， IL-1β， and IL-6 （P<0.01）. ConclusionTRQG exerts its therapeutic effect on acne by inhibiting the activation of the TLR2/MyD88/NF-κB signaling pathway and reducing the release of downstream inflammatory factors. Furthermore， it is less likely to induce bacterial resistance， indicating a promising application prospect.

ObjectiveTo explore the pharmacological mechanism of Tanreqing gel （TRQG） in the treatment of acne by regulating the Toll-like receptor 2 （TLR2）/myeloid differentiation 88 （MyD88）/nuclear factor-κB （NF-κB） signaling pathway. MethodsThe minimum inhibitory concentration （MIC） of Tanreqing injection （TRQ） against Cutibacterium acnes was determined through the microdilution broth method， and drug resistance induction experiments were conducted. A mouse model of acne was established through inoculation of C. acnes on the back. The effects of TRQG on skin structure， pustule formation， collagen repair， and inflammatory response were evaluated by dermoscopy， histopathology， two-photon excitation fluorescence microscopy （TPEF）， and optical coherence tomography （OCT）. Differentially expressed genes （DEGs） were identified through transcriptome sequencing and then subjected to KEGG pathway enrichment analysis. In the RAW264.7 cell model， the optimal dosage of TRQG was determined through cell viability assays. Subsequently， the expression of key proteins in the TLR2/MyD88/NF-κB pathway and downstream inflammatory factors ［tumor necrosis factor-α （TNF-α）， interleukin （IL）-1β， and IL-6］ was determined by Western blot and real-time PCR. ResultsThe MIC of TRQ against C. acnes was 4 125 mg·L^-1. Although TRQ had weaker antibacterial activity than erythromycin （ERY） and fusidic acid （FA）， it was less likely to induce bacterial resistance. In vivo experiments showed that TRQG improved the skin structure， promoted collagen repair （P<0.01）， inhibited pustule formation （P<0.01）， alleviated follicular keratinization and sebaceous gland hyperplasia， and reduced inflammatory responses in the mouse model of acne compared with erythromycin gel （ERYG）. Transcriptome analysis suggested that the TLR/MyD88/NF-κB signaling pathway was a key enriched pathway. Further experimental validation indicated that TRQG inhibited the expression of TLR2， MyD88， TNF receptor-associated factor 6 （TRAF6）， phosphorylated （p）-transforming growth factor-β-activated kinase 1 （TAK1）， p-inhibitor of NF-κB subunit α （IκBα）， and p-NF-κB in this pathway （P<0.01）. Meanwhile， Real-time PCR results confirmed that TRQG reduced the mRNA levels of TNF-α， IL-1β， and IL-6 （P<0.01）. ConclusionTRQG exerts its therapeutic effect on acne by inhibiting the activation of the TLR2/MyD88/NF-κB signaling pathway and reducing the release of downstream inflammatory factors. Furthermore， it is less likely to induce bacterial resistance， indicating a promising application prospect.

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 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 develop a simple digital chylous plasma device and validate its ability to accurately, standardly, and non-destructively determine chylous plasma in blood banks and clinical transfusions in hospitals. Methods: A digital chylous plasma assessment device was designed and manufactured. This device was used to measure the chylous degrees of chylous plasma samples before freezing, after freeze-thawing, before viral inactivation, and after viral inactivation. The measured chylosity index values were categorized according to the requirements specified in Appendix A of the Chinese national standard GB 18469-2001 "Quality Requirements for Whole Blood and Blood Components". This process established a digital standard for chylous plasma, enabling the identification of severe, moderate and mild chylous plasma, and non-chylous plasma. Results: The initial simple product of the digital chylous assessment device was successfully designed and manufactured. There was no significant difference in the degree of chylous plasma between pre-freezing 468.11±217.73 lux and post-thawing 538.91±273.39 lux of chylous plasma (P>0.05), or between pre-viral inactivation 858.33±387.79 lux and post-viral inactivation 928.33±166.51 lux of chylous plasma (P>0.05). The median of chylous degree values for plasma chylous index grades 0 to 6 were 45 lux, 250 lux, 620 lux, 835 lux, 1 130 lux, 1 390 lux, and 1 700 lux, respectively. The defined cutoff values/ranges for the chylous degree values corresponding to plasma chylous index grade 0 to 6 were ≤125 lux, 126-465 lux, 466-740 lux, 741-1 000 lux, 1 001-1 233 lux, 1 234-1 560 lux, and ≥1 561 lux. Conclusion: This study successfully developed the initial product of the digital chylous device and established digital standards for classifying chylous plasma. The device demonstrates the potential to meet the needs for assessment of chylous plasma in both blood banks and clinical transfusions in hospitals, thereby promoting the development and application of standardized, non-destructive chylous plasma assessment technology.

Objective: To evaluate the effect of weight monitoring feedback intervention among primary school students, so as to provide the evidence for strengthening children's weight management. Methods: In October 2023, students from grades four to six in a primary school in Hangzhou City were selected and randomly assigned to a control group and an intervention group on a class-by-class basis. The included primary school students had their height and weight measured at a fixed time each week, and the results were fed back to their parents in the form of cards. The cards for the control group contained knowledge about healthy lifestyles, while those for the intervention group additionally included information on body mass index (BMI), BMI grouping, and BMI ranking. Overweight and obesity were determined according to the age- and gender-specific criteria in the Screening for Overweight and Obesity among School-aged Children and Adolescents. After a 9-month intervention period, the prevalence rates of overweight and obesity and lifestyle behavior data between the two groups before and after the intervention were compared by a generalized linear mixed model, in order to assess the effectiveness of the weight monitoring information feedback intervention. Results: The intervention group consisted of 368 students, including 208 boys (56.52%) and 160 girls (43.48%). The majority of students were 11 years, with 153 students accounting for 41.58%. The prevalence rate of overweight and obesity was 24.18%. The control group had 324 students, with 180 boys (55.56%) and 144 girls (44.44%). The predominant age was also 11 years, with 128 students accounting for 39.51%. The prevalence rate of overweight and obesity was 25.31%. Before the intervention, there were no statistically significant differences between the two groups in terms of gender, age, prevalence rate of overweight and obesity, eating habits, exercise situation, and sleep patterns (all P>0.05). After the intervention, there were significant interactions between group and time for the prevalence rate of overweight and obesity, the frequency of moderate-intensity exercise per week, and adequate sleep in the two groups (all P<0.05). The prevalence rate of overweight and obesity in the intervention group (OR=0.461, 95%CI: 0.252-0.845) was lower than that in the control group. The proportions of students in the intervention group who engaged in moderate-intensity exercise ≥4 times per week (OR=1.315, 95%CI: 1.033-1.675) and had adequate sleep (OR=1.402, 95%CI: 1.049-1.875) were higher than those in the control group. Conclusion Weight monitoring feedback can reduce the incidence of overweight and obesity among primary school students and has a certain improving effect on lifestyle behaviors such as exercise and sleep.