105 research outputs found

    Table_1_Altered resting-state brain functional activities and networks in Crohn’s disease: a systematic review.DOCX

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    BackgroundCrohn’s disease (CD) is a non-specific chronic inflammatory disease of the gastrointestinal tract and is a phenotype of inflammatory bowel disease (IBD). The current study sought to compile the resting-state functional differences in the brain between CD patients and healthy controls.MethodsThe online databases PubMed, Web of Science Core, and EMBASE were used to find the published neuroimage studies. The search period was from the beginning through December 15, 2023. The predetermined inclusion and exclusion criteria allowed for the identification of the studies. The studies were assembled by two impartial reviewers, who also assessed their quality and bias.ResultsThis review comprised 16 resting-state fMRI studies in total. The included studies generally had modest levels of bias. According to the research, emotional processing and pain processing were largely linked to increased or decreased brain activity in patients with CD. The DMN, CEN, and limbic systems may have abnormalities in patients with CD, according to research on brain networks. Several brain regions showed functional changes in the active CD group compared to the inactive CD group and the healthy control group, respectively. The abnormalities in brain areas were linked to changes in mood fluctuations (anxiety, melancholy) in patients with CD.ConclusionFunctional neuroimaging helps provide a better understanding of the underlying neuropathological processes in patients with CD. In this review, we summarize as follows: First, these findings indicate alterations in brain function in patients with CD, specifically affecting brain regions associated with pain, emotion, cognition, and visceral sensation; second, disease activity may have an impact on brain functions in patients with CD; and third, psychological factors may be associated with altered brain functions in patients with CD.</p

    Sesamin inhibits lipopolysaccharide-induced inflammation and extracellular matrix catabolism in rat intervertebral disc

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    <p>Intervertebral disc (IVD) degeneration contributes to most spinal degenerative diseases, while treatment inhibiting IVD degeneration is still in the experimental stage. Sesamin, a bioactive component extracted from sesame, has been reported to exert chondroprotective and anti-inflammatory effects. Here, we analyzed the anti-inflammatory and anti-catabolic effects of sesamin on rat IVD <i>in vitro</i> and <i>ex vivo</i>. Results show that sesamin significantly inhibits the lipopolysaccharide (LPS)-induced expression of catabolic enzymes (MMP-1, MMP-3, MMP-13, ADAMTS-4, ADAMTS-5) and inflammation factors (IL-1β, TNF-α, iNOS, NO, COX-2, PGE2) in a dose-dependent manner <i>in vitro</i>. It is also proven that migration of macrophages induced by LPS can be inhibited by treatment with sesamin. Organ culture experiments demonstrate that sesamin protects the IVD from LPS-induced depletion of the extracellular matrix ex vivo. Moreover, sesamin suppresses LPS-induced activation of the mitogen-activated protein kinase (MAPK) pathway through inhibiting phosphorylation of JNK, the common downstream signaling pathway of LPS and IL-1β, which may be the potential mechanism of the effects of sesamin. In light of our results, sesamin protects the IVD from inflammation and extracellular matrix catabolism, presenting positive prospects in the treatment of IVD degenerative diseases.</p

    Visual results of scale factor <i>η</i>.

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    Visual results of scale factor η.</p

    Quantitative comparisons results for CRPGAN ablations study in terms of SIFID.

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    The best scores are in bold.</p

    MOESM1 of Rapid determination of chemical composition and classification of bamboo fractions using visible–near infrared spectroscopy coupled with multivariate data analysis

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    Additional file 1: Table S1. The experimental values and estimated values for cellulose, xylan and lignin. Table S2. Results of calibration and prediction PLS2 models for the quantitative compositional analysis of bamboo using raw spectra. Table S3. Results of calibration and prediction PLS1 models for the quantitative compositional analysis of bamboo using pretreated visible-near infrared spectra. Table S4. The experimental values and estimated values for glucose and xylose

    Table_1_Radiomics-based infarct features on CT predict hemorrhagic transformation in patients with acute ischemic stroke.docx

