Occipital connectivity networks mediate the neural effects of childhood maltreatment on depressive symptoms in major depressive disorder.

BACKGROUND: Childhood maltreatment (CM) is a well-established risk factor for major depressive disorder (MDD). The neural mechanisms linking childhood maltreatment experiences to changes in brain functional networks and the onset of depression are not fully understood. METHODS: In this study, we enrolled 66 patients with MDD and 31 healthy controls who underwent resting-state fMRI scans and neuropsychological assessments. We employed multivariate linear regression to examine the neural associations of CM and depression, specifically focusing on the bilateral occipital functional connectivity (OFC) networks relevant to MDD. Subsequently, a two-step mediation analysis was conducted to assess whether the OFC network mediated the relationship between CM experiences and the severity of depression. RESULTS: Our study showed that patients with MDD exhibited reduced OFC strength, particularly in the occipito-temporal, parietal, and premotor regions. These reductions were negatively correlated with CM scores and the severity of depression. Notably, the overlapping regions in the bilateral OFC networks, affected by both CM experiences and depressive severity, were primarily observed in the bilateral cuneus, left angular and calcarine, as well as the right middle frontal cortex and superior parietal cortex. Furthermore, the altered strengths of the OFC networks were identified as positive mediators of the impact of CM history on depression symptoms in patients with MDD. CONCLUSION: We have demonstrated that early exposure to CM may increase vulnerability to depression by influencing the brain's network. These findings provide new insights into understanding the pathological mechanism underlying depressive symptoms induced by CM.

Transarterial chemoembolization combined with radiofrequency ablation for medium and large hepatocellular carcinoma: insufficient ablation is associated with intrahepatic distant metastasis and extrahepatic metastasis.

Purpose: To compare the prognosis of complete and insufficient ablation of transarterial chemoembolization (TACE) combined with radiofrequency ablation (RFA) in treating medium and large hepatocellular carcinoma (HCC) and to explore the differences in recurrence patterns between the two groups. Patients and methods: Patients´ medical records and imaging data of patients with confirmed HCC from January 2014 to January 2022 were collected. These patients were divided into 2 groups: complete ablation (n=172) and insufficient ablation (n=171). Overall survival (OS) and progression-free survival (PFS) were estimated by the Kaplan-Meier curve and the log-rank test was used to compared. Fisher's exact test was used to compare recurrence patterns between the two groups. Results: The median OS time was 72.8 months (95%CI:69.5-76.1) and 62.0 months (95%CI: 55.3-68.7) in the complete and insufficient ablation groups, respectively. The median PFS time in the complete ablation group was 67.8 months (95% CI: 65.2-70.4) and 38.6 months (95%CI: 29.8-47.4) in the insufficient ablation group. The OS and PFS rates of the complete ablation group were significantly better than those of the insufficient ablation group (P<0.001). In the complete ablation group, 25(41%) patients experienced local tumor progression(LTP), 36(59%) experienced intrahepatic distant progression(IDP), and 0(0%) experienced extrahepatic progression (EP). In the insufficient ablation group, 51 (32.1%) patients experienced LTP, 96 (60.4%) experienced IDP, and 12 (7.5%) experienced EP. The progression patterns of the two groups were statistically significant (P=0.039). Conclusion: Insufficient ablation indicates a poor survival outcome of TACE combined with RFA for medium and large HCC and can promote intrahepatic distant and extrahepatic metastasis.

MicroRNA-124 influenced depressive symptoms via large-scale brain connectivity in major depressive disorder patients.

This study aimed to investigate the neurobiological mechanisms by which microRNA 124 (miR-124) is involved in major depressive disorder (MDD). We enrolled 53 untreated MDD patients and 38 healthy control (HC) subjects who completed behavior assessments and resting-state functional MRI (rs-fMRI) scans. MiR-124 expression levels were detected in the peripheral blood of all participants. We determined that miR-124 levels could influence depressive symptoms via disrupted large-scale intrinsic intra- and internetwork connectivity, including the default mode network (DMN)-DMN, dorsal attention network (DAN)-salience network (SN), and DAN-cingulo-opercular network (CON). This study deepens our understanding of how miR-124 dysregulation contributes to depression.

