Objective:To analyze the differences in emergency rescue service capacities between county-level hospitals in Zhejiang and Jiangsu provinces, and propose targeted improvement suggestions.Methods:Cluster sampling was employed, with representative county-level hospitals recommended by provincial hospital alliances in Zhejiang and Jiangsu. Emergency departments voluntarily submitted data, resulting in 24 hospitals from Zhejiang and 34 from Jiangsu. Data were collected through the China County-Level Hospital Emergency Service Capability Evaluation System. Differences between provinces were analyzed. Spearman correlation analysis explored relationships between multidimensional indicators, and multivariate logistic regression identified factors influencing hospital grade B++ and above.Results:In terms of department scale, Jiangsu had a larger emergency department area (4 853 m 2vs. 1 927 m 2, P<0.001), more beds of comprehensive inpatient wards in the emergency department (25.5 beds vs. 5.5 beds, P=0.011), and higher scores for the ratio of emergency department area to beds (4.8 vs. 3.4, P=0.005) than Zhejiang. In terms of staffing, Jiangsu had a higher proportion of doctors with master's degrees or above (13.8% vs. 0.0%, P<0.001), a higher proportion of frontline emergency medical staff with over three years of clinical experience (100.0% vs. 91.5%, P=0.001), and more doctors (19 vs. 15, P=0.039) and nurses (46 vs. 32, P=0.039). In terms of quality and safety and medical service capacity, Zhejiang had a higher severe trauma resuscitation success rate (92.9% vs. 83.75%, P=0.003), annual emergency surgery volume (1 002 vs. 428, P=0.015), and number of emergency surgery procedures (125 vs. 42, P=0.027), but Jiangsu had a shorter emergency room stay time (87.5 min vs. 136.2 min, P=0.029). In terms of informatization, Zhejiang outperformed Jiangsu in all indicators, especially in patient information interconnection and sharing (100.0% vs. 82.4%, P=0.030) and in-hospital diagnosis and treatment timeline (91.7% vs. 73.5%, P=0.043). Correlation analysis showed that the total specialty scale score was significantly positively correlated with the in-hospital emergency rescue service capacity score ( r=0.576) and the staffing score ( r=0.455). The total evaluation score was significantly positively correlated with the informatization total score ( r=0.397) and the pre-hospital emergency rescue service capacity score ( r=0.322). Binary Logistic regression analysis showed that the emergency rescue service capacity score was significantly positively correlated with hospital grade ( B=1.431, P=0.03, OR=4.185, 95% CI:1.152-15.205). For every 1-unit increase in the emergency department area ( B=0.002, P=0.05), the probability of a hospital being upgraded to B++ or above increased by 0.2% ( OR=1.002, 95% CI:1.000-1.004). Conclusions:Both provinces exhibit distinct advantages in emergency department development. Departmental scale and configuration significantly impact emergency service capabilities, while staffing and quality management constitute core elements for improvement. Informatization construction notably enhances emergency service efficiency and quality.