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    ObjectiveTo develop and validate a model based on the radiomics features of the infarct areas on non-contrast-enhanced CT to predict hemorrhagic transformation (HT) in acute ischemic stroke.Materials and methodsA total of 118 patients diagnosed with acute ischemic stroke in two centers from January 2019 to February 2022 were included. The radiomics features of infarcted areas on non-contrast-enhanced CT were extracted using 3D-Slicer. A univariate analysis and the least absolute shrinkage and selection operator (LASSO) were used to select features, and the radiomics score (Rad-score) was then constructed. The predictive model of HT was constructed by analyzing the Rad-score and clinical and imaging features in the training cohort, and it was verified in the validation cohort. The model was evaluated with the receiver operating characteristic curve, calibration curve and decision curve, and the prediction performance of the model in different scenarios was further discussed hierarchically.ResultsOf the 118 patients, 52 developed HT, including 21 cases of hemorrhagic infarct (HI) and 31 cases of parenchymal hematoma (PH). The Rad-score was constructed from five radiomics features and was the only independent predictor for HT. The predictive model was constructed from the Rad-score. The area under the curve (AUCs) of the model for predicting HT in the training and validation cohorts were 0.845 and 0.750, respectively. Calibration curve and decision curve analyses showed that the model performed well. Further analysis found that the model predicted HT for different infarct sizes or treatment methods in the training and validation cohorts with 78.3 and 71.4% accuracy, respectively. For all samples, the model predicted an AUC of 0.754 for HT in patients within 4.5 h since stroke onset, and predicted an AUC of 0.648 for PH.ConclusionThis model, which was based on CT radiomics features, could help to predict HT in the setting of acute ischemic stroke for any infarct size and provide guiding suggestions for clinical treatment and prognosis evaluation.</p

    Data_Sheet_1_Radiomics-based infarct features on CT predict hemorrhagic transformation in patients with acute ischemic stroke.CSV

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    ObjectiveTo develop and validate a model based on the radiomics features of the infarct areas on non-contrast-enhanced CT to predict hemorrhagic transformation (HT) in acute ischemic stroke.Materials and methodsA total of 118 patients diagnosed with acute ischemic stroke in two centers from January 2019 to February 2022 were included. The radiomics features of infarcted areas on non-contrast-enhanced CT were extracted using 3D-Slicer. A univariate analysis and the least absolute shrinkage and selection operator (LASSO) were used to select features, and the radiomics score (Rad-score) was then constructed. The predictive model of HT was constructed by analyzing the Rad-score and clinical and imaging features in the training cohort, and it was verified in the validation cohort. The model was evaluated with the receiver operating characteristic curve, calibration curve and decision curve, and the prediction performance of the model in different scenarios was further discussed hierarchically.ResultsOf the 118 patients, 52 developed HT, including 21 cases of hemorrhagic infarct (HI) and 31 cases of parenchymal hematoma (PH). The Rad-score was constructed from five radiomics features and was the only independent predictor for HT. The predictive model was constructed from the Rad-score. The area under the curve (AUCs) of the model for predicting HT in the training and validation cohorts were 0.845 and 0.750, respectively. Calibration curve and decision curve analyses showed that the model performed well. Further analysis found that the model predicted HT for different infarct sizes or treatment methods in the training and validation cohorts with 78.3 and 71.4% accuracy, respectively. For all samples, the model predicted an AUC of 0.754 for HT in patients within 4.5 h since stroke onset, and predicted an AUC of 0.648 for PH.ConclusionThis model, which was based on CT radiomics features, could help to predict HT in the setting of acute ischemic stroke for any infarct size and provide guiding suggestions for clinical treatment and prognosis evaluation.</p

    Gene ontology (GO) analysis used for analysis of the altered genes.

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    <p>A, The bar plot showed the top ten up-regulated Enrichment Score values of the significant enrichment; B, The bar plot showed the top ten down-regulated Enrichment Score values of the significant enrichment BP.</p
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