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19 pages, 5458 KiB  
Article
Differentially Expressed Genes in Rat Brain Regions with Different Degrees of Ischemic Damage
by Ivan B. Filippenkov, Yana Yu. Shpetko, Vasily V. Stavchansky, Alina E. Denisova, Vadim V. Yuzhakov, Natalia K. Fomina, Leonid V. Gubsky, Svetlana A. Limborska and Lyudmila V. Dergunova
Int. J. Mol. Sci. 2025, 26(5), 2347; https://doi.org/10.3390/ijms26052347 (registering DOI) - 6 Mar 2025
Abstract
Ischemic stroke is a multifactorial disease that leads to brain tissue damage and severe neurological deficit. Transient middle cerebral artery occlusion (tMCAO) models are actively used for the molecular, genetic study of stroke. Previously, using high-throughput RNA sequencing (RNA-Seq), we revealed 3774 differentially [...] Read more.
Ischemic stroke is a multifactorial disease that leads to brain tissue damage and severe neurological deficit. Transient middle cerebral artery occlusion (tMCAO) models are actively used for the molecular, genetic study of stroke. Previously, using high-throughput RNA sequencing (RNA-Seq), we revealed 3774 differentially expressed genes (DEGs) in the penumbra-associated region of the frontal cortex (FC) of rats 24 h after applying the tMCAO model. Here, we studied the gene expression pattern in the striatum that contained an ischemic focus. Striatum samples were obtained from the same rats from which we previously obtained FC samples. Therefore, we compared DEG profiles between two rat brain tissues 24 h after tMCAO. Tissues were selected based on magnetic resonance imaging (MRI) and histological examination (HE) data. As a result, 4409 DEGs were identified 24 h after tMCAO in striatum. Among them, 2609 DEGs were overlapped in the striatum and FC, whereas more than one thousand DEGs were specific for each studied tissue. Furthermore, 54 DEGs exhibited opposite changes at the mRNA level in the two brain tissues after tMCAO. Thus, the spatial regulation of the ischemic process in the ipsilateral hemisphere of rat brain at the transcriptome level was revealed. We believe that the targeted adjustment of the genome responses identified can be the key for the induction of regeneration processes in brain cells after stroke. Full article
(This article belongs to the Special Issue New Insights of Biomarkers in Neurodegenerative Diseases)
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Figure 1
<p>The morphometry zones of Nissl-stained neurons and RNA-Seq analysis of the effect of ischemia on the transcriptome of the striatum of rats 24 h after tMCAO. (<b>a</b>) A serial coronal brain section at a level of +1.0 mm from the bregma. (<b>b</b>) High-magnification images of the striatum of the right (ipsilateral) hemisphere at a level of +0.48 mm from the bregma. The zone of the penumbra and normal tissues is shown by “1”, whereas the zone of necrotic tissue is shown by “2”. (<b>c</b>) RNA-Seq results for IR-s vs. SO-s. The numbers in the diagram sectors indicate the quantity of DEGs. (<b>d</b>) Volcano plots show a comparison of the gene distribution between the IR-s and SO-s groups. Upregulated and downregulated DEGs are represented as red and green dots, respectively (fold change &gt; 1.50; <span class="html-italic">Padj</span> &lt; 0.05). Non-differentially expressed genes (non-DEGs) are represented as blue dots (fold change ≤ 1.50; <span class="html-italic">Padj</span> ≥ 0.05). Each group includes three rats.</p>
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<p>The RNA-Seq analysis of ischemia-induced gene expression changes in the striatum and FC 24 h after tMCAO. (<b>a</b>–<b>c</b>) Venn diagrams represent results obtained in comparisons between IR-s and SO-s in the striatum and IR-f and SO-f in the FC. All (<b>a</b>), upregulated (<b>b</b>), and downregulated (<b>c</b>) DEGs are shown for comparison. (<b>d</b>–<b>f</b>) The top ten genes among the 2609 overlapped DEGs (<b>d</b>), 1800 DEGs specific for IR-s vs. SO-s (<b>e</b>), and 1165 DEGs specific for IR-s vs. SO-s (<b>f</b>) are shown in the Venn diagram (<b>a</b>), respectively. DEGs were chosen with the greatest fold changes in the IR-s vs. SO-s (<b>d</b>,<b>e</b>) and IR-f vs. SO-f (<b>f</b>) comparison groups. (<b>g</b>) Hierarchical cluster analysis of all DEGs in IR-s vs. SO-s and IR-f vs. SO-f, where each row represents a DEG; n = 3 per group. Only those genes with a cut-off &gt;1.5 and <span class="html-italic">Padj</span> &lt; 0.05 were selected as significant results. The data are presented as the mean ± standard error of the mean (SEM).</p>
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<p>DEGs exhibited opposite changes at the mRNA level in the two brain tissues 24 h after tMCAO. (<b>a</b>,<b>b</b>) The Venn diagrams represent results obtained in comparisons between the upregulated DEGs in the IR-s and SO-s and downregulated DEGs in the IR-f and SO-f groups (<b>a</b>), as well as in comparisons between the downregulated DEGs in the IR-s and SO-s and upregulated DEGs in the IR-f and SO-f groups in the striatum (<b>b</b>). The cut-off for gene expression changes was 1.50-fold. Only genes with <span class="html-italic">Padj</span> &lt; 0.05 and a cut-off &gt; 1.5 were selected for analysis. (<b>c</b>) The top ten overlapped DEGs that changed expression in the opposite direction in the IR-s vs. SO-s and IR-f vs. SO-f pairwise comparisons. Data are presented as the mean ± SEM.</p>
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<p>Overlapped and unique signaling KEGG pathways associated with DEGs in the striatum and FC of rats 24 h after tMCAO. The pathway enrichment analysis of DEGs was carried out according to DAVID (2021 Update). (<b>a</b>) A schematic comparison of DEG-related annotations in the IR-s vs. SO-s and IR-f vs. SO-f pairwise comparisons using a Venn diagram. The number of annotations is indicated using numbers on the chart segments. (<b>b</b>–<b>d</b>) The most significant pathways among the 120 overlapped pathways (<b>b</b>), among 31 pathways that were unique for IR-s vs. SO-s (<b>c</b>), and among 14 pathways that were unique for IR-f vs. SO-f (<b>d</b>). The number of upregulated (green) and downregulated (red) DEGs in the two pairwise comparisons—IR-s vs. SO-s (<b>b</b>,<b>c</b>) and IA-s vs. IR-s (<b>b</b>,<b>d</b>)—as well as the corresponding <span class="html-italic">Padj</span> values are presented. Only DEGs and pathways with <span class="html-italic">Padj</span> &lt; 0.05 were selected for analysis, with n = 3 animals per group. <span class="html-italic">Padj</span> ≥ 0.05 is enclosed in the gray background.</p>
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<p>Gene regulatory networks demonstrating common and specific effects of ischemia on striatum and FC cells 24 h after tMCAO. (<b>a</b>–<b>c</b>) The genes that overlapped between IR-s and SO-s and IR-f and SO-f (<b>a</b>), were DEGs in IR-s vs. SO-s but non-DEGs in IR-f vs. SO-f (<b>b</b>), and were DEGs in IR-f vs. SO-f but non-DEGs in IR-s vs. SO-s (<b>c</b>) are shown. Furthermore, each network includes genes that participate in the maximum number of pathways: common (PC1) (<b>a</b>) and unique to the striatum (PC2) (<b>b</b>) and FC (PC3) (<b>c</b>). The genes in each network are arranged in two rings. Each ring includes the same genes, but the color in the inner ring identifies DEGs in IR-s vs. SO-s and the color in the outer ring determines the DEGs in IR-f vs. SO-f. Lines connecting genes and PCs indicate the participation of the protein products of the genes in the functioning of the pathway of the PCs. DAVID v2021 software was used for annotation of DEG functions based on the KEGG database. The network was constructed with the help of Cytoscape 3.9.2 program.</p>
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<p>The involvement of genes that changed their mRNA level in the opposite direction in the IR-s vs. SO-s and IR-f vs. SO-f pairwise comparisons in the presentation of the pathways of PC1–PC3 with minimal <span class="html-italic">Padj</span>-values. The pathway enrichment analysis of DEGs was carried out according to DAVID (2021 update). Only DEGs and pathways with <span class="html-italic">Padj</span> &lt; 0.05 were selected for analysis, with n = 3 animals per group. The network was constructed using Cytoscape 3.9.2 (Institute for Systems Biology, Seattle, WA, USA).</p>
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12 pages, 1011 KiB  
Article
Prognostic Impact of Malnutrition Evaluated via Bioelectrical Impedance Vector Analysis (BIVA) in Acute Ischemic Stroke: Findings from an Inverse Probability Weighting Analysis
by Simone Dal Bello, Laura Ceccarelli, Yan Tereshko, Gian Luigi Gigli, Lucio D’Anna, Mariarosaria Valente and Giovanni Merlino
Nutrients 2025, 17(5), 919; https://doi.org/10.3390/nu17050919 (registering DOI) - 6 Mar 2025
Abstract
Background. The association between malnutrition and poor outcomes in stroke patients has, to date, been evaluated using composite scores derived from laboratory measurements. However, Bioelectrical Impedance Analysis (BIA) and its advanced application, Bioelectrical Impedance Vector Analysis (BIVA), offer a non-invasive, cost-efficient, and rapid [...] Read more.
