Objective To explore the pathogenic mechanisms of Legionella pneumophila(L.pneumophila)infection inhibiting the fusion of endosome-lysosome fusion in mouse macrophages.Methods Twelve C57 mice were randomly divided into control group and L.pneumophila infection group(n=6 each).After anesthesia,an equal volume of physiological saline or L.pneumophila solution was administered nasally.Body weight changes were monitored for 3 consecutive days,and the lungs were extracted to assess injury.Hematoxylin and eosin(HE)staining and immunohistochemical staining were performed to observe the pathological characteristics of lung tissue in both groups.Transcriptome sequencing was utilized to analyze differentially expressed genes(DEGs)and associated signaling pathways in lung tissues.Mouse bone marrow macrophages(BMDMs)were isolated and co-cultured with L.pneumophila,with infection status confirmed by immunofluorescence staining.Transcriptome sequencing was employed to analyze DEGs and enriched related signaling pathways before and after infection.Core genes involved in the post-infection signaling pathway were identified,and the consistency of their mRNA expression levels in vivo and in vitro was verified using RT-qPCR.The expression of relevant proteins was detected by Western Blotting,and bacterial proliferation assays were conducted to evaluate the intracellular replication of L.pneumophila.Results Compared with control group,the body weight of mice in L.pneumophila infection group significantly decreased(P<0.001)on the second and third day post-infection.Edema and red hepatoid degeneration were observed in both left and right lung tissues,with lesion areas spreading from the hilum to the lung periphery.HE staining revealed increased inflammatory cell infiltration in the alveolar spaces,thickening of alveolar septa and increased fibrin exudation in L.pneumophila infection group.Immunohistochemistry results showed a significant increase in myeloperoxidase(MPO)activity in the lung tissue infected mice(P<0.001).Transcriptome sequencing identified 2550 DEGs,with 1444 up-regulated genes and 1106 down-regulated genes.KEGG enrichment analysis indicated that these DEGs were mainly involved in pathways related to tumor necrosis factor,rheumatoid arthritis,Rap1,PI3K-Ak,and phagosome pathways.Immunofluorescence results showed in vitro proliferation of L.pneumophila within mouse BMDMs.Transcriptome sequencing identified 2550 DEGs,including 1677 up-regulated genes and 873 down-regulated genes.KEGG enrichment analysis showed that enrichment in pathway related to transcription dysregulation in cancer,PI3K-Akt and phagosome pathways.Thirteen core genes,including tubulin β1(Tubb1),were identified from the overlap between mouse lung tissue and BMDMs.RT-qPCR results demonstrated a significant decrease in Tubb1 expression in both lung tissue and BMDMs infected with L.pneumophila(P<0.001).Western Blotting results revealed significant decreases in Rab7,Tubb1,and LAMP2 protein expression(P<0.05),and increases in iNOS and MPO expression(P<0.05).Intracellular proliferation experiments indicated that L.pneumophila gradually increased within BMDMs over time.Conclusion The potential mechanism of L.pneumophila infection in mouse macrophages involves the down-regulation of Rab7/Tubb1/LAMP2 which inhibits the endosome-lysosome fusion.

