NUCLEIC-ACID AMPLIFICATION TESTS

NUCLEIC-ACID AMPLIFICATION TESTS

Nucleic-acid amplification tests, also known as NATs or NAATs, are used to identify small amounts of DNA or RNA in test samples. When it comes to STD testing, there are NATs available that can detect a variety of different STDs. In fact, most urine tests for STDs are performed using nucleic-acid amplification tests.

MECHANISM

A NAT uses a series of repeated chemical reactions to make numerous copies of the DNA or RNA that doctors are trying to detect. These reactions amplify the signal of the nucleic acids in the test sample so that they are easier to identify. It's much simpler to find 10,000 copies of a gene than 10. 

once the amount of DNA or RNA has been increased in the sample using PCR or LCR, more conventional tests are used to detect it. These tests usually involve some form of nucleic acid hybridization. In those tests, the sample is probed with an artificially produced complementary strand of DNA or RNA that has been labelled in some way that makes it easy to detect. It may help to picture it as a glow in the dark tag that only sticks to one very specific piece of identifying information.

STD TESTING

Nucleic-acid amplification tests are incredibly useful for STD testing. They allow doctors to detect an STD pathogen even when only a very small number of organisms are present. It is this sort of technology that has made it possible to do urine testing for STDs that were previously only detectable by swab.

Furthermore, since nucleic-acid amplification tests are incredibly sensitive to even small amounts of viral DNA, they are very important for screening the blood supply. These tests make it possible to detect tiny amounts of HIV and other blood-borne pathogens that might otherwise be missed.

There are also non-amplified nucleic acid tests available for certain STDs, such as gonorrhea and chlamydia. Non-amplified nucleic acid hybridization tests are more likely to be used when large amounts of bacterial or viral DNA (or RNA) would be expected to be present, such as in a urethral swab or in a bacterial culture sample. In such circumstances, no amplification is necessary. In these samples, if DNA or RNA is present, it should be present in detectable amounts.

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Longer Period of HIV Suppression shown reducing Cancer Risk in Aging HIV Population

Early, sustained antiretroviral therapy (ART), which results in long-term viral suppression, helps to prevent AIDS-defining cancers and also non-AIDS-defining cancers, to a lesser degree. However, patients with long-term viral suppression still had excess cancer risk compared to uninfected patients. The study, published in Annals of Internal Medicine, is the first to examine the effects of prolonged periods of viral suppression and potential cancer prevention benefits for the aging population of persons living with HIV.

Persons with HIV are at increased risk for AIDS-defining cancers (Kaposi sarcoma, non-Hodgkin lymphoma, invasive cervical cancer) and non-AIDS-defining cancers (lung, larynx, melanoma, leukemia, etc.). Some of these cancers are known to be caused by viruses (certain anal, liver, Hodgkin lymphoma, etc.) that are common among persons with HIV. Viral suppression is a key component of HIV treatment, and studies have shown an association between prolonged viral suppression and decreased risk for some types of cancer. However, no studies have specifically focused on the effect of sustained viral suppression on overall cancer risk.

Researchers from the Stanford Center for Population Health Sciences compared cancer rates for 42,441 HIV-positive veterans with those of 104,712 demographically-matched uninfected veterans from 1999-2015 to determine whether long-term viral suppression was associated with decreased cancer risk. They found that cancer risk was highest in the unsuppressed state, lower in early suppression, lower still in long-term suppression, and lowest in uninfected patients for all cancer, AIDS-defining cancer, virus non-AIDS-defining-cancer, and several cancer types.

According to the authors, these findings are helpful to both infectious disease and internal medicine clinicians who care for the population of aging HIV-positive patients. Understanding how HIV interacts with viral coinfections and results in higher risks of cancer may offer critical insight in how to better prevent and treat these cancers for everyone.

Source: https://www.sciencedaily.com/releases/2018/06/180611172420.htm

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Algorithm to Reduce Spread of Infectious Diseases

Public outreach campaigns can prevent the spread of devastating yet treatable diseases such as tuberculosis (TB), malaria and gonorrhea. But ensuring these campaigns effectively reach undiagnosed patients, who may unknowingly spread the disease to others, is a major challenge for cash-strapped public health agencies. Now, a team of USC Viterbi School of Engineering researchers has created an algorithm that can help policymakers reduce the overall spread of disease. The algorithm is also optimized to make the most of limited resources, such as advertising budgets.

To create the algorithm, the researchers used data, including behavioral, demographic and epidemic disease trends, to create a model of disease spread that captures underlying population dynamics and contact patterns between people.

Using computer simulations, the researchers tested the algorithm on two real-world cases: tuberculosis (TB) in India and gonorrhea in the United States. In both cases, they found the algorithm did a better job at reducing disease cases than current health outreach policies by sharing information about these diseases with individuals who might be most at risk.

The study was published in the AAAI Conference on Artificial Intelligence. The authors are Bryan Wilder, a candidate for a PhD in computer science, Milind Tambe, the Helen N. and Emmett H. Jones Professor in Engineering, a professor of computer science and industrial and systems engineering and co-founder of the USC Center for AI in Society and Sze-chuan Suen, an assistant professor in industrial and systems engineering.

"Our study shows that a sophisticated algorithm can substantially reduce disease spread overall," says Wilder, the first author of the paper. "We can make a big difference, and even save lives, just by being a little bit smarter about how we use resources and share health information with the public."

Revealing disease dynamics

The algorithm also appeared to make more strategic use of resources. The team found it concentrated heavily on particular groups and did not simply allocate more budget to groups with a high prevalence of the disease. This seems to indicate that the algorithm is leveraging non-obvious patterns and taking advantage of sometimes-subtle interactions between variables that humans may not be able to pinpoint.

The team's mathematical models also consider that people move, age, and die, reflecting more realistic population dynamics than many existing algorithms for disease control. For instance, people may not be cured instantly, so reducing prevalence at age 30 could mean creating targeted public health communications for people at age 27.

"While there are many methods to identify patient populations for health outreach campaigns, not many consider the interaction between changing population patterns and disease dynamics over time," says Suen, who also holds an appointment as assistant professor in the Leonard D. Schaeffer Center for Health Policy and Economics.

"Fewer still consider how to use an algorithmic approach to optimize these policies given the uncertainty of our estimates of these disease dynamics. We take both effects into account in our approach."

Since transmission patterns for infection vary with age, the research team used age-stratified data to determine the optimal targeted audience demographic for public health communications. But the algorithm could also segment populations using other variables, including gender and location.

In the future, the study's insights could also shed light on health outcomes for other infectious disease interventions, such as HIV or the flu.

Source:

https://www.news-medical.net/news/20180221/Researchers-create-algorithm-to-reduce-spread-ofc2a0infectious-diseases.aspx

Explore more about the advancements in the field of sexually transmitted diseases at STD 2018. To speak at the conference, submit your abstracts here: https:/std-hiv-aids.cmesociety.com/abstract-submission