Long-Term Oral Administration of Theaphenon-E Improves Cardiomyocyte Mechanics and Calcium Dynamics by Affecting Phospholamban Phosphorylation and ATP Production

Background/Aims: Dietary polyphenols from green tea have been shown to possess cardio-protective activities in different experimental models of heart diseases and age-related ventricular dysfunction. The present study was aimed at evaluating whether long term in vivo administration of green tea extracts (GTE), can exert positive effects on the normal heart, with focus on the underlying mechanisms. Methods: The study population consisted of 20 male adult Wistar rats. Ten animals were given 40 mL/day tap water solution of GTE (concentration 0.3%) for 4 weeks (GTE group). The same volume of water was administered to the 10 remaining control rats (CTRL). Then, in vivo and ex vivo measurements of cardiac function were performed in the same animal, at the organ (hemodynamics) and cellular (cardiomyocyte mechanical properties and intracellular calcium dynamics) levels. On cardiomyocytes and myocardial tissue samples collected from the same in vivo studied animals, we evaluated: (1) the intracellular content of ATP, (2) the endogenous mitochondrial respiration, (3) the expression levels of the Sarcoplasmic Reticulum Ca2+-dependent ATPase 2a (SERCA2), the Phospholamban (PLB) and the phosphorylated form of PLB, the L-type Ca2+ channel, the Na+-Ca2+ exchanger, and the ryanodine receptor 2. Results: GTE cardiomyocytes exhibited a hyperdynamic contractility compared with CTRL (the rate of shortening and re-lengthening, the fraction of shortening, the amplitude of calcium transient, and the rate of cytosolic calcium removal were significantly increased). A faster isovolumic relaxation was also observed at the organ level. Consistent with functional data, we measured a significant increase in the intracellular ATP content supported by enhanced endogenous mitochondrial respiration in GTE cardiomyocytes, as well as higher values of the ratios phosphorylated-PLB/PLB and SERCA2/PLB. Conclusions: Long-term in vivo administration of GTE improves cell mechanical properties and intracellular calcium dynamics in normal cardiomyocytes, by increasing energy availability and removing the inhibitory effect of PLB on SERCA2

To what degree have the non-police public services adopted the National Intelligence Model? : what benefits could the National Intelligence Model deliver?

It is claimed that the National Intelligence Model (NIM) consolidated intelligence-led policing principles in investigative practice and decision making in British policing. Subsequently, encouraged by the Home Office, the NIM was adopted by a number of other public services with an investigative capability. However, that transfer took place without a sufficiently rigorous evaluation of the model’s value to the police service and without any meaningful analysis of its relevance to the investigative functions of other public sector agencies. This research examined the adoption of the NIM by three public sector bodies: The Department for Work and Pensions (DWP), The Identity and Passport Service (IPS) and the Driving Standards Agency (DSA). It drew on archival materials, associated literature and the analysis of semi-structured interviews with the personnel of these and associated agencies. Research respondents also assessed a simplified version of the NIM that was designed to remove many of the original model’s inconsistencies and ambiguities. The research identified that the reviewed public services are not compliant with the NIM minimum standards and that the model has not delivered any meaningful improvement in the consistency of process, investigative efficiency, improved partnership working, or in fraud reduction in those agencies. The NIM failed because of perceived complexity, the language of the model and supplementary guidance; its exclusive ‘fit’ with the police; and a suspicion by the agencies’ personnel that its adoption was intended as a performance management and governance tool. Moreover, the revised version of the NIM’s minimum standards did not improve comprehension or conformity, or resolve the model’s perceived police bias. It was concluded that the model is not fit for purpose for the agencies studied and that an alternative model that is more finely tuned to the needs of those agencies is required.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Incorporating Baseline Outcome Data in Individual Participant Data Meta-Analysis of Non-randomized Studies

Background: In non-randomized studies (NRSs) where a continuous outcome variable (e.g., depressive symptoms) is assessed at baseline and follow-up, it is common to observe imbalance of the baseline values between the treatment/exposure group and control group. This may bias the study and consequently a meta-analysis (MA) estimate. These estimates may differ across statistical methods used to deal with this issue. Analysis of individual participant data (IPD) allows standardization of methods across studies. We aimed to identify methods used in published IPD-MAs of NRSs for continuous outcomes, and to compare different methods to account for baseline values of outcome variables in IPD-MA of NRSs using two empirical examples from the Thyroid Studies Collaboration (TSC). Methods: For the first aim we systematically searched in MEDLINE, EMBASE, and Cochrane from inception to February 2021 to identify published IPD-MAs of NRSs that adjusted for baseline outcome measures in the analysis of continuous outcomes. For the second aim, we applied analysis of covariance (ANCOVA), change score, propensity score and the naïve approach (ignores the baseline outcome data) in IPD-MA from NRSs on the association between subclinical hyperthyroidism and depressive symptoms and renal function. We estimated the study and meta-analytic mean difference (MD) and relative standard error (SE). We used both fixed- and random-effects MA. Results: Ten of 18 (56%) of the included studies used the change score method, seven (39%) studies used ANCOVA and one the propensity score (5%). The study estimates were similar across the methods in studies in which groups were balanced at baseline with regard to outcome variables but differed in studies with baseline imbalance. In our empirical examples, ANCOVA and change score showed study results on the same direction, not the propensity score. In our applications, ANCOVA provided more precise estimates, both at study and meta-analytical level, in comparison to other methods. Heterogeneity was higher when change score was used as outcome, moderate for ANCOVA and null with the propensity score. Conclusion: ANCOVA provided the most precise estimates at both study and meta-analytic level and thus seems preferable in the meta-analysis of IPD from non-randomized studies. For the studies that were well-balanced between groups, change score, and ANCOVA performed similarly

