Thursday April 2, 2020

Here’s Why Aspirin May Help in Reducing Colorectal Cancer Risk, Tumour Growth

The study, published in the journal Carcinogenesis, used mouse models and mathematical modeling to parallel the amount of daily aspirin people in the US and Europe are taking in clinical trials

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Aspirin
The trick now is to determine the right dosage of aspirin that can be used as a daily prophylactic without triggering dangerous side effects such as stomach and brain bleeds. Pixabay

The benefits of a daily aspirin may extend beyond heart health to colorectal cancer treatment, say researchers, adding that they have found that aspirin appears to reduce tumour growth and inhibit recurrence of the disease.

The trick now is to determine the right dosage of aspirin that can be used as a daily prophylactic without triggering dangerous side effects such as stomach and brain bleeds, the research said.

“Some might say aspirin is a ‘miracle drug’ because of its potential to prevent diseases that result from chronic inflammation, such as cancer, Alzheimer’s, Parkinson’s and arthritis,” said Indian-origin study researhcher Ajay Goel from the City of Hope clinic in the US.

The reason aspirin isn’t currently being used to prevent these diseases is because taking too much of any anti-inflammatory eats at the stomach’s mucus lining and causes gastrointestinal and other problems. “We are getting closer to discovering the right amount of daily aspirin needed to treat and prevent colorectal cancer without causing scary side effects,” Goel added.

The study, published in the journal Carcinogenesis, used mouse models and mathematical modeling to parallel the amount of daily aspirin people in the US and Europe are taking in clinical trials. The research team tested three varying daily doses of aspirin in four colorectal cancer cell lines, including tumours with microsatellite instability and mutations in the PIK3CA gene, which has been tied to increased risk of endometrial, colon and aggressive breast cancers.

Then the researchers divided 432 mice into four groups: control, low-dose aspirin (15mg/kg), medium-dose aspirin (50mg/kg) and high-dose aspirin (100mg/kg) — the mouse equivalent of 100mg, 300mg and 600mg for humans. The tumours from three mice in each treatment group were analysed on days three, five, seven, nine and 11.

Researchers inspected “cellular apoptosis” (programmed cell death) and found that the percentage of cells programmed to die increased in all cell lines. Exactly how much, however, depended on the amount of aspirin that was consumed, suggesting that aspirin triggers a domino effect of cell death in all colorectal cell lines regardless of genetic background.

Aspirin
The benefits of a daily aspirin may extend beyond heart health to colorectal cancer treatment, say researchers, adding that they have found that aspirin appears to reduce tumour growth and inhibit recurrence of the disease. Pixabay

The research found that as the aspirin doses increased, the rate of cell death increased while the division rates of cells decreased, meaning tumour cells were more likely to die and not proliferate. Notably, the scientists observed that low-dose aspirin was especially effective in suppressing tumour growth in animal models that had more PIK3CA genes.

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The finding was significant because the mutated version of these genes has been associated with increased risk of certain cancers, the researchers said. “We are now working with some of the people conducting those human clinical trials to analyse data and use mathematical modelling. This process adds a layer of confidence to the findings and guides future human trial designs,” Goel said. (IANS)

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Researchers Develop AI Algorithm That Can Diagnose Skin Diseases

The researchers caution that AI cannot definitively interpret images, that it is not trained to interpret even when the problem presented is straightforward

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AI
Recently, there have been remarkable advances in the use of AI in medicine. Pixabay

Researchers have developed a deep learning-based artificial intelligence (AI) algorithm that can accurately classify cutaneous skin disorders, predict malignancy, suggest primary treatment options, and serve as an ancillary tool to enhance the diagnostic accuracy of clinicians. With the assistance of this system, the diagnostic accuracy of dermatologists, as well as the general public, was significantly improved, said the study, published in the Journal of Investigative Dermatology.

Skin diseases are common, but it is not always easy to visit a dermatologist quickly or distinguish malignant from benign conditions. “Recently, there have been remarkable advances in the use of AI in medicine. For specific problems, such as distinguishing between melanoma and nevi, AI has shown results comparable to those of human dermatologists,” said lead investigator Jung-Im Na from Seoul National University in South Korea. Most prior studies have been limited to specific binary tasks, such as differentiating melanoma from nevi.

“Our results suggest that our algorithm may serve as an Augmented Intelligence that can empower medical professionals in diagnostic dermatology,” Na added.

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Using a “convolutional neural network,” a specialised AI algorithm, the research team developed an AI system capable of predicting malignancy, suggesting treatment options, and classifying skin disorders. They collected 220,000 images of Asians and Caucasians with 174 skin diseases and trained neural networks to interpret those images.

AI
Researchers have developed a deep learning-based artificial intelligence (AI) algorithm that can accurately classify cutaneous skin disorders. Pixabay

They found that the algorithm could diagnose 134 skin disorders and suggest primary treatment options, render multi-class classification among disorders, and enhance the performance of medical professionals through Augmented Intelligence.”Rather than AI replacing humans, we expect AI to support humans as Augmented Intelligence to reach diagnoses faster and more accurately,” Na said.

The researchers caution that AI cannot definitively interpret images, that it is not trained to interpret even when the problem presented is straightforward. For example, an algorithm trained only to differentiate between melanoma and nevi cannot differentiate between an image of a nail hematoma and either a melanoma or a nevus. If the shape of the hematoma is irregular, the algorithm may diagnose it as melanoma.

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They also pointed out that the algorithm was trained and tested using high-quality images and its performance is generally suboptimal if the input images are of low quality. “We anticipate that the use of our algorithm with a smartphone could encourage the public to visit specialists for cancerous lesions such as melanoma that might have been neglected otherwise, however, there are issues with the quality or composition of photographs taken by the general public that may affect the results of the algorithm, Na said. (IANS)