BACKGROUND:Chitosan has a place in the biomedical field due to its good biological properties and unique physicochemical properties,especially in tissue engineering and drug delivery with good application prospects.OBJECTIVE:To summarize the research progress of the role of chitosan in the repair and regeneration of oral soft and hard tissues.METHODS:A computerized search of CNKI and PubMed databases was performed with the search terms"chitosan,oral mucosal diseases,periodontal diseases,tissue regeneration,bacteriostatic,drug carrier,wound healing"in Chinese and English.The search time limit was from 2010 to 2024.After screening according to the inclusion and exclusion criteria,88 articles were finally included for summary analysis.RESULTS AND CONCLUSION:Chitosan is a promising biomaterial in bone and pulp regeneration as it has the ability to stimulate the recruitment and adhesion of osteogenic progenitor cells and dental pulp stem cells.Chitosan prevents caries,periodontal disease,and candidiasis by inhibiting Streptococcus pyogenes,Porphyromonas gingivalis,and Candida in the oral cavity.Chitosan nanocomposites have higher stability,better biocompatibility,and slow-release properties of drugs and can be enhanced by combining with other chemical reagents to enhance their anticancer properties.Chitosan possesses drug delivery,antibacterial activity,hemostasis and wound healing,which in turn can block the erosion of wounds by saliva and oral flora,relieve pain,repair and promote wound healing.Chitosan promotes the deposition of calcified material,which is conducive to the remineralisation of enamel and dentin.

BACKGROUND:Chitosan has a place in the biomedical field due to its good biological properties and unique physicochemical properties,especially in tissue engineering and drug delivery with good application prospects.OBJECTIVE:To summarize the research progress of the role of chitosan in the repair and regeneration of oral soft and hard tissues.METHODS:A computerized search of CNKI and PubMed databases was performed with the search terms"chitosan,oral mucosal diseases,periodontal diseases,tissue regeneration,bacteriostatic,drug carrier,wound healing"in Chinese and English.The search time limit was from 2010 to 2024.After screening according to the inclusion and exclusion criteria,88 articles were finally included for summary analysis.RESULTS AND CONCLUSION:Chitosan is a promising biomaterial in bone and pulp regeneration as it has the ability to stimulate the recruitment and adhesion of osteogenic progenitor cells and dental pulp stem cells.Chitosan prevents caries,periodontal disease,and candidiasis by inhibiting Streptococcus pyogenes,Porphyromonas gingivalis,and Candida in the oral cavity.Chitosan nanocomposites have higher stability,better biocompatibility,and slow-release properties of drugs and can be enhanced by combining with other chemical reagents to enhance their anticancer properties.Chitosan possesses drug delivery,antibacterial activity,hemostasis and wound healing,which in turn can block the erosion of wounds by saliva and oral flora,relieve pain,repair and promote wound healing.Chitosan promotes the deposition of calcified material,which is conducive to the remineralisation of enamel and dentin.

To summarize the current status in the application of digital and intelligent technologies in the field of pharmacovigilance and to provide reference to the selection and development of methods for pharmacovigilance of rare diseases.

Research indicates that microbe activity within the human body significantly influences health by being closely linked to various diseases. Accurately predicting microbe-disease interactions (MDIs) offers critical insights for disease intervention and pharmaceutical research. Current advanced AI-based technologies automatically generate robust representations of microbes and diseases, enabling effective MDI predictions. However, these models continue to face significant challenges. A major issue is their reliance on complex feature extractors and classifiers, which substantially diminishes the models' generalizability. To address this, we introduce a novel graph autoencoder framework that utilizes decoupled representation learning and multi-scale information fusion strategies to efficiently infer potential MDIs. Initially, we randomly mask portions of the input microbe-disease graph based on Bernoulli distribution to boost self-supervised training and minimize noise-related performance degradation. Secondly, we employ decoupled representation learning technology, compelling the graph neural network (GNN) to independently learn the weights for each feature subspace, thus enhancing its expressive power. Finally, we implement multi-scale information fusion technology to amalgamate the multi-layer outputs of GNN, reducing information loss due to occlusion. Extensive experiments on public datasets demonstrate that our model significantly surpasses existing top MDI prediction models. This indicates that our model can accurately predict unknown MDIs and is likely to aid in disease discovery and precision pharmaceutical research. Code and data are accessible at: https://github.com/shmildsj/MDI-IFDRL.

