Objective:To summarize the research on oral health education for pregnant women.Methods:The literature was described and analyzed using a scoping review method. Seven databases, such as PubMed, Embase, Web of Science, China National Knowledge Infrastructure, and WanFang Data, were electronically searched, and the search period was from database establishment to October 30, 2023.Results:A total of 43 articles were included. The implementers of health education were mainly dental professionals and prenatal healthcare personnel. The theoretical basis included the health belief model, planned behavior theory, social cognitive model and so on. The methods involved traditional teaching or lectures, family-centered, internet-based, and motivational interviews. The contents contained many aspects of oral health for pregnant women. The evaluation indicators mainly covered oral health knowledge, attitude and practice, and self-efficacy, oral health beliefs, oral health status, the incidence of oral diseases, adverse pregnancy outcomes of pregnant and postpartum women, and childhood caries incidence.Conclusions:We should establish a cooperation team of the Department of Stomatology and Obstetrics and Gynecology, incorporate oral health for pregnant women into prenatal care projects, fully utilize the platform of pregnant women's schools, explore the optimal theoretical basis for oral health education, and improve the content of oral health education for pregnant women.

The aim of this study was to explore the impact of chronic apical periodontitis(CAP)on atherosclerosis in apoE-/-mice fed high-fat diet(HFD).This investigation focused on the gut microbiota,metabolites,and intestinal barrier function to uncover potential links between oral health and cardiovascular disease(CVD).In this study,CAP was shown to exacerbate atherosclerosis in HFD-fed apoE-/-mice,as evidenced by the increase in plaque size and volume in the aortic walls observed via Oil Red O staining.16S rRNA sequencing revealed significant alterations in the gut microbiota,with harmful bacterial species thriving while beneficial species declining.Metabolomic profiling indicated disruptions in lipid metabolism and primary bile acid synthesis,leading to elevated levels of taurochenodeoxycholic acid(TCDCA),taurocholic acid(TCA),and tauroursodeoxycholic acid(TDCA).These metabolic shifts may contribute to atherosclerosis development.Furthermore,impaired intestinal barrier function,characterized by reduced mucin expression and disrupted tight junction proteins,was observed.The increased intestinal permeability observed was positively correlated with the severity of atherosclerotic lesions,highlighting the importance of the intestinal barrier in cardiovascular health.In conclusion,this research underscores the intricate interplay among oral health,gut microbiota composition,metabolite profiles,and CVD incidence.These findings emphasize the importance of maintaining good oral hygiene as a potential preventive measure against cardiovascular issues,as well as the need for further investigations into the intricate mechanisms linking oral health,gut microbiota,and metabolic pathways in CVD development.

Objective:To explore the application value of artificial intelligence (AI) model based on deep learning in breast nodules classification of Breast Imaging Reporting and Data System of ultrasound (BI-RADS-US).Methods:The ultrasound images of 2 426 breast nodules from 1 558 female patients with breast diseases at Beijing Tongren Hospital, Capital Medical University between December 2006 and December 2019 were collected . The image data sets were divided into training (63%), verification (7%), and test (30%) subsets for the construction of AI model. The diagnostic efficiencies of AI model, doctors' arbitration results and doctors' diagnosis with or without AI model assistance were analyzed by using receiver operating characteristic (ROC) curve. The Cohen weighted Kappa statistic was used to compare the consistency of BI-RADS-US classification among 5 ultrasound doctors' diagnosis with or without AI model assistance. And the changes of BI-RADS-US classification were analyzed before and after each doctor adopted AI model assistance.Results:The differences in diagnostic efficiencies of AI model, doctors' arbitration results and doctors' diagnosis with or without AI model assistance were statistically significant (all P > 0.05). The consistency among 5 ultrasound doctors was improved due to AI model assistance and Kappa value was increased from 0.433 (category 3), 0.600 (category 4a), 0.614 (category 4b), 0.570 (category 4c) and 0.495 (category 5) to 0.812, 0.704, 0.823, 0.690 and 0.509 (all P < 0.05), respectively. The upgrade and downgrade of BI-RADS-US classification occurred in 5 doctors after the classification of AI model assistance. Downgrade from category 4 to 3 in benign nodules of 56.6% (47/76) and upgrade from category 4 to 5 in malignant nodules of 69.4% (34/49) were mostly observed. Conclusions:AI-assisted BI-RADS-US classification can effectively improve the consistency of classification among the doctors without reducing the diagnostic efficiency. AI model shows clinical values in reducing unnecessary biopsy of partial benign lesions and increasing diagnostic accuracy of partial malignant lesions through the adjustment of breast nodule classification.

