The findings showed controversies in anthropometric,glycemic and lipid profile effects. CV may have beneficial effects on obesity-related metabolic disorders; however, collected studies lacked statistical power to reach a definite conclusion. More well-designed studies are required.

CRD42019123683.

CRD42019123683.

Khat (

) is a stimulant plant, and it is abusive to induce euphoria, alertness and activity. Concomitant use of medications and khat chewing predisposes to the appearance of drug interactions result in treatment failure or toxicity. This study determined the changes in the urinary inorganic profile in adult healthy males who are chewing khat compared with non-khat chewer males.

A total of 40 adult non-smoker healthy males (20 khat chewer and 20 non-khat chewer) aged 24-30 years were selected. Khat chewer samples were positive for cathinone and cathine and negative for other drug of abuse, while non-khat chewer samples were negative for drug of abuse include cathinone and cathine. Samples were selected according to their results in immunoassay and gas chromatography mass spectrometry (GCMS) analysis. Cathine and cathinone were confirmed using liquid chromatography mass spectrometry (LC-MSMS) analysis. Inorganic profile includes titanium (Ti), cobalt (Co), copper (Cu), zinc (Zn), cadmium (Cd), and lead (Pb) were determined by using inductively coupled plasma-mass spectrometry (ICP-MS).

The levels of Ti, Co, Zn, Cd, and Pb in urine were significantly higher among the khat chewer group compared with non-khat chewer. Ti, Cd, Co, Pb and Zn urine levels were 0.5-, 1.5-, 1.15-, 5-, and 8.2-fold higher in the khat chewer group compared to non-khat chewer, respectively.

We suggested that continuous khat chewing has a long term effect on metabolic pathway of therapeutic drugs that result in toxicity or failure of therapy.

We suggested that continuous khat chewing has a long term effect on metabolic pathway of therapeutic drugs that result in toxicity or failure of therapy.In order to take advantage of the continuously increasing number of transcriptome studies, it is important to develop strategies that integrate multiple expression datasets addressing the same biological question to allow a robust analysis. Here, we propose a meta-analysis framework that integrates enriched pathways identified through the Gene Set Enrichment Analysis (GSEA) approach and calculates for each meta-pathway an empirical p-value. Validation of our approach on benchmark datasets showed comparable or even better performance than existing methods and an increase in robustness with increasing number of integrated datasets. We then applied the meta-analysis framework to 15 functional genomics datasets of physiological and pathological cardiac hypertrophy. Within these datasets we grouped expression sets measured at time points that represent the same hallmarks of heart tissue remodeling (‘aggregated time points’) and performed meta-analysis on the expression sets assigned to each aggregated time point. To facilitate biological interpretation, results were visualized as gene set enrichment networks. Here, our meta-analysis framework identified well-known biological mechanisms associated with pathological cardiac hypertrophy (e.g., cardiomyocyte apoptosis, cardiac contractile dysfunction, and alteration in energy metabolism). In addition, results highlighted novel, potentially cardioprotective mechanisms in physiological cardiac hypertrophy involving the down-regulation of immune cell response, which are worth further investigation.

This updated meta-analysis aimed to assess the diagnostic accuracy of circulating cell-free DNA (cfDNA) in breast cancer (BC).

An extensive systematic search was performed in PubMed, Scopus, Embase, and Science Direct databases to retrieve all related literature. Various diagnostic estimates, including sensitivity (SE), specificity (SP), likelihood ratios (LRs), diagnostic odds ratio (DOR), and area under the curve (AUC) of summary receiver operating characteristic (sROC) curve, were also calculated using bivariate linear mixed models.

In this meta-analysis, 57 unique articles (130 assays)on 4246 BC patients and 2,952 controls, were enrolled. For quantitative approaches, pooled SE, SP, PLR, NLR, DOR, and AUC were obtained as 0.80, 0.88, 6.7, 0.23, 29, and 0.91, respectively. Moreover, for qualitative approaches, pooled SE and SP for diagnostic performance were obtained as 0.36 and 0.98, respectively. In addition, PLR was 14.9 and NLR was 0.66. As well, the combined DOR was 23, and the AUC was 0.79.

Regardless of promising SE and SP, analysis of LRs suggested that quantitative assays are not robust enough neither for BC confirmation nor for its exclusion. On the other hand, qualitative assays showed satisfying performance only for confirming the diagnosis of BC, but not for its exclusion. Furthermore, qualitative cfDNA assays showed a better diagnostic performance in patients at the advanced stage of cancer, which represented no remarkable clinical significance as a biomarker for early detection.

Regardless of promising SE and SP, analysis of LRs suggested that quantitative assays are not robust enough neither for BC confirmation nor for its exclusion. selleck chemicals On the other hand, qualitative assays showed satisfying performance only for confirming the diagnosis of BC, but not for its exclusion. Furthermore, qualitative cfDNA assays showed a better diagnostic performance in patients at the advanced stage of cancer, which represented no remarkable clinical significance as a biomarker for early detection.This study tested the hypothesis that analyzing longitudinal item scores of the Unified Parkinson’s Disease Rating Scale could allow a smaller trial size and describe a drug’s effect on symptom progression. Two historical studies of the dopaminergic drug ropinirole were analyzed a cross-over formulation comparison trial in 161 patients with early-stage Parkinson’s disease, and a 24-week, parallel-group, placebo-controlled efficacy trial in 393 patients with advanced-stage Parkinson’s disease. We applied item response theory to estimate the patients’ symptom severity and developed a longitudinal model using the symptom severity to describe the time course of the placebo response and the drug effect on the time course. Similarly, we developed a longitudinal model using the total score. We then compared sample size needs for drug effect detection using these two different models. Total score modeling estimated median changes from baseline at 24 weeks (90% confidence interval) of -3.7 (-5.4 to -2.0) and -9.3 (-11 to -7.