Objective To investigate the expression of serum long non-coding RNA X-inactive specific transcript(lncRNA XIST)and microRNA-126-3p(miR-126-3p)in patients with non-small cell lung cancer(NSCLC),and their relationship with recurrence after radical surgery.Methods A total of 108 NSCLC patients who underwent radical surgery and were admitted to General Hospital of Southern Theater Command of the People's Liberation Army of China from February 2019 to October 2020 were selected as the NSCLC group,and 52 healthy volunteers who underwent physical examination in this hospital were selected as the control group.qRT-PCR method was used to detect the relative expression levels of serum lncRNA XIST and miR-126-3p.Pearson method was used to analyze the correlation between serum lncRNA XIST and miR-126-3p expression.Logistic regression analysis was used to analyze the influencing factors of postoperative recurrence in NSCLC patients.Receiver operating characteristic(ROC)curve was used to evaluate the predictive value of serum lncRNA XIST and miR-126-3p on postoperative recurrence in patients.Results The expression level of serum lncRNA XIST in the NSCLC group was higher than that in the control group(P<0.05),while the expression level of miR-126-3p was lower than that in the control group(P<0.05).The expression levels of serum lncRNA XIST and miR-126-3p were related to TNM staging and lymph node metastasis(P<0.05).Bioinformatics website predicted that there was a targeted binding site between lncRNA XIST and miR-126-3p,and serum lncRNA XIST was negatively correlated with miR-126-3p expression(r=-0.579,P<0.05).Compared with the non-recurrent group,the expression level of serum lncRNA XIST in the recurrent group increased(P<0.05),while the expression level of miR-126-3p in the recurrent group decreased(P<0.05).Logistic regression analysis results showed that lncRNA XIST,miR-126-3p,lymph node metastasis,and TNM staging were the influencing factors for postoperative recurrence in NSCLC patients(P<0.05).The area under the curve(AUC)of serum lncRNA XIST,miR-126-3p,and the combination of the two in prediction of postoperative recurrence in NSCLC patients were 0.750,0.886,and 0.933,respectively,the combined prediction of the two was superior to individual prediction(Zcombination vs.lncRNA XIST=4.076,Zcombination vs.miR-126-3p=2.065,P<0.05).Conclusion The expression of serum lncRNA XIST is increased and miR-126-3p is decreased in NSCLC patients,both of which have certain predictive value for recurrence after radical surgery in patients.

Research indicates that microbe activity within the human body significantly influences health by being closely linked to various diseases.Accurately predicting microbe-disease interactions(MDIs)offers critical insights for disease intervention and pharmaceutical research.Current advanced AI-based technologies automatically generate robust representations of microbes and diseases,enabling effec-tive MDI predictions.However,these models continue to face significant challenges.A major issue is their reliance on complex feature extractors and classifiers,which substantially diminishes the models' generalizability.To address this,we introduce a novel graph autoencoder framework that utilizes decoupled representation learning and multi-scale information fusion strategies to efficiently infer po-tential MDIs.Initially,we randomly mask portions of the input microbe-disease graph based on Bernoulli distribution to boost self-supervised training and minimize noise-related performance degradation.Secondly,we employ decoupled representation learning technology,compelling the graph neural network(GNN)to independently learn the weights for each feature subspace,thus enhancing its expressive power.Finally,we implement multi-scale information fusion technology to amalgamate the multi-layer outputs of GNN,reducing information loss due to occlusion.Extensive experiments on public datasets demonstrate that our model significantly surpasses existing top MDI prediction models.This indicates that our model can accurately predict unknown MDIs and is likely to aid in disease discovery and precision pharmaceutical research.Code and data are accessible at:https://github.com/shmildsj/MDI-IFDRL.

