BACKGROUND:In vitro construction of tissue-engineered intestinal models plays an important role in intestinal regeneration and intestinal disease research.The interaction of intestinal nervous system and intestinal epithelial barrier to maintain body homeostasis is a hot topic in the bionic construction of tissue-engineered intestinal tract.OBJECTIVE:To construct a bionic model that can mimic the enteric nervous system in vivo.METHODS:Using fibroin protein with villus structure as scaffold,human induced neural stem cells solidified with collagen were added to intestinal epithelial cells(Caco-2 and HT29-MTX-E12)for 3-day culture to construct a co-culture system of intestinal epithelial cells and nerve cells(co-culture group).Human induced neural stem cells or intestinal epithelial cells cultured alone that were inoculated with fibroin scaffolds were set as controls.Cell morphology was observed by scanning electron microscopy and hematoxylin-eosin staining.Cell activity was detected by Live/Dead cell staining.Human induced neural stem cell differentiation was detected by β-microtubulin immunofluorescence staining.Intestinal epithelial histological properties and barrier function were detected by microvillin,sucrase-isomaltase,tight junction protein 1,E-calmodulin,and mucin-2 immunofluorescence staining.The function of mucus secretion from intestinal epithelial cells was detected by Alcian blue staining.Alkaline phosphatase staining was performed to detect differentiation of intestinal epithelial cells,at the same time,sucrase-isomaltase,tight junction protein 1,and alkaline phosphatase mRNAs were detected by RT-qRCR.RESULTS AND CONCLUSION:The neuralized intestinal mucosal co-culture model with villi structure was successfully constructed,and neural stem cells and intestinal epithelial cells on the fibroin scaffold showed good cellular activities.After neuralization,the activity of alkaline phosphatase and sucrase-isomaltase in intestinal epithelial cells was enhanced,while the expression level of tight junction protein 1 was up-regulated.To conclude,the neuralized bionic intestinal epithelial model is beneficial to the maturation of intestinal mucosal epithelial cells and the formation of barrier function.

BACKGROUND:In vitro construction of tissue-engineered intestinal models plays an important role in intestinal regeneration and intestinal disease research.The interaction of intestinal nervous system and intestinal epithelial barrier to maintain body homeostasis is a hot topic in the bionic construction of tissue-engineered intestinal tract.OBJECTIVE:To construct a bionic model that can mimic the enteric nervous system in vivo.METHODS:Using fibroin protein with villus structure as scaffold,human induced neural stem cells solidified with collagen were added to intestinal epithelial cells(Caco-2 and HT29-MTX-E12)for 3-day culture to construct a co-culture system of intestinal epithelial cells and nerve cells(co-culture group).Human induced neural stem cells or intestinal epithelial cells cultured alone that were inoculated with fibroin scaffolds were set as controls.Cell morphology was observed by scanning electron microscopy and hematoxylin-eosin staining.Cell activity was detected by Live/Dead cell staining.Human induced neural stem cell differentiation was detected by β-microtubulin immunofluorescence staining.Intestinal epithelial histological properties and barrier function were detected by microvillin,sucrase-isomaltase,tight junction protein 1,E-calmodulin,and mucin-2 immunofluorescence staining.The function of mucus secretion from intestinal epithelial cells was detected by Alcian blue staining.Alkaline phosphatase staining was performed to detect differentiation of intestinal epithelial cells,at the same time,sucrase-isomaltase,tight junction protein 1,and alkaline phosphatase mRNAs were detected by RT-qRCR.RESULTS AND CONCLUSION:The neuralized intestinal mucosal co-culture model with villi structure was successfully constructed,and neural stem cells and intestinal epithelial cells on the fibroin scaffold showed good cellular activities.After neuralization,the activity of alkaline phosphatase and sucrase-isomaltase in intestinal epithelial cells was enhanced,while the expression level of tight junction protein 1 was up-regulated.To conclude,the neuralized bionic intestinal epithelial model is beneficial to the maturation of intestinal mucosal epithelial cells and the formation of barrier function.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Aim To mine the competing ceRNA net-works associated with programmed cell death in the pathophysiological mechanisms of atherosclerosis(AS)based on bioinformatics,in order to identify new targets for the diagnosis and treatment of AS.Methods Firstly,the GSE97210 and GSE28858 datasets were screened from the GEO database.Differentially ex-pressed lncRNA,mRNA and miRNA were identified,following which a IncRNA-miRNA-mRNA regulatory network was constructed in Cytoscape 3.7.2 software based on ceRNA theory.Second,GO and KEGG en-richment analysis of mRNA in the ceRNA network was performed.Finally,the mRNAS within the ceRNA net-work were compared with genes related to autophagy,pyroptosis and ferroptosis to establish a ceRNA network related to programmed cell death.Results A total of 1208 DElncRNAS,4723 DEmRNAS and 139 DEmiR-NAS were identified.A ceRNA network was estab-lished,comprising 64 lncRNAS,8 miRNAS and 167 mRNAS.The mRNAS within the CeRNA network were mainly enriched in biological processes such as positive regulation of transcription and migration,protein bind-ing,and signaling pathways including PI3K-Akt signa-ling pathway,and mTOR signaling pathway.Finally,this study established 7 lncRNA-mediated ceRNA regu-latory pathways associated with pyroptosis and 23 ln-cRNA-mediated regulatory pathways for ferroptosis and autophagy.Conclusion This study has successfully constructed a ceRNA network related to programmed cell death,which helps us understand the mechanism by which programmed cell death leads to AS.