Background. The association between malnutrition and poor outcomes in stroke patients has, to date, been evaluated using composite scores derived from laboratory measurements. However, Bioelectrical Impedance Analysis (BIA) and its advanced application, Bioelectrical Impedance Vector Analysis (BIVA), offer a non-invasive, cost-efficient, and rapid alternative. These methods enable precise assessment of body composition, nutritional status, and hydration levels, making them valuable tools in the clinical evaluation of stroke patients. Objective. This study aimed to compare the ordinal distribution of modified Rankin Scale (mRS) scores at 90 days following an acute ischemic stroke, stratifying patients based on their nutritional status at the time of Stroke Unit admission, as determined by the Bioelectrical Impedance Vector Analysis (BIVA) malnutrition parameter. Methods. We conducted a single-centre prospective observational study on all consecutive patients admitted for acute ischemic stroke to our Stroke Unit between 1 April 2024, and 30 September 2024. We applied the IPW (Inverse Probability Weighting) statistical technique and ordinal logistic regression to compare mRS scores in malnourished and non-malnourished patients. Results. Overall, our study included 195 patients with ischemic stroke assessed using BIVA. Of these, 37 patients (19%) were malnourished. After IPW, we found that malnourished patients had significantly lower rates of favorable 90-day functional outcomes (cOR 3.34, 95% CI 1.74–6.41; p = 0.001). Even after accounting for relevant covariates, malnutrition remained an independent predictor of unfavorable outcomes (acOR 2.79, 95% CI 1.37–5.70; p = 0.005), along with NIHSS score at admission (acOR 1.19, 95% CI 1.11–1.28; p < 0.001), intravenous thrombolysis (acOR 0.28, 95% CI 0.15–0.52; p < 0.001), absolute lymphocyte count (cOR 1.01, 95% CI 1.00–1.02; p = 0.027), and albumin concentration (cOR 0.82, 95% CI 0.75–0.89; p < 0.001). Conclusions. Malnutrition, assessed through Bioelectrical Impedance Vector Analysis (BIVA) at the time of admission to the Stroke Unit, is associated with worse clinical outcomes at 90 days following the ischemic cerebrovascular event. Full article
(This article belongs to the Section Nutrition and Neuro Sciences)
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<p>Flowchart of patients admitted to the Stroke Unit during the study period, assessed using the BIA method, and included or excluded from the study (Legend: ICU = intensive care unit; BIA = Bioelectrical Impedance Analysis; TIA = transient ischemic attack; CVT = cerebral venous thrombosis).</p>
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<p>SMDs before and after weighting.</p>
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<p>Distribution of modified Rankin Scale (mRS) scores at 90 days post-cerebrovascular event in patients with and without malnutrition.</p>
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42 pages, 31756 KiB  
Article
Models to Identify Small Brain White Matter Hyperintensity Lesions
by Darwin Castillo, María José Rodríguez-Álvarez, René Samaniego and Vasudevan Lakshminarayanan
Appl. Sci. 2025, 15(5), 2830; https://doi.org/10.3390/app15052830 (registering DOI) - 6 Mar 2025
Abstract
According to the World Health Organization (WHO), peripheral and central neurological disorders affect approximately one billion people worldwide. Ischemic stroke and Alzheimer’s Disease and other dementias are the second and fifth leading causes of death, respectively. In this context, detecting and classifying brain [...] Read more.
According to the World Health Organization (WHO), peripheral and central neurological disorders affect approximately one billion people worldwide. Ischemic stroke and Alzheimer’s Disease and other dementias are the second and fifth leading causes of death, respectively. In this context, detecting and classifying brain lesions constitute a critical area of research in medical image processing, significantly impacting clinical practice. Traditional lesion detection, segmentation, and feature extraction methods are time-consuming and observer-dependent. In this sense, research in the machine and deep learning methods applied to medical image processing constitute one of the crucial tools for automatically learning hierarchical features to get better accuracy, quick diagnosis, treatment, and prognosis of diseases. This project aims to develop and implement deep learning models for detecting and classifying small brain White Matter hyperintensities (WMH) lesions in magnetic resonance images (MRI), specifically lesions concerning ischemic and demyelination diseases. The methods applied were the UNet and Segmenting Anything model (SAM) for segmentation, while YOLOV8 and Detectron2 (based on MaskRCNN) were also applied to detect and classify the lesions. Experimental results show a Dice coefficient (DSC) of 0.94, 0.50, 0.241, and 0.88 for segmentation of WMH lesions using the UNet, SAM, YOLOv8, and Detectron2, respectively. The Detectron2 model demonstrated an accuracy of 0.94 in detecting and 0.98 in classifying lesions, including small lesions where other models often fail. The methods developed give an outline for the detection, segmentation, and classification of small and irregular morphology brain lesions and could significantly aid clinical diagnostics, providing reliable support for physicians and improving patient outcomes. Full article
(This article belongs to the Special Issue MR-Based Neuroimaging)
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<p>The flowchart of the proposed methodology: (<b>a</b>) the dataset, acquisition, number of images, preprocessing steps, and data augmentation methods applied to the dataset; (<b>b</b>) the DL methods, feature extraction, segmentation using UNET and SAM, and classification and detection using YOLOv8 and Detectron2.</p>
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<p>Some examples of images used in the dataset for training.</p>
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<p>Slices from the volume of a real patient with two lesions: ischemia (red circles) and demyelination (yellow circles). It is seen that the lesions are not continuous between slices. The lesions change in each slice, as well as their size, shape, and location. The different colors refer to the two types of lesions in the slice.</p>
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<p>Examples of images before and after the noise and artifact reduction.</p>
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<p>Examples of data augmentation. Some artifacts and noise are also generated.</p>
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<p>Examples of data augmentation using SNGAN at 275 epochs. The synthetic images do not show good confidence in the lesions of ischemia and demyelination diseases.</p>
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<p>The UNet model for segmenting brain lesions related to ischemia and demyelination.</p>
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<p>Segment Anything Model (SAM) architecture with the data and trained models used in this project. The model consists of an image encoder to extract image embeddings, a prompt encoder, and a mask decoder to predict segmentation masks using the image and prompt embeddings. This figure was adapted from [<a href="#B57-applsci-15-02830" class="html-bibr">57</a>].</p>
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<p>Architecture overview YOLOv8 applied in this project. The input image passes to the training process between the Bounding Boxes and Class Probability to give the input image with the bounding boxes and the probability of detection. This figure was adapted from [<a href="#B60-applsci-15-02830" class="html-bibr">60</a>].</p>
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<p>Architecture overview of Detectron2—R50-FPN applied in this project. The input image passes to the Backbone Network, Region Proposal Network, and Box Head with Fast R-CNN for object identification.</p>
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<p>Correlogram of lesions distribution and characteristics. (<b>a</b>) Spatial distribution of lesions, showing their tendency to occur in specific brain regions. In (<b>b</b>), width and (<b>c</b>) height are shown an analysis of lesion dimensions, indicating that the majority are small, with sizes below 0.2 pixels. It is observed that despite the localization trends, there is no strong correlation pattern between lesion occurrence and size.</p>
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<p>Training and validation loss curves from UNet model. The training and validation loss curves converge after 100 epochs and stabilize around 0.35, indicating that the model has learned most of the features and is refining its predictions.</p>
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<p>Examples of the lesion prediction using the UNet model. (<b>a</b>) Original images with their corresponding (<b>b</b>) masks and (<b>c</b>) prediction results.</p>
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<p>Examples of the lesion prediction using the UNet model. (<b>a</b>) Original images with their corresponding (<b>b</b>) masks and (<b>c</b>) prediction results.</p>
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<p>The training and validation loss of the SAM model with each of the trained models: (<b>a</b>) vit-base, (<b>b</b>) vit-large, and (<b>c</b>) vit-huge.</p>
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<p>Some examples of segmentation lesion predictions using the SAM model with 25 epochs of training and different processors. (<b>a</b>) MR Image. (<b>b</b>) Ground truth mask. (<b>c</b>) Predicted mask. The orange areas correspond to the WMH lesions.</p>
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<p>Some examples of segmentation lesion predictions using the SAM model with 25 epochs of training and different processors. (<b>a</b>) MR Image. (<b>b</b>) Ground truth mask. (<b>c</b>) Predicted mask. The orange areas correspond to the WMH lesions.</p>
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<p>Confusion matrix of the YOLO detection model using the pre-trained “yolov8n-seg.pt” model. On the right side, and below it, are some examples of the classification of the lesions.</p>
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<p>The experimental results with the pre-trained “yolov8n-seg.pt” model. The graphs are concerned with the trend of training and validation loss scores over the epochs and their corresponding precision and recall metrics related to bounding box prediction, segmentation, and classification of lesions. The values of the loss or metric are plotted on the <span class="html-italic">y</span>-axis, and the epochs are represented on the <span class="html-italic">x</span>-axis.</p>
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<p>Examples of detection of the lesions. The original image without detecting a lesion is shown on the left side, and the lesion prediction with model Detectron2 is shown on the right. In the center column, the classification done by the radiologist expert is shown; the “yellow” color refers to demyelination lesions and the violet color refers to ischemia lesions.</p>
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<p>Graphs of loss and accuracy metrics for the lesion detection model. (<b>a</b>) Loss curves over training iterations illustrate the optimization of different loss components, including classification, bounding box regression, mask loss, and total loss. (<b>b</b>) Accuracy trends over training iterations, depicting the performance of the Fast R-CNN classifier and Mask R-CNN segmentation accuracy. The stability and convergence of these metrics indicate the model’s learning progress and effectiveness.</p>
