Skin fibrosis is primarily characterized by excessive fibroblasts proliferation and aberrant extracellular matrix accumulation, leading to pathological conditions such as hypertrophic scars, keloids, and systemic sclerosis. This dynamic and complex process involves intricate interactions among various resident skin cells and inflammatory cells, ultimately resulting in extracellular matrix deposition and even invasive growth. The maintenance of cellular phenotypes and functions relies on dynamic metabolic responses, and cellular signal transduction is closely coupled with metabolic processes. Given that the coupling of cell metabolism and signaling in the skin fibrosis microenvironment plays a critical role in inflammatory responses and fibrotic activation, modulation of these metabolic pathways may offer novel therapeutic strategies for inhibiting or even reversing the progression of skin fibrosis. This review systematically summarizes the metabolic characteristics of various cell types involved in skin fibrosis, with a focus on core metabolic reprogramming mechanisms such as hyperactive glycolysis, dysregulated fatty acid metabolism, cellular metabolic dysfunction and dysregulated mTOR/AMPK signaling. Furthermore, potential intervention strategies targeting these metabolic pathways are explored, thereby providing new research perspectives for the treatment of skin fibrosis.
Humans ; Fibrosis/metabolism* ; Skin/metabolism* ; Signal Transduction ; Fibroblasts/pathology* ; TOR Serine-Threonine Kinases/metabolism* ; Skin Diseases/pathology* ; Cellular Reprogramming ; Metabolic Reprogramming

Objective:To investigate the alterations of individual metabolic brain network properties in patients with mild cognitive impairment (MCI) and their correlations with cognitive function.Methods:One hundred and five participants from Alzheimer's Disease Neuroimaging Initiative (ADNI) database enrolled from March 2012 to February 2016 were chosen, including 61 MCI patients and 44 normal controls (NC). Cognitive assessments, including mini-mental state examination (MMSE), auditory verbal learning test (AVLT), trail making test (TMT), and semantic verbal fluency (SVF) score, were performed in both groups; differences of above scores and clinical data between the participants from the two groups were compared. T1-weighted imaging and fluorodeoxyglucose positron emission tomography (FDG-PET) images were collected in both groups; individual metabolic brain networks were constructed based on differences in effect sizes between brain regions and network properties were calculated. Spatial correlation analysis was used to compare the correlations of metabolic brain networks at the individual and group levels. General linear model was employed to compare the differences in network properties between the two groups. Partial correlation analysis was used to examine the correlations of differential network properties with cognitive function in MCI patients. A support vector machine (SVM) classification model was constructed based on individual metabolic brain network properties, and receiver operating characteristic (ROC) curve was used to explore the diagnostic value of this SVM classification model in MCI.Results:(1) Compared with the NC group, the MCI group had significantly lower MMSE and AVLT-immediate recall scores, and longer TMT-A completion time ( P<0.05). (2) Spatial correlation analysis revealed a positive correlation between individual metabolic brain networks and group-level metabolic brain networks in patients of the MCI group ( r=0.825, P<0.001). No significant differences in global network properties were noted between the two groups ( P>0.05). Compared with the NC group, the MCI group significantly decreased degree centrality in the left A8vl, right A39c, and right V5/MT+ regions, increased degree centrality in the left anterior cuneus, decreased nodal efficiency in the left A8vl, right V5/MT+, and right caudal hippocampus regions, increased nodal shortest path length and nodal clustering coefficient in the left A8vl region ( P<0.05). (3) The degree centrality at the A8vl of ventral part of the left middle frontal gyrus and nodal efficiency in right caudal hippocampus region were positively correlated with AVLT-immediate recall scores ( r=0.331, P=0.010; r=0.282, P=0.030), nodal efficiency in the left A8vl region was negatively correlated with TMT-A completion time ( r=-0.470, P<0.001), and nodal efficiency in the left A8vl region was positively correlated with SVF score ( r=0.263, P=0.044). (4) Area under the curve of SVM classification model in diagnosing MCI was 0.880 (95% CI: 0.813-0.945, P<0.001), with an accuracy rate of 0.790. Conclusions:Patients with MCI have alterations in individual metabolic brain network properties, among which the degree centrality and nodal efficiency of some nodes are closely related to cognitive function changes. Models constructed based on individual metabolic brain network properties can help to effectively diagnose MCI.

