1,721,038 research outputs found
Current therapeutic strategies in connective tissue disease-associated interstitial lung disease: introduction to the special issue
No full textSARS-CoV-2 and endothelial cell interaction in COVID-19: molecular perspectives
No full textSARS-CoV-2 is the agent responsible for the coronavirus disease (COVID-19), which has been declared a pandemic by the World Health Organization. The clinical evolution of COVID-19 ranges from asymptomatic infection to death. Older people and patients with underlying medical conditions, particularly diabetes, cardiovascular and chronic respiratory diseases are more susceptible to develop severe forms of COVID-19. Significant endothelial damage has been reported in COVID-19 and growing evidence supports the key pathophysiological role of this alteration in the onset and the progression of the disease. In particular, the impaired vascular homeostasis secondary to the structural and functional damage of the endothelium and its main component, the endothelial cells, contributes to the systemic pro-inflammatory state and the multiorgan involvement observed in COVID-19 patients. This review summarizes the current evidence supporting the proposition that the endothelium is a key target of SARS-CoV-2, with a focus on the molecular mechanisms involved in the interaction between SARS-CoV-2 and endothelial cells
Metformin-mediated epigenetic modifications in diabetes and associated conditions: Biological and clinical relevance
No full textAn intricate interplay between genetic and environmental factors contributes to the development of type 2 diabetes (T2D) and its complications. Therefore, it is not surprising that the epigenome also plays a crucial role in the pathogenesis of T2D. Hyperglycemia can indeed trigger epigenetic modifications, thereby regulating different gene expression patterns. Such epigenetic changes can persist after normalizing serum glucose con-centrations, suggesting the presence of a 'metabolic memory' of previous hyperglycemia which may also be epigenetically regulated. Metformin, a derivative of biguanide known to reduce serum glucose concentrations in patients with T2D, appears to exert additional pleiotropic effects that are mediated by multiple epigenetic modifications. Such modifications have been reported in various organs, tissues, and cellular compartments and appear to account for the effects of metformin on glycemic control as well as local and systemic inflammation, oxidant stress, and fibrosis. This review discusses the emerging evidence regarding the reported metformin-mediated epigenetic modifications, particularly on short and long non-coding RNAs, DNA methylation, and histone proteins post-translational modifications, their biological and clinical significance, potential therapeutic applications, and future research directions
Blood cell count indexes of systemic inflammation in carotid artery disease: current evidence and future perspectives
No full textCarotid artery disease is commonly encountered in clinical practice and accounts for approximately 30% of ischemic strokes in the general population. Numerous biomarkers have been investigated as predictors of the onset and progression of carotid disease, the occurrence of cerebrovascular complications, and overall prognosis. Among them, blood cell count (BCC) indexes of systemic inflammation might be particularly useful, from a pathophysiological and clinical point of view, given the inflammatory nature of the atherosclerotic process. The aim of this review is to discuss the available evidence regarding the role of common BCC indexes, such as the neutrophil to lymphocyte ratio (NLR), monocyte to lymphocyte ratio (MLR), platelet to lymphocyte ratio (PLR), mean platelet volume (MPV), platelet distribution width (PDW), and red cell distribution width (RDW), in the diagnosis and risk stratification of carotid artery disease, and their potential clinical applications
An isotope dilution capillary electrophoresis/tandem mass spectrometry (CE-MS/MS) method for the simultaneous measurement of choline, betaine, and dimethylglycine concentrations in human plasma
No full textPlasma concentrations of choline, betaine, and dimethylglycine provide valuable information on the flow of methyl groups in key biological processes, particularly during folate deficiency states. We developed a new method to simultaneously measure these analytes in human plasma. Following sample deproteinization using acetonitrile, an aliquot was evaporated to dryness under vacuum to be then taken up by water. Finally, analytes were separated by capillary electrophoresis and detected by electrospray ionization triple-quadrupole mass spectrometry, in multiple reaction monitoring mode, using two stable isotope-labeled internal standards. Linearity of the calibration curves of each analyte was good (R(2) > 0.99). Average limits of detection (LODs) and limits of quantification (LOQs) for choline, betaine, and dimethylglycine were, respectively, 0.43, 0.62, and 0.31 μmol/L and 1.52, 2.11, and 0.97 μmol/L. Mean recovery of three replicates of two spiked concentrations levels was close to 100 % for all of the analytes. Repeatability and intermediate precision, expressed as %RSD of measurements, were <9 %. The method, applied to measure analytes in samples from 30 patients with chronic kidney disease and 30 age- and sex-matched healthy controls, was able to detect differences between groups and the sexes
Serum albumin concentrations are associated with disease severity and outcomes in coronavirus 19 disease (COVID-19): a systematic review and meta-analysis
No full textCoronavirus disease 2019 (COVID-19), an infectious disease caused by the severe acute respiratory syndrome coronavirus
2 (SARS-CoV-2), is responsible for the most threatening pandemic in modern history. The aim of this systematic review
and meta-analysis was to investigate the associations between serum albumin concentrations and COVID-19 disease severity
and adverse outcomes. A systematic literature search was conducted in PubMed, from inception to October 30, 2020.
