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 explore associated factors of post-discharge depressive symptom severity in patients with bipolar disorder.Methods:A longitudinal follow-up was conducted to investigate the demographic,behavioral,and clinical characteristics,and social function among discharged patients with bipolar disorder who met the DSM-5 diagnostic criteria.Clinical characteristics were assessed with the Hamilton Depression Scale(HAMD)and Brief Psychiatric Rating Scale(BPRS).Single factor and multivariate regression were carried out to explore the associat-ed factors of depressive symptom severity in patients with bipolar disorder.Results:A total of 298 discharged pa-tients with bipolar disorder were face-to-face interviewed to complete the follow-up survey.At follow-up time,psy-chotic symptoms(standardized(β)=0.18),housework((β)=0.23),social interaction((β)=0.17)and BPRS total score((β)=0.46)were positively associated with HAMD total score.Productive labor and work((β)=-0.27)and person-al life management((β)=-0.15)were negatively associated with HAMD total scores.Conclusion:Post-discharge depressive symptom severity in bipolar disorder patients is influenced by multiple factors.Effective management of psychotic symptoms,combined with enhanced community-based social rehabilitation and functional recovery,may help reduce the persistence or worsening of depressive symptoms and improve prognosis.

Objective:To explore associated factors of post-discharge depressive symptom severity in patients with bipolar disorder.Methods:A longitudinal follow-up was conducted to investigate the demographic,behavioral,and clinical characteristics,and social function among discharged patients with bipolar disorder who met the DSM-5 diagnostic criteria.Clinical characteristics were assessed with the Hamilton Depression Scale(HAMD)and Brief Psychiatric Rating Scale(BPRS).Single factor and multivariate regression were carried out to explore the associat-ed factors of depressive symptom severity in patients with bipolar disorder.Results:A total of 298 discharged pa-tients with bipolar disorder were face-to-face interviewed to complete the follow-up survey.At follow-up time,psy-chotic symptoms(standardized(β)=0.18),housework((β)=0.23),social interaction((β)=0.17)and BPRS total score((β)=0.46)were positively associated with HAMD total score.Productive labor and work((β)=-0.27)and person-al life management((β)=-0.15)were negatively associated with HAMD total scores.Conclusion:Post-discharge depressive symptom severity in bipolar disorder patients is influenced by multiple factors.Effective management of psychotic symptoms,combined with enhanced community-based social rehabilitation and functional recovery,may help reduce the persistence or worsening of depressive symptoms and improve prognosis.

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 investigate the relationship between prolonged PR interval and carotid atherosclerosis(CAS)in middle-aged and elderly patients with type 2 diabetes mellitus(T2DM).Methods:A total of 537 middle-aged and elderly inpatients with T2DM in the Southern Branch of the Sixth People′s Hospital of Shanghai Jiaotong University from January 2019 to January 2021 were selected as the research objects. Color Doppler ultrasound was used to detect bilateral carotid artery intima-media thickness(CIMT). The subjects were divided into carotid atherosclerosis group(CAS group, n=352)and non-carotid atherosclerosis group(NCAS group, n=185). The difference in the PR interval of ECG between the two groups was compared. Pearson or Spearman rank correlation analysis was used for evaluating the correlation of PR interval and CAS lesions with various clinical index. The relationship between PR interval and CAS lesions was adopted by multivariate logistic regression analysis. Results:The average PR interval of middle-aged and elderly patients with T2DM was(164.57±23.02)ms. The average PR interval in CAS group was significantly higher than that in NCAS group [(169.76±24.28) vs (154.70±16.42)ms, P<0.01]. The results of multifactorial logistic regression analysis showed that age, low density lipoprotein-cholesterol, serum osteocalcin, and PR interval were independent factors influencing the development of CAS lesions in middle-aged and elderly patients with T2DM( OR=1.079, 1.936, 0.879, 1.039, respectively, P<0.05 or P<0.01)where each 1 ms increase in PR interval was associated with a 3.9% increase in the risk of CAS in middle-aged and elderly patients with T2DM( OR=1.039, 95% CI 1.006-1.073, P=0.020). Multivariate logistic regression analysis showed that middle-aged and elderly type 2 diabetic patients with PR interval≥158 ms were 4.072 times more likely to have CAS lesions than those with PR interval<158 ms( OR=4.072, 95% CI 1.417-11.702, P<0.01). Conclusion:The PR interval of electrocardiogram is correlated with CAS lesions in middle-aged and elderly patients with T2DM. Middle-aged and elderly type 2 diabetic patients with significantly prolonged PR interval should be reminded to screen for CAS lesions early.