Objective:To assess the practical value of visual scores of magnetic resonance imaging(MRI)features in the diagnosis and classification of dementia with Lewy bodies(DLB).Methods:In this study, 102 DLB patients were prospectively recruited, with 102 cognitively normal elderly people as the normal control group(NC).All included subjects underwent MRI examinations and neuropsychological assessments.Based on the clinical dementia rating(CDR)scale, DLB patients were divided into a mild(CDR=1.0), a moderate(CDR=2.0)and a severe(CDR=3.0)group.The results of MRI were scored visually and the rating scales included medial temporal lobe atrophy(MTA), global cortical atrophy-frontal subscale(GCA-F), posterior cortical atrophy(PCA), white matter lesions(the Fazekas scale), cerebral microbleeds(CMBs), and the Evans Index(EI).Statistical differences were compared between the DLB and NC groups and between DLB patients with different degrees of cognitive impairment.Results:In terms of neuropsychology, the Mini-Mental State Examination(MMSE) score of the DLB group[16.0(11.0, 21.0)]was statistically significantly lower than that of the NC group[29.0(28.0, 30.0)]( Z=-12.31, P<0.001), the Montreal Cognitive Assessment(MoCA)score of the DLB group[9.5(6.0, 15.0)]was statistically significantly lower than that of the NC group[28.0(27.0, 29.0)]( Z=-12.40, P<0.001), and the Activities of Daily Living(ADL)score of the DLB group[32.0(23.8, 40.0)]was statistically significantly higher than that of the NC group[20.0(20.0, 20.0)]( Z=-11.98, P<0.001).The scores of all MRI visual assessment scales in DLB patients were statistically significantly higher than those in the NC group( P<0.001).There were significant differences in MTA scores between DLB patients with different degrees of cognitive impairment( P0<0.001).The MTA score of the mild group[1.0(1.0, 1.0)]was statistically significantly lower than that of the moderate group[2.0(1.0, 2.0)]( P1<0.001, P2<0.001); The MTA score of the moderate group[2.0(1.0, 2.0)]was statistically significantly lower than that of the severe group[2.0(2.0, 3.0)]( P1=0.003, P2=0.010). Conclusions:This study has for the first time after comprehensively evaluated the value of various visual scores in DLB diagnosis, MTA can be used to help diagnose DLB and distinguish the severity of DLB, providing a new supplemental tool for clinical diagnosis.

This paper aims to study the difference in the intestinal absorption kinetics of main active components of Sini decoction and its separated recipes and explain the scientificity and rationality of the compatibility of Sini Decoction. A in situ intestinal perfusion rat model was established to evaluate the differences in the absorption of benzoylmesaconine, benzoylaconine, benzoylhypacoitine, mesaconitine, hypaconitine, glycyrrhizic acid, liquiritin and 6-gingerol from Sini Decoction and its separated recipes in the duodenum, jejunum and ileum by high performance liquid chromatography(HPLC). The results indicated that the Sini Decoction group was superior to the Aconiti Lateralis Radix Praeparata group in terms of absorption degree and rate for aconitum alkaloids. The absorption of benzoylmesaconine and hypaconitine in the duodenum, jejunum and ileum was faster and stronger in the Sini Decoction group(P<0.05). The absorption degree of glycyrrhizic acid in the duodenum was significantly higher in the Sini Decoction group than in the Glycyrrhizae Radix et Rhizoma group and the Glycyrrhizae Radix et Rhizoma-Zingiberis Rhizoma group(P<0.05). The absorption rate and degree of 6-gingerol in the ileum in the Sini Decoction group were significantly higher than those in the Zingiberis Rhizoma group(P<0.05). In short, Zingiberis Rhizoma and Glycyrrhizae Radix et Rhizoma can promote the absorption of aconitum alkaloids in different intestinal segments, which reflects the scientific composition of Sini Decoction.
Aconitine/analogs & derivatives* ; Aconitum ; Alkaloids ; Animals ; Catechols ; Drugs, Chinese Herbal ; Fatty Alcohols ; Glycyrrhizic Acid ; Intestinal Absorption ; Kinetics ; Rats

Dementia with Lewy bodies(DLB)is the second most common neurodegenerative dementia after Alzheimer's Disease(AD). This article will mainly elaborate the relationship between DLB and blood-brain barrier(BBB)from the following five aspects: (1)The structure and function of BBB; (2)In vivo assessment methods for the blood-brain barrier damage; (3)Evidence for the damage of blood-brain barrier in DLB; (4)The relationship between α-synuclein and the blood-brain barrier; (5)The relationship between APOE and the blood-brain barrier.Future research should focus on the pathogenesis of BBB damage in DLB patients, by which new drug targets for disease diagnosis and treatment may be found.