Sixty-seven studies in 19,760 COVID-19 patients (6141 with severe disease or poor outcome) were selected for analysis.
Pooled results showed that serum albumin concentrations were significantly lower in patients with severe disease or poor
outcome (standard mean difference, SMD: − 0.99 g/L; 95% CI, − 1.11 to − 0.88, p < 0.001). In multivariate meta-regression
analysis, age (t = − 2.13, p = 0.043), publication geographic area (t = 2.16, p = 0.040), white blood cell count (t = − 2.77,
p = 0.008) and C-reactive protein (t = − 2.43, p = 0.019) were significant contributors of between-study variance. Therefore,
lower serum albumin concentrations are significantly associated with disease severity and adverse outcomes in COVID-19
patients. The assessment of serum albumin concentrations might assist with early risk stratification and selection of appropriate
care pathways in this group
Effect of cholesterol lowering treatment on plasma markers of endothelial dysfunction in chronic kidney disease
No full textThe elevated cardiovascular morbidity and mortality in chronic kidney disease (CKD) is linked with endothelial dysfunction secondary to the pro-inflammatory and pro-oxidative state typical of this pathology. In consideration of the well-known pleiotropic effect of statins, we investigated the effect of cholesterol lowering treatment on endothelial dysfunction markers (MED), asymmetric dimethylarginine (ADMA), vascular cell (VCAM) and intercellular (ICAM) adhesion molecule. Plasma MED concentrations, inflammation and oxidative stress indices [Kynurenine/Tryptophan (Kyn/Trp) ratio, malondialdehyde (MDA) and allantoin/uric acid (All/UA) ratio] were measured in 30 CKD patients randomized to three cholesterol lowering regimens for 12 months (simvastatin 40mg/day, ezetimibe/simvastatin 10/20mg/day, or ezetimibe/simvastatin 10/40mg/day). Treatment significantly reduced ADMA concentrations in all patients [0.694μmol/L (0.606-0.761) at baseline vs. 0.622μmol/L (0.563-0.681) after treatment, p<0.001]. ADMA reduction was paralleled by a significant decrease of MDA, All/AU ratio and Kyn/Trp ratio, but not VCAM and ICAM plasma concentrations. Cholesterol lowering treatment was associated with a significant reduction in plasma ADMA concentrations in CKD patients. This might be mediated by reduced oxidative stress and inflammation
D-Dimer Concentrations and COVID-19 Severity: A Systematic Review and Meta-Analysis
Full text linkCoronavirus disease 2019 (COVID-19) is a recently described infectious disease caused
by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Since late 2019,
COVID-19 has rapidly spread in virtually all countries, imposing the adoption of significant
lockdown and social distancing measures. The activation of the coagulation cascade
is a common feature of disseminated intravascular coagulation and adverse clinical
outcomes in COVID-19 patients. In this study, we conducted a meta-analysis aiming
to investigate differences in serum D-dimer concentrations in patients with and without
severe COVID-19 disease. An electronic search in Medline (PubMed), Scopus and Web
of Science was performed with no language restrictions, and 13 articles were reporting on
1,807 patients (585, 32.4% with severe disease) were finally identified and included in the
meta-analysis. The pooled results of all studies revealed that the D-dimer concentrations
were significantly higher in patients with more severe COVID-19 (SMD: 0.91 mg/L; 95%
CI, 0.75 to 1.07 mg/L, p < 0.0001). The heterogeneity was moderate (I
2 = 46.5%;
p = 0.033). Sensitivity analysis showed that the effect size was not modified when any
single study was in turn removed (effect size range, 0.87 mg/L to 0.93 mg/L). The Begg’s
(p = 0.76) and Egger’s tests (p = 0.38) showed no publication bias. In conclusion,
our systematic review and meta-analysis showed that serum D-dimer concentrations
in patients with severe COVID-19 are significantly higher when compared to those with
non-severe forms.This research was funded by Qatar University [IRCC-2019-007] to GN and GP, Regione Autonoma della Sardegna [RASSR82005] to GP and AZ, and fondo UNISS di Ateneo per la ricerca 2019 to GP and AZ
