Tag Archives: Infection Control

Covid-19: ‘Intranasal Vaccines’ Might Be More Effective Than Needles

From Scientific American (March 1, 2021):

Enter the intranasal vaccine, which abandons the needle and syringe for a spray container that looks more like a nasal decongestant. With a quick spritz up the nose, intranasal vaccines are designed to bolster immune defenses in the mucosa, triggering production of an antibody known as immunoglobulin A, which can block infection. This overwhelming response, called sterilizing immunity, reduces the chance that people will pass on the virus.

The development of highly effective COVID vaccines in less than a year is an extraordinary triumph of science. But several coronavirus variants have emerged that could at least partly evade the immune response induced by the vaccines. These variants should serve as a warning against complacency—and encourage us to explore a different type of vaccination, delivered as a spray in the nose. Intranasal vaccines could provide an additional degree of protection, and help reduce the spread of the virus.

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Covid-19: ‘The Evolving Science Of Masks’ (Video)

How can you tell if the mask you’re wearing is protective enough against the coronavirus? Correspondent David Pogue volunteers as a test subject to see how N95s work and learns about the science of face coverings.

Future Of Health Care: New Machine Learning System Detects Infections 48 Hours In Advance

From a Health IT Analytics online release:

Philips Machine Learning SystemThe prototype revealed that using artificial intelligence and machine learning to examine certain combinations of vital signs and other biomarkers could strongly predict the likelihood of infection up to 48 hours in advance of clinical suspicion, including observable symptoms.

Royal Philips, in collaboration with the Defense Threat Reduction Agency (DTRA) and Defense Innovation Unit (DIU) of the US Department of Defense (DoD), are building a machine learning algorithm that will be able to detect an infection before a patient shows signs or symptoms.

The partnering organizations recently announced results from an 18-month project, called Rapid Analysis of Threat Exposure (RATE), the first large-scale exploration of pre-symptomatic infection in humans. The project aims to develop an early warning system that accelerates diagnosis and treatment of infection, containing the spread of communicable disease.

To read more: https://healthitanalytics.com/news/philips-dod-build-machine-learning-system-to-detect-infection