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Retrovirus Life Cycles

Retrovirus Life Cycles

Retroviruses have a single-stranded RNA genome that undergoes a special form of replication. Once the retrovirus has entered the host cell, an enzyme called reverse transcriptase synthesizes double-stranded DNA from the retroviral RNA genome. This DNA copy of the genome is then integrated into the host’s genome inside the nucleus via an enzyme called integrase. Consequently, the retroviral genome is transcribed into RNA whenever the host’s genome is transcribed, allowing the...

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Related Experiment Video

Updated: Dec 28, 2025

Rapid Screening of HIV Reverse Transcriptase and Integrase Inhibitors

Rapid Screening of HIV Reverse Transcriptase and Integrase Inhibitors

Published on: April 9, 2014

Optimizing Screening for HIV.

Antoine Chaillon¹, Martin Hoenigl¹, Lorri Freitas²

¹Department of Medicine, University of California San Diego, San Diego, California, USA.

Open Forum Infectious Diseases

|February 15, 2020

Summary

This summary is machine-generated.

HIV testing centers effectively reached at-risk populations in San Diego County, California. Testing numbers correlated with HIV and sexually transmitted infection (STI) prevalence, indicating efficient resource allocation for HIV screening.

Keywords:

HIV prevalence screening sexually transmitted infections

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Area of Science:

Public Health
Epidemiology
Infectious Disease Prevention

Background:

The Human Immunodeficiency Virus (HIV) epidemic distribution is geographically varied within the United States.
Understanding testing efficacy in reaching at-risk populations is crucial for targeted interventions.

Purpose of the Study:

To evaluate the effectiveness of current HIV testing strategies in reaching at-risk individuals across San Diego County.
To analyze the correlation between HIV/STI diagnoses and screening efforts at a granular geographic level.

Main Methods:

Utilized HIV case, testing, and sexually transmitted infection (STI) data from San Diego Health and Human Services Agency and the Early Test program (1998-2016).
Employed ZIP code-level analysis to assess relationships between HIV/STI diagnoses and HIV screening numbers.

Main Results:

A strong correlation was observed between the number of HIV tests performed and prevalent HIV cases (R² = .714), new HIV diagnoses (R² = .798), and STI diagnoses (R² = .655–.836).
High-prevalence ZIP codes showed the highest test numbers per resident but fewer tests per diagnosis, suggesting efficient targeting.
Screening in lower-prevalence areas was proportional to local HIV and STI rates, indicating broad reach.

Conclusions:

Standalone HIV testing centers demonstrated effectiveness in reaching dispersed at-risk populations in San Diego County.
The methodology can identify geographic areas or demographic groups needing intensified HIV screening efforts.