With the growing emphasis on maternal and child oral health, the significance of managing oral health across preconception, pregnancy, and infancy stages has become increasingly apparent. Oral health challenges extend beyond affecting maternal well-being, exerting profound influences on fetal and neonatal oral development as well as immune system maturation. This expert consensus paper, developed using a modified Delphi method, reviews current research and provides recommendations on maternal and child oral health management. It underscores the critical role of comprehensive oral assessments prior to conception, diligent oral health management throughout pregnancy, and meticulous oral hygiene practices during infancy. Effective strategies should be seamlessly integrated across the life course, encompassing preconception oral assessments, systematic dental care during pregnancy, and routine infant oral hygiene. Collaborative efforts among pediatric dentists, maternal and child health workers, and obstetricians are crucial to improving outcomes and fostering clinical research, contributing to evidence-based health management strategies.
Humans ; Pregnancy ; Female ; Infant ; Consensus ; Mouth Diseases/therapy* ; Pregnancy Complications/therapy* ; Oral Health ; Infant, Newborn ; Delphi Technique ; Oral Hygiene

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective To investigate the antimicrobial resistance of clinical isolates from elderly patients(≥65 years)in major medical institutions across China.Methods Bacterial strains were isolated from elderly patients in 52 hospitals participating in the CHINET Antimicrobial Resistance Surveillance Program during the period from 2015 to 2021.Antimicrobial susceptibility test was carried out by disk diffusion method and automated systems according to the same CHINET protocol.The data were interpreted in accordance with the breakpoints recommended by the Clinical and Laboratory Standards Institute(CLSI)in 2021.Results A total of 514 715 nonduplicate clinical isolates were collected from elderly patients in 52 hospitals from January 1,2015 to December 31,2021.The number of isolates accounted for 34.3％of the total number of clinical isolates from all patients.Overall,21.8％of the 514 715 strains were gram-positive bacteria,and 78.2％were gram-negative bacteria.Majority(90.9％)of the strains were isolated from inpatients.About 42.9％of the strains were isolated from respiratory specimens,and 22.9％were isolated from urine.More than half(60.7％)of the strains were isolated from male patients,and 39.3％isolated from females.About 51.1％of the strains were isolated from patients aged 65-＜75 years.The prevalence of methicillin-resistant strains(MRSA)was 38.8％in 32 190 strains of Staphylococcus aureus.No vancomycin-or linezolid-resistant strains were found.The resistance rate of E.faecalis to most antibiotics was significantly lower than that of Enterococcus faecium,but a few vancomycin-resistant strains(0.2％,1.5％)and linezolid-resistant strains(3.4％,0.3％)were found in E.faecalis and E.faecium.The prevalence of penicillin-susceptible S.pneumoniae(PSSP),penicillin-intermediate S.pneumoniae(PISP),and penicillin-resistant S.pneumoniae(PRSP)was 94.3％,4.0％,and 1.7％in nonmeningitis S.pneumoniae isolates.The resistance rates of Klebsiella spp.(Klebsiella pneumoniae 93.2％)to imipenem and meropenem were 20.9％and 22.3％,respectively.Other Enterobacterales species were highly sensitive to carbapenem antibiotics.Only 1.7％-7.8％of other Enterobacterales strains were resistant to carbapenems.The resistance rates of Acinetobacter spp.(Acinetobacter baumannii 90.6％)to imipenem and meropenem were 68.4％and 70.6％respectively,while 28.5％and 24.3％of P.aeruginosa strains were resistant to imipenem and meropenem,respectively.Conclusions The number of clinical isolates from elderly patients is increasing year by year,especially in the 65-＜75 age group.Respiratory tract isolates were more prevalent in male elderly patients,and urinary tract isolates were more prevalent in female elderly patients.Klebsiella isolates were increasingly resistant to multiple antimicrobial agents,especially carbapenems.Antimicrobial resistance surveillance is helpful for accurate empirical antimicrobial therapy in elderly patients.

