AIM:To establish a stable and efficient rat model of alcoholic liver disease(ALD),we investigat-ed the effects of different alcoholic beverage concentrations and alcohol dosing regimens.METHODS:(1)SPF-grade male SD rats were randomized into 5 groups(n=10):blank,ALD1,ALD2,ALD3,and ALD4.Except for the blank group,rats received intragastric administration of 56%alcohol(6 mL/kg twice daily with an 8-hour interval)for 4 weeks,along with free access to 0%,5%,10%,or 15%alcoholic beverage to evaluate concentration-dependent effects.(2)An-other cohort was divided into three groups(n=10):blank,ALD5,and ALD6.Rats(except blank)were gavaged with 56%alcohol twice daily for 9 weeks(8 mL/kg for ALD5;6 mL/kg in week 1,increasing by 0.5 mL/kg weekly for ALD6),with 10%alcoholic beverage available ad libitum to assess dose-dependent effects.Serum biochemical markers[alanine aminotransferase(ALT),aspartate aminotransferase(AST),total cholesterol(TC),triglycerides(TG),high-density li-poprotein cholesterol(HDL-C),and low-density lipoprotein cholesterol(LDL-C)]and inflammatory cytokines[interleu-kin-6(IL-6),IL-1β and tumor necrosis factor-α(TNF-α)]were analyzed after modeling,complemented by imaging(B-ultrasound,CT,and MRI).Success and survival rates were calculated.RESULTS:(1)ALD1～4 groups exhibited sig-nificantly elevated ALT,AST,TC,TG,LDL-C,IL-1β,IL-6 and TNF-α(P<0.05 or P<0.01)and reduced HDL-C and liver-to-spleen CT density ratio vs blank.ALD3(10%alcoholic beverage)showed the highest modeling success rate with low mortality.(2)ALD5 and ALD6 groups also had siginificant differin terms(P<0.01),with ALD6(gradually increas-ing dose)displaying more severe liver injury,higher success rate,and better survival.CONCLUSION:The optimal ALD model was induced by intragastric administration of 56%alcohol(6 mL/kg twice daily in week 1,increasing by 0.5 mL/kg weekly for 9 weeks)combined with 10%alcoholic beverage.This protocol offers a reliable approach for ALD re-search and drug development.

AIM:To establish a stable and efficient rat model of alcoholic liver disease(ALD),we investigat-ed the effects of different alcoholic beverage concentrations and alcohol dosing regimens.METHODS:(1)SPF-grade male SD rats were randomized into 5 groups(n=10):blank,ALD1,ALD2,ALD3,and ALD4.Except for the blank group,rats received intragastric administration of 56%alcohol(6 mL/kg twice daily with an 8-hour interval)for 4 weeks,along with free access to 0%,5%,10%,or 15%alcoholic beverage to evaluate concentration-dependent effects.(2)An-other cohort was divided into three groups(n=10):blank,ALD5,and ALD6.Rats(except blank)were gavaged with 56%alcohol twice daily for 9 weeks(8 mL/kg for ALD5;6 mL/kg in week 1,increasing by 0.5 mL/kg weekly for ALD6),with 10%alcoholic beverage available ad libitum to assess dose-dependent effects.Serum biochemical markers[alanine aminotransferase(ALT),aspartate aminotransferase(AST),total cholesterol(TC),triglycerides(TG),high-density li-poprotein cholesterol(HDL-C),and low-density lipoprotein cholesterol(LDL-C)]and inflammatory cytokines[interleu-kin-6(IL-6),IL-1β and tumor necrosis factor-α(TNF-α)]were analyzed after modeling,complemented by imaging(B-ultrasound,CT,and MRI).Success and survival rates were calculated.RESULTS:(1)ALD1～4 groups exhibited sig-nificantly elevated ALT,AST,TC,TG,LDL-C,IL-1β,IL-6 and TNF-α(P<0.05 or P<0.01)and reduced HDL-C and liver-to-spleen CT density ratio vs blank.ALD3(10%alcoholic beverage)showed the highest modeling success rate with low mortality.(2)ALD5 and ALD6 groups also had siginificant differin terms(P<0.01),with ALD6(gradually increas-ing dose)displaying more severe liver injury,higher success rate,and better survival.CONCLUSION:The optimal ALD model was induced by intragastric administration of 56%alcohol(6 mL/kg twice daily in week 1,increasing by 0.5 mL/kg weekly for 9 weeks)combined with 10%alcoholic beverage.This protocol offers a reliable approach for ALD re-search and drug development.