Objective:To investigate the heterogeneity of tumor density-enhancement complex (TDEC) based on MRI parameters in predicting the treatment response and prognosis for recurrent glioblastoma (rGBM) to guide the formulation of personalized clinical treatment strategies.Methods:A prospective cohort study was performed; 66 patients with postoperative rGBM were enrolled from Department of Neurosurgery, Affiliated Hospital of Jiangsu University. Multi-sequence MRI was performed, and diffused and enhanced data of the rGBM were utilized to construct TDEC as intratumoral sub-regions via pixel co-localization technique. Correlations among rGBM with different volume proportions of TDEC types and correlations of rGBM with different volume proportions of TDEC types with rGBM volume were analyzed in rGBM after bevacizumab (BEV) combined with radiotherapy. A pixel co-localization decoupling method was applied to assess the treatment response efficiency in individual TDEC subcomponents. The rGBM imaging phenotypes were identified through unsupervised clustering analysis, and progression-free survival (PFS) and overall survival (OS) between patients with different phenotypes were compared. The predictive value of TDEC heterogeneity in PFS and OS of rGBM patients under BEV plus radiotherapy was assessed. Results:Four distinct TDEC sub-regions (TDEC1-4) were identified; a significant negative correlation was observed between volume proportions of TDEC2 and TDEC3 ( r s=-0.558, P<0.001), as well as between volume proportions of TDEC3 and TDEC4 ( r s=-0.782, P<0.001), while TDEC composition (volume proportions of TDEC2-4) showed no significant correlation with tumor volume ( P>0.05). Following BEV combined with radiotherapy, significant sub-region-specific TDEC volume changes were observed (tumor volume minification rate of TDEC1[ΔV TDEC1]: 16.7% [13.8%, 20.1%]; ΔV TDEC2: 25.4% [21.9%, 29.0%]; ΔV TDEC3: 27.6% [23.5%, 31.2%]; ΔV TDEC4: 8.4% [6.1%, 10.7%], P<0.05); volume proportion of TDEC3 was positively correlated with tumor volume minification ( r s=0.702, P<0.001), whereas volume proportion of TDEC4 was negatively correlated tumor volume minification ( r s=-0.933, P<0.001). The volume reduction of TDEC1-3 was driven by combined effects of tumor cellular and enhancement components, while volume reduction of TDEC4 was primarily attributed to changes in tumor cellularity (ΔV ADC: 9.3%; ΔV T1C: 0.8%). Two distinct TDEC phenotypes with different survival outcomes were identified in rGBM patients (silhouette coefficient=0.584; TDEC type I: n=23; type II: n=43); significant difference in PFS and OS was noted between patients with TDEC type I and type II (PFS: χ2=11.191, P=0.001; OS: χ2=9.733, P=0.002). TDEC phenotype was an independent influencing factor for survival of rGBM patients under BEV combined with radiotherapy (PFS: HR=2.738, 95% CI: 1.815-3.938 , P=0.003; OS: HR=2.507, 95% CI: 1.851-3.660, P=0.007). Conclusion:TDEC sub-region helps efficiently characterize the rGBM heterogeneity; rGBM imaging phenotypes identified based on TDEC sub-region can independently predict the clinical outcomes: the prognosis of TDEC type I patients is better than that of TDEC type II patients.

Objective:To explore the RNA expression and alterations in brain structure in individuals who have experienced stressful life events (SLE), as well as the correlation patterns between them and their association with the occurrence of depression.Methods:Prospectively, a total of 80 SLE subjects were recruited from the psychiatry and psychology clinic of the Jiangsu University Affiliated Yixing Hospital between January 2021 and December 2022, with 16 normal controls (NC) enrolled concurrently. The 17 items Hamilton depression scale (HAMD-17) and social readjustment rating scale (SRRS) were used to assess depressive symptoms and stress levels. RNA sequencing information of peripheral blood and imaging data at baseline were collected. Based on whether depression occurred during the 2-year follow-up period, SLE subjects were divided into the SLE-depression group ( n=15) and the SLE-non-depression group ( n=65). Differentially expressed genes (DEGs) were screened using differential analysis and protein-protein interaction (PPI) networks. Fractional anisotropy (FA) of white matter tracts and gray matter volume (GMV) were extracted using tract-based spatial statistics and voxel-based morphometry.Using analysis of variance compared inter-group differences in gene expression, GMV and white matter FA values. Partial correlation analysis was used to explore correlations between DEGs, altered GMV and white matter microstructure. Gene set enrichment analysis (GSEA) was performed on key genes to identify potential biological pathways. Propensity score matching constructed sensitivity subgroups to verify result robustness. Results:The SLE-depression group showed significantly higher SRRS and HAMD-17 scores at baseline and at the end of follow-up compared to the SLE-non-depression group and the NC group ( H=47.773, 35.427, 41.114, all P<0.05). Expression levels of IL-10 (2.12±0.28, 2.43±0.44), EZH2 (2.11±0.43, 2.45±0.51), NCAM1 (3.60±0.30, 3.03±0.39), CD3E (4.95±0.37, 4.57±0.48), CCK (3.29±0.28, 3.02±0.42), and CX3CR1 (5.55±0.40, 5.91±0.34) were significantly different between the SLE-depression group and SLE-non-depression group( F=5.549~28.371, all P<0.05). Compared with the SLE-non-depression group, the SLE-depression group exhibited significantly lower FA values in the genu of the corpus callosum (0.29±0.04, 0.31±0.04) and the left uncinate fasciculus (0.31±0.02, 0.33±0.02), as well as significantly smaller GMV in the right hippocampus (0.29±0.07, 0.33±0.06), bilateral middle frontal gyrus (left: 0.27±0.05, 0.31±0.05; right: 0.28±0.06, 0.32±0.06), right insula (0.36±0.03, 0.38±0.04), and left precentral gyrus (0.19±0.04, 0.24±0.05) ( F=4.593-12.064, all P<0.05, FDR correction). GMV in the right anterior cingulate and paracingulate gyri was significantly larger than that in the SLE-non-depression group (0.34±0.05, 0.29±0.06) ( F=6.704, P=0.034, FDR correction). Partial correlation analysis revealed significantly stronger correlations between hub DEGs and altered brain regions in the SLE-depression group ( r=0.017-0.801) compared to the SLE-non-depression group ( r=0.002-0.382), with a statistically significant difference ( U=629, P<0.001; Cliff's Delta=0.454). GSEA indicated that the aforementioned genes were primarily involved in pathways including the ribosome, spliceosome, ribosome biogenesis in eukaryotes, and neuroactive ligand-receptor interaction. Sensitivity analysis confirmed that the above results remained statistically significant after balancing sample sizes (all P<0.05). Conclusion:The SLE-depression group showed specific RNA expression and brain structure alterations compared to the SLE-non-depression group, and the correlation between RNA and brain structure was significantly enhanced in the SLE-depression group. This suggests that the correlation between genes and brain structure in the SLE population may be related to their susceptibility to depression.

Objective:To explore the RNA expression and alterations in brain structure in individuals who have experienced stressful life events (SLE), as well as the correlation patterns between them and their association with the occurrence of depression.Methods:Prospectively, a total of 80 SLE subjects were recruited from the psychiatry and psychology clinic of the Jiangsu University Affiliated Yixing Hospital between January 2021 and December 2022, with 16 normal controls (NC) enrolled concurrently. The 17 items Hamilton depression scale (HAMD-17) and social readjustment rating scale (SRRS) were used to assess depressive symptoms and stress levels. RNA sequencing information of peripheral blood and imaging data at baseline were collected. Based on whether depression occurred during the 2-year follow-up period, SLE subjects were divided into the SLE-depression group ( n=15) and the SLE-non-depression group ( n=65). Differentially expressed genes (DEGs) were screened using differential analysis and protein-protein interaction (PPI) networks. Fractional anisotropy (FA) of white matter tracts and gray