Age- and gender-related dispersion of brain networks across the lifespan.

The effects of age and gender on large-scale resting-state networks (RSNs) reflecting within- and between-network connectivity in the healthy brain remain unclear. This study investigated how age and gender influence the brain network roles and topological properties underlying the ageing process. Ten RSNs were constructed based on 998 participants from the REST-meta-MDD cohort. Multivariate linear regression analysis was used to examine the independent and interactive influences of age and gender on large-scale RSNs and their topological properties. A support vector regression model integrating whole-brain network features was used to predict brain age across the lifespan and cognitive decline in an Alzheimer's disease spectrum (ADS) sample. Differential effects of age and gender on brain network roles were demonstrated across the lifespan. Specifically, cingulo-opercular, auditory, and visual (VIS) networks showed more incohesive features reflected by decreased intra-network connectivity with ageing. Further, females displayed distinctive brain network trajectory patterns in middle-early age, showing enhanced network connectivity within the fronto-parietal network (FPN) and salience network (SAN) and weakened network connectivity between the FPN-somatomotor, FPN-VIS, and SAN-VIS networks. Age - but not gender - induced widespread decrease in topological properties of brain networks. Importantly, these differential network features predicted brain age and cognitive impairment in the ADS sample. By showing that age and gender exert specific dispersion of dynamic network roles and trajectories across the lifespan, this study has expanded our understanding of age- and gender-related brain changes with ageing. Moreover, the findings may be useful for detecting early-stage dementia.

Resting-state cerebral blood flow and functional connectivity abnormalities in depressed patients with childhood maltreatment: Potential biomarkers of vulnerability?

AIM: Childhood maltreatment (CM) is an important risk factor for major depressive disorder (MDD). This study aimed to explore the specific effect of CM on cerebral blood flow (CBF) and brain functional connectivity (FC) in MDD patients. METHODS: A total of 150 subjects were collected including 55 MDD patients with CM, 34 MDD patients without CM, 19 healthy controls (HC) with CM, and 42 HC without CM. All subjects completed MRI scans and neuropsychological tests. Two-way analysis of covariance was used to detect the main and interactive effects of disease and CM on CBF and FC across subjects. Then, partial correlation analyses were conducted to explore the behavioral significance of altered CBF and FC in MDD patients. Finally, a support vector classifier model was applied to differentiate MDD patients. RESULTS: MDD patients represented increased CBF in bilateral temporal lobe and decreased CBF in right visual cortex. Importantly, significant depression-by-CM interactive effects on CBF were primarily located in the frontoparietal regions, including orbitofrontal cortex (OFC), lateral prefrontal cortex (PFC), and parietal cortex. Moreover, significant FC abnormalities were seen in OFC-PFC and frontoparietal-visual cortex. Notably, the abnormal CBF and FC were significantly associated with behavioral performance. Finally, a combination of altered CBF and FC behaved with a satisfactory classification ability to differentiate MDD patients. CONCLUSIONS: These results highlight the importance of frontoparietal and visual cortices for MDD with CM experience, proposing a potential neuroimaging biomarker for MDD identification.

Identification and discovery of imaging genetic patterns using fusion self-expressive network in major depressive disorder.