Dynamic tracking analysis of monoclonal antibodies (mAbs) biotransformation in vivo is crucial, as certain modifications could inactivate the protein and reduce drug efficacy. However, a particular challenge (i.e. immune recognition deficiencies) in biotransformation studies may arise when modifications occur at the paratope recognized by the antigen. To address this limitation, a multi-epitope affinity technology utilizing the metal organic framework (MOF)@Au@peptide@aptamer composite material was proposed and developed by simultaneously immobilizing complementarity determining region (CDR) mimotope peptide (HH24) and non-CDR mimotope aptamer (CH1S-6T) onto the surface of MOF@Au nanocomposite. Comparative studies demonstrated that MOF@Au@peptide@aptamer exhibited significantly enhanced enrichment capabilities for trastuzumab variants in comparison to mono-epitope affinity technology. Moreover, the higher deamidation ratio for LC-Asn-30 and isomerization ratio for HC-Asn-55 can only be monitored by the novel bioanalytical platform based on MOF@Au@peptide@aptamer and liquid chromatography-quadrupole time of flight-mass spectrometry (LC-QTOF-MS). Therefore, multi-epitope affinity technology could effectively overcome the biases of traditional affinity materials for key sites modification analysis of mAb. Particularly, the novel bioanalytical platform can be successfully used for the tracking analysis of trastuzumab modifications in different biological fluids. Compared to the spiked phosphate buffer (PB) model, faster modification trends were monitored in the spiked serum and patients' sera due to the catalytic effect of plasma proteins and relevant proteases. Differences in peptide modification levels of trastuzumab in patients' sera were also monitored. In summary, the novel bioanalytical platform based on the multi-epitope affinity technology holds great potentials for in vivo biotransformation analysis of mAb, contributing to improved understanding and paving the way for future research and clinical applications.

Objective to establish a combined diagnosis model of scleroderma related genes based on gene expression comprehensive database(GEO)and artificial neural network(ANN)and to evaluate its effect and to predict and analyze targeted traditional Chinese medicine.Methods two scleroderma chips GSE23741 and GSE95065 were obtained from the GEO database as the training group data set.Random forest and lasso regression algorithms were used to screen the key genes of scleroderma and construct the ANN model for the diagnosis of scleroderma.The validation data sets GSE76807,GSE32413 and GSE59785 were used to verify the model,and the area under curve(AUC)analysis was used to evaluate the clinical application value of ANN model.The relative expression of key gene mRNA was verified by RT-qPCR experiment.The CIBERSORT algorithm was used to estimate the bioinformatics association between scleroderma and the screened biomarkers.Finally,the key genes were used to screen the targeted traditional Chinese medicine.Results A total of 167 differential genes were obtained.Furthermore,the five most relevant key genes(SERPINE2,SFRP4,SUGCT,FBLN5,NRXN2)were screened by machine learning,and the artificial neural network diagnosis model was constructed.The model was used to draw the subject operating characteristic(ROC)curves diagnosed by the training group and the verification group,and the AUC value of the training group was 1.000.The AUC of verification group were 0.770,0.795 and 0.872 respectively.The result of RT-qPCR experiment is consistent with that of machine learning algorithm.Immune cell infiltration analysis showed that the relative content of memory CD4+T cells was significantly increased in scleroderma group,while the relative content of γ δ T cells in normal group was significantly increased.Key genes are associated with macrophage M1,T cells,memory activated CD4+T cells,resting mast cells,CD8+T cells and so on.According to the key genes,12 traditional Chinese medicines were screened.Most of the four qi and five flavors belong to warm,cold,flat,sweet,pungent and bitter,and most of them belong to the meridians of liver,spleen and lung.Conclusion the artificial neural network diagnosis model of key genes of scleroderma is constructed,which can be used in clinical diagnosis of scleroderma,and the potential targeted traditional Chinese medicine for the treatment of scleroderma is predicted,which provides a new perspective for exploring the pathogenesis of scleroderma.

