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.

AIM To evaluate the quality of Gegen Formula Granules.METHODS Linear calibration with two reference substances(LCTRS)was adopted in the predicting of retention time with puerarin and daidzein as internal standards.UPLC characteristic chromatograms were established.The contents of 3'-hydroxy puerarin,puerarin(internal standard),3'-methoxy puerarin,puerarin 6"-O-xyloside,puerarin apioside and daidzin were determined by quantitative determination analysis multi-components by a single marker(QAMS),after which their transfer rates were calculated.RESULTS Compared with relative retention time method,LCTRS demonstrated higher positional accuracy for characteristic peaks and wider application range for columns.There were 9 characteristic peaks in the characteristic chromatograms for 14 batches of formula granules and 15 batches of standard decoctions with the similarities of more than 0.95.The contents and transfer rates of various constituents in formula granules and standard decoctions were basically consistent.CONCLUSION The chemical constituents in formula granules and their standard decoctions of Puerariae lobatae Radix display good consistency,reliable preparation process is observable in the former.

AIM To evaluate the chemical constituent consistency in formula granules and traditional decoctions of Gouteng Jiangya Formula.METHODS HPLC characteristic chromatograms were established,the analysis was performed on a 30 ℃ thermostatic YMC-Triart C18 column(4.6 mm× 250 mm,5 μm),with the mobile phase comprising of acetonitrile-0.2％phosphoric acid flowing at 1.0 mL/min in a gradient elution manner,and the detection wavelength was set at 240 nm.Puerarin was used as an internal standard to calculate the relative correction factors of 3'-methoxy puerarin,puerarin apioside,magnolflorine,paeoniflora,daidzin,baicalin,palmatine,berberine,wogonoside and benzoylpaeoniflorin,after which the content detemination was made by quantitative analysis of multi-components by single-marker(QAMS).RESULTS The characteristic chromatograms of 9 batches of formula granules and 15 bacthes of traditional decoctions demonstrated the similarities of more than 0.90 at the detection wavelengths of 192,210,240,260,280,300,320,360 nm,along with similar total peak areas.Eleven constituents showed good linear relationships within their own ranges(r＞0.999 0),whose average recoveries were 97.27％-101.64％with the RSDs of 0.36％-1.11％,the result obtained by QAMS and external standard method demonstrated no significant differences(P＞0.05).The contents of various constituents in the formula granules approximated those in the traditional decoctions.CONCLUSION The consistent kinds and contents of various constituents are obversable in formula granules and traditional decoctions of Gouteng Jiangya Formula,which can provide a reference for the reasonable clinical application of this formula.

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.

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.

Intracranial atherosclerotic stenosis (ICAS) is a common cause of ischemic stroke. The evaluation of its structure and function is of great significance for formulating clinical intervention strategies. The indications for endovascular treatment of ICAS lesions in the past were mainly based on the degree of luminal stenosis showed by cerebral angiography, which had certain limitations. The translesional pressure ratio (PR), as an important indicator for functional assessment after arterial stenosis, has gradually received attention in the evaluation of ICAS lesions in recent years. This article reviews the evaluation methods and clinical significance of PR in ICAS lesions.

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.

AIM To evaluate the quality of Chuanxiong Chatiao Pills.METHODS The UPLC fingerprints were established,after which hierarchical cluster analysis,principal component analysis,orthogonal partial least squares discriminant analysis were performed,the contents of cimifugin,ferulic acid,liquiritin,nodakenin,senkyunolide Ⅰ,5-O-methylvisamminol,rosmarinic acid,ammonium glycyrrhizinate,imperatorin and isoimperatorin were determined.RESULTS There were 18 common peaks in the fingerprints for 44 batches of samples with the similarities of 0.75-1.00.Various batches of samples were clustered into 2 catagories,3 principal components demonstrated the accumulative variance contribution rate of 87.1％,5 quality difference markers were screened.Ten constituents showed good linear relationships within their own ranges(r≥0.999 3),whose average recoveries were 90.22％-105.30％with the RSDs of 0.66％-1.98％,whose content ranges were 0.070-0.438,0.147-0.529,0.052-0.444,1.228-6.934,0.016-0.545,0.049-1.554,0.018-0.415,0.382-2.187,0.568-3.700,0.069-0.996 mg/g,respectively.CONCLUSION This accurate,reliable and specific method can provide scientific evidence for the quality control and standardization of Chuanxiong Chatiao Pills.

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.