Simultaneous determination of the main amino thiol and thione in human whole blood by CE and LC
No full textBackground: Two precolumn fluorescence derivatization procedures by two different sulfhydryl-reactive iodoacetyl reagents were established to measure simultaneously glutathione and l-ergothioneine in human whole blood by means of CE and LC. Materials & methods: Separations were achieved in <5 min on a reverse-phase column (100 mm × 4.6 mm Zorbax Eclipse Plus C18 3.5 μm) for LC analysis, and on an uncoated fused-silica capillary (60 cm × 50 μm) for CE analysis, monitoring the fluorescence of derivatives. Results: Performance of the assays was good in terms of linearity, recovery, intra- and inter-day precision and LOD and LOQ. Conclusion: This novel approach allows rapid assessment of circulating glutathione and l-ergothioneine concentrations for clinical and research purposes
Use of Multiprognostic Index Domain Scores, Clinical Data, and Machine Learning to Improve 12-Month Mortality Risk Prediction in Older Hospitalized Patients: Prospective Cohort Study
Full text linkBackgroundThe Multidimensional Prognostic Index (MPI) is an aggregate, comprehensive, geriatric assessment scoring system derived from eight domains that predict adverse outcomes, including 12-month mortality. However, the prediction accuracy of using the three MPI categories (mild, moderate, and severe risk) was relatively poor in a study of older hospitalized Australian patients. Prediction modeling using the component domains of the MPI together with additional clinical features and machine learning (ML) algorithms might improve prediction accuracy.
ObjectiveThis study aims to assess whether the accuracy of prediction for 12-month mortality using logistic regression with maximum likelihood estimation (LR-MLE) with the 3-category MPI together with age and gender (feature set 1) can be improved with the addition of 10 clinical features (sodium, hemoglobin, albumin, creatinine, urea, urea-to-creatinine ratio, estimated glomerular filtration rate, C-reactive protein, BMI, and anticholinergic risk score; feature set 2) and the replacement of the 3-category MPI in feature sets 1 and 2 with the eight separate MPI domains (feature sets 3 and 4, respectively), and to assess the prediction accuracy of the ML algorithms using the same feature sets.
MethodsMPI and clinical features were collected from patients aged 65 years and above who were admitted to either the general medical or acute care of the elderly wards of a South Australian hospital between September 2015 and February 2017. The diagnostic accuracy of LR-MLE was assessed together with nine ML algorithms: decision trees, random forests, extreme gradient boosting (XGBoost), support-vector machines, naïve Bayes, K-nearest neighbors, ridge regression, logistic regression without regularization, and neural networks. A 70:30 training set:test set split of the data and a grid search of hyper-parameters with 10-fold cross-validation—was used during model training. The area under the curve was used as the primary measure of accuracy.
ResultsA total of 737 patients (female: 370/737, 50.2%; male: 367/737, 49.8%) with a median age of 80 (IQR 72-86) years had complete MPI data recorded on admission and had completed the 12-month follow-up. The area under the receiver operating curve for LR-MLE was 0.632, 0.688, 0.738, and 0.757 for feature sets 1 to 4, respectively. The best overall accuracy for the nine ML algorithms was obtained using the XGBoost algorithm (0.635, 0.706, 0.756, and 0.757 for feature sets 1 to 4, respectively).
ConclusionsThe use of MPI domains with LR-MLE considerably improved the prediction accuracy compared with that obtained using the traditional 3-category MPI. The XGBoost ML algorithm slightly improved accuracy compared with LR-MLE, and adding clinical data improved accuracy. These results build on previous work on the MPI and suggest that implementing risk scores based on MPI domains and clinical data by using ML prediction models can support clinical decision-making with respect to risk stratification for the follow-up care of older hospitalized patients
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