Objective:To explore the current status of central venous catheter (CVC) maintenance knowledge, attitude and practice among Intensive Care Unit (ICU) nurses, and analyze its influencing factors.Methods:From March to May 2021, convenience sampling was used to select 445 ICU nurses from 9 ClassⅢ Grade A hospitals in Anhui Province as the research object. The self-designed General Information Questionnaire and CVC Maintenance Knowledge, Attitude and Practice Questionnaire for ICU Nurses were used to investigate the nurses. Multiple linear regression was used to explore the influencing factors of CVC maintenance knowledge, attitude and practice among ICU nurses. A total of 445 questionnaires were distributed, 11 invalid questionnaires were excluded, and 434 valid questionnaires were recovered, and the valid recovery rate was 97.5%.Results:Among 434 ICU nurses, the scores of CVC maintenance knowledge, attitude, and practice dimensions were (22.14±3.16) , (37.31±3.28) , and (75.65±6.35) , respectively. The results of multiple linear regression analysis showed that hospital category and CVC training times were the influencing factors of ICU nurses' CVC maintenance knowledge ( P<0.05) . CVC training times, educational background, ICU working years, and ICU category were the influencing factors of ICU nurses' CVC maintenance attitude ( P<0.05) . ICU working years, CVC training times, professional title and hospital category were the influencing factors of ICU nurses' CVC maintenance practice ( P<0.05) . Conclusions:ICU nurses have a good attitude to CVC maintenance, but the level of knowledge and practice needs to be improved. Nursing managers should focus on ICU nurses with low educational background, short ICU working years, low professional titles, non-teaching hospitals and few trainings, strengthen relevant knowledge training, increase the level of CVC maintenance practice, and then improve the quality of nursing.

Objective:To explore the diagnostic value of endoscopic ultrasonography (EUS) for duodenal accessory papilla.Methods:Data of 122 cases of duodenal accessory papilla diagnosed by EUS at the endoscopy center of the First Affiliated Hospital of Zhejiang University School of Medicine from February 28, 2006 to February 28, 2018 were analyzed and summarized.Results:Of the 122 duodenal accessory papilla cases, the age was 52.1±12.9, with more males than females. The most common site of duodenal accessory papillae was the descending part above the papilla (88/122, 72.13%), followed by the junction of duodenal bulb and descending part (29/122, 23.77%), and a small proportion of lesions located in the duodenal bulb (5/122, 4.10%). Duodenal accessory papillae were all solitary, whose diameter mostly ranged 0.5-1.0 cm (88/122, 72.13%), a smaller proportion of diameter larger than 1.0 cm (23/122, 18.85%), and only a few with diameter less than 0.5 cm (11/122, 9.02%). Most duodenal accessory papillae were hypoechoic (71/122, 58.20%) or moderate to low echogenic (35/122, 28.68%), and the echoes were mostly homogeneous. The mucosa layer was smooth, with a sphincteroid structure in the submucosa and below. The boundary of the duodenal accessory papillae was mostly clear (121/122, 99.18%) and characteristic lacunar cavity structures were often seen in the center (83/122, 68.03%). The surrounding intestinal wall was normal and no associated enlarged lymph nodes were found around the intestine.Conclusion:EUS can clearly show the structure of duodenal accessory papilla and adjacent organs, and is of high value for the diagnosis of duodenal accessory papilla.

Objective: To screen and identify serum protein biomarkers for the differential diagnosis between ischemic colitis (IC) and ulcerative colitis (UC) by tandem mass tag (TMT) combined with liquid chromatography/tandem mass spectrometry (LC-MS/MS). Methods: From January 2018 to January 2019, at the First Affiliated Hospital of School of Medicine of Zhejiang University, patients with UC or IC, and health controls, each 10 cases, were enrolled into UC group, IC group and normal control (NC) group, respectively. Fasting serum samples of all the subjects were collected. After removal of high-abundance protein, followed by proteolysis, peptide labeling and fractionating, the samples were then processed by mass spectrometry. The protein with TMT data of three groups was obtained and protein with TMT value 0 were removed. Heat map of protein was constructed. The differential protein was defined as the protein fold change over 1.5 or less than 0.67. The Reactome database was used to cluster the pathways of differential proteins among groups. Statistical methods included t test, hypergeometry test and corrected by BH multiple test. Results: A total of 357 serum proteins were identified by proteomic profiling. There were 27 differential proteins between the IC group and the NC group, including six up-regulated proteins and 21 down-regulated proteins. There were 228 differential proteins between the UC group and the NC group, including 75 up-regulated proteins and 153 down-regulated proteins. There were 49 differential proteins between UC group and IC group, including 22 up-regulated proteins and 27 down-regulated proteins. In the comparison of differential proteins between the NC group, IC group and UC group, only the expression of fibrin 3 was statistically significant (the fold change between UC and NC, between UC and IC, between IC and NC were 0.24, 0.46 and 0.53, respectively; t=-5.089, -7.298 and -3.919, all P<0.01). The results of pathway cluster analysis showed that in the comparison of differential proteins between IC group and NC group, only the platelet degranulation pathway was enriched, and 10 proteins were involved in this pathway (P<0.01). In the comparison of differential proteins between UC group and NC group, there were 58 pathways enriched, of which 38 proteins were involved in the platelet degranulation pathway, 16 proteins were involved in the initial complement trigger pathway, 13 proteins were involved in the complement cascade pathway, and 11 proteins were involved in antibody-mediated complement activation pathway (all P<0.01). In the comparison of differential proteins between UC group and IC group, three different pathways were obtained. Among them, nine proteins were involved in the platelet degranulation pathway, seven proteins were involved in the initial complement trigger pathway, and five proteins were involved in the complement cascade pathway (all P<0.01). Conclusions The difference in serum proteome between IC patients and UC patients was significant, and the differential proteins are mainly involved in platelet activation and complement activation. The candidate proteins identified in this study may be used as biomarkers for the differential diagnosis of UC and IC in the future.