Objective:To investigate the differences in hippocampal subfield volumes and structural covariance network properties among patients with mild cognitive impairment (MCI) exhibiting different cognitive outcomes and normal controls (NCs), and to further evaluate the predictive value of these imaging indicators for cognitive deterioration in MCI patients.Methods:A total of 43 NCs, 65 stable MCI (sMCI), and 26 progressive MCI (pMCI) patients enrolled in the Alzheimer′s Disease Neuroimaging Initiative (ADNI) database between December 2012 and May 2016 were included in this study. Baseline demographic information and T 1-weighted magnetic resonance imaging scans were collected. Hippocampal subfield volumes were extracted using freesurfer software, and structural covariance networks of hippocampal subfields were constructed. Multivariate analysis of covariance was used to compare hippocampal subfield volumes among the 3 groups. A general linear model was applied to examine group differences in hippocampal subfield structural covariance network properties. Least absolute shrinkage and selection operator (LASSO)-Logistic regression was employed to identify imaging predictors associated with conversion to Alzheimer′s disease (AD), based on which structural, network-based, and combined predictive models were constructed. Model discrimination was evaluated using the area under the curve (AUC); internal validation was performed using Bootstrap resampling; model calibration was assessed with the Hosmer-Lemeshow test; and clinical utility was evaluated through decision curve analysis. Results:Significant differences in hippocampal subfield volumes (mm3) were observed among the 3 groups (all P<0.05, Bonferroni-corrected). Specifically, left parasubiculum (65.58±13.30, 61.96±17.56, 49.56±11.82, F=9.900), right parasubiculum (65.92±15.21, 59.45±16.65, 47.69±15.48, F=11.612), left presubiculum (277.09±39.85, 258.15±44.86, 224.05±45.05, F=14.513), right presubiculum (262.85±40.43, 247.41±43.27, 209.97±46.11, F=14.500), left subiculum (399.66±32.19, 374.25±55.83, 306.12±51.62, F=32.923), right subiculum (417.93±48.92, 376.59±51.01, 316.82±70.22, F=28.764), left cornu ammonis 1 (CA1) (592.10±83.87, 561.96±94.72, 490.06±86.89, F=13.352), right CA1 (632.15±100.09, 601.24±88.88, 531.05±110.29, F=10.579), left CA3 (191.58±30.08, 180.47±34.66, 155.08±37.82, F=12.182), right CA3 (210.42±28.92, 203.84±34.80, 176.69±41.47, F=9.597), left CA4 (224.61±28.94, 210.49±35.04, 183.98±36.89, F=16.521), right CA4 (238.49±28.14, 227.43±30.65, 200.23±42.74, F=13.702), left granule cell-molecular layer-dentate gyrus (GC-ML-DG) (259.96±36.76, 239.42±41.17, 207.61±41.84, F=19.831), right GC-ML-DG (273.98±35.12, 258.79±36.82, 227.81±49.07, F=14.204), left molecular layer (505.62±66.16, 468.58±75.17, 402.68±75.47, F=22.293), right molecular layer (527.39±72.39, 493.14±70.39, 423.81±88.09, F=19.588), left hippocampal amygdala transition area (HATA) (54.91±9.99, 49.52±9.93, 43.27±9.59, F=13.571), right HATA (58.43±9.83, 54.55±10.80, 47.12±12.54, F=10.037), left fimbria (69.94±25.04, 56.63±23.74, 40.58±19.83, F=14.846), right fimbria (68.61±26.24, 53.95±23.16, 45.25±17.04, F=10.424), left hippocampal tail (488.37±83.44, 463.54±80.33, 393.83±77.73, F=13.570), and right hippocampal tail (519.78±80.22, 498.84±81.68, 419.75±93.29, F=14.339) all showed significant group differences. Significant group differences were also observed in small-worldness metric γ (0.51±0.10, 0.51±0.08, 0.62±0.14, F=9.317), small-worldness metric λ (0.39±0.02, 0.39±0.02, 0.43±0.04, F=9.925), global efficiency (0.19±0.01, 0.20±0.01, 0.18±0.01, F=3.189), local efficiency (0.26±0.02, 0.26±0.01, 0.27±0.01, F=3.068), clustering coefficient (0.23±0.01, 0.23±0.01, 0.24±0.02, F=4.274), and characteristic path length (0.73±0.06, 0.72±0.06, 0.76±0.07, F=4.477) of the hippocampal subfield structural covariance network (all P<0.05). Specifically, the pMCI group exhibited higher γ ( t=3.773, P<0.001), λ ( t=4.060, P<0.001), local efficiency ( t=2.445, P=0.047), and clustering coefficient ( t=2.849, P=0.015) than the NCs group, and higher γ ( t=4.074, P<0.001), λ ( t=4.068, P<0.001), and characteristic path length ( t=2.986, P=0.010) but lower global efficiency ( t=-2.444, P=0.047) than the sMCI group. The AUC of the structural, network, and combined models based on LASSO-Logistic regression was 0.837, 0.861, and 0.899, respectively. After internal validation, the corrected AUC was 0.835, 0.855, and 0.889, respectively. All models demonstrated good calibration ( P>0.05), and decision curve analysis indicated favorable clinical net benefit across models. Conclusions:Both sMCI and pMCI patients exhibit widespread hippocampal subfield atrophy and altered global properties of hippocampal subfield structural covariance networks compared to NCs. The models constructed based on hippocampal subfield volumes and structural covariance networks show strong potential for predicting cognitive decline in MCI patients.

