Publications by authors named "Jianan Ye"

Background: Effective clearance of lipid-rich debris by macrophages is critical for neural repair and regeneration after spinal cord injury (SCI). Interleukin-3 (IL-3) has been implicated in programming microglia to cluster and clear pathological aggregates in neurodegenerative disease. Yet, the influence of IL-3 on lipid debris clearance post-SCI is not well characterized.

View Article and Find Full Text PDF

Objective: This study examines the association between dietary intake of live microbes (LM) and the risk of cardiovascular disease (CVD) and cardiovascular mortality in adults with diabetes.

Methods: A retrospective cohort study was conducted using National Health and Nutrition Examination Survey (NHANES) data from 2001 to 2010, with follow-up mortality data through December 31, 2019. A total of 3,955 adults with diabetes were included.

View Article and Find Full Text PDF

Background: In the context of spinal cord injury (SCI), infiltrating macrophages assume prominence as the primary inflammatory cells within the lesion core, where the fibrotic scar is predominantly orchestrated by platelet-derived growth factor receptor beta (PDGFRβ) fibroblasts. Galectin-3, a carbohydrate-binding protein of the lectin family, is notably expressed by infiltrating hematogenous macrophages and mediates cell-cell interactions. Although Galectin-3 has been shown to contribute to the endocytic internalization of PDGFRβ in vitro, its specific role in driving fibrotic scar formation after SCI has not been determined.

View Article and Find Full Text PDF

Objective: Develop a time-dependent deep learning model to accurately predict the prognosis of pediatric glioma patients, which can assist clinicians in making precise treatment decisions and reducing patient risk.

Study Design: The study involved pediatric glioma patients from the Surveillance, Epidemiology, and End Results (SEER) Registry (2000-2018) and Tangdu Hospital in China (2010-2018) within specific time frames. For training, we selected two neural network-based algorithms (DeepSurv, neural multi-task logistic regression [N-MTLR]) and one ensemble learning-based algorithm (random survival forest [RSF]).

View Article and Find Full Text PDF
Article Synopsis
  • * The study analyzed gene expression data from the Cancer Genome Atlas (TCGA) to evaluate the relationship between tumor microenvironment scores and differentially expressed genes (DEGs), identifying 2346 DEGs that are linked to immune and cellular processes.
  • * Ten hub genes, including ACTB and IL-6, were found to have predictive value related to patient outcomes, such as overall and disease-free survival, emphasizing their potential significance in BC prognosis.
View Article and Find Full Text PDF

Aim: To assess and verify the effect of the gut microbiome on the susceptibility and complications of type 1 diabetes (T1D).

Materials And Methods: To achieve this aim, a two-sample and reverse Mendelian randomization (MR) analysis was conducted. In addition, an external validation study was performed using individual microbiome data of patients with T1D from the gutMEGA datasets and the National Clinical Research Center for Metabolic Diseases.

View Article and Find Full Text PDF

Aims: The aims of the present study were to assess the effects of lipid-lowering drugs [HMG-CoA reductase inhibitors, proprotein convertase subtilisin/kexin type 9 inhibitors, and Niemann-Pick C1-Like 1 (NPC1L1) inhibitors] on novel subtypes of adult-onset diabetes through a Mendelian randomisation study.

Materials And Methods: We first inferred causal associations between lipid-related traits [including high-density lipoprotein cholesterol, low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), apolipoproteins A-I, and apolipoproteins B] and novel subtypes of adult-onset diabetes. The expression quantitative trait loci of drug target genes for three classes of lipid-lowering drugs, as well as genetic variants within or nearby drug target genes associated with LDL-C, were then utilised as proxies for the exposure of lipid-lowering drugs.

View Article and Find Full Text PDF

After spinal cord injury (SCI), the accumulation of myelin debris can serve as proinflammatory agents, hindering axon regrowth and exacerbating damage. While astrocytes have been implicated in the phagocytosis of myelin debris, the impact of this process on the phenotypic transformation of astrocytes and their characteristics following SCI in rats is not well understood. Here, we demonstrated that the conditioned medium of myelin debris can trigger apoptosis in rat primary astrocytes in vitro.

View Article and Find Full Text PDF

After spinal cord injury (SCI), the accumulation of myelin debris at the lesion exacerbates cell death and hinders axonal regeneration. Transplanted bone marrow mesenchymal stem cells (BMSCs) have been proven to be beneficial for SCI repair, but they are susceptible to apoptosis. It remains unclear whether this apoptotic process is influenced by myelin debris.

View Article and Find Full Text PDF

A cerebral contrast-enhanced electrical impedance tomography perfusion method is developed for acute ischemic stroke during intravenous thrombolytic therapy. Several clinical contrast agents with stable impedance characteristics and high-conductivity contrast were screened experimentally as electrical impedance contrast agent candidates. The electrical impedance tomography perfusion method was tested on rabbits with focal cerebral infarction, and its capability for early detection was verified based on perfusion images.

View Article and Find Full Text PDF

Electrical impedance tomography (EIT) is a noninvasive and radiation-free imaging method. As a "soft-field" imaging technique, in EIT, the target signal in the center of the measured field is frequently swamped by the target signal at the edge, which restricts its further application. To alleviate this problem, this study presents an enhanced encoder-decoder (EED) method with an atrous spatial pyramid pooling (ASPP) module.

View Article and Find Full Text PDF

Context: Diabetes is a major health problem and metabolically unhealthy is an important risk factor.

Objective: To conduct the first nationally representative study on epidemiological data of metabolically unhealthy normal weight (MUNW) focused only on nondiabetic subjects and determine the predictive effect on diabetes in China.