OBJECTIVE: The present study evaluated the effects of deep acupuncture at Weizhong acupoint (BL40) on bladder function and brain activity in a rat model of overactive bladder (OAB), and investigated the possible mechanisms around the acupuncture area that initiate the effects of acupuncture. METHODS: Adult female Sprague-Dawley rats were randomly divided into six groups, comprising a control group, model group, group treated with deep acupuncture at BL40, group treated with shallow acupuncture at BL40, group treated with acupuncture at non-acupoint next to BL40, and group treated with acupuncture at Xuanzhong (GB39). Urodynamic evaluation was used to observe the urination, and functional magnetic resonance imaging was used to observe the brain activation. The mechanism of acupuncture at BL40 in regulating bladder function was explored by toluidine blue staining and enzyme-linked immunosorbent assay, and the mechanism was verified by stabilizing mast cells (MCs) or blocking tibial nerve. RESULTS: Deep acupuncture at BL40 significantly increased the intercontraction interval in OAB rats and enhanced the mean amplitude of low frequency fluctuation of primary motor cortex (M1), periaquaductal gray matter (PAG), and pontine micturition center (PMC). It also increased the zero-lag functional connectivity between M1 and PAG and between PAG and PMC. Shallow acupuncture at BL40 and acupuncture at non-acupoint or GB39 had no effect on these indexes. Further studies suggested that deep acupuncture at BL40 increased the number and degranulation rate of MCs as well as the contents of 5-hydroxytryptamine, substance P, and histamine in the tissues around BL40. Blocking the tibial nerve by lidocaine injection or inhibiting MC degranulation by sodium cromoglycate injection obstructed the effects of acupuncture on restoring urinary function and modulating brain activation in OAB rats. CONCLUSION Deep acupuncture at BL40 may be more effective for inhibiting OAB by promoting degranulation of MCs around the acupoint and stimulating tibial nerve, thereby regulating the activation of the brain area that controls the lower urinary tract. Please cite this article as: Liu X, Zhang CY, Du XY, Li SS, Wang YQ, Zheng Y, Deng HZ, Fang XQ, Li JY, Wang ZQ, Xu SF, Mi YQ. Acupuncture at Weizhong (BL40) attenuates acetic acid-induced overactive bladder in rats by regulating brain neural activity through the modulation of mast cells and tibial nerves. J Integr Med. 2025; 23(1): 46-55.
Animals ; Urinary Bladder, Overactive/physiopathology* ; Mast Cells/physiology* ; Rats, Sprague-Dawley ; Female ; Acupuncture Therapy ; Acupuncture Points ; Rats ; Brain/physiopathology* ; Tibial Nerve/physiopathology* ; Acetic Acid ; Urinary Bladder/physiopathology*

Objective To explore the effects of different glucose concentrations on the synthesis and secretion of bone collagen in osteoblasts and the impact of diabetes on bone quality in mice.Methods(1)Primary osteoblasts were extracted from the skulls of neonatal mice via collagenase digestion and cultured in four groups under different glucose concentrations:normal glucose(5.5 mmol/L),moderate glucose(11.5 mmol/L),moderate-high glucose(16.5 mmol/L),and high glucose(25 mmol/L).EdU staining was performed to evaluate cell proliferation,while the Transwell assay was used to assess cell migration.Immunofluorescence and Western blotting were performed to detect and quantitatively analyze the content of type Ⅰ collagen(Col-1).Alizarin red S(ARS)staining and alkaline phosphatase(ALP)staining were applied to assess the effects of different glucose concentrations on osteogenic differentiation.(2)Six-week-old male C57BL/6 mice were randomly divided into control group and model group(5 in each group).The model group was fed a high-fat diet for 4 weeks followed by streptozotocin(STZ)injection to establish a diabetic mouse model.The osteogenic differentiation capacity of primary osteoblasts from both groups was assessed.(3)Micro-computed tomography(Micro-CT)was employed to analyze femoral bone mineral density(BMD),bone volume/tissue volume(BV/TV),trabecular number(Tb.N),and trabecular separation(Tb.Sp).Three-point bending test was conducted to evaluate mechanical parameters including maximum load,Young's modulus,fracture energy,and stiffness.RT-qPCR was employed to assess the expression of osteogenic differentiation genes(Alp,Opn,Col1a1,and Lox).Masson staining and Mallory staining were used to evaluate Col-1 content in trabecular bone.Results(1)EdU and Transwell assay results demonstrated that with the gradual increase in glucose concentration,the proliferation and migration abilities of osteoblasts were significantly decreased(P<0.001),and the protein expression levels of Col-1 and lysyl oxidase(LOX)were significantly reduced(P<0.01 or P<0.001).ARS and ALP staining revealed that calcium salt deposition and ALP activity in osteoblasts were significantly decreased with increasing glucose concentration(P<0.05 or P<0.001).(2)Compared with control group,mice in model group exhibited typical"three polies and one weight loss"symptoms(polyuria,polydipsia,polyphagia,and weight loss)of diabetes,and ARS and ALP staining showed a significant reduction in osteoblasts(P<0.001).(3)Micro-CT and three-point bending test results indicated that,compared with control group,mice in model group showed microarchitectural deterioration of bone,decreased Tb.N,increased Tb.Sp,and significantly reduced maximum load,Young's modulus,fracture energy,and stiffness(P<0.05).RT-qPCR results showed that the relative mRNA expression levels of osteogenic differentiation genes(Alp,Opn,Col1a1,and Lox)were significantly decreased in model group compared with control group(P<0.01 or P<0.001).Masson and Mallory staining indicated a significant reduction in collagen content in model group compared with control group(P<0.01).Conclusions High-glucose environment inhibits osteoblast proliferation,differentiation,and migration.Diabetic mice exhibit reduced bone quality and increased bone fragility,potentially mediated by decreased lysyl oxidase and collagen levels.