matter volume (GMV) were extracted using tract-based spatial statistics and voxel-based morphometry.Using analysis of variance compared inter-group differences in gene expression, GMV and white matter FA values. Partial correlation analysis was used to explore correlations between DEGs, altered GMV and white matter microstructure. Gene set enrichment analysis (GSEA) was performed on key genes to identify potential biological pathways. Propensity score matching constructed sensitivity subgroups to verify result robustness. Results:The SLE-depression group showed significantly higher SRRS and HAMD-17 scores at baseline and at the end of follow-up compared to the SLE-non-depression group and the NC group ( H=47.773, 35.427, 41.114, all P<0.05). Expression levels of IL-10 (2.12±0.28, 2.43±0.44), EZH2 (2.11±0.43, 2.45±0.51), NCAM1 (3.60±0.30, 3.03±0.39), CD3E (4.95±0.37, 4.57±0.48), CCK (3.29±0.28, 3.02±0.42), and CX3CR1 (5.55±0.40, 5.91±0.34) were significantly different between the SLE-depression group and SLE-non-depression group( F=5.549~28.371, all P<0.05). Compared with the SLE-non-depression group, the SLE-depression group exhibited significantly lower FA values in the genu of the corpus callosum (0.29±0.04, 0.31±0.04) and the left uncinate fasciculus (0.31±0.02, 0.33±0.02), as well as significantly smaller GMV in the right hippocampus (0.29±0.07, 0.33±0.06), bilateral middle frontal gyrus (left: 0.27±0.05, 0.31±0.05; right: 0.28±0.06, 0.32±0.06), right insula (0.36±0.03, 0.38±0.04), and left precentral gyrus (0.19±0.04, 0.24±0.05) ( F=4.593-12.064, all P<0.05, FDR correction). GMV in the right anterior cingulate and paracingulate gyri was significantly larger than that in the SLE-non-depression group (0.34±0.05, 0.29±0.06) ( F=6.704, P=0.034, FDR correction). Partial correlation analysis revealed significantly stronger correlations between hub DEGs and altered brain regions in the SLE-depression group ( r=0.017-0.801) compared to the SLE-non-depression group ( r=0.002-0.382), with a statistically significant difference ( U=629, P<0.001; Cliff's Delta=0.454). GSEA indicated that the aforementioned genes were primarily involved in pathways including the ribosome, spliceosome, ribosome biogenesis in eukaryotes, and neuroactive ligand-receptor interaction. Sensitivity analysis confirmed that the above results remained statistically significant after balancing sample sizes (all P<0.05). Conclusion:The SLE-depression group showed specific RNA expression and brain structure alterations compared to the SLE-non-depression group, and the correlation between RNA and brain structure was significantly enhanced in the SLE-depression group. This suggests that the correlation between genes and brain structure in the SLE population may be related to their susceptibility to depression.

Objective:To investigate the heterogeneity of tumor density-enhancement complex (TDEC) based on MRI parameters in predicting the treatment response and prognosis for recurrent glioblastoma (rGBM) to guide the formulation of personalized clinical treatment strategies.Methods:A prospective cohort study was performed; 66 patients with postoperative rGBM were enrolled from Department of Neurosurgery, Affiliated Hospital of Jiangsu University. Multi-sequence MRI was performed, and diffused and enhanced data of the rGBM were utilized to construct TDEC as intratumoral sub-regions via pixel co-localization technique. Correlations among rGBM with different volume proportions of TDEC types and correlations of rGBM with different volume proportions of TDEC types with rGBM volume were analyzed in rGBM after bevacizumab (BEV) combined with radiotherapy. A pixel co-localization decoupling method was applied to assess the treatment response efficiency in individual TDEC subcomponents. The rGBM imaging phenotypes were identified through unsupervised clustering analysis, and progression-free survival (PFS) and overall survival (OS) between patients with different phenotypes were compared. The predictive value of TDEC heterogeneity in PFS and OS of rGBM patients under BEV plus radiotherapy was