Tuesday December 10, 2019

A Molecule in Immune System Can Target and Kill Cancer Cells: Study

Crucially, there is a need to induce the immune system to ensure long-term protection against the recurrence of cancer

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Immune
Current approaches to achieve this involve killing Cancer cells by using chemotherapeutics and other agents which could be harmful and have uncertain outcomes other than Immune Bacteria. Pixabay

Researchers have found a naturally occurring molecule and a component of the Immune system that could successfully target and kill Cancer cells, according to a study.

The study, published in British Journal of Cancer, discovered that beta-galactoside-binding protein, a naturally occurring molecule produced by immune cells can non-specifically target cancer cells, make them undergo cell death and through a stress response pathway make the cancer cells visible to the immune system to prompt an anti-cancer immune response that would secure protection against recurrences.

“By contrast, the anti-tumour property of the molecule is selective and not harmful to normal cells. It is effective against the most aggressive colorectal cancer cells and a wide range of other cancer cells equally unresponsive to current therapies,” said study lead author Professor Livio Mallucci from King’s College London.

“This research presents experimental evidence for a strategy where the targeting of cancer cells and the stimulation of immunity combine to prompt immediate and long-term responses against aggressive cancer,” he said.

According to the researchers, major developments in anti-cancer therapies have taken place over the last decade, but as only a subset of patients respond to treatments, there is a need for further development.

Crucially, there is a need to induce the immune system to ensure long-term protection against the recurrence of cancer.

Immune
Researchers have found a naturally occurring molecule and a component of the Immune system that could successfully target and kill Cancer cells, according to a study. Pixabay

Current approaches to achieve this involve killing cells by using chemotherapeutics and other agents which could be harmful and have uncertain outcomes, the study said.

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“Translation of the molecule to the clinic could open a new therapeutic opportunity which safely combines direct killing of cancer cells and the stimulation of the immune system against recurrences, a significant step forward in the management of cancer,” he added. (IANS)

Next Story

Machine Learning Can Help Doctors to Improve End-Of-Life Conversation with Patients

A deeper understanding of these conversations, which are often freighted with emotion and uncertainty, will also help reveal what aspects or behaviors associated with these conversations are more valuable for patients and families

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Machine Learning
A Research used Machine Learning algorithms to analyze 354 transcripts of palliative care conversations collected by the Palliative Care Communication Research Initiative, involving 231 patients. Pixabay

Researchers at University of Vermont have used Machine Learning and natural language processing (NLP) to better understand conversations about death, which could eventually help doctors improve their end-of-life communication.

Some of the most important, and difficult, conversations in healthcare are the ones that happen amid serious and life-threatening illnesses.

Discussions of the treatment options and prognoses in these settings are a delicate balance for doctors and nurses who are dealing with people at their most vulnerable point and may not fully understand what the future holds.

“We want to understand this complex thing called a conversation. Our major goal is to scale up the measurement of conversations so we can re-engineer the healthcare system to communicate better,” said Robert Gramling, director of the Vermont Conversation Lab in the study published in the journal Patient Education and Counselling.

Gramling and his colleagues used machine learning algorithms to analyze 354 transcripts of palliative care conversations collected by the Palliative Care Communication Research Initiative, involving 231 patients.

They broke each conversation into 10 parts with an equal number of words in each, and examined how the frequency and distribution of words referring to time, illness terminology, sentiment and words indicating possibility and desirability changed between each decile.

“We picked up some strong signals,” said Gramling.

Conversations tended to progress from talking about the past to talking about the future, and from sadder to happier sentiments. “There was quite a range, they went from pretty sad to pretty happy,” Gramling added.

Machine Learning
Researchers at University of Vermont have used Machine Learning and natural language processing (NLP) to better understand conversations about death, which could eventually help doctors improve their end-of-life communication. Pixabay

The consistent results across multiple conversations show just how much people make meaning out of stories in healthcare.

“What we found supports the importance of narrative in medicine,” he said.

That knowledge could eventually help healthcare practitioners understand what makes a “good” conversation about palliative care, and how different kinds of conversations might require different responses.
That could help create interventions that are matched to what the conversation indicates the patient needs the most.

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A deeper understanding of these conversations, which are often freighted with emotion and uncertainty, will also help reveal what aspects or behaviors associated with these conversations are more valuable for patients and families. (IANS)