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<p>Graphs of false positive and false negative rates over training iterations. The false negative rate (orange) decreases steadily, indicating improved sensitivity. The false positive rate (blue) remains relatively stable, suggesting consistent precision in lesion detection. These trends highlight the model’s learning process and ability to refine segmentation accuracy over time.</p>
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<p>Correlogram of the balanced number of instances (lesions) used for the YOLO model to detect and classify ischemia and demyelination. The bar plots at the top represent the distribution of lesion instances across classes. The scatter plots (<span class="html-italic">x</span>-<span class="html-italic">y</span> plane) illustrate the location and distribution of the lesions (scattered points) within the image, as well as the correlation between lesion height and width, providing insights into lesion size variability. This visualization includes information on the lesion characteristics.</p>
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<p>Experimental analysis results, including training and validation metrics, precision–recall curves, and mean average precision (mAP) metrics. These graphs illustrate the model’s learning progression, performance across evaluation metrics, and effectiveness in detecting and segmenting lesions.</p>
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<p>Confusion matrix of the YOLO classification to distinguish between ischemic and demyelination lesions using the pre-trained “yolov8n-seg.pt” model. The images on the top right side and lower sections are examples of the classification of the lesions with their corresponding confidence scores in predicting each lesion type. These results highlight the model’s effectiveness in lesion classification while revealing potential challenges in differentiating lesions with similar radiological characteristics.</p>
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<p>Examples of detection and classification of the lesions. The image without a classification is shown on the left side, and the classification prediction with model Detectron2 is shown on the right. The labels “ische” refer to ischemia, and “demy” refer to demyelination diseases, respectively. In the center column, the classification performed by the radiologist expert is shown; the “yellow” color refers to demyelination lesions and the violet color refers to ischemia lesions.</p>
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<p>Training performance metrics for classifying and detecting lesions using the Detectron2 model. (<b>a</b>) Loss curves over iterations, including classification loss (orange), bounding box regression loss (blue), mask loss (green), and total loss (brown), indicate the behavior convergence of the model. (<b>b</b>) Accuracy metrics over iterations, illustrating the classification performance of the Fast R-CNN and Mask R-CNN models. The increasing accuracy trends suggest improved learning stability and effectiveness in lesion detection and classification between ischemia and demyelination.</p>
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<p>Graphs of the metrics concerning the model’s false positive and false negative rates. The graph illustrates how the model refines its predictions over time, with the false negative rate (red) decreasing as the model improves sensitivity and correctly identifies more lesions. Similarly, the false positive rate (yellow) stabilizes, indicating enhanced precision in lesion classification.</p>
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<p>Graphs of performance metrics of the classification model over training iterations. (<b>a</b>) Accuracy, (<b>b</b>) recall, (<b>c</b>) precision, and (<b>d</b>) F1_score values of the classification model. All values maintain an average value above 0.9, indicating high classification reliability.</p>
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<p>Visual comparison of the detection and classification of the lesions using the Detectron2 and YOLOv8 models against the radiologist expert. In the Detectron2 Model, a threshold for lesion detection of 0.8 and 0.5 is used. In the YOLOv8 model, the threshold used for lesion detection is 0.2.</p>
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<p>Visual comparison of the detection and classification of the lesions using the Detectron2 and YOLOv8 models against the radiologist expert. In the Detectron2 Model, a threshold for lesion detection of 0.8 and 0.5 is used. In the YOLOv8 model, the threshold used for lesion detection is 0.2.</p>
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<p>Visual comparison of the detection and classification of the lesions using the Detectron2 and threshold for lesion detection of 0.8 and 0.5. The threshold level allows for a change in the sensitivity of the detection of lesions.</p>
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<p>Comparative analysis of detection and classification performance metrics of accuracy, precision, recall, F1-score, sensitivity, and specificity for detection and classification of the lesions across three evaluations: criteria by Experts (blue bars), Detectron2 (orange bars), and YOLOv8 (green bars). The graph highlights the strong performance of the Detectron2 model comparable to the reliability of the expert’s criteria and the lower performance of YOLOv8, particularly for recall and sensitivity.</p>
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<p>ROC curve comparison for detection and classification of the lesions using the criteria by Experts (AUC = 0.976), Detectron2 (AUC = 0.929), and YOLOv8 (AUC = 0.524). The curve illustrates the trade-off between true positive rate (sensitivity) and false positive rate, highlighting the good performance of Detectron2, comparable to experts, while YOLOv8 shows limited discriminatory power for lesion classification. The dashed diagonal line represents a random classifier (AUC = 0.5).</p>
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12 pages, 1847 KiB  
Article
Blood Biomarkers Reflect Dementia Symptoms and Are Influenced by Cerebrovascular Lesions
by Taizen Nakase, Yasuko Tatewaki, Yumi Takano, Shuko Nomura, Hae Woon Baek and Yasuyuki Taki
Int. J. Mol. Sci. 2025, 26(5), 2325; https://doi.org/10.3390/ijms26052325 - 5 Mar 2025
Abstract
Dementia blood biomarkers are becoming increasingly important. Various factors, such as ischemic lesions and inflammation, can influence the pathomechanism of dementia. We aimed to evaluate the effects of past stroke lesions on blood biomarkers (BMs). Following approval from the institutional ethics committee, patients [...] Read more.
Dementia blood biomarkers are becoming increasingly important. Various factors, such as ischemic lesions and inflammation, can influence the pathomechanism of dementia. We aimed to evaluate the effects of past stroke lesions on blood biomarkers (BMs). Following approval from the institutional ethics committee, patients who were admitted to the memory clinic and were consented to written documents were enrolled (n = 111, average [standard deviation] age: 74.5 [9.1] years-old). Brain magnetic resonance imaging, cognitive function, and neuropsychological symptoms were analyzed. The amyloid-β 42 (Aβ42)/Aβ40 ratio, phosphorylated tau181 (p-tau181), glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), and Aβ42/p-tau181 ratio were assessed as plasma BMs. The patients were diagnosed with Alzheimer’s disease (n = 45), mild cognitive impairment (n = 56), depression (n = 8), and subjective cognitive impairment (n = 4). Bivariate analysis exhibited that all measured BM indicators were significantly associated with cognitive decline in patients without past stroke lesions. Whereas the patients with stroke lesions presented a significant association only between GFAP and cognitive decline (p = 0.0011). Multiple regression analysis showed that NfL significantly correlated with cognitive decline only in patients without stroke lesions (r = 0.4988, p = 0.0003) and with delusion only in those with stroke lesions (r = 0.5492, p = 0.0121). Past stroke lesions should be addressed in the assessment of the correlation between blood biomarkers and cognitive decline in dementia patients. Full article
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Graphical abstract

Graphical abstract
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<p>Representative amyloid PET images of patients who were amyloid-positive (top two images) and amyloid-negative (bottom two images) are depicted in (<b>A</b>). (<b>B</b>) presents ROC curves for the Aβ42/Aβ40 ratio (green), Aβ42/p-tau181 ratio (red), and plasma GFAP level (blue). The X and Y axes represent 1-specificity and sensitivity, respectively.</p>
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<p>Correlation between plasma biomarker levels and cognitive decline in patients with and without stroke lesions. Scatter graphs of patients without stroke lesions (left) and with stroke lesions (right) exhibited the correlation of blood biomarkers of (<b>A</b>) p-tau181, (<b>B</b>) NfL, (<b>C</b>) GFAP, (<b>D</b>) Aβ42/Aβ40, and (<b>E</b>) Aβ42/p-tau181 with MMSE (blue) or ADAS (red) scores. Linear lines indicate the correlation lines. The formulae for each line are as follows: (<b>A</b>) y = 97.76 − 2.046x(MMSE), <span class="html-italic">p</span> = 0.0005, and y = 36.65 + 1.009x(ADAS), <span class="html-italic">p</span> = 0.0340 in patients without stroke, and y = 91.21 − 1.676x(MMSE), <span class="html-italic">p</span> = 0.2957 and y = 36.58 + 1.106x(ADAS), <span class="html-italic">p</span> = 0.3080 in patients with stroke; (<b>B</b>) y = 54.46 − 1.047x(MMSE), <span class="html-italic">p</span> = 0.0186 and y = 16.57 + 0.873x(ADAS), <span class="html-italic">p</span> = 0.0005 in patients without stroke, and y = 29.73 + 0.577x(MMSE), <span class="html-italic">p</span> = 0.7626 and y = 25.14 + 1.374x(ADAS), <span class="html-italic">p</span> = 0.2799 in patients with stroke; (<b>C</b>) y = 337.8 − 5.352x(MMSE), <span class="html-italic">p</span> = 0.0334 and y = 142.7 + 5.479x(ADAS), <span class="html-italic">p</span> = 0.0048 in patients without stroke, and y = 346.1 − 6.183x(MMSE), <span class="html-italic">p</span> = 0.2928 and y = 46.9 + 11.4x(ADAS), <span class="html-italic">p</span> = 0.0011 in patients with stroke; (<b>D</b>) y = 0.029 + 0.0096x(MMSE), <span class="html-italic">p</span> = 0.0089 and y = 0.061 − 0.001x(ADAS), <span class="html-italic">p</span> = 0.0047 in patients without stroke, and y = 0.0294 + 0.001x(MMSE), <span class="html-italic">p</span> = 0.2159 and y = 0.059 − 0.0004x(ADAS), <span class="html-italic">p</span> = 0.4227 in patients with stroke; and (<b>E</b>) y = −0.088 + 0.010x(MMSE), <span class="html-italic">p</span> &lt; 0.0001 and y = 0.218 − 0.005x(ADAS), <span class="html-italic">p</span> = 0.0017 in patients without stroke, and y = 0.029 + 0.006x(MMSE), <span class="html-italic">p</span> = 0.2096 and y = 0.223 − 0.004x(ADAS), <span class="html-italic">p</span> = 0.1809 in patients with stroke.</p>
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<p>Flow chart of patients’ selection.</p>
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13 pages, 754 KiB  
Article
Assessment of the Psychometric Properties of the Tampa Scale of Kinesiophobia (TSK) Questionnaire in Poland Based on Patients with Type 2 Diabetes Complicated by Stroke
by Ewelina Bąk, Wojciech Kustrzycki, Robert Skalik and Sylwia Krzemińska
J. Clin. Med. 2025, 14(5), 1751; https://doi.org/10.3390/jcm14051751 - 5 Mar 2025
Abstract
Background/Objectives: Kinesiophobia, or the fear of movement, is a significant problem in the rehabilitation of patients after a stroke, especially in individuals with diabetes, who have an increased risk of health complications. The aim of the study was to validate the Tampa Scale [...] Read more.