Introduction: Major depressive disorder (MDD) is a prevalent mental illness, with severe symptoms that can significantly impair daily routines, social interactions, and professional pursuits. Recently, imaging genetics has received considerable attention for understanding the pathogenesis of human brain disorders. However, identifying and discovering the imaging genetic patterns between genetic variations, such as single nucleotide polymorphisms (SNPs), and brain imaging data still present an arduous challenge. Most of the existing MDD research focuses on single-modality brain imaging data and neglects the complex structure of brain imaging data. Methods: In this study, we present a novel association analysis model based on a self-expressive network to identify and discover imaging genetics patterns between SNPs and multi-modality imaging data. Specifically, we first build the multi-modality phenotype network, which comprises voxel node features and connectivity edge features from structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI), respectively. Then, we apply intra-class similarity information to construct self-expressive networks of multi-modality phenotype features via sparse representation. Subsequently, we design a fusion method guided by diagnosis information, which iteratively fuses the self-expressive networks of multi-modality phenotype features into a single new network. Finally, we propose an association analysis between MDD risk SNPs and the multi-modality phenotype network based on a fusion self-expressive network. Results: Experimental results show that our method not only enhances the association between MDD risk SNP rs1799913 and the multi-modality phenotype network but also identifies some consistent and stable regions of interest (ROIs) multi-modality biological markers to guide the interpretation of MDD pathogenesis. Moreover, 15 new potential risk SNPs highly associated with MDD are discovered, which can further help interpret the MDD genetic mechanism. Discussion: In this study, we discussed the discriminant and convergence performance of the fusion self-expressive network, parameters, and atlas selection.

Altered brain functional networks in schizophrenia with persistent negative symptoms: an activation likelihood estimation meta-analysis.

Objective: To investigate brain structural and functional characteristics of three brain functional networks including default mode network (DMN), central executive network (CEN), and salience network (SN) in persistent negative symptoms (PNS) patients. Methods: We performed an activation likelihood estimation (ALE) meta-analysis of functional connectivity (FC) studies and voxel-based morphometry (VBM) studies to detect specific structural and functional alterations of brain networks between PNS patients and healthy controls. Results: Seventeen VBM studies and twenty FC studies were included. In the DMN, PNS patients showed decreased gray matter in the bilateral medial frontal gyrus and left anterior cingulate gyrus and a significant reduction of FC in the right precuneus. Also, PNS patients had a decrease of gray matter in the left inferior parietal lobules and medial frontal gyrus, and a significant reduction of FC in the bilateral superior frontal gyrus in the CEN. In comparison with healthy controls, PNS patients exhibited reduced gray matter in the bilateral insula, anterior cingulate gyrus, left precentral gyrus and right claustrum and lower FC in these brain areas in the SN, including the left insula, claustrum, inferior frontal gyrus and extra-nuclear. Conclusion: This meta-analysis reveals brain structural and functional imaging alterations in the three networks and the interaction among these networks in PNS patients, which provides neuroscientific evidence for more personalized treatment.Systematic Review RegistrationThe PROSPERO (https://www.crd.york.ac.uk/PROSPERO/, registration number: CRD42022335962).

Connectomics-based resting-state functional network alterations predict suicidality in major depressive disorder.

Suicidal behavior is a major concern for patients who suffer from major depressive disorder (MDD). However, dynamic alterations and dysfunction of resting-state networks (RSNs) in MDD patients with suicidality have remained unclear. Thus, we investigated whether subjects with different severity of suicidal ideation and suicidal behavior may have different disturbances in brain RSNs and whether these changes could be used as the diagnostic biomarkers to discriminate MDD with or without suicidal ideation and suicidal behavior. Then a multicenter, cross-sectional study of 528 MDD patients with or without suicidality and 998 healthy controls was performed. We defined the probability of dying by the suicide of the suicidality components as a 'suicidality gradient'. We constructed ten RSNs, including default mode (DMN), subcortical (SUB), ventral attention (VAN), and visual network (VIS). The network connections of RSNs were analyzed among MDD patients with different suicidality gradients and healthy controls using ANCOVA, chi-squared tests, and network-based statistical analysis. And support vector machine (SVM) model was designed to distinguish patients with mild-to-severe suicidal ideation, and suicidal behavior. We found the following abnormalities with increasing suicidality gradient in MDD patients: within-network connectivity values initially increased and then decreased, and one-versus-other network values decreased first and then increased. Besides, within- and between-network connectivity values of the various suicidality gradients are mainly negatively correlated with HAMD anxiety and positively correlated with weight. We found that VIS and DMN-VIS values were affected by age (p < 0.05), cingulo-opercular network, and SUB-VAN values were statistically influenced by sex (p < 0.05). Furthermore, the SVM model could distinguish MDD patients with different suicidality gradients (AUC range, 0.73-0.99). In conclusion, we have identified that disrupted brain connections were present in MDD patients with different suicidality gradient. These findings provided useful information about the pathophysiological mechanisms of MDD patients with suicidality.

