相关实验视频

Updated: Jul 16, 2025

Identifying Dysregulated Genes Induced by Kaposi's Sarcoma-associated Herpesvirus KSHV
07:02

Identifying Dysregulated Genes Induced by Kaposi's Sarcoma-associated Herpesvirus KSHV

Published on: September 14, 2010

12.7K

卡波西肉瘤发病率的全球模式和趋势:基于人口的研究

Leiwen Fu1, Tian Tian1, Bingyi Wang1

  • 1School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.

The Lancet. Global health
|September 21, 2023
PubMed
概括

卡波西肉瘤是艾滋病毒感染者常见的一种癌症,其全球发病率和死亡率显著,特别是在非洲. 解决医疗保健差异和改善艾滋病毒/艾滋病治疗是预防的关键.

更多相关视频

An In Vitro Model for Studying Cellular Transformation by Kaposi Sarcoma Herpesvirus09:53

An In Vitro Model for Studying Cellular Transformation by Kaposi Sarcoma Herpesvirus

Published on: August 25, 2017

8.4K
Quantitative Fluorescence In Situ Hybridization FISH and Immunofluorescence IF of Specific Gene Products in KSHV-Infected Cells00:06

Quantitative Fluorescence In Situ Hybridization FISH and Immunofluorescence IF of Specific Gene Products in KSHV-Infected Cells

Published on: August 27, 2019

12.5K

相关概念视频

Prevalence and Incidence01:08

Prevalence and Incidence

In statistical epidemiology and health sciences, two essential metrics—prevalence and incidence—are fundamental for understanding disease dynamics within a population. These measures enable public health officials, epidemiologists, and researchers to assess the burden of diseases, allocate resources effectively, and design impactful public health policies and interventions.
Prevalence indicates the proportion of individuals in a population who have a specific disease or health...
619
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
178
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...
380
Rous Sarcoma Virus (RSV) and Cancer01:03

Rous Sarcoma Virus (RSV) and Cancer

Rous Sarcoma virus or RSV was discovered by F. Peyton Rous in the year 1911 as a filterable transmissible agent that could cause tumors in chickens. He won a Nobel Prize for this discovery in 1966. His experiments clearly demonstrated that some cancers could be caused by infectious agents and led to the discovery of many more cancer-causing viruses in animals as well as humans.
RSV is a retrovirus that contains two copies of a plus-strand  RNA genome. Its genome consists of four main open...
5.1K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
13.6K
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
339