Background/Objectives: Kinesiophobia, or the fear of movement, is a significant problem in the rehabilitation of patients after a stroke, especially in individuals with diabetes, who have an increased risk of health complications. The aim of the study was to validate the Tampa Scale for Kinesiophobia (TSK) for assessing kinesiophobia in the context of patients with diabetes complicated by stroke to ensure its adequacy and reliability in this specific group of patients. Methods: After considering exclusion criteria, 166 patients with type 2 diabetes after ischemic stroke, hospitalized in the neurological rehabilitation ward, were included in the analysis. A survey using the TSK was conducted in the study group. A reliability analysis of the questionnaire was conducted, and then exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to disclose the number of factors that characterize the study group. Results: The Cronbach’s alpha value for the entire scale is 0.875. The value for all the questions on the scale was also above 0.86, so they are considered reliable. Removing any question does not increase the value of Cronbach’s alpha or Guttman index. Based on the scree plot, two factors were identified. The first factor includes 12 items and forms a physical factor, while the second factor includes 5 items and forms a psychological factor. The fit of the two-factor model was checked using confirmatory factor analysis. The final two-factor model has an acceptable fit. All the factor loadings are statistically significant. The factor loadings range from 0.262 to 0.729 for the physical factor and from 0.543 to 0.822 for the psychological factor. Conclusions: The TSK is a reliable and valid tool for assessing the level of kinesiophobia in a group of patients with type 2 diabetes complicated by stroke. The results of the study using this tool may contribute to the development of more effective therapeutic strategies that take into account the specific physical and psychological needs of this group of patients. Full article
(This article belongs to the Special Issue Clinical Perspectives in Stroke Rehabilitation)
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<p>Recruitment process.</p>
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<p>Scree plot for exploratory factor analysis.</p>
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<p>Graphical representation of factor loadings.</p>
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30 pages, 1571 KiB  
Review
Omentin—General Overview of Its Role in Obesity, Metabolic Syndrome and Other Diseases; Problem of Current Research State
by Hubert Mateusz Biegański, Krzysztof Maksymilian Dąbrowski and Anna Różańska-Walędziak
Biomedicines 2025, 13(3), 632; https://doi.org/10.3390/biomedicines13030632 - 5 Mar 2025
Abstract
Background: Omentin (omentin-1, intelectin-1, ITLN-1) is an adipokine considered to be a novel substance. Many chronic, inflammatory, or civilization diseases are linked to obesity, in which omentin plays a significant role. Methods: MEDLINE and SCOPUS databases were searched using the keywords “omentin” [...] Read more.
Background: Omentin (omentin-1, intelectin-1, ITLN-1) is an adipokine considered to be a novel substance. Many chronic, inflammatory, or civilization diseases are linked to obesity, in which omentin plays a significant role. Methods: MEDLINE and SCOPUS databases were searched using the keywords “omentin” or “intelectin-1”. Then the most recent articles providing new perspectives on the matter and the most important studies, which revealed crucial insight, were selected to summarize the current knowledge on the role of omentin in a literature review. Results and Conclusions: The valid role of this adipokine is evident in the course of metabolic syndrome. In most cases, elevated omentin expression is correlated with the better course of diseases, including: type 2 diabetes mellitus, polycystic ovary syndrome, rheumatoid arthritis, metabolic dysfunction-associated steatotic liver disease, Crohn’s disease, ulcerative colitis, atherosclerosis, or ischemic stroke, for some of which it can be a better marker than the currently used ones. However, results of omentin studies are not completely one-sided. It was proven to participate in the development of asthma and atopic dermatitis and to have different concentration dynamics in various types of tumors. All of omentin’s effects and properties make it an attractive subject of research, considering still unexplored inflammation mechanisms, in which it may play an important role. Omentin was proven to prevent osteoarthritis, hepatocirrhosis, and atherosclerosis in mouse models. All of the above places omentin among potential therapeutic products, and not only as a biomarker. However, the main problems with the omentin’s research state are the lack of standardization, which causes many contradictions and disagreements in this field. Full article
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<p>Brief summary of conditions, diseases, and prognosis correlated with low plasma omentin’s levels.</p>
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<p>Summary of selected important functions of omentin. T2DM—type 2 diabetes myelitis; UC—ulcerative colitis; CD—Crohn’s disease; BMI—body’s mass index; CRP—C-reactive protein; TNF-α—tumor necrosis factor alpha; IFN-γ—interferon gamma; MMPs matrix metalloproteinases; OC—ovarian cancer; NO—nitric oxide; LDL—low-density lipoprotein; MASLD—metabolic dysfunction-associated steatotic liver disease.</p>
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9 pages, 1953 KiB  
Case Report
Chronic Central Nervous System Graft-Versus-Host Disease to Unravel Progressive Visual Loss and Ischemic Stroke Recurrence Post-Allogeneic Hematopoietic Stem Cell Transplant: A Case Report
by Francesco Crescenzo, Alessandra Danese, Francesco Dall’Ora and Michelangelo Turazzini
Int. J. Mol. Sci. 2025, 26(5), 2289; https://doi.org/10.3390/ijms26052289 - 4 Mar 2025
Abstract
Chronic graft-versus-host disease (cGVHD) is a prognostically negative event following hematopoietic stem cell transplant (HSCT). While cGVHD mainly affects the muscles, skin, oral mucosa, eyes, lungs, gastrointestinal tract, and liver, central nervous system (CNS) involvement remains possible and, moreover, is rare when it [...] Read more.