ObjectiveTo explore the mechanism of Shouhui Tongbian capsules in treating constipation based on the research foundation of its active components combined with network pharmacology and animal experiments. MethodsThe drug components were imported into SwissTargetPrediction to predict the targets of Shouhui Tongbian capsules， and constipation-related targets were collected from disease databases. A protein-protein interaction （PPI） network was constructed for the common targets shared by Shouhui Tongbian capsules and constipation to screen key targets， which was followed by gene ontology （GO） function and Kyoto encyclopedia of genes and genomes （KEGG） pathway enrichment analyses. A "bioactive component-target-pathway" network was constructed， and the core components of Shouhui Tongbian capsules in treating constipation were screened based on the topological parameters of this network. Molecular docking was employed to predict the binding affinity of core components to key targets. A mouse model of constipation was constructed to screen the key pathways and targets of the drug intervention in constipation. ResultsThe PPI network revealed six key constipation-related targets： protein kinase B （Akt1）， B-cell lymphoma-2 （Bcl-2）， glycogen synthase kinase-3β （GSK-3β）， cyclooxygenase-2 （PTGS2）， estrogen receptor 1 （ESR1）， and epidermal growth factor receptor （EGFR）. The KEGG pathway analysis showed that the phosphatidylinositol 3-kinase （PI3K）/Akt signaling pathway was the most enriched. The topological parameter analysis of the "bioactive component-target-pathway" network screened out the top 10 core components： auranetin， isosinensetin， naringin， diosmetin， quercetin， apigenin， luteolin， hesperidin， isorhapontigenin， and chrysophanol. Molecular docking results showed that the 10 core components had strong binding affinity with the 6 key targets. Animal experiments showed that after intervention with different doses of Shouhui Tongbian capsules， the time to the first black stool excretion was reduced and the fecal water content and small intestine charcoal propulsion rate of mice were improved. After treatment with Shouhui Tongbian capsules， the colonic mucosal injury and glandular arrangement were alleviated， and the muscle layer thickness was increased. Western blot results showed that Shouhui Tongbian capsules recovered the expression of apoptosis-related molecules mediated by the PI3K/Akt pathway in the colonic tissue of constipated mice. Terminal-deoxynucleotidyl transferase-mediated nick end labeling （TUNEL） results showed that the cell apoptosis rate of the colon significantly reduced after intervention with Shouhui Tongbian capsules. ConclusionThe results of network pharmacology and animal experiments confirmed that Shouhui Tongbian capsules can treat constipation through multiple targets and pathways. The capsules can effectively intervene in loperamide-induced constipation in mice by regulating the constipation indicators and reducing cell apoptosis in the colon tissue via activating the PI3K/Akt signaling pathway.

Objective To examine the changing prevalence and antimicrobial resistance profiles of Burkholderia cepacia in 52 hospitals across China from 2015 to 2021.Methods A total of 9 261 strains of B.cepacia were collected from 52 hospitals between January 1,2015 and December 31,2021.Antimicrobial susceptibility of the strains was tested using Kirby-Bauer method or automated antimicrobial susceptibility testing systems according to a unified protocol.The results were interpreted according to the breakpoints released in the Clinical & Laboratory Standards Institute(CLSI)guidelines(2023 edition).Results A total of 9 261 strains of B.cepacia were isolated from all age groups,especially elderly patients.The proportion was 11.1％(1 032 strains)in children,significantly lower than the proportion in adults.About half(46.5％,4 310/9 261)of the strains were isolated from patients at least 60 years old and 42.3％(3 919/9 261)of the strains were isolated from young adults.Most isolates(71.1％)were isolated from sputum and respiratory secretions,followed by urine(10.7％)and blood samples(8.1％).B.cepacia isolates were highly susceptible to the five antimicrobial agents recommended in the CLSI M100 document(33rd edition,2023).B.cepacia isolates showed relatively higher resistance rates to meropenem and levofloxacin.However,the resistance rates to ceftazidime,trimethoprim-sulfamethoxazole,and minocycline remained below 8.1％.The percentage of B.cepacia strains resistant to levofloxacin was the highest compared to other antibiotics in any of the three age groups(from 12.4％in the patients＜18 years old to 20.6％in the patients aged 60 years or older).Conclusions B.cepacia is one of the clinically important non-fermenting gram-negative bacteria.Accurate and timely reporting of antimicrobial susceptibility test results and ongoing antimicrobial resistance surveillance are helpful for rational prescription of antimicrobial agents and proper prevention and control of nosocomial infections.