Objective:To establish a scientific and standardized evaluation index system of neonatal breastfeeding nursing quality under the state of mother-infant separation, in order to provide a tool for the detection and management of neonatal breastfeeding nursing quality under mother-infant separation, so as to promote the implementation of breastfeeding in China.Methods:A systematic and comprehensive search of Chinese and English databases was conducted to collect guidelines on neonatal breastfeeding in the state of mother-infant separation. Based on the "structure-process - result" three-dimensional quality structure model, the preliminary draft of evaluation indexes of neonatal breastfeeding nursing quality in the state of mother-infant separation was formed. The evaluation indexes of neonatal breastfeeding nursing quality in the state of mother-infant separation were constructed by Delphi expert correspondence method from July to October, 2023.Results:The effective recovery rates were 95.83% (23/24) and 91.30% (21/23), respectively. The expert authority coefficients were 0.895 and 0.870, respectively. The Kendall harmony coefficients of the whole index were 0.134 and 0.178 (both P<0.01), respectively. The final evaluation index system of neonatal breastfeeding nursing quality under the state of mother-infant separation includes 3 first-level indicators, 18 second-level indicators and 82 third-level indicators. Conclusions:The evaluation index system of neonatal breastfeeding nursing quality in the state of mother-infant separation established in this study is highly scientific and reliable, highlighting the characteristics of neonatal breastfeeding nursing work in the state of mother-infant separation, and can provide a reference for the evaluation of neonatal breastfeeding nursing quality in the state of mother-infant separation.

Objective To explore the effects of M1 polarization of microglia on secondary damage in the thalamus after cerebral cortical infarction.Methods A focal cortical infarct model of adult male SD rats was pre-pared using eletrocoagulation and randomized into Sham and model groups at different time points 1～4 weeks after surgery.Based on the assessment of neurofunctional changes in each group of rats,immunohistochemistry was used to observe the number and morphology of NeuN,GFAP and Iba-1 positive cells in(Ventral posterior nucleus of thalamus,VPN)of the ipsilateral thalamus after distal middle cerebral artery occlusion(dMCAO).Immunofures-cence was used to detect the number and morphology of M1 microglia marker(Iba-1+/CD68+cells)and M2 microg-lia marker(Iba-1+/CD206+cells)in VPN of the ipsilateral thalamus after dMCAO.Western blot was used to detect the expression levels of IL-1β,TNF-α,IL-10 and Arg-1 in VPN of the ipsilateral thalamus after dMCAO.Results The results of immunohistochemistry showed a significant decrease in NeuN positive cells and an increase in the density of GFAP and Iba-1 in the ipsilateral VPN of rats after dMCAO when compared with Sham group(P<0.001).Compared with sham group,the protein levels of TNF α and IL-1β were elevated in the ipsilateral VPN elevated(P<0.05).In addition,the model group rats exhibited higher Bederson scores,beam-walking test and adhesive removal test scores after dMCAO compared with Sham group(P<0.05).The numbers of M1 microglia marker(Iba-1+/CD68+cells)were significantly increased when compared with M2 microglia marker(Iba-1+/CD206+cells)in ipsilateral VPN of rats after dMCAO.Conclusion M1 polarization of microglia may play an essential role in secondary damage of thalamus after cerebral cortical infarction.

Dynamic tracking analysis of monoclonal antibodies(mAbs)biotransformation in vivo is crucial,as certain modifications could inactivate the protein and reduce drug efficacy.However,a particular chal-lenge(i.e.immune recognition deficiencies)in biotransformation studies may arise when modifications occur at the paratope recognized by the antigen.To address this limitation,a multi-epitope affinity technology utilizing the metal organic framework(MOF)@Au@peptide@aptamer composite material was proposed and developed by simultaneously immobilizing complementarity determining region(CDR)mimotope peptide(HH24)and non-CDR mimotope aptamer(CH1S-6T)onto the surface of MOF@Au nanocomposite.Comparative studies demonstrated that MOF@Au@peptide@aptamer exhibited signifi-cantly enhanced enrichment capabilities for trastuzumab variants in comparison to mono-epitope af-finity technology.Moreover,the higher deamidation ratio for LC-Asn-30 and isomerization ratio for HC-Asn-55 can only be monitored by the novel bioanalytical platform based on MOF@Au@peptide@aptamer and liquid chromatography-quadrupole time of flight-mass spectrometry(LC-QTOF-MS).Therefore,multi-epitope affinity technology could effectively overcome the biases of traditional affinity materials for key sites modification analysis of mAb.Particularly,the novel bioanalytical platform can be suc-cessfully used for the tracking analysis of trastuzumab modifications in different biological fluids.Compared to the spiked phosphate buffer(PB)model,faster modification trends were monitored in the spiked serum and patients'sera due to the catalytic effect of plasma proteins and relevant proteases.Differences in peptide modification levels of trastuzumab in patients'sera were also monitored.In summary,the novel bioanalytical platform based on the multi-epitope affinity technology holds great potentials for in vivo biotransformation analysis of mAb,contributing to improved understanding and paving the way for future research and clinical applications.