Heart failure(HF)is a cardiovascular disease with a high prevalence and mortality rate worldwide,and despite the widespread use of existing drugs,device intervetions and surgical procedures,the clinical outcomes are still unsatisfactory.The exploration of new methods to treat HF is still an urgent problem.Gene therapy provides a new therapeutic strategy for HF by targeting the regulation of pathogenic genes.This article systematically reviewed the delivery system optimization,key targets and clinical translational challenges of gene therapy for HF,aiming to provide a theoretical basis for the optimization of treatment strategies.

Monoamine neurotransmitters mainly include serotonin,dopamine,epinephrine,and norepinephrine.They play an indispensable regulatory role in key physiological activities such as emotion,sleep,and memory within the central nervous system.Precise detection of these neurotransmitters holds great significance in the field of neuroscience research.Detection methods for monoamine neurotransmitters encompass high-performance liquid chromatography,mass spectrometry,capillary electrophoresis,fluorescence spectroscopy,and electrochemical methods,etc.Compared with other methods,electrochemical methods offer advantages such as high sensitivity,good selectivity,low cost,strong portability,convenient operation,and capability for in vivo real-time detection.This article reviewed recent research progress in electrochemical detection of monoamine neurotransmitters,focusing on a retrospective and summary from three aspects:sensor electrode materials,detection of various monoamine neurotransmitters,and in vivo real-time analysis.Furthermore,the future development of electrochemical sensors for monoamine neurotransmitters was prospected.

Objective To investigate the association of insulin resistance(IR)and its related indices,including triacylglycerol-glucose(TyG)index and metabolic score of insulin resistance(Mets-IR),with the occurrence of early neurological deterioration(END)in patients with branch atheromatous disease(BAD),and develop a risk prediction model based on these factors.Methods A total of 189 BAD patients were consecutively recruited from the Department of Neurology of the Second Affiliated Hospital of Zhengzhou University between March 2020 and August 2024.Based on the occurrence of END within 7 d after admission,the participants were stratified into END(75 cases)and non-END(114 cases)groups.Demographic characteristics,clinical parameters,TyG index and Mets-IR values were systematically collected and analyzed.Multivariate logistic regression analysis was performed to identify independent risk factors for END in BAD-related stroke.Receiver operating characteristic curve(ROC)analysis was subsequently conducted to evaluate the predictive performance of significant variables,followed by construction of a nomogram prediction model.Results The END group exhibited significantly elevated fasting blood glucose[6.47(5.74,7.86)mmol/L vs 5.83(5.14,6.70)mmol/L]and triacylglycerol[1.65(1.21,2.04)mmol/L vs 1.27(0.99,1.57)mmol/L]levels,higher body mass index[25.02(23.88,26.67)kg/m2 vs 23.71(22.66,25.27)kg/m2]and TyG index(9.03±0.41 vs 8.71±0.45),and increased Mets-IR(39.98±4.23 vs 36.85±4.38)and NIHSS score[5.00(3.00,7.00)vs 3.00(2.00,5.00)]at admission when compared with the non-END group(P＜0.05).Multivariate logistic regression analysis showed that high TyG index(OR=3.751,95％CI:1.592-9.202,P＜0.01),Mets-IR(OR=1.146,95％CI:1.049-1.252,P＜0.01),and NIHSS score at admission(OR=1.279,95％CI:1.128-1.451,P＜0.01)were risk factors for the occurrence of END in BAD patients(P＜0.05).ROC curve indicated that the AUC value of TyG index,Mets-IR,and NIHSS score at admission in predicting END occurrence was 0.698(95％CI:0.623-0.774,P＜0.01),0.698(95％CI:0.620-0.775,P＜0.01),and 0.666(95％CI:0.586-0.745,P＜0.01),respectively.The nomogram prediction model based on these factors demonstrated significant clinical benefits by decision curve analysis and goodness of fit in internal calibration analysis.Conclusion IR shows significant association with END in BAD patients.The IR related indices,TyG index and Mets-IR,have certain predictive efficiency for occurrence and progression of END.