A Novel Gene Pair CSTF2/DPE2A Impacts Prognosis and Cell Cycle of Hepatocellular Carcinoma.

Background: Hepatocellular carcinoma (HCC), one of the commonest cancers at present, possesses elevated mortality. This study explored the predictive value of CSTF2/PDE2A for HCC prognosis. Methods: In this study, clinical information and RNA sequencing expression profiles of HCC patients were acquired from common databases. Kaplan-Meier curve compound with time-dependent ROC curve, nomogram model, and univariate/multivariate Cox analysis were carried out to access the prediction capacity of CSTF2/PDE2A. The immune status, tumor microenvironment, drug sensitivity, biological function and pathway between HCC and adjacent non-tumorous tissue were analyzed and compared. Finally, RT-qPCR, Western blot, and apoptosis assays were performed to verify the effect on HCC cells of CSTF2/PDE2A. Results: The optimal cut-off value of CSTF2, PDE2A and CSTF2/PDE2A was 6.95, 0.95 and 3.63, respectively. In TCGA and ICGC cohorts, the high group of CSTF2/PDE2A presented higher OS compared to low group. The area under the curve (AUC) for OS at 1-, 2-, and 3-years predicted by CSTF2/PDE2A were 0.731/0.695, 0.713/0.732 and 0.689/0.755, higher than the counterparts of the single gene CSTF2 and PDE2A. Multivariate Cox analysis revealed that CSTF2/PDE2A (HR = 1.860/3.236, 95% CI = 1.265-2.733/1.575-6.645) was an independent prognostic factor for HCC. The OS nomogram model created according to five independent factors including CSTF2/PDE2A showed excellent capacity for HCC prognosis. Furthermore, the immune status of the CSTF2/PDE2A high group was deleted, cell cycle-related genes and chemotherapy resistance were increased. Finally, cell experiments revealed distinct differences in the proliferation, apoptosis, protein and mRNA expression of HCC cells after si-CSTF2 transfection compared with the negative control. Conclusion: Taken together, the gene pair CSTF2/PDE2A is able to forecast the prognosis of HCC and regulates cell cycle, which is promising as a novel prognostic predictor of HCC.

Childhood trauma and social support affect symptom profiles through cortical thickness abnormalities in major depressive disorder: A structural equation modeling analysis.

BACKGROUND: Childhood trauma, low social support, and alexithymia are recognized as risk factors for major depressive disorder (MDD). However, the mechanisms of risk factors, symptoms, and corresponding structural brain abnormalities in MDD are not fully understood. Structural equation modeling (SEM) has advantages in studying multivariate interrelationships. We aim to illustrate their relationships using SEM. METHODS: 313 MDD patients (213 female; mean age 42.49 years) underwent magnetic resonance imaging and completed assessments. We integrated childhood trauma, alexithymia, social support, anhedonia, depression, anxiety, suicidal ideation and cortical thickness into a multivariate SEM. RESULTS: We first established the risk factors-clinical phenotype SEM with an adequate fit. Cortical thickness results show a negative correlation of childhood trauma with the left middle temporal gyrus (MTG) (p = 0.012), and social support was negatively correlated with the left posterior cingulate cortex (PCC) (p < 0.001). The final good fit SEM (χ2 = 32.92, df = 21, χ2/df = 1.57, CFI = 0.962, GFI = 0.978, RMSEA = 0.043) suggested two pathways, with left PCC thickness mediating the relationship between social support and suicidal ideation, and left MTG thickness mediating between childhood trauma and anhedonia/anxiety. CONCLUSION: Our findings provide evidence for the impact of risk factor variables on the brain structure and clinical phenotype of MDD patients. Insufficient social support and childhood trauma might lead to corresponding cortical abnormalities in PCC and MTG, affecting the patient's mood and suicidal ideation. Future interventions should aim at these nodes.