Objective:To report the nationwide surveillance results of pathogenic profiles and antimicrobial resistance patterns of Gram-positive bloodstream infections in China in 2023.Methods:The clinical isolates of Gram-posttive bacteria from blood cultures were collected in member hospitals of National Bloodstream Infection Bacterial Resistant Investigation Collaborative System（BRICS）during January to December 2023. Antimicrobial susceptibility testing was performed using the dilution method recommended by the Clinical and Laboratory Standards Institute（CLSI）. Statistical analyses were conducted using WHONET 5.6 and SPSS 25.0 software.Results:A total of 4 385 Gram-positive bacterial isolates were obtained from 60 participating center. The top five pathogens were Staphylococcus aureus（ n=1 544，35.2%），coagulase-negative Staphylococci（ n=1 441，32.9%）， Enterococcus faecium（ n=574，13.1%）， Enterococcus faecalis（ n=385，8.8%），and α-hemolytic Streptococci（ n=187，4.3%）. The prevalence of methicillin-resistant Staphylococcus aureus（MRSA）and methicillin-resistant coagulase-negative Staphylococci（MRCNS）was 26.2%（405/1 544）and 69.8%（1 006/1 441），respectively. Notably，all Staphylococci remained susceptible to glycopeptide or daptomycin. Staphylococcus aureus demonstrated excellent susceptibility（>97.0%）to cephalobiol，rifampicin，trimethoprim-sulfamethoxazole，linezolid，minocycline，tigecycline，and eravacycline. No Enterococcus exhibiting resistance to linezolid were detected. Glycopeptide resistance was uncommon but more frequent in Enterococcus faecium（resistance to vancomycin and teicoplanin：both 1.7%）compared to Enterococcus faecalis（both 0.3%）. The detection rates of MRSA and MRCNS exhibited significant regional variations across the country（ χ2=17.674 and 148.650，respectively，both P<0.001）. No vancomycin-resistant Enterococci were detected in central China. Institutional comparison demonstrated higher prevalence of MRSA（ χ2=14.111， P<0.001）and MRCNS（ χ2=4.828， P=0.028）in provincial hospitals than that in municipal hospitals. Socioeconomic analysis identified elevated detection rates of both MRSA（ χ2=18.986， P<0.001）and MRCNS（ χ2=4.477， P=0.034）in less developed regions（per capita GDP

Objective:To report the results of bacterial resistant investigation collaborative system（BRICS）on the distribution and antimicrobial resistance profile of clinical Gram-negative bacteria isolates from bloodstream infections in China in 2023，and provide reference for clinical tretment of bloodstream infections and prevention and control of bacterial resistance.Methods:The clinical isolates of Gram-negative bacteria from blood cultures in member hospitals of BRICS were collected during January 2023 to December 2023. Antibiotic susceptibility tests were conducted by agar dilution or broth dilution methods recommended by Clinical and Laboratory Standards Institute（CLSI）. WHONET 5.6 and SPSS 25.0 were used to analyze the data.Results:During the study period，11 492 strains of Gram-negative bacteria were collected from 60 hospitals，of which 10 098（87.9%）were Enterobacterales and 1 394（12.1%）were non-fermentative bacteria. The top 5 bacterial species were Escherichia coli（50.0%）， Klebsiella pneumoniae（26.1%）， Pseudomonas aeruginosa（5.1%）， Acinetobacter baumannii complex（5.0%）and Enterobacter cloacae complex（4.1%）. The ESBL-producing rates in Escherichia coli， Klebsiella pneumoniae and Proteus mirablilis were 46.8%（2 685/5 741），18.3%（549/2 999）and 44.0%（77/175），respectively. The prevalence of carbapenem-resistant Escherichia coli（CREC）and carbapenem-resistant Klebsiella pneumoniae（CRKP）were 1.3%（76/5 741）and 15.0%（450/2 999）；32.9%（25/76）and 78.0%（351/450）of CREC and CRKP were sensitive to ceftazidime/avibactam combination，respectively. 94.7%（72/76）and 90.2%（406/450）of CREC and CRKP were sensitive to aztreonam/avibactam combination. Furthermore，57.9%（44/76）and 79.1%（356/450）were sensitive to imipenem/relebactam combination. The prevalence of carbapenem-resistant Acinetobacter baumannii（CRAB）complex was 64.6%（370/573），while more than 80.0% of CRAB complex was sensitive to tigecycline，eravacycline and polymyxin B. The prevalence of carbapenem-resistant Pseudomonas aeruginosa（CRPA）was 17.0%（99/581）. There were differences in the composition ratio of Gram-negative bacteria in bloodstream infections and the prevalence of important Gram-negative bacteria resistance among different regions in China，with statistically significant differences in the prevalence of CREC，CRKP，CRPA and CRAB complex（ χ2=10.6，28.6，10.8 and 19.3， P<0.05）. The prevalence of ESBL-producing Escherichia coli， CREC，CRAB complex and CRKP were higher in provincial hospitals than those in municipal hospitals（ χ2=12.5，9.8，12.7 and 57.8，all P<0.01）. Conclusions:Gram-negative bacteria are the main pathogens causing bloodstream infections in China，and Escherichia coli is ranked in the top，while the trend of Klebsiella pneumoniae increases continuously with time. CRKP infection shows a slow upward trend，CREC infecton maintains a low prevalence level，and CRAB complex infection continues to exhibit a high prevalence rate. The composition and resistance patterns of pathogens causing bloodstream infections vary to some extent across different regions and levels of hospitals in China.