assessed. Results:Four distinct TDEC sub-regions (TDEC1-4) were identified; a significant negative correlation was observed between volume proportions of TDEC2 and TDEC3 ( r s=-0.558, P<0.001), as well as between volume proportions of TDEC3 and TDEC4 ( r s=-0.782, P<0.001), while TDEC composition (volume proportions of TDEC2-4) showed no significant correlation with tumor volume ( P>0.05). Following BEV combined with radiotherapy, significant sub-region-specific TDEC volume changes were observed (tumor volume minification rate of TDEC1[ΔV TDEC1]: 16.7% [13.8%, 20.1%]; ΔV TDEC2: 25.4% [21.9%, 29.0%]; ΔV TDEC3: 27.6% [23.5%, 31.2%]; ΔV TDEC4: 8.4% [6.1%, 10.7%], P<0.05); volume proportion of TDEC3 was positively correlated with tumor volume minification ( r s=0.702, P<0.001), whereas volume proportion of TDEC4 was negatively correlated tumor volume minification ( r s=-0.933, P<0.001). The volume reduction of TDEC1-3 was driven by combined effects of tumor cellular and enhancement components, while volume reduction of TDEC4 was primarily attributed to changes in tumor cellularity (ΔV ADC: 9.3%; ΔV T1C: 0.8%). Two distinct TDEC phenotypes with different survival outcomes were identified in rGBM patients (silhouette coefficient=0.584; TDEC type I: n=23; type II: n=43); significant difference in PFS and OS was noted between patients with TDEC type I and type II (PFS: χ2=11.191, P=0.001; OS: χ2=9.733, P=0.002). TDEC phenotype was an independent influencing factor for survival of rGBM patients under BEV combined with radiotherapy (PFS: HR=2.738, 95% CI: 1.815-3.938 , P=0.003; OS: HR=2.507, 95% CI: 1.851-3.660, P=0.007). Conclusion:TDEC sub-region helps efficiently characterize the rGBM heterogeneity; rGBM imaging phenotypes identified based on TDEC sub-region can independently predict the clinical outcomes: the prognosis of TDEC type I patients is better than that of TDEC type II patients.

In 2009,the World Health Organization included snakebite on the list of neglected tropical diseases,acknowledging it as a common occupational hazard for farmers,plantation workers,and others,causing tens of thousands of deaths and chronic physical disabilities every year.This guideline aims to provide practical information to help clinical professionals evaluate and treat snakebite victims.These recommendations are based on clinical experience and clinical research evidence.This guideline focuses on the following topics:snake venom,clinical manifestations,auxiliary examination,diagnosis,treatments,and prevention.

Objective:To establish a method for rapid detection of OXA and par C resistance genes of Acinetobacter baumannii(Ab)by double nucleic acid colloidal gold strip and to develop kit.Methods:DNA of Ab was extracted by heating and boiling method.OXA and par C genes sequences of Ab were selected as target gene fragments based on NCBI.Primers were designed and labeled with 6-FAM,digoxin and biotin,respectively.Drug resistance gene detection reagents were developed,and nucleic acid gold test strips were used for rapid and visual detection.Molecular cloning and sequencing techniques were used to clone positive control samples and evaluate specificity,sensitivity and stability of kit.Results:DNA concentration and purity of Ab extracted by boiling method were good.Homology between cloned and sequenced plasmid DNA and gene sequence in GenBank database was 100%,respectively.Speci-ficity of kit was good,with only Ab showing positive results and other bacterial genera showing negative results;DNA concentration of Ab in double nucleic acid colloidal gold test strip decreased to 10-3 ng/μl,a red line still appeared,whose sensitivity was 10 times higher consistent with minimum detection limit of electrophoresis 10-2 ng/μl;test kits were tested at 3rd,6th and 9th months,and showed good stability.Conclusion:Double resistance detection kit established in this study can simultaneously detect OXA and par C resis-tance of Ab,who has advantages of high sensitivity,strong specificity,rapid and simple,and provides a new method for detection of carbapenem and quinolone antibiotic resistance of Ab.