Objective:To investigate the differences in hippocampal subfield volumes and structural covariance network properties among patients with mild cognitive impairment (MCI) exhibiting different cognitive outcomes and normal controls (NCs), and to further evaluate the predictive value of these imaging indicators for cognitive deterioration in MCI patients.Methods:A total of 43 NCs, 65 stable MCI (sMCI), and 26 progressive MCI (pMCI) patients enrolled in the Alzheimer′s Disease Neuroimaging Initiative (ADNI) database between December 2012 and May 2016 were included in this study. Baseline demographic information and T 1-weighted magnetic resonance imaging scans were collected. Hippocampal subfield volumes were extracted using freesurfer software, and structural covariance networks of hippocampal subfields were constructed. Multivariate analysis of covariance was used to compare hippocampal subfield volumes among the 3 groups. A general linear model was applied to examine group differences in hippocampal subfield structural covariance network properties. Least absolute shrinkage and selection operator (LASSO)-Logistic regression was employed to identify imaging predictors associated with conversion to Alzheimer′s disease (AD), based on which structural, network-based, and combined predictive models were constructed. Model discrimination was evaluated using the area under the curve (AUC); internal validation was performed using Bootstrap resampling; model calibration was assessed with the Hosmer-Lemeshow test; and clinical utility was evaluated through decision curve analysis. Results:Significant differences in hippocampal subfield volumes (mm3) were observed among the 3 groups (all P<0.05, Bonferroni-corrected). Specifically, left parasubiculum (65.58±13.30, 61.96±17.56, 49.56±11.82, F=9.900), right parasubiculum (65.92±15.21, 59.45±16.65, 47.69±15.48, F=11.612), left presubiculum (277.09±39.85, 258.15±44.86, 224.05±45.05, F=14.513), right presubiculum (262.85±40.43, 247.41±43.27, 209.97±46.11, F=14.500), left subiculum (399.66±32.19, 374.25±55.83, 306.12±51.62, F=32.923), right subiculum (417.93±48.92, 376.59±51.01, 316.82±70.22, F=28.764), left cornu ammonis 1 (CA1) (592.10±83.87, 561.96±94.72, 490.06±86.89, F=13.352), right CA1 (632.15±100.09, 601.24±88.88, 531.05±110.29, F=10.579), left CA3 (191.58±30.08, 180.47±34.66, 155.08±37.82, F=12.182), right CA3 (210.42±28.92, 203.84±34.80, 176.69±41.47, F=9.597), left CA4 (224.61±28.94, 210.49±35.04, 183.98±36.89, F=16.521), right CA4 (238.49±28.14, 227.43±30.65, 200.23±42.74, F=13.702), left granule cell-molecular layer-dentate gyrus (GC-ML-DG) (259.96±36.76, 239.42±41.17, 207.61±41.84, F=19.831), right GC-ML-DG (273.98±35.12, 258.79±36.82, 227.81±49.07, F=14.204), left molecular layer (505.62±66.16, 468.58±75.17, 402.68±75.47, F=22.293), right molecular layer (527.39±72.39, 493.14±70.39, 423.81±88.09, F=19.588), left hippocampal amygdala transition area (HATA) (54.91±9.99, 49.52±9.93, 43.27±9.59, F=13.571), right HATA (58.43±9.83, 54.55±10.80, 47.12±12.54, F=10.037), left fimbria (69.94±25.04, 56.63±23.74, 40.58±19.83, F=14.846), right fimbria (68.61±26.24, 53.95±23.16, 45.25±17.04, F=10.424), left hippocampal tail (488.37±83.44, 463.54±80.33, 393.83±77.73, F=13.570), and right hippocampal tail (519.78±80.22, 498.84±81.68, 419.75±93.29, F=14.339) all showed significant group differences. Significant group differences were also observed in small-worldness metric γ (0.51±0.10, 0.51±0.08, 0.62±0.14, F=9.317), small-worldness metric λ (0.39±0.02, 0.39±0.02, 0.43±0.04, F=9.925), global efficiency (0.19±0.01, 0.20±0.01, 0.18±0.01, F=3.189), local efficiency (0.26±0.02, 0.26±0.01, 0.27±0.01, F=3.068), clustering coefficient (0.23±0.01, 0.23±0.01, 0.24±0.02, F=4.274), and characteristic path length (0.73±0.06, 0.72±0.06, 0.76±0.07, F=4.477) of the hippocampal subfield structural covariance network (all P<0.05). Specifically, the pMCI group exhibited higher γ ( t=3.773, P<0.001), λ ( t=4.060, P<0.001), local efficiency ( t=2.445, P=0.047), and clustering coefficient ( t=2.849, P=0.015) than the NCs group, and higher γ ( t=4.074, P<0.001), λ ( t=4.068, P<0.001), and characteristic path length ( t=2.986, P=0.010) but