312 research outputs found

    Mesenchymal stem cells and immunomodulation: current status and future prospects

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    Animal models of atherosclerosis.

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    Atherosclerosis is a significant cause of morbidity and mortality globally. Many animal models have been developed to study atherosclerosis, and permit experimental conditions, diet and environmental risk factors to be carefully controlled. Pathophysiological changes can be produced using genetic or pharmacological means to study the harmful consequences of different interventions. Experiments using such models have elucidated its molecular and pathophysiological mechanisms, and provided platforms for pharmacological development. Different models have their own advantages and disadvantages, and can be used to answer different research questions. In the present review article, different species of atherosclerosis models are outlined, with discussions on the practicality of their use for experimentation.GT was supported by a BBSRC Doctoral Training Award and thanks the Croucher Foundation of Hong Kong for the generous support of his clinical assistant professorship. YC is supported by the ESRC

    Animal models of atherosclerosis.

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    Atherosclerosis is a significant cause of morbidity and mortality globally. Many animal models have been developed to study atherosclerosis, and permit experimental conditions, diet and environmental risk factors to be carefully controlled. Pathophysiological changes can be produced using genetic or pharmacological means to study the harmful consequences of different interventions. Experiments using such models have elucidated its molecular and pathophysiological mechanisms, and provided platforms for pharmacological development. Different models have their own advantages and disadvantages, and can be used to answer different research questions. In the present review article, different species of atherosclerosis models are outlined, with discussions on the practicality of their use for experimentation.GT was supported by a BBSRC Doctoral Training Award and thanks the Croucher Foundation of Hong Kong for the generous support of his clinical assistant professorship. YC is supported by the ESRC

    Identification of Postoperative Prognostic MicroRNA Predictors in Hepatocellular Carcinoma

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    Comparison of microRNA (miRNA) expression profiles in the noncancerous liver tissues adjacent to hepatocelluar carcinomas (HCCs) was a strategy to identify postoperative prognostic predictors in this study. Expression profiles of 270 miRNAs were determined in the paraneoplastic liver tissues of 12 HCC patients with known postoperative prognosis. A panel of candidate miRNA predictors was identified. The prognostic predictive value of these candidate miRNAs was then verified in 216 postoperative HCC patients. Univariate analysis identified 8 and 3 miRNA predictors for recurrence-free (RFS) and overall (OS) survivals, respectively. Multivariate analysis revealed high expression levels of miR-155 (HR, 2.002 [1.324–3.027]; P = .001), miR-15a (HR, 0.478 [0.248–0.920]; P = .027), miR-432 (HR, 1.816 [1.203–2.740]; P = .015), miR-486-3p (HR, 0.543 [0.330–0.893]; P = .016), miR-15b (HR, 1.074 [1.002–1.152]; P = .043) and miR-30b (HR, 1.102 [1.025–1.185]; P = .009) were significantly associated with RFS. When clinicopathological predictors were included, multivariate analysis revealed that tumor number and miR-432, miR-486-3p, and miR-30b expression levels remained significant as independent predictors for RFS. Additionally, expression knockdown of miR-155 in J7 and Mahlavu hepatoma cells resulted in decreased cell growth and enhanced cell death in xenograft tumors, suggesting an oncogenic effect of miR-155. In conclusion, significant prognostic miRNA predictors were identified through examination of miRNA expression levels in paraneoplastic liver tissues. Functional analysis of a miRNA predictor, miR-155, suggested that the prognostic miRNA predictors identified under this strategy could serve as potential molecular targets for anticancer therapy

    Elevated BCRP/ABCG2 Expression Confers Acquired Resistance to Gefitinib in Wild-Type EGFR-Expressing Cells