Chronic graft-versus-host disease (cGVHD) is a prognostically negative event following hematopoietic stem cell transplant (HSCT). While cGVHD mainly affects the muscles, skin, oral mucosa, eyes, lungs, gastrointestinal tract, and liver, central nervous system (CNS) involvement remains possible and, moreover, is rare when it occurs isolated. CNS-cGVHD can manifest with a wide spectrum of CNS disorders, including cerebrovascular diseases, autoimmune demyelinating diseases, and immune-mediated encephalitis. We present a case of 65-year-old man previously treated with HSCT presenting with progressive cerebrovascular disorder and optic neuropathy without any clear alternative causal processes except for immune-mediated CNS microangiopathy in the context of possible CNS-cGVHD, along with suggestive imaging and instrumental and laboratory findings. Starting one year after HSCT for acute myeloid leukemia, when the first cerebral ischemic event occurred and was then associated with a reduction in visual acuity, an extensive diagnostic work-up had remained inconclusive over many years, leading us to the hypothesis of CNS-cGVHD and, therefore, to the start of immunosuppressive therapy. Our experience highlighted not ignoring the possibility of cGVHD as the underlying mechanism of CNS disorder, even in the absence of other systemic presentations, once more common etiologies of CNS pathological processes have been ruled out. Full article
(This article belongs to the Special Issue New Insights of Biomarkers in Neurodegenerative Diseases)
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<p>Timeline of the main patient’s clinical events and related therapy. The worsening of visual acuity and the accrual of brain lesion load are represented by arrows in red and blue, respectively.* Idarucibin and cytarabine (4 cycles of induction and consolidation); ** idarucibin, cytarabine, and fludarabine (3 cycles of induction and consolidation); § busulfan 220 mg/day for 4 days (day −6 to day −3), fludarabine 70 mg/day for 4 days (day −6 to day −3), anti-thymocyte globulin 200 and 400 mg every other day for 4 days (day −4 to day −1); # methotrexate (day +1, day +3, day +6) and cyclosporine (for 4 months).</p>
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<p>Exemplification of the evolution of brain ischemic damage over time. Axial fluid-attenuated inversion recovery (FLAIR) MRI images showing the progressive accumulation of subcortical microinfarcts associated with the development of cortical–subcortical brain atrophy. <b>Top</b> row (2018), <b>middle</b> row (2022), <b>bottom</b> row (2023).</p>
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<p>Visual conduction pathway assessment. The reversal pattern of VEP showed a predominant reduction in the amplitude of P100 in both eyes (worse on the right panel—right eye), which was not associated with a substantial modification of latency.</p>
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<p>Cerebral acute microangiopathy. Axial MRI diffusion-weighted images (DWI) showing multiple bilateral small acute infarcts that occurred after steroid withdrawal.</p>
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16 pages, 1015 KiB  
Systematic Review
Association Between Stroke and Traumatic Brain Injury: A Systematic Review and Meta-Analysis
by Mohammed Maan Al-Salihi, Maryam Sabah Al-Jebur, Ahmed Abd Elazim, Ram Saha, Ahmed Saleh, Farhan Siddiq, Ali Ayyad and Adnan I. Qureshi
NeuroSci 2025, 6(1), 21; https://doi.org/10.3390/neurosci6010021 - 4 Mar 2025
Viewed by 100
Abstract
Background: Stroke and traumatic brain injury (TBI) represent two major health concerns worldwide. There is growing evidence suggesting a potential association between TBI and stroke. In this systematic review and meta-analysis, we aim to explore the association between TBI and stroke risk, with [...] Read more.
Background: Stroke and traumatic brain injury (TBI) represent two major health concerns worldwide. There is growing evidence suggesting a potential association between TBI and stroke. In this systematic review and meta-analysis, we aim to explore the association between TBI and stroke risk, with a specific focus on overall stroke risk and subgroup variations based on stroke type, severity, and the post-TBI time period. Methods: PubMed, Web of Science (WOS), Scopus, and Cochrane Library were systematically searched for studies exploring the link between stroke and TBI. The pooled hazard ratios (HRs) with a 95% confidence interval (CI) were calculated. The Comprehensive Meta-Analysis (CMA) software was used for the analysis. Subgroup analyses were conducted based on stroke type, TBI severity, and post-TBI phase. The Newcastle–Ottawa Scale (NOS) was utilized for the quality assessment. Results: We included a total of 13 observational studies, with data from 8 studies used for quantitative analysis. A history of TBI was associated with a significantly higher odds of stroke compared to controls (HR = 2.3, 95% CI (1.79 to 2.958), p < 0.001). The risk was greater for hemorrhagic stroke (HR = 4.8, 95% CI (3.336 to 6.942), p < 0.001) than for ischemic stroke (HR = 1.56, 95% CI (1.28 to 1.9), p < 0.001). Both moderate-to-severe TBI (HR = 3.64, 95% CI (2.158 to 6.142), p < 0.001) and mild TBI (HR = 1.81, 95% CI (1.17 to 2.8), p = 0.007) were associated with a significantly higher risk of stroke. The risk was also higher in the early post-TBI phase (1–30 days) (HR = 4.155, 95% CI (2.25 to 7.67), p < 0.001) compared to later phases (HR = 1.68, 95% CI (1.089 to 2.59), p = 0.019) from 30 days to 1 year and (HR = 1.87, 95% CI (1.375 to 2.544), p < 0.001) after 1 year. Conclusions: This systematic review confirms a significant association between TBI and an increased risk of stroke, regardless of TBI severity, type, or timing of stroke. The findings highlight the need for early monitoring and advocating preventive strategies for stroke in patients with a history of TBI. Full article
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<p>PRISMA flowchart.</p>
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<p>Forest plot showing the pooled stroke risk after TBI based on the stroke type.</p>
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<p>Forest plot showing the pooled stroke risk after TBI based on stroke severity.</p>
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<p>Forest plot showing the pooled stroke risk after TBI based on post-TBI phase.</p>
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16 pages, 910 KiB  
Article
Nutritional Status Is Associated with Mortality but Not Appropriate Discharge of Implantable Cardioverter Defibrillators in Patients with Heart Failure
by Idris Yakut, Yücel Kanal, Atik Aksoy, Ozcan Ozeke, Ozgür Ulaş Ozcan, Yasin Ozen and Dursun Aras
Diagnostics 2025, 15(5), 610; https://doi.org/10.3390/diagnostics15050610 - 4 Mar 2025
Viewed by 72
Abstract
Objective: To investigate the predictive value of nutritional status in heart failure (HF) patients with an implantable cardioverter defibrillator (ICD), and to identify factors associated with ICD discharge and mortality. Methods: This retrospective study was conducted by analyzing data from 2017 to 2021. [...] Read more.
Objective: To investigate the predictive value of nutritional status in heart failure (HF) patients with an implantable cardioverter defibrillator (ICD), and to identify factors associated with ICD discharge and mortality. Methods: This retrospective study was conducted by analyzing data from 2017 to 2021. HF patients who underwent ICD implantation for primary prevention were included. Follow-up visits were continued until December 2022. Patients were examined based on ICD shock occurrence (ICD-A: appropriate shock), ICD non-discharge (ICD-X), and mortality. Nutritional status was assessed by the Prognostic Nutritional Index (PNI) and the Controlling Nutritional Status (CONUT) scores. Results: A total of 221 patients were included in the study, 86 of whom were in the ICD-A group (135 in the ICD-X group). Age and sex distribution were similar in these groups. The all-cause mortality rate was 20.36%. A PNI with a cut-off value of <47.25 and a CONUT score with a cut-off value of >2.5 were able to significantly predict all-cause mortality. The PNI had a greater area under the curve compared to the CONUT. Non-ischemic cardiomyopathy and high left-ventricle end-systolic diameter (ESD) were independently associated with appropriate ICD shock. Low systolic blood pressure, high ESD, low sodium, low total cholesterol, low (<47.25) PNI, and ICD shock were independently associated with all-cause mortality. Conclusions: Malnutrition appears to be associated with mortality in patients with primary-prevention ICDs, and the PNI appears to be a more useful indicator than the CONUT for determining the risk of mortality in these patients. Full article
(This article belongs to the Section Pathology and Molecular Diagnostics)
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<p>Flow diagram of the study (ICD: implantable cardioverter defibrillator, LVEF: left ventricular ejection fraction).</p>
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<p>ROC curves of the PNI and CONUT score to predict mortality (CONUT: the Controlling Nutritional Status, PNI: the Prognostic Nutritional Index, and ROC: receiver operating characteristic).</p>
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11 pages, 698 KiB  
Article
The Determinants of Delays and Their Impact on Clinical End Points in Acute ST-Segment Elevation Myocardial Infarction: A Single-Center Experience
by Kardelen Ohtaroglu Tokdil, Hasan Tokdil, Eser Durmaz, Bilgehan Karadag, Burcak Kilickiran Avci, Baris Ikitimur, Emre Ozmen, Alpin Mert Tekin, Betul Zehra Pirdal and Zeki Ongen
Medicina 2025, 61(3), 447; https://doi.org/10.3390/medicina61030447 - 4 Mar 2025
Viewed by 157
Abstract
Background and Objectives: The purpose of this study was to determine the factors that cause delay time in patients admitted to the hospital with STEMI. In addition, the effect of this delay on the patient’s prognosis has also been investigated. Materials and [...] Read more.
Background and Objectives: The purpose of this study was to determine the factors that cause delay time in patients admitted to the hospital with STEMI. In addition, the effect of this delay on the patient’s prognosis has also been investigated. Materials and Methods: a total of 301 patients diagnosed with STEMI treated with primary percutaneous coronary intervention (pPCI) were included in the study. Reinfarction, revascularization, cerebrovascular event, and cardiac death were determined as major cardiac clinical events. The follow-up period of our study was 475 ± 193 days. Results: Univariate analysis revealed that factors influencing delay time included BMI, hypertension diabetes, smoking habit and variability in pain intensity. In multivariate logistic regression analysis, BMI, diabetes, hypertension, smoking, variation in pain intensity, and infarct-related artery other than the LAD were identified as independent factors associated with increased delay times. We determined the cut-off values predicting the composite endpoint as 122.5 min for patient delay, 95.5 min for system delay, and 371 min for total ischemic time. It was observed that the in-hospital NT pro-BNP values of the patients presenting early were lower (181 vs. 594 pg/mL p < 0.001), had a higher ejection fraction at the first measurement, and even improved at the sixth week of follow-up (p = 0.047). Conclusions: Prolonged ischemia duration was associated with several factors. Early reperfusion in STEMI patients reduces both cardiac death and clinical events. Delays are influenced by patient awareness, emergency care efficiency, and hospital-specific factors. Improving education, response times, and hospital protocols is essential to minimize delays and improve outcomes. Full article
(This article belongs to the Section Cardiology)
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<p>ROC curves.</p>
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<p>Major Cardiac Clinical Events in Early and Late Group According to Total Ischemic Time.</p>
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10 pages, 10515 KiB  
Article
Clinical, Immunological and Pathological Characteristics of Ischemic Dermatopathy in Dogs with Leishmaniosis
by Nuria García, Àlex Cobos, Laia Solano-Gallego, Marina García and Laura Ordeix
Pathogens 2025, 14(3), 246; https://doi.org/10.3390/pathogens14030246 - 3 Mar 2025
Viewed by 183
Abstract
Cutaneous lesions suggestive of vasculitis and/or ischemic dermatopathy (ID) are anecdotally reported in canine leishmaniosis, and the clinicopathological features of these conditions have not been fully characterized. The objective of this case series was to describe six dogs with leishmaniosis and ID. In [...] Read more.