lower global efficiency ( t=-2.444, P=0.047) than the sMCI group. The AUC of the structural, network, and combined models based on LASSO-Logistic regression was 0.837, 0.861, and 0.899, respectively. After internal validation, the corrected AUC was 0.835, 0.855, and 0.889, respectively. All models demonstrated good calibration ( P>0.05), and decision curve analysis indicated favorable clinical net benefit across models. Conclusions:Both sMCI and pMCI patients exhibit widespread hippocampal subfield atrophy and altered global properties of hippocampal subfield structural covariance networks compared to NCs. The models constructed based on hippocampal subfield volumes and structural covariance networks show strong potential for predicting cognitive decline in MCI patients.

Objective:To investigate the alterations of individual metabolic brain network properties in patients with mild cognitive impairment (MCI) and their correlations with cognitive function.Methods:One hundred and five participants from Alzheimer's Disease Neuroimaging Initiative (ADNI) database enrolled from March 2012 to February 2016 were chosen, including 61 MCI patients and 44 normal controls (NC). Cognitive assessments, including mini-mental state examination (MMSE), auditory verbal learning test (AVLT), trail making test (TMT), and semantic verbal fluency (SVF) score, were performed in both groups; differences of above scores and clinical data between the participants from the two groups were compared. T1-weighted imaging and fluorodeoxyglucose positron emission tomography (FDG-PET) images were collected in both groups; individual metabolic brain networks were constructed based on differences in effect sizes between brain regions and network properties were calculated. Spatial correlation analysis was used to compare the correlations of metabolic brain networks at the individual and group levels. General linear model was employed to compare the differences in network properties between the two groups. Partial correlation analysis was used to examine the correlations of differential network properties with cognitive function in MCI patients. A support vector machine (SVM) classification model was constructed based on individual metabolic brain network properties, and receiver operating characteristic (ROC) curve was used to explore the diagnostic value of this SVM classification model in MCI.Results:(1) Compared with the NC group, the MCI group had significantly lower MMSE and AVLT-immediate recall scores, and longer TMT-A completion time ( P<0.05). (2) Spatial correlation analysis revealed a positive correlation between individual metabolic brain networks and group-level metabolic brain networks in patients of the MCI group ( r=0.825, P<0.001). No significant differences in global network properties were noted between the two groups ( P>0.05). Compared with the NC group, the MCI group significantly decreased degree centrality in the left A8vl, right A39c, and right V5/MT+ regions, increased degree centrality in the left anterior cuneus, decreased nodal efficiency in the left A8vl, right V5/MT+, and right caudal hippocampus regions, increased nodal shortest path length and nodal clustering coefficient in the left A8vl region ( P<0.05). (3) The degree centrality at the A8vl of ventral part of the left middle frontal gyrus and nodal efficiency in right caudal hippocampus region were positively correlated with AVLT-immediate recall scores ( r=0.331, P=0.010; r=0.282, P=0.030), nodal efficiency in the left A8vl region was negatively correlated with TMT-A completion time ( r=-0.470, P<0.001), and nodal efficiency in the left A8vl region was positively correlated with SVF score ( r=0.263, P=0.044). (4) Area under the curve of SVM classification model in diagnosing MCI was 0.880 (95% CI: 0.813-0.945, P<0.001), with an accuracy rate of 0.790. Conclusions:Patients with MCI have alterations in individual metabolic brain network properties, among which the degree centrality and nodal efficiency of some nodes are closely related to cognitive function changes. Models constructed based on individual metabolic brain network properties can help to effectively diagnose MCI.

