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Background The triglyceride-glucose (TyG) index is a predictor of cardiovascular diseases; however, to what extent the TyG index is associated with cardiovascular diseases through renal function is unclear. This study aimed to evaluate the complex association of the TyG index and renal function with cardiovascular diseases using a cohort design. Methods This study included participants from the China Health and Retirement Longitudinal Study (CHARLS) free of cardiovascular diseases at baseline. We performed adjusted regression analyses and mediation analyses using Cox models. The TyG index was calculated as Ln [fasting triglyceride (mg/dL) × fasting glucose (mg/dL)/2]. Renal function was defined by the estimated glomerular filtration rate (eGFR). Results A total of 6 496 participants were included in this study. The mean age of the participants was 59.6 ± 9.5 years, and 2996 (46.1%) were females. During a maximum follow-up of 7.0 years, 1 996 (30.7%) people developed cardiovascular diseases, including 1 541 (23.7%) cases of heart diseases and 651 (10.0%) cases of stroke. Both the TyG index and eGFR level were significantly associated with cardiovascular diseases. Compared with people with a lower TyG index (median level) and eGFR ≥ 60 ml/minute/1.73 m 2 , those with a higher TyG index and decreased eGFR had the highest risk of cardiovascular diseases (HR, 1.870; 95% CI 1.131–3.069). Decreased eGFR significantly mediated 29.6% of the associations between the TyG index and cardiovascular diseases. Conclusions The combination of a higher TyG index and lower eGFR level was associated with the highest risk of cardiovascular diseases. Renal function could mediate the association between the TyG index and cardiovascular risk.

By employing deep learning-based automatic whole-brain region segmentation technology, we aim to investigate the cross-sectional associations between regional brain volumes and disease duration in patients with Parkinson's disease (PD). A retrospective study design was implemented on 83 patients diagnosed with idiopathic PD who had complete clinical and imaging data. Cranial magnetic resonance images (MRI) were imported into the uAI platform for automated regional segmentation of brain tissue. Volumetric data from five major brain regions and 80 subregions were extracted to explore their potential associations with disease progression in PD patients. Statistical analysis was conducted using a multiple linear regression model within the framework of linear regression analysis, with statistical significance defined as < 0.05. Cross-sectional analysis revealed that in PD patients, volume ratios of multiple brain regions-including the bilateral precentral gyrus, right medial frontal gyrus, bilateral postcentral gyrus, bilateral superior and inferior parietal lobules, bilateral precuneus, right cuneus, right lingual gyrus, bilateral lateral occipital gyrus, and right globus pallidus-were negatively associated with disease duration ( < 0.05). In contrast, the right hippocampus, right inferior temporal gyrus, and left superior temporal gyrus showed positive correlations ( < 0.05). The combined volume ratios of these brain regions also decreased with longer disease duration ( < 0.05). Furthermore, absolute volume differences in the hippocampus, fusiform gyrus, isthmus of the cingulate gyrus, and cerebellar white matter increased as the disease progressed ( < 0.05). In PD patients, volume ratios and absolute volume differences in specific brain subregions associated with lateralized intracranial changes may serve as potential biomarkers for assessing brain tissue alterations during disease progression.

In this work, we propose a novel cascaded V-Nets method to segment brain tumor substructures in multimodal brain magnetic resonance imaging. Although V-Net has been successfully used in many segmentation tasks, we demonstrate that its performance could be further enhanced by using a cascaded structure and ensemble strategy. Briefly, our baseline V-Net consists of four levels with encoding and decoding paths and intra- and inter-path skip connections. Focal loss is chosen to improve performance on hard samples as well as balance the positive and negative samples. We further propose three preprocessing pipelines for multimodal magnetic resonance images to train different models. By ensembling the segmentation probability maps obtained from these models, segmentation result is further improved. In other hand, we propose to segment the whole tumor first, and then divide it into tumor necrosis, edema, and enhancing tumor. Experimental results on BraTS 2018 online validation set achieve average Dice scores of 0.9048, 0.8364, and 0.7748 for whole tumor, tumor core and enhancing tumor, respectively. The corresponding values for BraTS 2018 online testing set are 0.8761, 0.7953, and 0.7364, respectively. We also evaluate the proposed method in two additional data sets from local hospitals comprising of 28 and 28 subjects, and the best results are 0.8635, 0.8036, and 0.7217, respectively. We further make a prediction of patient overall survival by ensembling multiple classifiers for long, mid and short groups, and achieve accuracy of 0.519, mean square error of 367240 and Spearman correlation coefficient of 0.168 for BraTS 2018 online testing set.