Objective Using event-related potentials(ERPs)technology to study the effect of long term emotional distress on selective attention distraction inhibition function in college students and its neuroelectrophysiological mechanism.Methods The Eysenck personality questionnaire(EPQ)adult version was used to screen the high and low neuroticism groups among college students,and 35 subjects in each group were included in the long term emotional distress group and the emotional smoothness control group,respectively,and the response time,correct rate,N2 and P3 amplitude and latency results of the participants to complete the negative priming paradigm task were collected and analyzed.Results Compared with the control group,① the long term emotional distress group showed a prolonged response trend(P=0.072).② the long term emotional distress group had a prolonged N2 and P3 latency(P<0.05).Conclusion Selective attention distraction inhibition in college students with long term emotional distress decreased,and the decline mechanism may be related to the decline of inhibition processing and attention resource allocation ability.

Objective:To explore the abnormal patterns of brain functional network connectivity in depression adolescents and their diagnostic value in adolescent depression.Methods:A total of 94 depression adolescents (adolescent depression group) admitted to Outpatient Department of Psychiatric Imaging, West China Hospital, Sichuan University from January 2020 to December 2022 were selected. In addition, 78 age- and gender-matched healthy adolescents were recruited from local community advertisements at the same time-period as healthy control group. Resting-state functional magnetic resonance imaging was performed; after image preprocessing, group-level spatial independent component analysis was performed to identify the intrinsic network connectivity, and differences in network connectivity between the two groups were compared. Functional connectivity edges were employed as classification features, and feature ranking and screening were then performed. A support vector machine (SVM) with linear kernel function was used to construct a classification model, and receiver operating characteristic (ROC) curve was used to analyze the diagnostic value of this classification model in adolescent depression.Results:No significant difference was noted in age, gender, years of education, and body mass index between the two groups ( P>0.05). Compared with the healthy control group, the adolescent depression group had significantly decreased functional connectivity intensity within and between the networks of sensorimotor network (SMN), visual network (VN), auditory network (AN), default mode network (DMN), and cognitive control network (CCN), and significantly increased functional connectivity intensity within CCN ( P<0.05). When using the 75 top-ranked functional connectivity features, this classification model had the best performance (accuracy rate: 70.35%, sensitivity: 70.21%, specificity: 71.80%, P<0.001). ROC curve showed that area under the curve of this classification model in diagnosing adolescent depression was 0.724 (95% CI: 0.648-0.800, P<0.001). A total of 51 consistent functional connectivities were identified and they were mainly located within or between the networks of SMN, VN, AN, DMN, and CCN. Conclusion:The abnormal resting-state brain functional connectivity in depression adolescents can provide imaging basis for their clinical diagnosis.