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    The sensitivity of non-small cell lung cancer (NSCLC) patients to EGFR tyrosine kinase inhibitors (TKIs) is strongly associated with activating EGFR mutations. Although not as sensitive as patients harboring these mutations, some patients with wild-type EGFR (wtEGFR) remain responsive to EGFR TKIs, suggesting that the existence of unexplored mechanisms renders most of wtEGFR-expressing cancer cells insensitive.Here, we show that acquired resistance of wtEGFR-expressing cancer cells to an EGFR TKI, gefitinib, is associated with elevated expression of breast cancer resistance protein (BCRP/ABCG2), which in turn leads to gefitinib efflux from cells. In addition, BCRP/ABCG2 expression correlates with poor response to gefitinib in both cancer cell lines and lung cancer patients with wtEGFR. Co-treatment with BCRP/ABCG2 inhibitors enhanced the anti-tumor activity of gefitinib.Thus, BCRP/ABCG2 expression may be a predictor for poor efficacy of gefitinib treatment, and targeting BCRP/ABCG2 may broaden the use of gefitinib in patients with wtEGFR

    A model for predicting physical function upon discharge of hospitalized older adults in Taiwan—a machine learning approach based on both electronic health records and comprehensive geriatric assessment

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    BackgroundPredicting physical function upon discharge among hospitalized older adults is important. This study has aimed to develop a prediction model of physical function upon discharge through use of a machine learning algorithm using electronic health records (EHRs) and comprehensive geriatrics assessments (CGAs) among hospitalized older adults in Taiwan.MethodsData was retrieved from the clinical database of a tertiary medical center in central Taiwan. Older adults admitted to the acute geriatric unit during the period from January 2012 to December 2018 were included for analysis, while those with missing data were excluded. From data of the EHRs and CGAs, a total of 52 clinical features were input for model building. We used 3 different machine learning algorithms, XGBoost, random forest and logistic regression.ResultsIn total, 1,755 older adults were included in final analysis, with a mean age of 80.68 years. For linear models on physical function upon discharge, the accuracy of prediction was 87% for XGBoost, 85% for random forest, and 32% for logistic regression. For classification models on physical function upon discharge, the accuracy for random forest, logistic regression and XGBoost were 94, 92 and 92%, respectively. The auROC reached 98% for XGBoost and random forest, while logistic regression had an auROC of 97%. The top 3 features of importance were activity of daily living (ADL) at baseline, ADL during admission, and mini nutritional status (MNA) during admission.ConclusionThe results showed that physical function upon discharge among hospitalized older adults can be predicted accurately during admission through use of a machine learning model with data taken from EHRs and CGAs

    Prevalence of JC Virus in Chinese Patients with Colorectal Cancer

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    BACKGROUND: JCV is a DNA polyomavirus very well adapted to humans. Although JCV DNA has been detected in colorectal cancers (CRC), the association between JCV and CRC remains controversial. In China, the presence of JCV infection in CRC patients has not been reported. Here, we investigated JCV infection and viral DNA load in Chinese CRC patients and to determine whether the JCV DNA in peripheral blood (PB) can be used as a diagnostic marker for JCV-related CRC. METHODOLOGY/PRINCIPAL FINDINGS: Tumor tissues, non-cancerous tumor-adjacent tissues and PB samples were collected from 137 CRC patients. In addition, 80 normal colorectal tissue samples from patients without CRC and PB samples from 100 healthy volunteers were also harvested as controls. JCV DNA was detected by nested PCR and glass slide-based dot blotting. Viral DNA load of positive samples were determined by quantitative real-time PCR. JCV DNA was detected in 40.9% (56/137) of CRC tissues at a viral load of 49.1 to 10.3×10(4) copies/µg DNA. Thirty-four (24.5%) non-cancerous colorectal tissues (192.9 to 4.4×10(3) copies/µg DNA) and 25 (18.2%) PB samples (81.3 to 4.9×10(3) copies/µg DNA) from CRC patients were positive for JCV. Tumor tissues had higher levels of JCV than non-cancerous tissues (P = 0.003) or PB samples (P<0.001). No correlation between the presence of JCV and demographic or medical characteristics was observed. The JCV prevalence in PB samples was significantly associated with the JCV status in tissue samples (P<0.001). Eleven (13.8%) normal colorectal tissues and seven (7.0%) PB samples from healthy donors were positive for JCV. CONCLUSIONS/SIGNIFICANCE: JCV infection is frequently present in colorectal tumor tissues of CRC patients. Although the association between JCV presence in PB samples and JCV status in tissue samples was identified in this study, whether PB JCV detection can serve as a marker for JCV status of CRC requires further study
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