Non-motor symptoms (NMS) in Parkinson's disease (PD) often precede motor manifestations and are challenging to detect with conventional MRI. This study investigates the use of the Multi-Flip-Angle and Multi-Echo Gradient Echo Sequence (MULTIPLEX) in MRI to detect previously undetectable microstructural changes in brain tissue associated with NMS in PD. A prospective study was conducted on 37 patients diagnosed with PD. Anxiety and depression levels were assessed using the Hamilton Anxiety Scale (HAMA) and Hamilton Depression Scale (HAMD), respectively. MRI techniques, including 3D T1-weighted imaging (3D T1WI) and MULTIPLEX - which encompasses T2*-mapping, T1-mapping, proton density-mapping, and quantitative susceptibility mapping (QSM)-were performed. Brain subregions were automatically segmented using deep learning, and their volume and quantitative parameters were correlated with NMS-related assessment scales using Spearman's rank correlation coefficient. Correlations were observed between QSM and T2* values of certain subregions within the left frontal and bilateral temporal lobes and both anxiety and depression (absolute -values ranging from 0.358 to 0.480, < 0.05). Additionally, volume measurements of regions within the bilateral frontal, temporal, and insular lobes exhibited negative correlations with anxiety and depression (absolute -values ranging from 0.354 to 0.658, < 0.05). In T1-mapping and proton density-mapping, no specific brain regions were found to be significantly associated with the NMS of PD under investigation. Quantitative parameters derived from MULTIPLEX MRI show significant associations with clinical evaluations of NMS in PD. Multiparametric MR neuroimaging may serve as a potential early diagnostic tool for PD.

BackgroundPost-stroke epilepsy (PSE) is a major complication of stroke. However, data about the predictors of PSE in patients with acute ischemic stroke (AIS) undergoing mechanical thrombectomy are limited.ObjectiveTo evaluate the relationship between intraoperative angiographic signs and PSE risk in patients with anterior circulation AIS who underwent mechanical thrombectomy.MethodsWe conducted a retrospective study. A total of 800 patients with AIS who underwent mechanical thrombectomy were classified into case and control groups based on the occurrence of PSE. Propensity score matching (PSM) (1:4) was applied using covariates such as age, sex, National Institutes of Health Stroke Scale score at admission, and baseline modified Rankin Scale score. Conditional logistic regression and mediation analysis were performed. Subgroup analyses were conducted to assess the effect of modification. A diagnostic model based on the angiographic signs and clinical characteristics was developed.ResultsAfter PSM, 67 and 234 patients with and without PSE, respectively, were selected. The PSE group had significantly higher incidences of hemorrhagic transformation, early seizures, early venous filling (EVF) sign, inferior frontal gyrus (IFG), hippocampus, basal ganglia blush sign, and larger infarct size. After adjusting for hypertension, diabetes, hemorrhagic transformation, infarct size, early seizure, IFG, and hippocampus involvement, EVF remained independently associated with PSE. Hemorrhagic transformation mediated 14.87% of the EVF–PSE associations. Comparison of the evaluation metrics of each model showed that model 3 exhibited the best overall performance.ConclusionHemorrhagic transformation mediates the EVF–PSE association. EVF signs are key predictors of PSE following mechanical thrombectomy.