Objective To explore the value of deep learning image reconstruction(DLIR)algorithm to improve the quality of low keV monochromatic portal vein images of energy spectrum CT.Methods Fifty patients who underwent enhanced upper abdominal energy spectrum CT scan were selected.Mixed-model adaptive statistical iterative reconstruction-Veo(50%ASIR-V)algorithm and high-deep learning image reconstruction(DLIR-H)algorithm were used to obtain monochromatic images at 40-70 keV(with intervals of 10 keV).The CT and standard deviation(SD)values of the portal vein trunk,left and right branches,and erector spinae muscle were measured in the transverse position,and the signal-to-noise ratio(SNR)and portal vein contrast-to-noise ratio(CNR)were calculated for objective evaluation.The portal vein image quality between the two algorithms and different energy was subjectively scored by two physicians.Results In terms of objective evaluation:compared with 50%ASIR-V,the CNR and SNR of portal vein in monochromatic DLIR-H images at the same keV between 40-70 keV energy levels were increased while the SD value was decreased(P＜0.05),and the CT value was unchanged;there was no statistical difference in the magnitude of change in CNR between the two algorithms at different energy levels(P＞0.05);there was a statistically significant difference in the magnitude of change in SNR and SD value(P＜0.05)and the magnitude of change was the largest at 40 keV;comparison between different energy levels of DLIR-H,the CNR and SD value of 40 keV DLIR-H were the highest(P＜0.05),and there was no significant difference in the SNR(P＞0.05).In terms of subjective evaluation:there was no significant difference between the subjective scores of the two algorithms at the same keV from 40-70 keV(P＞0.05),and the two reconstruction algorithms at 40 keV and 50 keV had the highest subjective scores between different keV.Conclusion The DLIR algorithm can reduce the noise of low keV monochromatic images,improve the image quality of portal vein.

AIM To compare the chemical constituents in traditional decoction and formula granule decoction of classical famous prescription Wendan Decoction.METHODS The HPLC fingerprints were established,after which the contents of adenosine,synephrine,liquiritin,naringin,hesperidin,6-gingerol and adenosine cyclophosphate were determined,cluster analysis,principal component analysis and multidimensional scaling analysis were adopted in the investigation of component differences,and the equivalent of formula granules was adjusted.RESULTS The similarities of HPLC fingerprints for 10 batches of traditional decoctions were higher than those of HPLC fingerprints for 9 batches of formula granule decoctions(P＜0.01).Adenosine,synephrine,liquiritin,hesperidin and cyclic adenosine monophosphate demonstrated higher contents in traditional decoctions than those in formula granule decoctions(P＜0.05),6-gingerol displayed lower content than that in the latter produced by manufacturers A,C(P＜0.05),which was higher than that in the latter produced by manufacturer B(P＜0.01).Various batches of traditional decoctions and formula granule decoctions could be obviously distinguished,adenosine,synephrine and hesperidin exhibited great influences on the classification of principal component analysis,and the quality of formula granule decoctions produced by manufacturer C was closer to that of traditional decoctions.After equivalent correction,the contents of various constituents in formula granule decoctions produced by manufacturers A,C showed no significant differences as compared with those in traditional decoction(P＞0.05).CONCLUSION The formula granules of Wendan Decoction from different manufacturers exist quality differences,so the preparation process and extraction process of this preparation should be optimized to improve quality,and equivalent ratio should be adjusted according to actual requirements to ensure its scientific and rational clinical application.