Objective To examine the changing prevalence and antimicrobial resistance profiles of Burkholderia cepacia in 52 hospitals across China from 2015 to 2021.Methods A total of 9 261 strains of B.cepacia were collected from 52 hospitals between January 1,2015 and December 31,2021.Antimicrobial susceptibility of the strains was tested using Kirby-Bauer method or automated antimicrobial susceptibility testing systems according to a unified protocol.The results were interpreted according to the breakpoints released in the Clinical & Laboratory Standards Institute(CLSI)guidelines(2023 edition).Results A total of 9 261 strains of B.cepacia were isolated from all age groups,especially elderly patients.The proportion was 11.1％(1 032 strains)in children,significantly lower than the proportion in adults.About half(46.5％,4 310/9 261)of the strains were isolated from patients at least 60 years old and 42.3％(3 919/9 261)of the strains were isolated from young adults.Most isolates(71.1％)were isolated from sputum and respiratory secretions,followed by urine(10.7％)and blood samples(8.1％).B.cepacia isolates were highly susceptible to the five antimicrobial agents recommended in the CLSI M100 document(33rd edition,2023).B.cepacia isolates showed relatively higher resistance rates to meropenem and levofloxacin.However,the resistance rates to ceftazidime,trimethoprim-sulfamethoxazole,and minocycline remained below 8.1％.The percentage of B.cepacia strains resistant to levofloxacin was the highest compared to other antibiotics in any of the three age groups(from 12.4％in the patients＜18 years old to 20.6％in the patients aged 60 years or older).Conclusions B.cepacia is one of the clinically important non-fermenting gram-negative bacteria.Accurate and timely reporting of antimicrobial susceptibility test results and ongoing antimicrobial resistance surveillance are helpful for rational prescription of antimicrobial agents and proper prevention and control of nosocomial infections.

Objective To investigate the antimicrobial resistance of clinical isolates from elderly patients(≥65 years)in major medical institutions across China.Methods Bacterial strains were isolated from elderly patients in 52 hospitals participating in the CHINET Antimicrobial Resistance Surveillance Program during the period from 2015 to 2021.Antimicrobial susceptibility test was carried out by disk diffusion method and automated systems according to the same CHINET protocol.The data were interpreted in accordance with the breakpoints recommended by the Clinical and Laboratory Standards Institute(CLSI)in 2021.Results A total of 514 715 nonduplicate clinical isolates were collected from elderly patients in 52 hospitals from January 1,2015 to December 31,2021.The number of isolates accounted for 34.3％of the total number of clinical isolates from all patients.Overall,21.8％of the 514 715 strains were gram-positive bacteria,and 78.2％were gram-negative bacteria.Majority(90.9％)of the strains were isolated from inpatients.About 42.9％of the strains were isolated from respiratory specimens,and 22.9％were isolated from urine.More than half(60.7％)of the strains were isolated from male patients,and 39.3％isolated from females.About 51.1％of the strains were isolated from patients aged 65-＜75 years.The prevalence of methicillin-resistant strains(MRSA)was 38.8％in 32 190 strains of Staphylococcus aureus.No vancomycin-or linezolid-resistant strains were found.The resistance rate of E.faecalis to most antibiotics was significantly lower than that of Enterococcus faecium,but a few vancomycin-resistant strains(0.2％,1.5％)and linezolid-resistant strains(3.4％,0.3％)were found in E.faecalis and E.faecium.The prevalence of penicillin-susceptible S.pneumoniae(PSSP),penicillin-intermediate S.pneumoniae(PISP),and penicillin-resistant S.pneumoniae(PRSP)was 94.3％,4.0％,and 1.7％in nonmeningitis S.pneumoniae isolates.The resistance rates of Klebsiella spp.(Klebsiella pneumoniae 93.2％)to imipenem and meropenem were 20.9％and 22.3％,respectively.Other Enterobacterales species were highly sensitive to carbapenem antibiotics.Only 1.7％-7.8％of other Enterobacterales strains were resistant to carbapenems.The resistance rates of Acinetobacter spp.(Acinetobacter baumannii 90.6％)to imipenem and meropenem were 68.4％and 70.6％respectively,while 28.5％and 24.3％of P.aeruginosa strains were resistant to imipenem and meropenem,respectively.Conclusions The number of clinical isolates from elderly patients is increasing year by year,especially in the 65-＜75 age group.Respiratory tract isolates were more prevalent in male elderly patients,and urinary tract isolates were more prevalent in female elderly patients.Klebsiella isolates were increasingly resistant to multiple antimicrobial agents,especially carbapenems.Antimicrobial resistance surveillance is helpful for accurate empirical antimicrobial therapy in elderly patients.