Cutaneous lesions suggestive of vasculitis and/or ischemic dermatopathy (ID) are anecdotally reported in canine leishmaniosis, and the clinicopathological features of these conditions have not been fully characterized. The objective of this case series was to describe six dogs with leishmaniosis and ID. In 5/6 dogs, leishmaniosis was diagnosed at the time of ID diagnosis, whereas in 1/6 dogs, ID developed during the first month of anti-Leishmania conventional treatment. One each of greyhound, Chihuahua, whippet, American bully, hound and mixed breeds were represented, and the median age at presentation was 6 years [2–8]. All patients presented high or very high levels of circulating anti-Leishmania infantum antibodies. The cutaneous lesions were multifocal alopecia with atrophic skin with hyper- or hypopigmentation (6/6), ulcers located on the extremities and trunk (3/6) and onychodystrophy (2/6). Histologically, ID was confirmed by the presence of follicular atrophy (faded follicles) (6/6), perivascular or interstitial lymphoplasmacytic dermatitis or panniculitis (6/6), collagen smudging (3/6), dermal fibrosis (3/6), lymphocytic interface dermatitis (3/6) and ulceration (3/6). Vasculopathy was observed in the superficial and mid-vascular plexuses in 4/6 dogs and characterized by the combination of some of the following lesions: vasocongestion, hemorrhagic foci, mild hyaline mural degeneration, thrombi and fragmented degenerating nuclear debris of neutrophils in the vascular wall. Moreover, myositis was observed in 1/6 cases. Leishmania-specific immunohistochemistry was positive in the skin of 4/6 cases. Leishmaniosis might be considered an underlying cause of ID in dogs. However, the immune mechanisms and pathogenesis need to be elucidated. Full article
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<p>Clinical presentations: (<b>a</b>,<b>c</b>) multifocal alopecia in the face, (<b>b</b>) onychodystrophy and (<b>d</b>) atrophic skin.</p>
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<p>Photomicrographs of skin sections of ischemic dermatopathy with H&amp;E: (<b>a</b>) lymphoplasmacytic perivascular to interstitial dermatitis, centered around dermal capillaries; (<b>b</b>) interface dermatitis composed of cell-poor infiltrates at the dermo–epithelial junction with the presence of intraepithelial lymphocytes in the epidermis; (<b>c</b>) follicular atrophy; (<b>d</b>) myositis composed of lymphoplasmacytic infiltrate between the muscle fibers; (<b>e</b>) dermal fibrosis and (<b>f</b>) immune-positive <span class="html-italic">Leishmania</span> spp. amastigote within a macrophage detected through immunochemistry (arrow).</p>
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<p>Histochemical and immunohistochemical findings: (<b>a</b>) a deep muscular artery displays mild eosinophilia of the tunica media; (<b>b</b>) the same artery reveals positive PTAH staining at the tunica media, consistent with fibrin; (<b>c</b>) abundant alcian blue positive material surrounding the hair follicle and (<b>d</b>) immunohistochemistry against IgG reveals positive staining in the lumen, albeit no labelling can be seen within the arterial wall.</p>
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Article
Dalbergia odorifera Trans-Nerolidol Protects Against Myocardial Ischemia via Downregulating Cytochrome- and Caspases-Signaling Pathways in Isoproterenol-Induced Rats
by Canhong Wang, Yulan Wu, Bao Gong, Xiangsheng Zhao, Hui Meng, Junyu Mou, Xiaoling Cheng, Yinfeng Tan and Jianhe Wei
Int. J. Mol. Sci. 2025, 26(5), 2251; https://doi.org/10.3390/ijms26052251 - 3 Mar 2025
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Abstract
Dalbergia odorifera is widely used to treat cardiovascular diseases. Our research group found that Dalbergia odorifera volatile oil has a good anti-myocardial ischemic effect, and its main pharmacodynamic components are trans-nerolol and its oxides. However, the exact mechanisms underlying this effect have not [...] Read more.
Dalbergia odorifera is widely used to treat cardiovascular diseases. Our research group found that Dalbergia odorifera volatile oil has a good anti-myocardial ischemic effect, and its main pharmacodynamic components are trans-nerolol and its oxides. However, the exact mechanisms underlying this effect have not yet been elucidated. This study aimed to explore the potential myocardial protective effects of trans-nerolol and its underlying molecular mechanisms. Molecular docking was used to predict and visualize the possible mechanism of the anti-apoptotic myocardial protection by trans-nerolol. The myocardial protective effect of trans-nerolol was evaluated by observing pathological injury, myocardial enzyme levels, oxidation, antioxidant levels, and the expression of related proteins. Molecular docking results showed that trans-nerolol binds closely to cytochrome C (Cytc) and apoptosis-related proteins, suggesting that it may play a role in interacting with these target proteins. The results showed that pre-treatment with dose-dependent trans-nerolol significantly mitigated the myocardial histological damage; decreased lactate dehydrogenase (LDH), creatinine kinase (CK), alanine transaminase (ALT), and aspartate transaminase (AST) levels; reduced nitric oxide (NO) production, hydrogen peroxide (H2O2), and lipid peroxide (LPO); and increased the total antioxidant content (T-AOC), glutathione (GSH), catalase (CAT), and superoxide dismutase (SOD) activities compared with the model group. In addition, dose-dependent trans-nerolol significantly increased the Na+-K+-ATPase and Ca2+-Mg2+-ATPase levels. Moreover, trans-nerolol markedly reduced the endogenous and external apoptotic pathways; downregulated the protein expression of Cytc, apoptotic protease activating factor-1 (Apaf1), Fibroblast-associated (Fas), Cysteine-aspartate protease 3 (Caspase3), Cysteine-aspartate protease 8 (Caspase8), and Cysteine-aspartate protease 9 (Caspase9); and upregulated the expression of Heat shock protein 70 (Hsp70) and B-cell lymphoma-2 (Bcl-2). These data indicate that trans-nerolol exerts protective effects against myocardial ischemia (MI), and its mechanism is associated with the suppression of the Cytc- and caspase-signaling pathways. Trans-nerolol has a therapeutic effect on MI, and its mechanism of action is related to its anti-apoptotic effect. These results suggest that Dalbergia odorifera has a potential role to be developed as an MI-promoting therapeutic agent. Full article
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<p>GC-MS total ion flow diagram of <span class="html-italic">D. odorifera</span> volatile oil.</p>
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<p>The proportion of components content of <span class="html-italic">D. odorifera</span> volatile oil.</p>
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<p>Molecular docking of anti-apoptotic mechanism of trans-nerolol.</p>
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<p>Molecular docking of anti-apoptotic mechanism of trans-nerolol.</p>
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<p>Effects of trans-nerol on myocardial enzymes in homogenate supernatants of heart: (<b>A</b>) LDH, (<b>B</b>) CK, (<b>C</b>) ALT, and (<b>D</b>) AST. Each value represents the mean ± SD with n = 6, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001 vs. the normal group, # <span class="html-italic">p</span> &lt; 0.05, ## <span class="html-italic">p</span> &lt; 0.01, and ### <span class="html-italic">p</span> &lt; 0.001 vs. the model group.</p>
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<p>Effects of trans-nerol on the lipid peroxidation in homogenate supernatants of the heart. (<b>A</b>) NO, (<b>B</b>) H<sub>2</sub>O<sub>2</sub>, and (<b>C</b>) LPO. Each value represents the mean ± SD with n = 6, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001 vs. the normal group, # <span class="html-italic">p</span> &lt; 0.05, ## <span class="html-italic">p</span> &lt; 0.01, and ### <span class="html-italic">p</span> &lt; 0.001 vs. the Model group.</p>