Chronic inflammatory diseases, subclinical atherosclerosis, and cardiovascular diseases: Design, objectives, and baseline characteristics of a prospective case-cohort study ‒ ELSA-Brasil

Objectives: This analysis describes the protocol of a study with a case-cohort to design to prospectively evaluate the incidence of subclinical atherosclerosis and Cardiovascular Disease (CVD) in Chronic Inflammatory Disease (CID) participants compared to non-diseased ones. Methods: A high-risk group for CID was defined based on data collected in all visits on self-reported medical diagnosis, use of medicines, and levels of high-sensitivity C-Reactive Protein >10 mg/L. The comparison group is the Aleatory Cohort Sample (ACS): a group with 10% of participants selected at baseline who represent the entire cohort. In both groups, specific biomarkers for DIC, markers of subclinical atherosclerosis, and CVD morbimortality will be tested using weighted Cox. Results: The high-risk group (n = 2,949; aged 53.6 ± 9.2; 65.5% women) and the ACS (n=1543; 52.2±8.8; 54.1% women) were identified. Beyond being older and mostly women, participants in the high-risk group present low average income (29.1% vs. 24.8%, p < 0.0001), higher BMI (Kg/m2) (28.1 vs. 26.9, p < 0.0001), higher waist circumference (cm) (93.3 vs. 91, p < 0.0001), higher frequencies of hypertension (40.2% vs. 34.5%, p < 0.0001), diabetes (20.7% vs. 17%, p = 0.003) depression (5.8% vs. 3.9%, p = 0.007) and higher levels of GlycA a new inflammatory marker (p < 0.0001) compared to the ACS. Conclusions: The high-risk group selected mostly women, older, lower-income/education, higher BMI, waist circumference, and of hypertension, diabetes, depression, and higher levels of GlycA when compared to the ACS. The strategy chosen to define the high-risk group seems adequate given that multiple sociodemographic and clinical characteristics are compatible with CID

Incorporating Baseline Outcome Data in Individual Participant Data Meta-Analysis of Non-randomized Studies.

Background In non-randomized studies (NRSs) where a continuous outcome variable (e.g., depressive symptoms) is assessed at baseline and follow-up, it is common to observe imbalance of the baseline values between the treatment/exposure group and control group. This may bias the study and consequently a meta-analysis (MA) estimate. These estimates may differ across statistical methods used to deal with this issue. Analysis of individual participant data (IPD) allows standardization of methods across studies. We aimed to identify methods used in published IPD-MAs of NRSs for continuous outcomes, and to compare different methods to account for baseline values of outcome variables in IPD-MA of NRSs using two empirical examples from the Thyroid Studies Collaboration (TSC). Methods For the first aim we systematically searched in MEDLINE, EMBASE, and Cochrane from inception to February 2021 to identify published IPD-MAs of NRSs that adjusted for baseline outcome measures in the analysis of continuous outcomes. For the second aim, we applied analysis of covariance (ANCOVA), change score, propensity score and the naïve approach (ignores the baseline outcome data) in IPD-MA from NRSs on the association between subclinical hyperthyroidism and depressive symptoms and renal function. We estimated the study and meta-analytic mean difference (MD) and relative standard error (SE). We used both fixed- and random-effects MA. Results Ten of 18 (56%) of the included studies used the change score method, seven (39%) studies used ANCOVA and one the propensity score (5%). The study estimates were similar across the methods in studies in which groups were balanced at baseline with regard to outcome variables but differed in studies with baseline imbalance. In our empirical examples, ANCOVA and change score showed study results on the same direction, not the propensity score. In our applications, ANCOVA provided more precise estimates, both at study and meta-analytical level, in comparison to other methods. Heterogeneity was higher when change score was used as outcome, moderate for ANCOVA and null with the propensity score. Conclusion ANCOVA provided the most precise estimates at both study and meta-analytic level and thus seems preferable in the meta-analysis of IPD from non-randomized studies. For the studies that were well-balanced between groups, change score, and ANCOVA performed similarly

A comparative analysis reveals weak relationships between ecological factors and beta diversity of stream insect metacommunities at two spatial levels.

The hypotheses that beta diversity should increase with decreasing latitude and increase with spatial extent of a region have rarely been tested based on a comparative analysis of multiple datasets, and no such study has focused on stream insects. We first assessed how well variability in beta diversity of stream insect metacommunities is predicted by insect group, latitude, spatial extent, altitudinal range, and dataset properties across multiple drainage basins throughout the world. Second, we assessed the relative roles of environmental and spatial factors in driving variation in assemblage composition within each drainage basin. Our analyses were based on a dataset of 95 stream insect metacommunities from 31 drainage basins distributed around the world. We used dissimilarity-based indices to quantify beta diversity for each metacommunity and, subsequently, regressed beta diversity on insect group, latitude, spatial extent, altitudinal range, and dataset properties (e.g., number of sites and percentage of presences). Within each metacommunity, we used a combination of spatial eigenfunction analyses and partial redundancy analysis to partition variation in assemblage structure into environmental, shared, spatial, and unexplained fractions. We found that dataset properties were more important predictors of beta diversity than ecological and geographical factors across multiple drainage basins. In the within-basin analyses, environmental and spatial variables were generally poor predictors of variation in assemblage composition. Our results revealed deviation from general biodiversity patterns because beta diversity did not show the expected decreasing trend with latitude. Our results also call for reconsideration of just how predictable stream assemblages are along ecological gradients, with implications for environmental assessment and conservation decisions. Our findings may also be applicable to other dynamic systems where predictability is low