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Chronic constipation （CC） is one of the most common functional gastrointestinal diseases. At present， the overall therapeutic effect of CC is still not satisfactory worldwide， which seriously affects the quality of life and social function of patients. The etiology and pathophysiological mechanism of constipation are still unclear. It involves comprehensive factors such as heredity， social psychology， diet， intestinal flora imbalance， intestinal motility disorder， visceral sensitivity change， pelvic floor muscle group dysfunction and enteric nervous system （ENS） disorder. Among them， the abnormal factors of the brain-gut-microbiome axis are particularly significant. The brain-gut-microbiome axis is a complex network of interactions between the intestine and the brain， integrating and coordinating the physiological functions and pathological processes of the gastrointestinal tract. Microorganisms in the intestinal lumen play an important role in it， and can communicate with the intestinal tract and the central nervous system through nerve， endocrine and immune pathways. As a key brain-gut peptide in the regulation pathway of the brain-gut-microbiome axis， 5-hydroxytryptamine （5-HT） is involved in the regulation of gastrointestinal motility， sensation and secretion. The abnormal conduction of the 5-HT signaling pathway is closely related to the occurrence and development of constipation. Traditional Chinese medicine（TCM） is a unique precious resource in China， which has good curative effects and safety. In recent years， it has received extensive attention in the treatment of constipation. TCM and active ingredients， acupuncture and massage specifically regulate 5-HT signal transmission， so that the expressions of related molecules tend to be suitable for individual disease state levels to effectively improve constipation symptoms， with unique advantages. Therefore， this study used ''constipation''， ''intestinal flora''， ''5-HT''， and ''traditional Chinese medicine'' as the keywords to search PubMed， China National Knowledge Infrastructure （CNKI） and other literature databases. The correlation between 5-HT and constipation as well as brain-gut-microbiome axis and the research progress of TCM intervention in the 5-HT signaling pathway in the treatment of constipation were reviewed in order to explore the potential therapeutic value of 5-HT system in this disease and provide references for subsequent research.

Chronic constipation （CC） is one of the common and frequent functional gastrointestinal diseases in clinic. At present， the overall therapeutic effect of CC is still not satisfactory in China and abroad， which seriously affects the quality of life of patients. The pathophysiological mechanism of constipation has not been fully elucidated， and studies have mainly focused on the dysfunction of the enteric nervous system （ENS）， abnormalities of Cajal interstitial cells （ICCs）， alterations in mucosal immune function （low-grade intestinal inflammation， intestinal barrier damage， and immune activation）， abnormalities of gastrointestinal hormones， dysregulation of intestinal flora， and dysfunctions of intestinal dynamics. Traditional Chinese medicine （TCM） is a unique and precious resource in China with good efficacy and safety. TCM and its active ingredients can effectively treat constipation， and its mechanism of action needs to be further investigated. Signaling pathways are involved in the development of constipation， which is one of the main targets in the study of pathological mechanisms of constipation and the pharmacological study of therapeutic drugs. Therefore， the authors searched PubMed， China National Knowledge Infrastructure （CNKI）， and other literature databases with the keywords of ''constipation''， ''signaling pathway''， and ''traditional Chinese medicine'' to systematically summarize the signaling pathways related to constipation regulated by TCM. The comprehensive analysis shows that TCM mainly plays a role in regulating autophagy， apoptosis， and proliferation and ensuring anti-oxidative stress， anti-inflammation， and protection of intestinal barrier of ICCs by modulating signaling pathways such as stem cell factor/tyrosine kinase receptor （SCF/c-kit）， phosphatidylinositol 3-kinase/protein kinase B （PI3K/Akt）， 5-hydroxytryptophan （5-HT）， mitogen-activated protein kinases （MAPK）， and Toll-like receptors （TLRs）. TCM regulates neurotransmitter secretion and intestinal water metabolism to improve constipation. This demonstrates the advantages of multi-level， multi-target， and multi-level effects of TCM， and in-depth research can provide new ideas for exploring the pathogenesis of constipation and developing new drugs.