Objective To develop and validate a nomogram model for predicting the risk of kinesiophobia in patients after lung cancer surgery.Methods A total of 164 lung cancer patients who underwent surgery in a Grade ⅢA hospital in Xiamen were recruited from October 2022 to May 2023 in this study.Logistic regression was conducted to identify independent risk factors of kinesiophobia in patients who recieved lung cancer surgery.A Nomogram model was developed using R software for predicting the risk of kinesiophobia.The predictive performance of the model was assessed by calculating the receiver-operating-characteristics curve(ROC)and the area under curve(AUC).Results The incidence of postoperative kinesiophobia in lung cancer patients was at 44.51％.Logistic regression analysis showed that pain and fatigue were the risk factors for the occurrence of postoperative kinesiophobia in the patients(P<0.05)and self-efficacy was a protective factor(P<0.05).Validation of the Nomogram model showed that the ROC curve indicated an AUC of 0.888(95％CI 0.836-0.940)for predicting kenesiophobia in patients after lung cancer surgery and the calibration curve presented as a line with a slope close to 1.The Hosmer-Lemeshow goodnee-of-fit test showed that the model could accurately predict the risk of postoperative kinesiophobia in lung cancer patients(χ 2=1.931,P=0.983).Conclusion Self-efficacy,pain and fatigue are the influencing factors for the occurrence of postoperative kinesiophobia in the patients who received lung cancer surgery.The nomogram prediction model has good accuracy and discrimination,and it may assist the healthcare professionals to predict the occurrence of postoperative kinesiophobia in the patients after lung cancer surgery and take pertinent measures to minimise the incidence.

Objective To develop and validate a nomogram model for predicting the risk of kinesiophobia in patients after lung cancer surgery.Methods A total of 164 lung cancer patients who underwent surgery in a Grade ⅢA hospital in Xiamen were recruited from October 2022 to May 2023 in this study.Logistic regression was conducted to identify independent risk factors of kinesiophobia in patients who recieved lung cancer surgery.A Nomogram model was developed using R software for predicting the risk of kinesiophobia.The predictive performance of the model was assessed by calculating the receiver-operating-characteristics curve(ROC)and the area under curve(AUC).Results The incidence of postoperative kinesiophobia in lung cancer patients was at 44.51％.Logistic regression analysis showed that pain and fatigue were the risk factors for the occurrence of postoperative kinesiophobia in the patients(P<0.05)and self-efficacy was a protective factor(P<0.05).Validation of the Nomogram model showed that the ROC curve indicated an AUC of 0.888(95％CI 0.836-0.940)for predicting kenesiophobia in patients after lung cancer surgery and the calibration curve presented as a line with a slope close to 1.The Hosmer-Lemeshow goodnee-of-fit test showed that the model could accurately predict the risk of postoperative kinesiophobia in lung cancer patients(χ 2=1.931,P=0.983).Conclusion Self-efficacy,pain and fatigue are the influencing factors for the occurrence of postoperative kinesiophobia in the patients who received lung cancer surgery.The nomogram prediction model has good accuracy and discrimination,and it may assist the healthcare professionals to predict the occurrence of postoperative kinesiophobia in the patients after lung cancer surgery and take pertinent measures to minimise the incidence.

Objective:To rapidly evaluate the level of healthcare resource demand for laboratory testing and prevention and control of corona virus disease 2019 (COVID-19) in different epidemic situation, and prepare for the capacity planning, stockpile distribution, and funding raising for infectious disease epidemic response.Methods:An susceptible, exposed, infectious, removed infectious disease dynamics model with confirmed asymptomatic infection cases and symptomatic hospitalized patients was introduced to simulate different COVID-19 epidemic situation and predict the numbers of hospitalized or isolated patients, and based on the current COVID-19 prevention and control measures in China, the demands of resources for laboratory testing and prevention and control of COVID-19 were evaluated.Results:When community or local transmission or outbreaks occur and total population nucleic acid testing is implemented, the need for human resources is 3.3-89.1 times higher than the reserved, and the current resources of medical personal protective equipment and instruments can meet the need. The surge in asymptomatic infections can also increase the human resource demand for laboratory testing and pose challenge to the prevention and control of the disease. When vaccine protection coverage reach ≥50%, appropriate adjustment of the prevention and control measures can reduce the need for laboratory and human resources.Conclusions:There is a great need in our country to reserve the human resources for laboratory testing and disease prevention and control for the response of the possible epidemic of COVID-19. Challenges to human resources resulted from total population nucleic acid testing and its necessity need to be considered. Conducting non-pharmaceutical interventions and encouraging more people to be vaccinated can mitigate the shock on healthcare resource demand in COVID-19 prevention and control.