Dendritic cells (DCs) play a crucial role in anti-tumor immunity by capturing, processing, and presenting tumor antigens to T cells, making DC-based immunotherapy a promising approach for cancer treatment. However, the most commonly used clinical strategy still relies on inducing DCs using granulocyte-macrophage colony-stimulating factor (GM-CSF) and interleukin-4 (IL - 4) (GM/IL4-DCs), which often results in a heterogeneous cell population with suboptimal anti-tumor function. Here, we compared DCs generated by co-stimulating with FMS-like tyrosine kinase 3 ligand (FLT3L) and GM-CSF (FL/GM-DCs) with the conventional GM/IL4-DCs. To compare the functional differences of DCs induced by different methods, we conducted a comprehensive study. Mouse bone marrow cells were continuously cultured for 9 days in a FLT3L/GM-CSF-containing medium. After cell collection, we analyzed the composition, subpopulations, and status of FL/GM-DCs using flow cytometry and scRNA-seq. Flow cytometry was also used to assess their antigen presentation and ability to stimulate T cells. experiments were performed to examine their distribution, anti-tumor effects, and therapeutic responses in tumor models. Finally, combining scRNA-seq and scTCR-seq, we explored the mechanisms by which FL/GM-DCs reshape the tumor microenvironment. The results showed that FL/GM-DCs exhibited a unique subpopulation distribution, characterized by an abundance of conventional cDC subpopulations, and demonstrated enhanced cross-antigen presentation capabilities. Notably, FL/GM-DCs were able to induce a broader and more tumor-specific CD8 T cell response, effectively reshaping the tumor microenvironment by promoting the infiltration of cytotoxic T lymphocytes (CTLs) and reducing immunosuppressive components. In contrast, GM/IL4-DCs contained fewer cDC subpopulations, eliciting a weaker initial CD8 T cell response and yielding relatively inferior anti-tumor effects. In summary, FLT3L combined with GM-CSF induced DCs, through their unique subpopulation composition and functional state, can more effectively expand tumor-specific CD8 T cells and reshape the tumor microenvironment, thereby achieving superior immunotherapy outcomes. This study highlights the potential of FL/GM-DCs as a next-generation DC platform, paving the way for improved clinical translation of DC-based adoptive cancer immunotherapies.

Purpose Magnetic resonance imaging (MRI) refers to one of the critical image modalities for diagnosis, whereas its long acquisition time limits its application. In this study, the aim was to investigate whether deep learning–based techniques are capable of using the common information in different MRI sequences to reduce the scan time of the most time‐consuming sequences while maintaining the image quality. Method Fully sampled T1‐FLAIR, T2‐FLAIR, and T2WI brain MRI raw data originated from 217 patients and 105 healthy subjects. The T1‐FLAIR and T2‐FLAIR sequences were subsampled using Cartesian masks based on four different acceleration factors. The fully sampled T1/T2‐FLAIR images were predicted from undersampled T1/T2‐FLAIR images and T2WI images through deep learning–based reconstruction. They were qualitatively assessed by two senior radiologists in accordance with the diagnosis decision and a four‐point scale image quality score. Furthermore, the images were quantitatively assessed based on regional signal‐to‐noise ratios (SNRs) and contrast‐to‐noise ratios (CNRs). The chi‐square test was performed, where p < 0.05 indicated a difference with statistical significance. Results The diagnosis decisions from two senior radiologists remained unchanged in accordance with the accelerated and fully sampled images. There were no significant differences in the regional SNRs and CNRs of most assessed regions (p > 0.05) between the accelerated and fully sampled images. Moreover, no significant difference was identified in the image quality assessed by two senior radiologists (p > 0.05). Conclusion Deep learning–based image reconstruction is capable of significantly expediting the brain MR imaging process and producing acceptable image quality without affecting diagnosis decisions. Deep learning‐based rapid MR reconstruction was assessed on the T1‐FLAIR and T2‐FLAIR sequences, and its performance was compared with that of conventional reconstruction strategies. Both quantitative and qualitative analyses were performed on the deep learning‐based MR reconstruction results across various acceleration factors, revealing image quality comparable to that of fully sampled images. Images produced through deep learning‐based reconstruction exhibit reduced artifacts and enhanced soft tissue contrast compared to those generated by conventional reconstruction methods.