The study was aimed at identifying the diversity of tick species in selected areas of Qinghai,to analyze the genetic differentiation characteristics of tick-borne spotted fever group rickettsiae(SFGR),and to provide the theoretical basis for SFGR prevention and control in the region.The 16S rRNA gene was used for molecular biological identification of 446 collected tick samples,and the infection characteristics of SFGR in tick samples were determined according to the SFGR outer membrane protein A(ompA)gene.Haplotype analysis,phylogenetic tree construction,and estimation of differentiation times for SFGR were conducted in DNASP v6,IQ-tree v2.2.0,and BEAST v2.7.4 software.The obtained 446 tick samples belonged to three categories:(1)Haemaphy-salis spp.,including Haemaphysalis qinghaiensis(n=192)and H.danieli(n=37);(2)Dermacentor spp.,including Dermacentor ever-estianus(n=121),D.nuttalli(n=55),and D.silvarum(n=36);and(3)Hyalomma marginatum(n=5).Rickettsia raoultii was de-tected in D.everestianus,D.silvarum,D.nuttalli,H.qinghaiensis,and H.danieli,with infection rates of 95.9%,80.6%,69.1%,4.1%,and 2.7%,respectively.R.sibirica subsp.sibirica BJ-90 was found only in D.silvarum and D.nuttalli,with infection rates of 5.6%and 1.8%,respectively.The Candidatus R.gannanii F107 was found in H.danieli and H.qinghaiensis,with infection rates of 16.2%and 7.8%,respectively.Ca.R.hongyuanensis was detected only in H.qinghaiensis,with a prevalence of 16.3%.The prevalence of R.aeschlimannii was 20%and 2.7%in Hy.marginatum and H.danieli,respectively.Haplotype and nucleotide polymorphism analy-ses revealed 13 haplotypes in R.raoultii,with haplotype H13 as the dominant haplotype(42/192);seven haplotypes in Ca.R.ganna-nii F107,with haplotype H4 as the dominant haplotype(4/18);and three haplotypes in Ca.R.hongyuanensis,with haplotype H1 as the dominant haplotype(11/13).The phylogenetic tree indicated that the sequences of R.raoultii in selected areas of Qinghai and R.rhipicephali clustered into one branch;Ca.R.hongyuanensis and Ca.R.gannanii F107 clustered into one branch;and R.sibirica subsp.sibirica BJ-90 clustered into one branch with R.sibirica.Estimates of differentiation time revealed that the mean differentiation time for the six Rickettsia was approximately 2 000 Mya(95%CI:1 999.08-2 001.02 Mya).The tick species distributed in selected ar-eas of Qinghai are diverse,and this study provides the first report of Hy.marginatum in Qinghai Province.SFGR significantly varied in prevalence among tick species and showed high genetic diversity.

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.