Methods: A longitudinal study was conducted using data from the Rich Healthcare Group in China.

View Article and Find Full Text PDF

Following spinal cord injury (SCI), fibrotic scar inhibits axon regeneration and impairs neurological function recovery. It has been reported that T cell-derived interferon (IFN)-γ plays a pivotal role in promoting fibrotic scarring in neurodegenerative disease. However, the role of IFN-γ in fibrotic scar formation after SCI has not been declared.

View Article and Find Full Text PDF

Glycemic variability (GV) in some patients with type 1 diabetes (T1D) remains heterogeneous despite comparable clinical indicators, and whether other factors are involved is yet unknown. Metabolites in the serum indicate a broad effect of GV on cellular metabolism and therefore are more likely to indicate metabolic dysregulation associated with T1D. To compare the metabolomic profiles between high GV (GV-H, coefficient of variation (CV) of glucose ≥ 36%) and low GV (GV-L, CV < 36%) groups and to identify potential GV biomarkers, metabolomics profiling was carried out on serum samples from 17 patients with high GV, 16 matched (for age, sex, body mass index (BMI), diabetes duration, insulin dose, glycated hemoglobin (HbA1c), fasting, and 2 h postprandial C-peptide) patients with low GV (exploratory set), and another 21 (GV-H/GV-L: 11/10) matched patients (validation set).

View Article and Find Full Text PDF
Article Synopsis
  • * The application of deep learning in EIT image reconstruction has grown significantly, focusing on single network approaches, combining deep learning with traditional methods, and using hybrid networks.
  • * Future challenges in EIT image reconstruction involve optimizing datasets, improving network structures, and integrating multimodal deep learning, which could lead to enhanced diagnostic systems.
View Article and Find Full Text PDF

Electrical impedance tomography (EIT) is low-cost and noninvasive and has the potential for real-time imaging and bedside monitoring of brain injury. However, brain injury monitoring by EIT imaging suffers from image noise (IN) and resolution problems, causing blurred reconstructions. To address these problems, a least absolute shrinkage and selection operator model is built, and a fast iterative shrinkage-thresholding algorithm with continuation (FISTA-C) is proposed.

View Article and Find Full Text PDF

Aims: To investigate glycaemic variability (GV) patterns in patients with type 1 diabetes (T1D), type 2 diabetes (T2D), and latent autoimmune diabetes in adults (LADA).

Materials And Methods: A total of 842 subjects (510 T1D, 105 LADA, 227 T2D) were enrolled and underwent 1 week of continuous glucose monitoring (CGM). Clinical characteristics and CGM parameters were compared among T1D, LADA, and T2D.

View Article and Find Full Text PDF

Background: We aimed to explore the performance of detrended fluctuation function (DFF) in distinguishing patients with latent autoimmune diabetes in adults (LADA) from type 2 diabetes mellitus (T2DM) with glucose data derived from continuous glucose monitoring.

Methods: In total, 71 LADA and 152 T2DM patients were enrolled. Correlations between glucose parameters including time in range (TIR), mean glucose, standard deviation (SD), mean amplitude of glucose excursions (MAGE), coefficient of variation (CV), DFF and fasting and 2-hour postprandial C-peptide (FCP, 2hCP) were analyzed and compared.

View Article and Find Full Text PDF

Aims: The comorbidity of metabolic syndrome (MetS) and type 1 diabetes mellitus (T1DM) is an obstacle to glucose control in patients with T1DM. We compared glycemic profiles using continuous glucose monitoring (CGM) systems in patients with T1DM with or without MetS.

Methods: This was a multicenter cross-sectional study of patients with T1DM (N = 207) with or without MetS.

View Article and Find Full Text PDF

Objective: Regarding the effects and practical application of insulin pumps on patients with type 1 diabetes mellitus (T1DM), the real-world evidence is limited especially concerning the incidence of hypoglycemia. This study aimed to compare the efficacy of continuous subcutaneous insulin infusion (CSII) therapy with multiple daily injection (MDI) therapy on glycemic metrics evaluated by retrospective continuous glucose monitoring (CGM) in Chinese patients with T1DM.

Methods: In total, 362 T1DM Chinese patients from the outpatient department of the Second Xiangya Hospital, Central South University, who underwent intensive insulin therapy and used a retrospective CGM system were included in this retrospective cross-sectional study.

View Article and Find Full Text PDF

Aims: There is limited evidence that evaluates the glycemic control of type 1 diabetes mellitus (T1DM) during the Chinese New Year public holiday in China. The Chinese New Year public holiday represents various challenges to glycemic control, especially in T1DM patients, in China. We aimed to assess the effect of the Chinese New Year public holiday on several glucose metrics using flash glucose monitoring (FGM) in patients with T1DM.

View Article and Find Full Text PDF

Synopsis of recent research by authors named "Jianan Ye"

  • - Jianan Ye's research primarily focuses on the intersection of neural injury, cancer prognosis, and metabolic diseases, highlighting the exploration of factors such as immune responses, microbiome influences, and the application of advanced technologies such as deep learning for medical predictions.
  • - Recent findings demonstrate that Galectin-3 plays a critical role in fibrotic scarring post spinal cord injury, and insights into the gut microbiome significantly affect the susceptibility and complications related to type 1 diabetes.
  • - Ye's work includes developing machine learning models for predicting survival in pediatric glioma patients, addressing the impact of lipid-lowering drugs on diabetes subtypes, and enhancing early detection methods for acute ischemic stroke, showcasing a commitment to improving patient outcomes through innovative approaches.