BACKGROUND: The current deep learning diagnosis of breast masses is mainly reflected by the diagnosis of benign and malignant lesions. In China, breast masses are divided into four categories according to the treatment method: inflammatory masses, adenosis, benign tumors, and malignant tumors. These categorizations are important for guiding clinical treatment. In this study, we aimed to develop a convolutional neural network (CNN) for classification of these four breast mass types using ultrasound (US) images. METHODS: Taking breast biopsy or pathological examinations as the reference standard, CNNs were used to establish models for the four-way classification of 3623 breast cancer patients from 13 centers. The patients were randomly divided into training and test groups (n = 1810 vs. n = 1813). Separate models were created for two-dimensional (2D) images only, 2D and color Doppler flow imaging (2D-CDFI), and 2D-CDFI and pulsed wave Doppler (2D-CDFI-PW) images. The performance of these three models was compared using sensitivity, specificity, area under receiver operating characteristic curve (AUC), positive (PPV) and negative predictive values (NPV), positive (LR+) and negative likelihood ratios (LR-), and the performance of the 2D model was further compared between masses of different sizes with above statistical indicators, between images from different hospitals with AUC, and with the performance of 37 radiologists. RESULTS: The accuracies of the 2D, 2D-CDFI, and 2D-CDFI-PW models on the test set were 87.9%, 89.2%, and 88.7%, respectively. The AUCs for classification of benign tumors, malignant tumors, inflammatory masses, and adenosis were 0.90, 0.91, 0.90, and 0.89, respectively (95% confidence intervals [CIs], 0.87-0.91, 0.89-0.92, 0.87-0.91, and 0.86-0.90). The 2D-CDFI model showed better accuracy (89.2%) on the test set than the 2D (87.9%) and 2D-CDFI-PW (88.7%) models. The 2D model showed accuracy of 81.7% on breast masses ≤1 cm and 82.3% on breast masses >1 cm; there was a significant difference between the two groups (P < 0.001). The accuracy of the CNN classifications for the test set (89.2%) was significantly higher than that of all the radiologists (30%). CONCLUSIONS: The CNN may have high accuracy for classification of US images of breast masses and perform significantly better than human radiologists. TRIAL REGISTRATION Chictr.org, ChiCTR1900021375; http://www.chictr.org.cn/showproj.aspx?proj=33139.
Area Under Curve ; Breast/diagnostic imaging* ; Breast Neoplasms/diagnostic imaging* ; China ; Deep Learning ; Humans ; ROC Curve ; Sensitivity and Specificity

Objective:To investigate the predictive value of cerebroplacental ratio (CPR) for adverse perinatal outcomes of induction of labor in prolonged pregnancy.Methods:This retrospective study recruited 315 singleton pregnant women who had induced labor due to prolonged pregnancy (≥41 gestational weeks) in the First Affiliated Hospital of Chongqing Medical University from January 1, 2019 to April 30, 2020. Based on the occurrence of adverse perinatal outcomes (emergency delivery due to persistent abnormal fetal heart rate monitoring, umbilical artery blood pH at birth <7.2, 5 min Apgar scores<7, transferring to neonatal intensive care unit after birth, chorioamnionitis and vaginal delivery converted to cesarean section), they were divided into two groups: case group ( n=76) and normal group ( n=239). Clinical features and umbilical artery blood flow, middle cerebral artery (MCA) flow and CPR measured in the last ultrasound scan before induction were compared between the two groups using student's t-test, Mann-Whitney U test and Chi-square test. Receiver operating characteristic (ROC) curve was used to analyze the predictive values of umbilical artery blood flow, MCA flow and CPR for the adverse perinatal outcomes. Multivariate logistic regression analysis was used to screen the meaningful predictors. Results:Compared with the normal group, the umbilical artery pulsatility index (PI) (0.9±0.1 vs 0.8±0.1, t=-5.458, P<0.001) and the percentage of abnormal CPR (<1.0) increased significantly [21.1%(16/76) vs 6.3%(15/239), χ2=14.190, P<0.001] in the case group, while the MCA-PI and CPR decreased significantly (1.1±0.2 vs 1.3±0.3, t=5.658, P<0.001; 1.2±0.3 vs 1.6±0.5, t=8.940, P<0.001). The areas under the ROC curves of umbilical artery PI, MCA-PI and CPR for predicting adverse perinatal outcomes were 0.71, 0.71 and 0.77, respectively. CPR had the highest sensitivity (0.74) compared with umbilical artery PI (0.68) and MCA-PI (0.71), but the specificity of them were similar (0.67, 0.66 and 0.66). Multivariate logistic regression analysis showed that only CPR was the independent risk factor for adverse perinatal outcomes ( OR=0.028, 95% CI: 0.010-0.080, P<0.001). Conclusions:As an indicator for early prediction of adverse perinatal outcomes of induction of labor in prolonged pregnancy, CPR was more sensitive but less specific.