Characterization of neutrophil extracellular traps related gene pair for predicting prognosis in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is a malignant disease with a high incidence rate, high mortality and poor prognosis. Neutrophil extracellular traps (NETs), as an extracellular reticular structure, promote the development and progression of cancer in the tumor microenvironment, and have a promising prospect as a prognostic indicator. In the present study, we elucidated the prognostic value of NET-related genes. METHODS: The NETs gene pair of The Cancer Genome Atlas cohort was constructed by least absolute shrinkage and selection operator analysis. Samples from the International Cancer Genome Consortium were performed to verify its feasibility. Kaplan-Meier analysis was used to compare the overall survival (OS) rate of the two subgroups. The independent predictors of OS were determined by univariate and multivariate Cox analysis. Furthermore, Gene Ontology term and Kyoto Encyclopedia of Genes and Genomes pathway were analyzed by gene set enrichment analysis. The single sample gene set enrichment analysis method was performed to deplore the relationship of risk score with tumor immune microenvironment. The GSE149614 dataset was applied as single cell RNA level validation. PCR was performed to the detect mRNA expression profiles of NETs-related genes. RESULTS: Our analysis of the NETs-related model provides a promising prospect as a prognostic indicator. The OS of high-risk group patients was significantly reduced. The risk score was an important independent predictor of HCC prognosis. The Nomogram model suggested a favorable classification performance. The drug resistance and sensitivity of tumor cells to chemotherapeutics was significantly correlated with the prognostic gene expression. The immune status of the two risk groups showed a marked difference. CONCLUSIONS: The novel prognostic gene pair and immune landscape could predict the prognosis of HCC patients and provide a new understanding of immunotherapy in HCC.

Disconnection of Network Hubs Underlying the Executive Function Deficit in Patients with Ischemic Leukoaraiosis.

BACKGROUND: Cognitive impairment is the most common clinical manifestation of ischemic leukoaraiosis (ILA), but the underlying neurobiological pathways have not been well elucidated. Recently, it was thought that ILA is a "disconnection syndrome". Disorganized brain connectome were considered the key neuropathology underlying cognitive deficits in ILA patients. OBJECTIVE: We aimed to detect the disruption of network hubs in ILA patients using a new analytical method called voxel-based eigenvector centrality (EC) mapping. METHODS: Subjects with moderate to severe white matters hyperintensities (Fazekas score ≥3) and healthy controls (HCs) (Fazekas score = 0) were included in the study. The resting-state functional magnetic resonance imaging and the EC mapping approach were performed to explore the alteration of whole-brain network connectivity in ILA patients. RESULTS: Relative to the HCs, the ILA patients exhibited poorer cognitive performance in episodic memory, information processing speed, and executive function (all ps < 0.0125). Additionally, compared with HCs, the ILA patients had lower functional connectivity (i.e., EC values) in the medial parts of default-mode network (i.e., bilateral posterior cingulate gyrus and ventral medial prefrontal cortex [vMPFC]). Intriguingly, the functional connectivity strength at the right vMPFC was positively correlated with executive function deficit in the ILA patients. CONCLUSION: The findings suggested disorganization of the hierarchy of the default-mode regions within the whole-brain network in patients with ILA and advanced our understanding of the neurobiological mechanism underlying executive function deficit in ILA.

Multi-Connectivity Representation Learning Network for Major Depressive Disorder Diagnosis.

