関連する実験動画

Updated: Mar 30, 2026

PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis
08:43

PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis

Published on: May 11, 2017

13.1K

コーヒー消費と3つの大きな前向きなコホートにおける全死亡率と因果死亡率との関連

Ming Ding1, Ambika Satija1, Shilpa N Bhupathiraju1

  • 1From Departments of Nutrition (M.D., A.S., S.N.B., Y.H., Q.S., W.W., R.M.v.D., F.B.H.) and Epidemiology (M.D., A.S., W.W., F.B.H.), Harvard School of Public Health, Boston, MA; Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA (Q.S., W.W., F.B.H.); Department of Epidemiology, Fairbanks School of Public Health (J.H.) and Melvin and Bren Simon Cancer Center (J.H.), Indiana University, Indianapolis; Department of Preventive Medicine and Public Health, School of Medicine, Universidad Autónoma de Madrid/IdiPAZ, CIBER of Epidemiology and Public Health, Madrid, Spain (E.L.-G.); and Saw Swee Hock School of Public Health and Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore (R.M.v.D.).

Circulation
|November 18, 2015
PubMed
まとめ

カフェインを含んだコーヒーも カフェインなしのコーヒーも 死亡リスクの低下と関連しています 適度な摂取量である1日最大5杯は 総死亡率において 最も有意な効果を示しました

キーワード:
コーヒー死亡率喫煙

さらに関連する動画

Caffeine Extraction, Enzymatic Activity and Gene Expression of Caffeine Synthase from Plant Cell Suspensions09:11

Caffeine Extraction, Enzymatic Activity and Gene Expression of Caffeine Synthase from Plant Cell Suspensions

Published on: October 2, 2018

13.6K
Assaying β-amyloid Toxicity using a Transgenic C. elegans Model13:59

Assaying β-amyloid Toxicity using a Transgenic C. elegans Model

Published on: October 9, 2010

26.9K

関連する概念動画

Cause and Effect01:53

Cause and Effect

While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
12.7K
Causality in Epidemiology01:21

Causality in Epidemiology

Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
2.0K
Observational Studies01:11

Observational Studies

Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One...
11.5K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
1.2K
Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
830
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
1.0K