Objective:To explore the application value of artificial intelligence-assisted diagnosis model based on convolutional neural network (CNN) in the differential diagnosis of benign and malignant breast masses.Methods:A total of 10 490 images of 2 098 patients with breast lumps (including 1 132 cases of benign tumor, 779 cases of malignant tumor, 32 cases of inflammation, 155 cases of adenosis) were collected from January 2016 to January 2018 in Beijing Tiantan Hospital Affiliated to the Capital University of Medical Sciences. They were divided into training set and test set and the auxiliary artificial intelligence diagnosis model was used for training and testing. Two sets of data training models were compared by two-dimensional imaging (2D) and two-dimensional and color Doppler flow imaging (2D-CDFI). The ROC curves of benign breast tumors, malignant tumors, inflammation and adenopathy were analyzed, and the area under the ROC curve (AUC) were calculated.Results:The accuracies of 2D-CDFI ultrasonic model for training group and testing group were significantly improved. ①For benign tumors, the result from training set with 2D image was: sensitivity 92%, specificity 95%, AUC 0.93; the result from training set with 2D-CDFI images was: sensitivity 93%, specificity 95%, AUC 0.93; the result for test set with 2D images was: sensitivity 91%, specificity 96%, AUC 0.94; the result for test set with 2D-CDFI images was: sensitivity 93%, specificity: 94%, AUC 0.94. ② For malignancies, the result for training set with 2D images was: sensitivity 93%, specificity 97%, AUC 0.94; the result for training set with 2D-CDFI images was: sensitivity 93%, specificity 96%, AUC 0.94; the result for test set with 2D images was: sensitivity 93%, specificity 96%, AUC 0.94; the result for test set with 2D-CDFI images was: sensitivity 93%, specificity 96%, AUC 0.94. ③For inflammation, the result for training set with 2D images was: sensitivity 81%, specificity 99%, AUC 0.91; the result for training set with 2D-CDFI images was: sensitivity 86%, specificity 99%, AUC 0.89; the result for test set with 2D images was: sensitivity 100%, specificity 98%, AUC 0.98; the result for test set with 2D-CDFI images was: sensitivity 100%, specificity 99%, AUC 0.96. ④For adenopathy, the result for training set with 2D images was: sensitivity 88%, specificity 97%, AUC 0.94; the result for training set with 2D-CDFI images was: sensitivity 93%, specificity 98%, AUC 0.94; the result for test set with 2D images was: sensitivity 94%, specificity 98%, AUC 0.93; the result for test set with 2D-CDFI images was: sensitivity 88%, specificity 99%, AUC 0.90. Its diastolic accuracy was not affected even if the maximum diameter of the tumor was less than 1 cm.Conclusions:Through the deep learning of artificial intelligence based on CNN for breast masses, it can be more finely classified and the diagnosis rate can be improved. It has potential guiding value for the treatment of breast cancer patients.

Objective: To investigate the prevalence of autonomic dysfunction and its influencing factors in the elderly in Jizhou community of Tianjin. Methods: By using a cross-sectional study, a questionnaire survey was conducted in the elderly in order to investigate the prevalence of autonomic dysfunction and its influencing factors. Results: A total of 1 292 elderly patients were enrolled.Of them, 196 cases had autonomic dysfunction(15.2%, 196/1 292). The main symptoms of autonomic dysfunction were frequent urination, urination urgency, urination incontinence(19.7%, 255/1 292)and constipation(15.9%, 205/1 292). Multivariate Logistic regression analysis showed that women(OR=1.808, 95%CI: 1.253~2.607), 75-85 years of age(OR=1.573, 95%CI: 1.109~2.232), general anesthesia history(OR=1.552, 95%CI: 1.044~2.307), sleep disorders(OR=2.906, 95%CI: 1.506~2.916), diabetes(OR=1.791, 95%CI: 1.197~2.678)and headache(OR=2.589, 95%CI: 1.482~4.520)were risk factors for autonomic dysfunction. Conclusions The prevalence of autonomic dysfunction is high in the elderly in Jizhou community of Tianjin city.It is necessary to pay great attention to potential risk factors of autonomic dysfunction in the elderly.And the autonomic dysfunction symptoms should be diagnosed and treated as early as possible.