The pathophysiology of major depressive disorder (MDD) has been demonstrated to be highly associated with the dysfunctional integration of brain activity. Existing studies only fuse multi-connectivity information in a one-shot approach and ignore the temporal property of functional connectivity. A desired model should utilize the rich information in multiple connectivities to help improve the performance. In this study, we develop a multi-connectivity representation learning framework to integrate multi-connectivity topological representation from structural connectivity, functional connectivity and dynamic functional connectivities for automatic diagnosis of MDD. Briefly, structural graph, static functional graph and dynamic functional graphs are first computed from the diffusion magnetic resonance imaging (dMRI) and resting state functional magnetic resonance imaging (rsfMRI). Secondly, a novel Multi-Connectivity Representation Learning Network (MCRLN) approach is developed to integrate the multiple graphs with modules of structural-functional fusion and static-dynamic fusion. We innovatively design a Structural-Functional Fusion (SFF) module, which decouples graph convolution to capture modality-specific features and modality-shared features separately for an accurate brain region representation. To further integrate the static graphs and dynamic functional graphs, a novel Static-Dynamic Fusion (SDF) module is developed to pass the important connections from static graphs to dynamic graphs via attention values. Finally, the performance of the proposed approach is comprehensively examined with large cohorts of clinical data, which demonstrates its effectiveness in classifying MDD patients. The sound performance suggests the potential of the MCRLN approach for the clinical use in diagnosis. The code is available at https://github.com/LIST-KONG/MultiConnectivity-master.

Incorporating multi-stage diagnosis status to mine associations between genetic risk variants and the multi-modality phenotype network in major depressive disorder.

Depression (major depressive disorder, MDD) is a common and serious medical illness. Globally, it is estimated that 5% of adults suffer from depression. Recently, imaging genetics receives growing attention and become a powerful strategy for discoverying the associations between genetic variants (e.g., single-nucleotide polymorphisms, SNPs) and multi-modality brain imaging data. However, most of the existing MDD imaging genetic research studies conducted by clinicians usually utilize simple statistical analysis methods and only consider single-modality brain imaging, which are limited in the deeper discovery of the mechanistic understanding of MDD. It is therefore imperative to utilize a powerful and efficient technology to fully explore associations between genetic variants and multi-modality brain imaging. In this study, we developed a novel imaging genetic association framework to mine the multi-modality phenotype network between genetic risk variants and multi-stage diagnosis status. Specifically, the multi-modality phenotype network consists of voxel node features and connectivity edge features from structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI). Thereafter, an association model based on multi-task learning strategy was adopted to fully explore the relationship between the MDD risk SNP and the multi-modality phenotype network. The multi-stage diagnosis status was introduced to further mine the relation among the multiple modalities of different subjects. A multi-modality brain imaging data and genotype data were collected by us from two hospitals. The experimental results not only demonstrate the effectiveness of our proposed method but also identify some consistent and stable brain regions of interest (ROIs) biomarkers from the node and edge features of multi-modality phenotype network. Moreover, four new and potential risk SNPs associated with MDD were discovered.

Linking individual variability in functional brain connectivity to polygenic risk in major depressive disorder.

BACKGROUND: Major depressive disorder (MDD) is a highly heterogeneous disease, which brings great difficulties to clinical diagnosis and therapy. Its mechanism is still unknown. Prior neuroimaging studies mainly focused on mean differences between patients and healthy controls (HC), largely ignoring individual differences between patients. METHODS: This study included 112 MDD patients and 93 HC subjects. Resting-state functional MRI data were obtained to examine the patterns of individual variability of brain functional connectivity (IVFC). The genetic risk of pathways including dopamine, 5-hydroxytryptamine (5-HT), norepinephrine (NE), hypothalamic-pituitary-adrenal (HPA) axis, and synaptic plasticity was assessed by multilocus genetic profile scores (MGPS), respectively. RESULTS: The IVFC pattern of the MDD group was similar but higher than that in HCs. The inter-network functional connectivity in the default mode network contributed to altered IVFC in MDD. 5-HT, NE, and HPA pathway genes affected IVFC in MDD patients. The age of onset, duration, severity, and treatment response, were correlated with IVFC. IVFC in the left ventromedial prefrontal cortex had a mediating effect between MGPS of the 5-HT pathway and baseline depression severity. LIMITATIONS: Environmental factors and differences in locations of functional areas across individuals were not taken into account. CONCLUSIONS: This study found MDD patients had significantly different inter-individual functional connectivity variations than healthy people, and genetic risk might affect clinical manifestations through brain function heterogeneity.