Objective:To examine the structural network changes in participants with amnestic mild cognitive impairment(aMCI)and investigate the correlation between these changes and the onset of Alzheimer's disease(AD).Methods:In this prospective study, a total of 100 individuals with amnestic mild cognitive impairment(aMCI)were enrolled as the research group.Additionally, 25 healthy individuals who were matched in terms of age and sex were enrolled as healthy controls.Upon enrollment, all participants underwent MRI scans, neuropsychological assessments, and clinical evaluations.The participants were then followed every 6 months for a period of 36 months or until they withdrew from the study.Based on the outcome of the follow-up(whether Alzheimer's disease occurred), the aMCI participants were divided into two groups: stable aMCI group and progressive aMCI group.The Chinese version of the Brief Mental State Examination(MMSE), the Montreal Cognitive Assessment(MoCA), the Clinical Dementia Rating Scale(CDR), and the Auditory Word Learning Test(AVLT)were utilized to evaluate the overall mental and cognitive status of the subjects.Pearson correlation analysis was employed to investigate the relationship between structural network changes and cognitive decline.Logistic regression was performed to analyze the predictive ability of structural network changes in determining the onset of AD.Results:Compared to the stable aMCI group, the progressive aMCI group exhibited lower levels of global efficiency( P=0.002), local efficiency( P=0.007), feeder connections( P=0.003), local connections( P=0.008), and right precuneus nodal efficiency( P=0.010).Correlation analysis revealed that global efficiency( r=0.604, P=0.002), feeder connections( r=0.513, P=0.012), and right precuneus nodal efficiency( r=0.504, P=0.014)were correlated with AVLT-delay scores(baseline)in the progressive aMCI group.A logistic regression model demonstrated that global efficiency, feeder connections, and right precuneus nodal efficiency could significantly predict the onset of AD(all P<0.05, AUCunited=0.797, 95% CI: 0.684-0.884, sensitivity=73.91, 95% CI: 51.6-89.8, specificity=76.60, 95% CI: 62.0-87.7). Conclusions:Among participants with aMCI, individuals who exhibit lower global efficiency, feeder connections, or right precuneus nodal efficiency are at a higher risk of developing AD.These indicators are anticipated to serve as new targets for clinical intervention.

Objective:To explore the association between changes in brain structural network during the early stage of stroke recovery and the onset of post-stroke depression (PSD).Methods:A total of 87 acute ischemic stroke patients scheduled for discharge, who were admitted to the Yixing Hospital Affiliated to Jiangsu University from March 2020 to May 2021, were prospectively collected. During the same period, 34 healthy control subjects matched with the stroke patients were also collected. All participants underwent systematic magnetic resonance imaging scans and scale assessments, and were followed up longitudinally for 2 years. Based on the occurrence of depression during follow-up, the stroke patients were divided into PSD group and post-stroke non-depression (PSND) group. Graph theoretical analysis was used to analyze the topological characteristics of brain structural network. Analysis of variance was used to explore the differences in brain structural network attributes among groups. Logistic regression model was used to analyze the predictive power of differential brain network attributes for PSD. Linear regression analysis was conducted to investigate the relationship between the synergism of non-rich-club regions and changes in rich-club connectivity.Results:The rich-club connectivity and synergism of the non-rich-club regions were significantly lower in the PSD group than in the PSND group (rich-club connectivity, P<0.01; synergism of feeder/local, P<0.001). The regression model demonstrated that the synergism of non-rich-club regions had a good predictive power for the occurrence of PSD ( OR=1.195, 95%CI 1.073-1.471, P<0.001). Furthermore, linear regression analysis revealed a significant correlation between the synergism of non-rich-club regions and Δrich-club connectivity ( r=-0.691, P<0.001). Conclusion:The good synergism of non-rich-club regions during the early stage of stroke recovery promotes the repair of rich-club connectivity and inhibits the onset of PSD.

