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- Galluzzi, L, et al.
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Guidelines for the use and interpretation of assays for monitoring cell death in higher eukaryotes.
- 2009
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In: Cell death and differentiation. - : Springer Science and Business Media LLC. - 1476-5403 .- 1350-9047. ; 16:8, s. 1093-107
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Research review (peer-reviewed)abstract
- Cell death is essential for a plethora of physiological processes, and its deregulation characterizes numerous human diseases. Thus, the in-depth investigation of cell death and its mechanisms constitutes a formidable challenge for fundamental and applied biomedical research, and has tremendous implications for the development of novel therapeutic strategies. It is, therefore, of utmost importance to standardize the experimental procedures that identify dying and dead cells in cell cultures and/or in tissues, from model organisms and/or humans, in healthy and/or pathological scenarios. Thus far, dozens of methods have been proposed to quantify cell death-related parameters. However, no guidelines exist regarding their use and interpretation, and nobody has thoroughly annotated the experimental settings for which each of these techniques is most appropriate. Here, we provide a nonexhaustive comparison of methods to detect cell death with apoptotic or nonapoptotic morphologies, their advantages and pitfalls. These guidelines are intended for investigators who study cell death, as well as for reviewers who need to constructively critique scientific reports that deal with cellular demise. Given the difficulties in determining the exact number of cells that have passed the point-of-no-return of the signaling cascades leading to cell death, we emphasize the importance of performing multiple, methodologically unrelated assays to quantify dying and dead cells.
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- Kriit, Hedi Katre, et al.
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Using Distributed Lag Non-Linear Models to Estimate Exposure Lag-Response Associations between Long-Term Air Pollution Exposure and Incidence of Cardiovascular Disease
- 2022
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In: International Journal of Environmental Research and Public Health. - : MDPI AG. - 1661-7827 .- 1660-4601. ; 19:5
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Journal article (peer-reviewed)abstract
- Long-term air pollution exposure increases the risk for cardiovascular disease, but little is known about the temporal relationships between exposure and health outcomes. This study aims to estimate the exposure-lag response between air pollution exposure and risk for ischemic heart disease (IHD) and stroke incidence by applying distributed lag non-linear models (DLNMs). Annual mean concentrations of particles with aerodynamic diameter less than 2.5 µm (PM2.5 ) and black carbon (BC) were estimated for participants in five Swedish cohorts using dispersion models. Simultaneous estimates of exposure lags 1–10 years using DLNMs were compared with separate year specific (single lag) estimates and estimates for lag 1–5-and 6–10-years using moving average exposure. The DLNM estimated no exposure lag-response between PM2.5 total, BC, and IHD. However, for PM2.5 from local sources, a 20% risk increase per 1 µg/m3 for 1-year lag was estimated. A risk increase for stroke was suggested in relation to lags 2–4-year PM2.5 and BC, and also lags 8–9-years BC. No associations were shown in single lag models. Increased risk estimates for stroke in relation to lag 1–5-and 6–10-years BC moving averages were observed. Estimates generally supported a greater contribution to increased risk from exposure windows closer in time to incident IHD and incident stroke. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
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- Kootar, S., et al.
(author)
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Validation of the CogDrisk Instrument as Predictive of Dementia in Four General Community-Dwelling Populations
- 2023
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In: The Journal of Prevention of Alzheimer's Disease. - : SERDI. - 2274-5807 .- 2426-0266. ; :10, s. 478-487
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Journal article (peer-reviewed)abstract
- Background: Lack of external validation of dementia risk tools is a major limitation for generalizability and translatability of prediction scores in clinical practice and research.Objectives: We aimed to validate a new dementia prediction risk tool called CogDrisk and a version, CogDrisk-AD for predicting Alzheimer’s disease (AD) using cohort studies.Design, Setting, Participants and Measurements: Four cohort studies were identified that included majority of the dementia risk factors from the CogDrisk tool. Participants who were free of dementia at baseline were included. The predictors were component variables in the CogDrisk tool that include self-reported demographics, medical risk factors and lifestyle habits. Risk scores for Any Dementia and AD were computed and Area Under the Curve (AUC) was assessed. To examine modifiable risk factors for dementia, the CogDrisk tool was tested by excluding age and sex estimates from the model.Results: The performance of the tool varied between studies. The overall AUC and 95% CI for predicting dementia was 0.77 (0.57, 0.97) for the Swedish National study on Aging and Care in Kungsholmen, 0.76 (0.70, 0.83) for the Health and Retirement Study - Aging, Demographics and Memory Study, 0.70 (0.67,0.72) for the Cardiovascular Health Study Cognition Study, and 0.66 (0.62,0.70) for the Rush Memory and Aging Project.Conclusions: The CogDrisk and CogDrisk-AD performed well in the four studies. Overall, this tool can be used to assess individualized risk factors of dementia and AD in various population settings.
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- Nilsson Sommar, Johan, et al.
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Long-term exposure to particulate air pollution and black carbon in relation to natural and cause-specific mortality: a multicohort study in Sweden
- 2021
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In: Bmj Open. - : BMJ. - 2044-6055. ; 11:9
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Journal article (peer-reviewed)abstract
- Objectives To estimate concentration-response relationships for particulate matter (PM) and black carbon (BC) in relation to mortality in cohorts from three Swedish cities with comparatively low pollutant levels. Setting Cohorts from Gothenburg, Stockholm and Umea, Sweden. Design High-resolution dispersion models were used to estimate annual mean concentrations of PM with aerodynamic diameter <= 10 mu m (PM10) and <= 2.5 mu m (PM2.5), and BC, at individual addresses during each year of follow-up, 1990-2011. Moving averages were calculated for the time windows 1-5 years (lag1-5) and 6-10 years (lag6-10) preceding the outcome. Cause-specific mortality data were obtained from the national cause of death registry. Cohort-specific HRs were estimated using Cox regression models and then meta-analysed including a random effect of cohort. Participants During the study period, 7 340 cases of natural mortality, 2 755 cases of cardiovascular disease (CVD) mortality and 817 cases of respiratory and lung cancer mortality were observed among in total 68 679 individuals and 689 813 person-years of follow-up. Results Both PM10 (range: 6.3-41.9 mu g/m(3)) and BC (range: 0.2-6.8 mu g/m(3)) were associated with natural mortality showing 17% (95% CI 6% to 31%) and 9% (95% CI 0% to 18%) increased risks per 10 mu g/m(3) and 1 mu g/m(3) of lag1-5 exposure, respectively. For PM2.5 (range: 4.0-22.4 mu g/m(3)), the estimated increase was 13% per 5 mu g/m(3), but less precise (95% CI -9% to 40%). Estimates for CVD mortality appeared higher for both PM10 and PM2.5. No association was observed with respiratory mortality. Conclusion The results support an effect of long-term air pollution on natural mortality and mortality in CVD with high relative risks also at low exposure levels. These findings are relevant for future decisions concerning air quality policies.
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