Episodic Memory-Related Imaging Features as Valuable Biomarkers for the Diagnosis of Alzheimer's Disease: A Multicenter Study Based on Machine Learning.

BACKGROUND: Individualized and reliable biomarkers are crucial for diagnosing Alzheimer's disease (AD). However, lack of accessibility and neurobiological correlation are the main obstacles to their clinical application. Machine learning algorithms can effectively identify personalized biomarkers based on the prominent symptoms of AD. METHODS: Episodic memory-related magnetic resonance imaging (MRI) features of 143 patients with amnesic mild cognitive impairment (MCI) were identified using a multivariate relevance vector regression algorithm. The support vector machine classification model was constructed using these MRI features and verified in 2 independent datasets (N = 994). The neurobiological basis was also investigated based on cognitive assessments, neuropathologic biomarkers of cerebrospinal fluid, and positron emission tomography images of amyloid-ß plaques. RESULTS: The combination of gray matter volume and amplitude of low-frequency fluctuation MRI features accurately predicted episodic memory impairment in individual patients with amnesic MCI (r = 0.638) when measured using an episodic memory assessment panel. The MRI features that contributed to episodic memory prediction were primarily distributed across the default mode network and limbic network. The classification model based on these features distinguished patients with AD from normal control subjects with more than 86% accuracy. Furthermore, most identified episodic memory-related regions showed significantly different amyloid-ß positron emission tomography measurements among the AD, MCI, and normal control groups. Moreover, the classification outputs significantly correlated with cognitive assessment scores and cerebrospinal fluid pathological biomarkers' levels in the MCI and AD groups. CONCLUSIONS: Neuroimaging features can reflect individual episodic memory function and serve as potential diagnostic biomarkers of AD.

Association of platelet-to-neutrophil ratios with 1-year outcome and mortality in patients with acute ischemic stroke.

BACKGROUND: Platelet-neutrophil crosstalk is being increasingly recognized as a driver of inflammation and thrombosis in patients with ischemic stroke. The aim of this study was to investigate the potential of PNR value in predicting the long-term prognosis and evaluate whether or not an available and routine blood cell biomarker could help predict the long-term neurological function and mortality in AIS patients. METHODS: A total of 718 patients with suspected acute ischemic stroke were involved and followed up for 1 year by standard telephone interview or reexamination. Kaplan-Meier curve, Univariate and Multivariate Cox Regression were analyzed using Statistical Packages for Social Sciences. RESULTS: ROC curve for PNR to evaluate 1-year outcomes was analyzed and the area under the curve (AUC) was 0.659 (P < 0.001). The cutoff point was observed at 38.30, with a sensitivity of 53.09 % and a specificity of 71.25 %. Moreover, patients in PNR ≤ 38.30 were more likely to have more serious NIHSS on admission, 1-year mRS and higher 1-year mortality (P < 0.001, respectively). The 1-year mortality in the low PNR group was significantly higher than that of the high PNR group (log-rank tests: P < 0.0001). Age, NIHSS, RBC and PNR were combined into model B which significantly increased the AUC value from 0.736 to 0.888 compared to model A (including Age, NIHSS and RBC). CONCLUSION: PNR may serve as a readily assessable biomarker for early predicting neurological deterioration and the long-term prognosis of AIS. The nomogram that included age, NIHSS, PNR and RBC may be a useful predictive tool for 1-year mortality.