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<p>Effects of trans-nerol on the lipid peroxidation in homogenate supernatant of heart: (<b>A</b>) T-AOC, (<b>B</b>) GSH, (<b>C</b>) CAT, and (<b>D</b>) SOD. Each value represents the mean ± SD with n = 6, * <span class="html-italic">p</span> &lt; 0.05 vs. the normal group, # <span class="html-italic">p</span> &lt; 0.05, ## <span class="html-italic">p</span> &lt; 0.01, and ### <span class="html-italic">p</span> &lt; 0.001 vs. the model group.</p>
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<p>Effects of trans-nerol on the ATPase in homogenate supernatant of heart. (<b>A</b>) Na<sup>+</sup>-K<sup>+</sup>-ATPase, (<b>B</b>) Ca<sup>2+</sup>-Mg<sup>2+</sup>-ATPase. Each value represents the mean ± SD with n = 6, ** <span class="html-italic">p</span> &lt; 0.01 and *** <span class="html-italic">p</span> &lt; 0.001 vs. the normal group, # <span class="html-italic">p</span> &lt; 0.05, ## <span class="html-italic">p</span> &lt; 0.01 and ### <span class="html-italic">p</span> &lt; 0.001 vs. the model group.</p>
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<p>Effects of trans-nerol on the myocardium histopathological examination of the heart: (<b>A</b>) normal group, (<b>B</b>) model group, (<b>C</b>) propranolol group, (<b>D</b>) 5 mg/kg group, (<b>E</b>) 10 mg/kg group, (<b>F</b>) 20 mg/kg group, (<b>G</b>) 20+TMDP group, (<b>H</b>) TMDP group and (<b>I</b>) Pathological damage index(PDI). Each value represents the mean ± SD with n = 3, *** <span class="html-italic">p</span> &lt; 0.001 vs. the normal group, ## <span class="html-italic">p</span> &lt; 0.01, ### <span class="html-italic">p</span> &lt; 0.001 vs. the model group.</p>
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<p>Effects of trans-nerol on the protein expressions of the endogenous apoptotic pathway in the heart tissue: (<b>a</b>) Apaf1, (<b>b</b>) Cytc, (<b>c</b>) Caspase9, and (<b>d</b>) Caspase3. (<b>A</b>–<b>D</b>) Scale bar = 50 μm. Relative protein expression. Each value represents the mean ± SD with n = 3, *** <span class="html-italic">p</span> &lt; 0.001 vs. the normal group, ## <span class="html-italic">p</span> &lt; 0.01, ### <span class="html-italic">p</span> &lt; 0.001 vs. the model group.</p>
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<p>Effects of trans-nerol on the protein expressions of exogenous apoptosis pathway in the heart tissue: (<b>a</b>) Fas, (<b>b</b>) Hsp70, (<b>c</b>) Caspase8, and (<b>d</b>) Bcl-2. (<b>A</b>–<b>D</b>) Scale bar = 50 μm. Relative protein expression. Each value represents the mean ± SD with n = 3, *** <span class="html-italic">p</span> &lt; 0.001 vs. the normal group, ### <span class="html-italic">p</span> &lt; 0.001 vs. the model group.</p>
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<p>Effects of trans-nerol on the protein expressions of apoptosis pathways in the heart tissue: (<b>a</b>) Apaf1, Cytc, Caspase9 and Caspase3. (<b>b</b>) Fas, Hsp70, Caspase8, and Bcl-2. (<b>A</b>–<b>D</b>) Relative protein expression of Figure a and b, respectively. Each value represents the mean ± SD with n = 3, *** <span class="html-italic">p</span> &lt; 0.001 vs. the normal group, ### <span class="html-italic">p</span> &lt; 0.001 vs. the model group.</p>
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<p>The mechanism of action of trans-nerolol against myocardial ischemia. Black arrows indicate activation, red arrows indicate inhibition.</p>
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<p>Structural formula of trans-nerol.</p>
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11 pages, 3208 KiB  
Case Report
Progressive Evaluation of Ischemic Occlusion in a Macaque Monkey with Sudden Exacerbation of Infarction During Acute Stroke: A Case Report
by Chun-Xia Li and Xiaodong Zhang
Vet. Sci. 2025, 12(3), 231; https://doi.org/10.3390/vetsci12030231 - 3 Mar 2025
Viewed by 168
Abstract
Early neurological deterioration is associated with poor functional outcomes in stroke patients, but the underlying mechanisms remain unclear. This study aims to understand the progression of stroke-related brain damage using a rhesus monkey model with ischemic occlusion. Multiparameter MRI was used to monitor [...] Read more.
Early neurological deterioration is associated with poor functional outcomes in stroke patients, but the underlying mechanisms remain unclear. This study aims to understand the progression of stroke-related brain damage using a rhesus monkey model with ischemic occlusion. Multiparameter MRI was used to monitor the progressive evolution of the brain lesion following stroke. Resting-state functional MRI, dynamic susceptibility contrast perfusion MRI, diffusion tensor imaging, and T1- and T2-weighted scans were acquired prior to surgery and at 4–6 h, 48 h, and 96 h following the stroke. The results revealed a sudden increase in infarction volume after the hyper-acute phase but before 48 h on diffusion-weighted imaging (DWI), with a slight extension by 96 h. Lower relative cerebral blood flow (CBF) and time to maximum (Tmax) prior to the stroke, along with a progressive decrease post-stroke, were observed when compared to other stroke monkeys in the same cohort. Functional connectivity (FC) in the ipsilesional secondary somatosensory cortex (S2) and primary motor cortex (M1) exhibited an immediate decline on Day 0 compared to baseline and followed by a slight increase on Day 2 and a further decrease on Day 4. These findings provide valuable insights into infarction progression, emphasizing the critical role of collateral circulation and its impact on early neurological deterioration during acute stroke. Full article
(This article belongs to the Special Issue Medical Interventions in Laboratory Animals)
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Figure 1

Figure 1
<p>Representative MR angiography map (<b>top left</b>), lesion volume changes post-stroke (<b>top right</b>) and DWIs (<b>bottom</b>) of the monkey at different time points post-stroke. The orange arrow points to the MCA (middle cerebral artery) occlusion location. The yellow arrow points to the different branches of the MCA. DWI: diffusion-weighted images, T2W: T<sub>2</sub>-weighted images, PWI: perfuse-weighted images.</p>
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<p>Serial axial cerebral blood flow (CBF) maps (<b>left</b>) illustrate temporal perfusion changes after permanent middle cerebral artery (MCA) occlusion and relative CBF/Tmax changes in final infarction regions of monkey brains in the monkey (PH1019) and the other 3 monkeys after MCA occlusion (<b>right</b>). The yellow and orange shaded areas represent the regions of interest (ROIs) used to measure CBF/Tmax from the contralesional (yellow) and ipsilesional (orange) areas of the brain.</p>
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<p>The representative template slices of the monkey brain with ROIs of contralesional S2 (S2-CON, seed for functional connectivity analysis, <b>top left</b>), contralesional M1 (M1-CON, seed used for functional connectivity analysis, <b>bottom left</b>), and ipsilateral S2 (S2-Ipsi), ipsilateral M1 (M1-Ipsi) (<b>left</b>), and representative functional connectivity maps in S2 (<b>top right</b>) and M1 (<b>bottom right</b>) before surgery (pre) and post-surgery at 6 h, 48 h, and 96 h. <span class="html-italic">p</span> = 0.044 with 100 voxels as threshold.</p>
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<p>Illustration of representative brain slides of H&amp;E (<b>left</b>), GFAP, FJB, NeuN, and DAPI staining of the monkey at 96 h post-stroke: (<b>a</b>) the contralesional S2 area, (<b>b</b>) the ipsilesional S2 area.</p>
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11 pages, 3649 KiB  
Article
Diabetes Differentially Alters Glial Cells in Different Brain Regions
by Rashmi Kumari, Lisa Willing and Patricia J. McLaughlin
Diabetology 2025, 6(3), 16; https://doi.org/10.3390/diabetology6030016 - 3 Mar 2025
Viewed by 80
Abstract
Background/Objectives: The chronic metabolic condition of hyperglycemia in type-2 diabetics is known to cause various neurological disorders and compromise recovery from brain insults. Previously, we reported a delayed and reduced glial cell response and a greater neuronal cell death in different brain regions [...] Read more.
