Red wine, an alcoholic beverage is composed of a spectrum of complex compounds such as water, alcohol, glycerol, organic acid, carbohydrates, polyphenols, and minerals as well as volatile compounds. ...Major factors that affect the levels of phenolic compounds in red wines are the variety of grapes and the storage of the wines. Among the constituents of red wine, phenolic compounds play a crucial role in attributes including color and mouthfeel and confer beneficial properties on health. Most importantly, phenolic compounds such as flavanols, flavonols, flavanones, flavones, tannins, anthocyanins, hydroxycinnamic acids, hydroxybenzoic acids, and resveratrol can prevent the development of cardiovascular diseases, cancers, diabetes, inflammation, and some other chronic diseases.
The contribution of Russian experts to the development of HPLC is significant; they have proposed and developed new chromatography methods, adsorbents, and instruments. New applications of HPLC are ...proposed, in particular, for the separation of viruses, nanoparticles, optical isomers, peptides, and other compounds. HPLC has become widely used in Russia for the control of contamination of food products, medicines, and environmental samples, for the early diagnosis of dangerous diseases, for solving the problems of forensic chemistry and criminalistics.
Health forecasting is an important aspect of ensuring that the health system can effectively respond to the changing epidemiological environment. Common models for forecasting Alzheimer's disease and ...related dementias (AD/ADRD) are based on simplifying methodological assumptions, applied to limited population subgroups, or do not allow analysis of medical interventions. This study uses 5 %-Medicare data (1991–2017) to identify, partition, and forecast age-adjusted prevalence and incidence-based mortality of AD as well as their causal components.
The core underlying methodology is the partitioning analysis that calculates the relative impact each component has on the overall trend as well as intertemporal changes in the strength and direction of these impacts. B-spline functions estimated for all parameters of partitioning models represent the basis for projections of these parameters in future.
Prevalence of AD is predicted to be stable between 2017 and 2028 primarily due to a decline in the prevalence of pre-AD-diagnosis stroke. Mortality, on the other hand, is predicted to increase. In all cases the resulting patterns come from a trade-off of two disadvantageous processes: increased incidence and disimproved survival. Analysis of health interventions demonstrates that the projected burden of AD differs significantly and leads to alternative policy implications.
We developed a forecasting model of AD/ADRD risks that involves rigorous mathematical models and incorporation of the dynamics of important determinative risk factors for AD/ADRD risk. The applications of such models for analyses of interventions would allow for predicting future burden of AD/ADRD conditional on a specific treatment regime.
•AD exhibits rising incidence and post-onset mortality; falling survival.•Mortality post AD onset expected to continue to grow through 2028.•AD prevalence post-stroke, is expected to decrease drastically.•AD prevalence for all non-stroke chronic morbidity profiles will increase.•Due to conflicting trends, total AD prevalence will appear stable through 2028.
Purpose
To quantitatively evaluate contributions of trends in incidence, relative survival, and stage at diagnosis to the dynamics in the prevalence of major cancers (lung, prostate, colon, breast, ...urinary bladder, ovaries, stomach, pancreas, esophagus, kidney, liver, and skin melanoma) among older U.S. adults age 65 +.
Methods
Trend partitioning was applied to the Surveillance, Epidemiology, and End Results Program data for 1973–2016.
Results
Growth of cancer prevalence in older adults decelerated or even decreased over time for all studied cancers due to decreasing incidence and improving survival for most of cancers, with a smaller contribution of the stage at cancer diagnosis. Changes in the prevalence of cancers of the lung, colon, stomach, and breast were predominantly due to decreasing incidence, increasing survival and more frequent diagnoses at earlier stages. Changes in prevalence of some other cancers demonstrated adverse trends such as decreasing survival in localized and regional stages (urinary bladder and ovarian) and growing impact of late-stage diagnoses (esophageal cancer).
Conclusion
While decelerating or decreasing prevalence of many cancers were due to a beneficial combination of decreasing incidence and increasing survival, there are cancers for which decelerating prevalence is due to lack of improvement in their stage-specific survival and/or increasing frequency of diagnosis at advanced stages. Overall, if the observed trends persist, it is likely that the burden associated with cancer prevalence in older U.S. adults will be lower comparing to projections based on constant increasing prevalence have previously estimated.
•A new approach to forecasting disease-specific prevalence is developed.•Approach is based on McKendrick–von Foerster's partial differential eqns.•Analytical solutions of formulas for disease ...prevalence are provided.•Approach uses minimal simplifying assumptions.•Validated through comparison of observed and predicted data for diabetes prevalence.
A new model for disease prevalence based on the analytical solutions of McKendric–von Foerster's partial differential equations is developed. Derivation of the model and methods to cross check obtained results are explicitly demonstrated. Obtained equations describe the time evolution of the healthy and unhealthy age-structured sub-populations and age patterns of disease prevalence. The projection of disease prevalence into the future requires estimates of time trends of age-specific disease incidence, relative survival functions, and prevalence at the initial age and year available in the data. The computational scheme for parameter estimations using Medicare data, analytical properties of the model, application for diabetes prevalence, and relationship with partitioning models are described and discussed. The model allows natural generalization for the case of several diseases as well as for modeling time trends in cause-specific mortality rates.
Quantification of biological aging in young adults Belsky, Daniel W; Avshalom Caspi; Renate Houts ...
