Wednesday December 11, 2019
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Exposure to airborne ultrasound causes head related diseases

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New Delhi: Places like railway station, museums, sports stadium, libraries, schools has higher expose to airborne ultrasound which can lead to diseases like nausea, dizziness, migraine, fatigue and tinnitus.

According to the university of Southampton, people are being exposed to airborne ultrasound without their knowledge in public places. Loudspeakers, door sensors, public addressing system can generate airborne ultrasound in public places.

Professor Tim Leighton collected readings of very high frequency/ultrasonic VHF/US fields in several public buildings using smartphones and tablets equipped with an app. The findings were then calibrated with two or three independent microphone and audio data systems.

Study author Professor Tim Leighton found that members of the public were exposed to VHF/US levels over 20 kHz, which is the threshold of the current guidelines.

People who are unaware of this airborne exposure are complaining, for themselves and their children.

One in 20 people between the age of 40-49 years is suffering from hearing threshold that is a minimum of 20(DB) which becomes more sensitive at 20KH than that of people aged between 30-39.

Five percent of the People between the age of 5-19 are likely to have 20KH threshold that of 60(DB) more sensitive than the median for the 30-39 year age group.

The current reports are not capable for fighting with the mass exposure, which a large number of people is facing. Current guidelines are not meeting the requirements for such a large public who is being exposed to airborne ultrasound. These guidelines are roughly collected on the basis of small groups’ mainly male adults.

Over a period of time, many workers are being exposed to occupational industrial ultrasound through industrial devices for cleaning and drilling which affects them negatively.

It’s very much necessary that suffers can able to identify whether they are suffering from  VHF/US exposure or not. The lack of research states that it is not possible to prove or disprove the health risk of public.(IANS)(Image-huffingtonpost)

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AI Can Better Help Doctors to Identify Cancer Cells in Human Body

The process of manually identifying all the cells in a pathology slide is extremely labor intensive and error-prone

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Cancer
The AI algorithm helps pathologists obtain the most accurate Cancer cell analysis - in a much faster way. Pixabay

Researchers at University of Texas Southwestern have developed a software tool that uses Artificial Intelligence (AI) to recognize Cancer cells from digital pathology images – giving clinicians a powerful way of predicting patient outcomes.

The spatial distribution of different types of cells can reveal a cancer’s growth pattern, its relationship with the surrounding microenvironment, and the body’s immune response.

But the process of manually identifying all the cells in a pathology slide is extremely labor intensive and error-prone.

“To make a diagnosis, pathologists usually only examine several ‘representative’ regions in detail, rather than the whole slide. However, some important details could be missed by this approach,” said Dr. Guanghua “Andy” Xiao, corresponding author of a study published in EbioMedicine.

A major technical challenge in systematically studying the tumor microenvironment is how to automatically classify different types of cells and quantify their spatial distributions.

The AI algorithm that Dr Xiao and his team developed, called “ConvPath”, overcomes these obstacles by using AI to classify cell types from lung cancer pathology images.

Cancer
Researchers at University of Texas Southwestern have developed a software tool that uses Artificial Intelligence (AI) to recognize Cancer Cells from digital pathology images – giving clinicians a powerful way of predicting patient outcomes. Pixabay

The ConvPath algorithm can “look” at cells and identify their types based on their appearance in the pathology images using an AI algorithm that learns from human pathologists.

The algorithm helps pathologists obtain the most accurate cancer cell analysis – in a much faster way.

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“It is time-consuming and difficult for pathologists to locate very small tumour regions in tissue images, so this could greatly reduce the time that pathologists need to spend on each image,” said Dr Xiao. (IANS)