Objective:To investigate the features of the brain structural network in patients with postpartum depression (PPD).Methods:This cross-sectional study included PPD patients who visited the mental health counseling clinic after delivery at the Jiangsu University Affiliated Yixing Hospital from June 2013 to September 2022 (PPD group). Matched non-PPD postpartum women based on age, years of education, and body mass index who came for postpartum follow-up (non-PPD postpartum group), and non-pregnant women who visited the hospital or underwent physical examinations during the same period (non-pregnant group) were also included. Demographic data and diffusion tensor imaging (DTI) data were collected for all three groups. The brain was partitioned into 90 regions using an anatomical template to construct the brain structural network. Network-based statistics (NBS) were applied to further screen and construct subnetworks. The efficacy of the subnetworks in identifying PPD was evaluated through multivariable logistics regression models and receiver operating characteristic curves. A comparison of the connectivity strength of white matter tracts and topological attributes of brain structural network parameters was conducted using independent samples t-tests, and the results were corrected using the false discovery rate (FDR) method. Results:(1) A total of 116 subjects were included, with 40 in the non-pregnant group, 40 in the non-PPD postpartum group, and 36 in the PPD group. PPD group had higher Edinburgh Postnatal Depression Scale (EPDS) scores than the non-pregnant and non-PPD postpartum groups [(18.0±4.1) scores vs. (2.5±1.2) and (6.1±2.1) scores, F=340.40; t=24.65,10.60 and 16.16 in pairwise comparison; all P<0.001]. (2) Compared to the non-pregnant group, there was a decrease in the connectivity strength of nine white matter tracts within the brain structural network of the postpartum group (including left dorsolateral superior frontal gyrus-left anterior cingulate and paracingulate gyrus, left dorsolateral superior frontal gyrus-right amygdala, left dorsolateral superior frontal gyrus-left insula, left insula-left lentiform nucleus, left insula-left hippocampus, left hippocampus-right amygdala, left hippocampus-left precuneus, left anterior cingulate and paracingulate gyrus-right amygdala, and right amygdala-right hippocampus) (all P<0.05, FDR corrected). No increased connection strengths were observed. There were no significant differences in the connection strengths of these nine tracts between the non-PPD and PPD groups. (3) A characteristic subnetwork for the maternal group was successfully constructed based on the nine tracts, which exhibited typical small-world properties (σ>1). Compared to the non-PPD maternal group, the characteristic path length in the PPD group was increased [(3.904±0.328) vs. (4.130±0.433), t=－2.58], and global efficiency was decreased [(0.361±0.036) vs. (0.331±0.053), t=2.91] (both P<0.05). Local property comparisons showed that the node efficiency values for the left dorsolateral superior frontal gyrus, left insula, left anterior cingulate and paracingulate gyrus, left hippocampus, right hippocampus, right amygdala, left precuneus and left putamen in the PPD group were significantly reduced [(0.273±0.023) vs. (0.267±0.030), t=0.98; (0.299±0.035) vs. (0.276±0.041), t=2.64; (0.265±0.019) vs. (0.258±0.025), t=1.38; (0.318±0.028) vs. (0.305±0.031), t=1.92; (0.312±0.027) vs. (0.302±0.031), t=1.50; (0.322±0.030) vs. (0.298±0.026), t=3.71; (0.356±0.040) vs. (0.338±0.056), t=1.62; (0.346±0.028) vs. (0.331±0.036), t=1.74; all P<0.05]. However, only the differences in node efficiency values for the left insula and right amygdala remained significant after FDR correction (corrected P=0.041 and 0.003). (4) Global efficiency, as well as node efficiency for the left insula and right amygdala, demonstrated good value for identifying PPD [areas under the curve (AUC) and their 95% CI were 0.827 (0.732-0.922), 0.741 (0.628-0.854), and 0.761 (0.653-0.867), respectively], with even better performance when combined [0.897 (0.828-0.969)]. (5) In the PPD group, global efficiency ( r=－0.43, P=0.008), node efficiency for the left insula ( r=－0.39, P=0.019), and node efficiency for the right amygdala ( r=－0.42, P=0.011) were all negatively correlated with EPDS scores. Conclusion:Aberrations in global efficiency, node efficiency for the left insula, and node efficiency for the right amygdala may serve as characteristic neuroimaging biomarkers for PPD.