Our objective was to use deep learning models to identify underlying brain regions associated with depression symptom phenotypes in late-life depression (LLD). Diagnosed with LLD ( = 116) and enrolled in a prospective treatment study. Cross-sectional. Structural magnetic resonance imaging (sMRI) was used to predict five depression symptom phenotypes from the Hamilton and MADRS depression scales previously derived from factor analysis: (1) Anhedonia, (2) Suicidality, (3) Appetite, (4) Sleep Disturbance, and (5) Anxiety. Our deep learning model was deployed to predict each factor score via learning deep feature representations from 3D sMRI patches in 34 a regions-of-interests (ROIs). ROI-level prediction accuracy was used to identify the most discriminative brain regions associated with prediction of factor scores representing each of the five symptom phenotypes. Factor-level results found significant predictive models for Anxiety and Suicidality factors. ROI-level results suggest the most LLD-associated discriminative regions in predicting all five symptom factors were located in the anterior cingulate and orbital frontal cortex. We validated the effectiveness of using deep learning approaches on sMRI for predicting depression symptom phenotypes in LLD. We were able to identify deep embedded local morphological differences in symptom phenotypes in the brains of those with LLD, which is promising for symptom-targeted treatment of LLD. Future research with machine learning models integrating multimodal imaging and clinical data can provide additional discriminative information.

The infant brain undergoes a remarkable period of neural development that is crucial for the development of cognitive and behavioral capacities (Hasegawa et al., 2018 ). Longitudinal magnetic resonance imaging (MRI) is able to characterize the developmental trajectories and is critical in neuroimaging studies of early brain development. However, missing data at different time points is an unavoidable occurrence in longitudinal studies owing to participant attrition and scan failure. Compared to dropping incomplete data, data imputation is considered a better solution to address such missing data in order to preserve all available samples. In this paper, we adapt generative adversarial networks (GAN) to a new application: longitudinal image prediction of structural MRI in the first year of life. In contrast to existing medical image-to-image translation applications of GANs, where inputs and outputs share a very close anatomical structure, our task is more challenging as brain size, shape and tissue contrast vary significantly between the input data and the predicted data. Several improvements over existing GAN approaches are proposed to address these challenges in our task. To enhance the realism, crispness, and accuracy of the predicted images, we incorporate both a traditional voxel-wise reconstruction loss as well as a perceptual loss term into the adversarial learning scheme. As the differing contrast changes in T1w and T2w MR images in the first year of life, we incorporate multi-contrast images leading to our proposed 3D multi-contrast perceptual adversarial network (MPGAN). Extensive evaluations are performed to assess the qualityand fidelity of the predicted images, including qualitative and quantitative assessments of the image appearance, as well as quantitative assessment on two segmentation tasks. Our experimental results show that our MPGAN is an effective solution for longitudinal MR image data imputation in the infant brain. We further apply our predicted/imputed images to two practical tasks, a regression task and a classification task, in order to highlight the enhanced task-related performance following image imputation. The results show that the model performance in both tasks is improved by including the additional imputed data, demonstrating the usability of the predicted images generated from our approach.

Previous studies have confirmed the separate effect of arterial stiffness and obesity on type 2 diabetes; however, the joint effect of arterial stiffness and obesity on diabetes onset remains unclear. This study aimed to propose the concept of arterial stiffness obesity phenotype and explore the risk stratification capacity for diabetes. This longitudinal cohort study used baseline data of 12,298 participants from Beijing Xiaotangshan Examination Center between 2008 and 2013 and then annually followed them until incident diabetes or 2019. BMI (waist circumference) and brachial-ankle pulse wave velocity were measured to define arterial stiffness abdominal obesity phenotype. The Cox proportional hazard model was used to estimate the hazard ratio (HR) and 95% CI. Of the 12,298 participants, the mean baseline age was 51.2 (SD 13.6) years, and 8448 (68.7%) were male. After a median follow-up of 5.0 (IQR 2.0-8.0) years, 1240 (10.1%) participants developed diabetes. Compared with the ideal vascular function and nonobese group, the highest risk of diabetes was observed in the elevated arterial stiffness and obese group (HR 1.94, 95% CI 1.60-2.35). Those with exclusive arterial stiffness or obesity exhibited a similar risk of diabetes, and the adjusted HRs were 1.63 (95% CI 1.37-1.94) and 1.64 (95% CI 1.32-2.04), respectively. Consistent results were observed in multiple sensitivity analyses, among subgroups of age and fasting glucose level, and alternatively using arterial stiffness abdominal obesity phenotype. This study proposed the concept of arterial stiffness abdominal obesity phenotype, which could improve the risk stratification and management of diabetes. The clinical significance of arterial stiffness abdominal obesity phenotype needs further validation for other cardiometabolic disorders.