Aim To predict the mechanism of action of ginsenoside Rg1(G-Rg1)paired with hirudin in the treatment of myocardial fibrosis in acute myocardial in-farction(AMI)based on the network pharmacology ap-proach,and to validate it by in vivo and in vitro experi-ments.Methods The corresponding targets of G-Rg1 and Hirudin were collected using SwissTargetPredic-tion,TargetNet,ETCM and ChEMBL databases,and the targets related to AMI and myocardial fibrosis were collected using GeneCards,OMIM and DisGeNET da-tabases.The drug-disease intersection targets were subjected to protein-protein interaction network(PPI)network analysis,gene ontology(GO)functional en-richment analysis and kyoto encyclopedia of genomes(KEGG)pathway enrichment analysis.Key targets and pathways were validated using an AMI mouse mod-el induced by ligation of the anterior descending branch of the left coronary artery in mice versus a hypoxia-in-duced injury model of human cardiac microvascular en-dothelial cells(HCMECs).Results G-Rg1 paired with hirudin had 229 drug targets,816 AMI and myo-cardial fibrosis disease targets,and 65 intersecting tar-gets.PPI analysis showed that tumor necrosis factor(TNF),interleukin-1[3(IL-1 β),transforming growth factor beta-1(TGF-β1),nuclear factor kappa-B(NF-κB),and interleukin-6(IL-6)might be the core tar-gets of G-Rg1 paired with Hirudin in the treatment of post-MI myocardial fibrosis;KEGG was enriched for a total of 141 pathways involving endocrine and metabo-lism,inflammation,and immunity,mainly TNF signa-ling pathway,PI3K/Akt signaling pathway and TGF-βsignaling pathway.In vivo experiments confirmed that G-Rg1 paired with hirudin attenuated myocardial fibro-sis after AMI in mice,and down-regulated the expres-sion of TNF-α,IL-1β,NF-κB,TGF-β1,and Smad2/3 proteins in myocardial tissues.In vitro experiments confirmed that G-Rg1 paired with Hirudin inhibited cellular NF-κB/TGF-β1 pathway,reduced hypoxia-in-duced cellular TNF-α and IL-1β expression,and su-perimposed NF-κB inhibitor significantly reduced IL-1 β expression and attenuated cellular inflammatory re-sponse.Conclusions G-Rg1 with Hirudin treats post-MI myocardial fibrosis by regulating TNF-α,IL-1 β and other targets and NF-KB/TGF-β1 pathway,reflecting its multi-pathway and multi-target action characteris-tics,and providing a pharmacological basis for the treatment of post-MI myocardial fibrosis with G-Rg1 with Hirudin.

Objective To investigate the distribution and antimicrobial resistance profiles of common pathogens isolated from cerebrospinal fluid(CSF)in CHINET program from 2015 to 2021.Methods The bacterial strains isolated from CSF were identified in accordance with clinical microbiology practice standards.Antimicrobial susceptibility test was conducted using Kirby-Bauer method and automated systems per the unified CHINET protocol.Results A total of 14 014 bacterial strains were isolated from CSF samples from 2015 to 2021,including the strains isolated from inpatients(95.3％)and from outpatient and emergency care patients(4.7％).Overall,19.6％of the isolates were from children and 80.4％were from adults.Gram-positive and Gram-negative bacteria accounted for 68.0％and 32.0％,respectively.Coagulase negative Staphylococcus accounted for 73.0％of the total Gram-positive bacterial isolates.The prevalence of MRSA was 38.2％in children and 45.6％in adults.The prevalence of MRCNS was 67.6％in adults and 69.5％in children.A small number of vancomycin-resistant Enterococcus faecium(2.2％)and linezolid-resistant Enterococcus faecalis(3.1％)were isolated from adult patients.The resistance rates of Escherichia coli and Klebsiella pneumoniae to ceftriaxone were 52.2％and 76.4％in children,70.5％and 63.5％in adults.The prevalence of carbapenem-resistant E.coli and K.pneumoniae(CRKP)was 1.3％and 47.7％in children,6.4％and 47.9％in adults.The prevalence of carbapenem-resistant Acinetobacter baumannii(CRAB)and Pseudomonas aeruginosa(CRPA)was 74.0％and 37.1％in children,81.7％and 39.9％in adults.Conclusions The data derived from antimicrobial resistance surveillance are crucial for clinicians to make evidence-based decisions regarding antibiotic therapy.Attention should be paid to the Gram-negative bacteria,especially CRKP and CRAB in central nervous system(CNS)infections.Ongoing antimicrobial resistance surveillance is helpful for optimizing antibiotic use in CNS 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 explore the value of deep learning image reconstruction(DLIR)algorithm to improve the quality of low keV monochromatic portal vein images of energy spectrum CT.Methods Fifty patients who underwent enhanced upper abdominal energy spectrum CT scan were selected.Mixed-model adaptive statistical iterative reconstruction-Veo(50%ASIR-V)algorithm and high-deep learning image reconstruction(DLIR-H)algorithm were used to obtain monochromatic images at 40-70 keV(with intervals of 10 keV).The CT and standard deviation(SD)values of the portal vein trunk,left and right branches,and erector spinae muscle were measured in the transverse position,and the signal-to-noise ratio(SNR)and portal vein contrast-to-noise ratio(CNR)were calculated for objective evaluation.The portal vein image quality between the two algorithms and different energy was subjectively scored by two physicians.Results In terms of objective evaluation:compared with 50%ASIR-V,the CNR and SNR of portal vein in monochromatic DLIR-H images at the same keV between 40-70 keV energy levels were increased while the SD value was decreased(P＜0.05),and the CT value was unchanged;there was no statistical difference in the magnitude of change in CNR between the two algorithms at different energy levels(P＞0.05);there was a statistically significant difference in the magnitude of change in SNR and SD value(P＜0.05)and the magnitude of change was the largest at 40 keV;comparison between different energy levels of DLIR-H,the CNR and SD value of 40 keV DLIR-H were the highest(P＜0.05),and there was no significant difference in the SNR(P＞0.05).In terms of subjective evaluation:there was no significant difference between the subjective scores of the two algorithms at the same keV from 40-70 keV(P＞0.05),and the two reconstruction algorithms at 40 keV and 50 keV had the highest subjective scores between different keV.Conclusion The DLIR algorithm can reduce the noise of low keV monochromatic images,improve the image quality of portal vein.