Objective To establish a diagnostic model for scleroderma by combining machine learning and artificial neural network based on mitochondria-related genes.Methods The GSE95065 and GSE59785 datasets of scleroderma from GEO database were used for analyzing expressions of mitochondria-related genes,and the differential genes were identified by Random forest,LASSO regression and SVM algorithms.Based on these differential genes,an artificial neural network model was constructed,and its diagnostic accuracy was evaluated by 10-fold crossover verification and ROC curve analysis using the verification dataset GSE76807.The mRNA expressions of the key genes were verified by RT-qPCR in a mouse model of scleroderma.The CIBERSORT algorithm was used to estimate the bioinformatic association between scleroderma and the screened biomarkers.Results A total of 24 differential genes were obtained,including 11 up-regulated and 13 down-regulated genes.Seven most relevant mitochondria-related genes(POLB,GSR,KRAS,NT5DC2,NOX4,IGF1,and TGM2)were screened using 3 machine learning algorithms,and the artificial neural network diagnostic model was constructed.The model showed an area under the ROC curves of 0.984 for scleroderma diagnosis(0.740 for the verification dataset and 0.980 for cross-over validation).RT-qPCR detected significant up-regulation of POLB,GSR,KRAS,NOX4,IGF1 and TGM2 mRNAs and significant down-regulation of NT5DC2 in the mouse models of scleroderma.Immune cell infiltration analysis showed that the differential genes in scleroderma were associated with follicular helper T cells,immature B cells,resting dendritic cells,memory activated CD4+T cells,M0 macrophages,monocytes,resting memory CD4+T cells and mast cell activation.Conclusion The artificial neural network diagnostic model for scleroderma established in this study provides a new perspective for exploring the pathogenesis of scleroderma.

Objective To analyze the current status,hot spots and trends of Chinese and English researches in the field of aspiration after dysphasia in the past twenty years. Methods The articles about aspiration after dysphasia were retrieved from CNKI and Web of Science(WOS)core collec-tion database,from January,2003 to June,2023,and were analyzed with CiteSpace 6.1.R6. Results A total of 3 231 articles were included.The annual articles were published more and more year by year.The most English literatures came from the United States.Hot spots mainly focused on the assessment of dysphasia,prevention of complication,nutrition and rehabilitation therapy.It would concentrate on the application of the volume-viscosity swallow test and assessment scales,rehabilitation,penetration aspiration,outcome and effect validation,quality of life,feeding and nutrition condition,and evidence-based nursing,etc.,in the future. Conclusion The researches in the field of aspiration after dysphasia have been increasing in recent years,and the themes and contents of researches have been deepening.

Objective To establish a diagnostic model for scleroderma by combining machine learning and artificial neural network based on mitochondria-related genes.Methods The GSE95065 and GSE59785 datasets of scleroderma from GEO database were used for analyzing expressions of mitochondria-related genes,and the differential genes were identified by Random forest,LASSO regression and SVM algorithms.Based on these differential genes,an artificial neural network model was constructed,and its diagnostic accuracy was evaluated by 10-fold crossover verification and ROC curve analysis using the verification dataset GSE76807.The mRNA expressions of the key genes were verified by RT-qPCR in a mouse model of scleroderma.The CIBERSORT algorithm was used to estimate the bioinformatic association between scleroderma and the screened biomarkers.Results A total of 24 differential genes were obtained,including 11 up-regulated and 13 down-regulated genes.Seven most relevant mitochondria-related genes(POLB,GSR,KRAS,NT5DC2,NOX4,IGF1,and TGM2)were screened using 3 machine learning algorithms,and the artificial neural network diagnostic model was constructed.The model showed an area under the ROC curves of 0.984 for scleroderma diagnosis(0.740 for the verification dataset and 0.980 for cross-over validation).RT-qPCR detected significant up-regulation of POLB,GSR,KRAS,NOX4,IGF1 and TGM2 mRNAs and significant down-regulation of NT5DC2 in the mouse models of scleroderma.Immune cell infiltration analysis showed that the differential genes in scleroderma were associated with follicular helper T cells,immature B cells,resting dendritic cells,memory activated CD4+T cells,M0 macrophages,monocytes,resting memory CD4+T cells and mast cell activation.Conclusion The artificial neural network diagnostic model for scleroderma established in this study provides a new perspective for exploring the pathogenesis of scleroderma.