Background/Objectives: The chronic metabolic condition of hyperglycemia in type-2 diabetics is known to cause various neurological disorders and compromise recovery from brain insults. Previously, we reported a delayed and reduced glial cell response and a greater neuronal cell death in different brain regions of diabetic, db/db, mice following cerebral hypoxic- ischemic injury. In this study, we explored the changes in baseline activation of astrocytes and microglia and its impact on vascular permeability in different brain regions. Methods: The numbers of activated astrocytes (GFAP-positive) and microglia/macrophage (Iba-1-positive) in the motor cortex, caudate and hippocampal regions of 12-week old, type-2 diabetic db/db and non-diabetic db/+ mice were quantitated. The leakage of serum IgG and loss of occludin, a tight junctional protein observed in the cortex and caudate of db/db mice, indicated a compromised blood brain barrier. Results: Results indicated significant differences in activation of glial cells in the cortex and caudate along with increased vessel permeability in diabetic mice. Conclusions: The study suggests that a constant activation of glial cells in the diabetic brain may be the cause of impaired inflammatory response and/or degenerating cerebral blood vessels which contribute to neuronal cell death upon CNS injury. Full article
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Graphical abstract

Graphical abstract
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<p><b>Baseline difference in body weight and blood glucose in <span class="html-italic">db</span>/+ &amp; <span class="html-italic">db</span>/<span class="html-italic">db</span> mice</b>. Body weight and blood glucose measured at 12 weeks of age. * <span class="html-italic">p</span> &lt; 0.05 vs. <span class="html-italic">db</span>/+ BL (n = 5).</p>
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<p><b>In situ hybridization of GFAP mRNA expression 10 and 24 h post hypoxia-ischemia in <span class="html-italic">db</span>+ and <span class="html-italic">db</span>/<span class="html-italic">db</span> mice.</b> GFAP mRNA expression at caudate and hippocampus level of brain section in <span class="html-italic">db</span>/+ &amp; <span class="html-italic">db</span>/<span class="html-italic">db</span> mice.</p>
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<p><b>Baseline activation of astrocytes (GFAP) in different brain regions of <span class="html-italic">db</span>/+ &amp; <span class="html-italic">db</span>/<span class="html-italic">db</span> mice</b>. (<b>A</b>) Lower resolution brain sections indicating areas of the motor cortex (box-1) and caudate (box-2) regions, or the hippocampus, CA3 (box-3) that were chosen for higher magnification imaging. (<b>B</b>) Immunofluorescence of GFAP positive astrocytes in the motor cortex, caudate and hippocampus (CA3) regions. DAPI = blue nuclear staining, Red = astrocytes (GFAP Alexa Fluor 546 conjugated). (<b>C</b>) Quantitative analysis of GFAP positive astrocytes in corresponding images shown in (<b>A</b>). Values are expressed as mean ± SEM (n = 4 to 5). * <span class="html-italic">p</span> &lt; 0.05 vs. <span class="html-italic">db</span>/+; BL = baseline. The red arrow indicates the GFAP positive astrocytes.</p>
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<p><b>Baseline microglial activation (Iba-1) in different brain regions of <span class="html-italic">db</span>/+ &amp; <span class="html-italic">db</span>/<span class="html-italic">db</span> mice.</b> (<b>A</b>) Immunofluorescences of Iba-1 positive microglia/macrophage in the motor cortex, caudate and hippocampus (CA3) regions. DAPI = blue nuclear staining, Green = microglia/macrophage (Iba-1 conjugated with Alexa Fluor 488). (<b>B</b>) Quantitative analysis of Iba-1 positive cells in corresponding images shown in (<b>A</b>). Values are expressed as mean ± SEM (n = 4 to 5). * <span class="html-italic">p</span> &lt; 0.05 vs. <span class="html-italic">db</span>/+; BL = baseline. Red arrow indicates the Iba-1 positive microglia/macrophage.</p>
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<p><b>Blood vessel permeability in baseline <span class="html-italic">db</span>/+ &amp; <span class="html-italic">db</span>/<span class="html-italic">db</span> mice.</b> Leakage of serum IgG in vessels in the cortex and caudate of diabetic <span class="html-italic">db</span>/<span class="html-italic">db</span> and non-diabetic <span class="html-italic">db</span>/+ mice. Green = tomato lectin conjugated with Alexa Fluor 488 and red = IgG conjugated with Alexa Fluor 594.</p>
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<p><b>Loss of occludin in cortex and caudate of <span class="html-italic">db</span>/+ &amp; <span class="html-italic">db</span>/<span class="html-italic">db</span> mice.</b> Immunofluorescence staining of anti-occludin in blood vessels in the cortex and caudate of diabetic <span class="html-italic">db</span>/<span class="html-italic">db</span> and non-diabetic <span class="html-italic">db</span>/+ mice. Red = blood vessel expressing occludin conjugated with Alexa Fluor 546.</p>
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25 pages, 2703 KiB  
Review
Role of Gut Microbial Metabolites in Ischemic and Non-Ischemic Heart Failure
by Mohammad Reza Hatamnejad, Lejla Medzikovic, Ateyeh Dehghanitafti, Bita Rahman, Arjun Vadgama and Mansoureh Eghbali
Int. J. Mol. Sci. 2025, 26(5), 2242; https://doi.org/10.3390/ijms26052242 - 2 Mar 2025
Viewed by 262
Abstract
The effect of the gut microbiota extends beyond their habitant place from the gastrointestinal tract to distant organs, including the cardiovascular system. Research interest in the relationship between the heart and the gut microbiota has recently been emerging. The gut microbiota secretes metabolites, [...] Read more.
The effect of the gut microbiota extends beyond their habitant place from the gastrointestinal tract to distant organs, including the cardiovascular system. Research interest in the relationship between the heart and the gut microbiota has recently been emerging. The gut microbiota secretes metabolites, including Trimethylamine N-oxide (TMAO), short-chain fatty acids (SCFAs), bile acids (BAs), indole propionic acid (IPA), hydrogen sulfide (H2S), and phenylacetylglutamine (PAGln). In this review, we explore the accumulating evidence on the role of these secreted microbiota metabolites in the pathophysiology of ischemic and non-ischemic heart failure (HF) by summarizing current knowledge from clinical studies and experimental models. Elevated TMAO contributes to non-ischemic HF through TGF-ß/Smad signaling-mediated myocardial hypertrophy and fibrosis, impairments of mitochondrial energy production, DNA methylation pattern change, and intracellular calcium transport. Also, high-level TMAO can promote ischemic HF via inflammation, histone methylation-mediated vascular fibrosis, platelet hyperactivity, and thrombosis, as well as cholesterol accumulation and the activation of MAPK signaling. Reduced SCFAs upregulate Egr-1 protein, T-cell myocardial infiltration, and HDAC 5 and 6 activities, leading to non-ischemic HF, while reactive oxygen species production and the hyperactivation of caveolin-ACE axis result in ischemic HF. An altered BAs level worsens contractility, opens mitochondrial permeability transition pores inducing apoptosis, and enhances cholesterol accumulation, eventually exacerbating ischemic and non-ischemic HF. IPA, through the inhibition of nicotinamide N-methyl transferase expression and increased nicotinamide, NAD+/NADH, and SIRT3 levels, can ameliorate non-ischemic HF; meanwhile, H2S by suppressing Nox4 expression and mitochondrial ROS production by stimulating the PI3K/AKT pathway can also protect against non-ischemic HF. Furthermore, PAGln can affect sarcomere shortening ability and myocyte contraction. This emerging field of research opens new avenues for HF therapies by restoring gut microbiota through dietary interventions, prebiotics, probiotics, or fecal microbiota transplantation and as such normalizing circulating levels of TMAO, SCFA, BAs, IPA, H2S, and PAGln. Full article
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Figure 1
<p><b>Microbiota metabolites in the gut–heart axis.</b> (<b>A</b>) After consuming a regular diet, gastrointestinal enzymes take them apart into micronutrients such as betaine, L-carnitine, phosphatidylcholine, tryptophan, cysteine, phenylalanine, and non-digestible carbohydrates, including fibers such as inulin, pectin, and resistant starch. (<b>B</b>) The gut microbiota converts food-derived compounds into TMA, SCFAs, IPA, H<sub>2</sub>S, and phenylacetic acid (PAA). Also, it changes the duodenum-released primary bile acids into secondary bile acids. (<b>C</b>,<b>D</b>) They are reabsorbed into the portal vein and enter the liver. Flavin-containing Monooxygenase (FMO) transforms TMA into TMAO and releases it into the hepatic vein. Hepatocytes and enterocytes consume most SCFAs and IPA to tighten their intercellular junction and maintain intestinal integrity; the rest are released into the systemic circulation. In addition, secondary bile acids are either reabsorbed by the liver and go back to enterohepatic circulation or enter the systemic circulation to ultimately affect the heart. Microbiota-driven H<sub>2</sub>S regulates inflammation and tissue repair within the GI tract and as released circular gasotransmitter facilitates vasodilation and other systemic effects. Liver PAA conjugation with glutamine results in PAGln production and secretion into the portal vein and subsequently in systemic circulation.</p>
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<p><b>Gut dysbiosis derivatives in experimental models of ischemic and non-ischemic HF.</b> (<b>A</b>) Following gut dysbiosis and increased TMAO, either myocardial hypertrophy and fibrosis through TGF-ß/Smad signaling pathways and altered DNA methylation pattern or myocardial dysfunction by calcium transport and energy production impairment can lead to non-ischemic HF. A high level of TMAO accelerates inflammatory pathways, platelet hyperactivity, cholesterol accumulation, and foam cell formation, which can all lead to thrombosis and clot formation in ischemic HF. Furthermore, TMAO primes MAPK signaling, leading to ferroptosis-mediated cardiomyopathy. In addition, histone methylation-mediated chromatin remodeling leading to endothelial–myofibroblast transition and vascular fibrosis results in ischemic HF. (<b>B</b>) Reduction in SCFAs after gut dysbiosis upregulates Egr-1 protein and T-cell myocardial infiltration, and enhances HDAC 5 and 6 activities that, through the MKK3/P38/PRAK pathway, causes less angiogenesis and more apoptosis, resulting in non-ischemic HF. Also, SCFAs decrement through enhancement in ROS and inflammatory cytokines production and C3/CAV-1/ACE-2 axis activation can lead to ischemic HF. (<b>C</b>) Changes in BAs, such as reduced deoxycholic acid and increased taurocholate, stimulate IL-1 and IL-1ß expression and worsen contractility, respectively, and affect mitochondrial apoptosis, leading to non-ischemic and ischemic HF through infarct expansion; in addition, with BAs reduction, cholesterol accumulates and plaque formation enhances and myocardium becomes prone to ischemic HF.</p>
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