Objective:To explore the protective effect of vitamin D in acute liver failure through a mouse model.Methods:Acute liver failure was induced by combining D-galactosamine (D-GalN) lipopolysaccharide (LPS) to observe the effect of long-term vitamin D deficiency on liver injury and inflammatory signals in a mouse model. Acute liver failure was induced by thioacetamide (TAA) to observe the effect of vitamin D deficiency on the survival rate, and further high-dose of vitamin D supplementation protective effect was determined in a mouse model. Liver function was evaluated by measuring serum alanine aminotransferase (ALT), aspartate aminotransferase (AST) and liver inflammation by hematoxylin-eosin staining. The expressions of tumor necrosis factor (TNF-α), interleukin (IL) -1β, NOD-like receptor family, pyrin domain containing 3 (NLRP-3), chemokines (CCL2, CXCL1 and CXCL2), etc. in liver tissues were detected by RT-qPCR. The quantitation of macrophages in liver tissue was detected by immunohistochemistry. The comparison between groups were performed by t-test. The survival curve was analyzed by log-rank (Mantel-Cox) test.Results:Long-term vitamin D deficiency had increased acute liver failure sensitivity in mice, which was manifested by increased blood cell extravasation, massive necrosis of parenchymal cells, up-regulation of TNF-α, IL-1β, and NLRP-3 mRNA expression ( P < 0.05), and increased macrophages quantitation ( P < 0.05) in liver tissues. At the same time, vitamin D deficiency had increased the mice mortality rate because of liver injury ( P < 0.01). On the contrary, pre-administration of high dose of vitamin D (100 IU/g) had significantly reduced liver injury, inhibited ALT and AST rise ( P < 0.01), alleviated liver necrosis, and down-regulated the mRNA expression of inflammatory factors in liver tissues ( P < 0.05). Conclusion:Mouse model shows that long-term vitamin D deficiency can aggravate drug-induced acute liver failure and reduce survival rates. Furthermore, high-dose of vitamin D has a certain hepatoprotective effect, which can significantly improve liver necrosis condition and inhibit inflammation. Therefore, adequate vitamin D can retain liver physiological balance to resist liver injury.

Objective To evaluate the clinical application value of transesophageal atrial pacing (TEAP) combined with atropine load experiment in the diagnosis of the lesions of sinoatrial node and atrioventricular node.Methods One hundred and forty-four cases selected from the outpatient and hospitalized patients in the People's Hospital of Changji Hui Autonomous Prefecture from September 2009 to December 2012,who with dizziness, syncope and other clinical symptoms and electrocardiogram showe.TEAP combined with atropine load experiment were given to these patients.Results (1) The authors detected in all patients,83 cases (57.6％) were positive, among which, 48 cases (57.8％) male, 35 cases (42.2％) female.(2) The authors detected 57 cases(39.6％) non-increased vagus nerve tension cases in 83 positive cases,among which 33 cases (57.9％) male, 24 cases (42.1％) female;Among which 29 cases (20.1％) were sinoatrial node hypofunction, and 16 cases(55.2％) male;8 cases(5.6％) were atrioventricular node hypofunction,and 4 cases(50％) male;14 cases(9.7％) were double node hypofunction, and 10 cases (71.4％) male;6 cases (4.2％) were tachycardia-bradycardia syndrome, and 3 cases (50％) male;among which, a long interval of greater than 3 seconds appeared when we stimulate one 84 years old man with S1S1 stimulate way, immediately pressed protective pacemaker until his own sinus rhythm was restored, as a safety precaution, stoped further examination and classified him as sick sinus group.Conclusion Detect the common causes of slow sinus and atrioventricular block,such as the sinoatrial node dysfunction, atrioventricular node dysfunction, double node dysfunction and increased vagus nerve tension through TEAP combined with atropine load experiment.Consider that this methods have the best diagnostic value in decreasing its rate of false positivity,and should be used as a necessary check before implantation of pacemaker in such patients, suitable used in clinical, especially in basic general hospitals.