Proceedings of the National Academy of Sciences - PNAS,
07/2015, Letnik:
112, Številka:
30
Journal Article
Recenzirano
Odprti dostop
Antiaging therapies show promise in model organism research. Translation to humans is needed to address the challenges of an aging global population. Interventions to slow human aging will need to be ...applied to still-young individuals. However, most human aging research examines older adults, many with chronic disease. As a result, little is known about aging in young humans. We studied aging in 954 young humans, the Dunedin Study birth cohort, tracking multiple biomarkers across three time points spanning their third and fourth decades of life. We developed and validated two methods by which aging can be measured in young adults, one cross-sectional and one longitudinal. Our longitudinal measure allows quantification of the pace of coordinated physiological deterioration across multiple organ systems (e.g., pulmonary, periodontal, cardiovascular, renal, hepatic, and immune function). We applied these methods to assess biological aging in young humans who had not yet developed age-related diseases. Young individuals of the same chronological age varied in their âbiological agingâ (declining integrity of multiple organ systems). Already, before midlife, individuals who were aging more rapidly were less physically able, showed cognitive decline and brain aging, self-reported worse health, and looked older. Measured biological aging in young adults can be used to identify causes of aging and evaluate rejuvenation therapies.
The global population is aging, driving up age-related disease morbidity. Antiaging interventions are needed to reduce the burden of disease and protect population productivity. Young people are the most attractive targets for therapies to extend healthspan (because it is still possible to prevent disease in the young). However, there is skepticism about whether aging processes can be detected in young adults who do not yet have chronic diseases. Our findings indicate that aging processes can be quantified in people still young enough for prevention of age-related disease, opening a new door for antiaging therapies. The science of healthspan extension may be focused on the wrong end of the lifespan; rather than only studying old humans, geroscience should also study the young.
There are prominent geographic disparities in the life expectancy (LE) of older US adults between the states with the highest (leading states) and lowest (lagging states) LE and their causes remain ...poorly understood. Heart failure (HF) has been proposed as a major contributor to these disparities. This study aims to investigate geographic disparities in HF outcomes between the leading and lagging states.
The study was a secondary data analysis of HF outcomes in older US adults aged 65+, using Center for Disease Control and Prevention sponsored Wide-Ranging Online Data for Epidemiologic Research (CDC WONDER) database and a nationally representative 5% sample of Medicare beneficiaries over 2000-2017. Empiric estimates of death certificate-based mortality from HF as underlying cause of death (CBM-UCD)/multiple cause of death (CBM-MCD); HF incidence-based mortality (IBM); HF incidence, prevalence, and survival were compared between the leading and lagging states. Cox regression was used to investigate the effect of residence in the lagging states on HF incidence and survival.
Between 2000 and 2017, HF mortality rates (per 100,000) were higher in the lagging states (CBM-UCD: 188.5-248.6; CBM-MCD: 749.4-965.9; IBM: 2656.0-2978.4) than that in the leading states (CBM-UCD: 79.4-95.6; CBM-MCD: 441.4-574.1; IBM: 1839.5-2138.1). Compared to their leading counterparts, lagging states had higher HF incidence (2.9-3.9% vs. 2.2-2.9%), prevalence (15.6-17.2% vs. 11.3-13.0%), and pre-existing prevalence at age 65 (5.3-7.3% vs. 2.8-4.1%). The most recent rates of one- (77.1% vs. 80.4%), three- (59.0% vs. 60.7%) and five-year (45.8% vs. 49.8%) survival were lower in the lagging states. A greater risk of HF incidence (Adjusted Hazards Ratio, AHR 95%CI: 1.29 1.29-1.30) and death after HF diagnosis (AHR: 1.12 1.11-1.13) was observed for populations in the lagging states. The study also observed recent increases in CBMs and HF incidence, and declines in HF prevalence, prevalence at age 65 and survival with a decade-long plateau stage in IBM in both leading and lagging states.
There are substantial geographic disparities in HF mortality, incidence, prevalence, and survival across the U.S.: HF incidence, prevalence at age 65 (age of Medicare enrollment), and survival of patients with HF contributed most to these disparities. The geographic disparities and the recent increase in incidence and decline in survival underscore the importance of HF prevention strategies.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
In the present work, we suggest an approach for describing dynamics of finite-dimensional quantum systems in terms of pseudostochastic maps acting on probability distributions, which are obtained via ...minimal informationally complete quantum measurements. The suggested method for probability representation of quantum dynamics preserves the tensor product structure, which makes it favourable for the analysis of multi-qubit systems. A key advantage of the suggested approach is that minimal informationally complete positive operator-valued measures (MIC-POVMs) are easier to construct in comparison with their symmetric versions (SIC-POVMs). We establish a correspondence between the standard quantum-mechanical formalism and the MIC-POVM-based probability formalism. Within the latter approach, we derive equations for the unitary von-Neumann evolution and the Markovian dissipative evolution, which is governed by the Gorini-Kossakowski-Sudarshan-Lindblad (GKSL) generator. We apply the MIC-POVM-based probability representation to the digital quantum computing model. In particular, for the case of spin-1/2 evolution, we demonstrate identifying a transition of a dissipative quantum dynamics to a completely classical-like stochastic dynamics. One of the most important findings is that the MIC-POVM-based probability representation gives more strict requirements for revealing the non-classical character of dissipative quantum dynamics in comparison with the SIC-POVM-based approach. Our results give a physical interpretation of quantum computations and pave a way for exploring the resources of noisy intermediate-scale quantum devices.
Interest in lignans is continually growing in recent years because of their strong antioxidant properties and other biological characteristics, positively affecting human health. Methods for the ...extraction, identification, and determination of lignans are developed; lignan species and their quantity in food products are determined, and databases of lignans in food products have been created in several countries (Finland, Netherlands, United States, Canada, United Kingdom, Japan, and Spain). In this review, we consider chromatographic methods (gas, liquid, supercritical fluid, and thin-layer chromatography) used to identify and determine natural lignans and isolate them in an individual state. Most natural lignans are found in flax and sesame seeds, cereals, some vegetables, fruits, and berries.
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