Sunday March 29, 2020

‘Epilepsy drug during pregnancy ups the oral cleft risk in babies’

The findings are based on data on more than one million live births over a period of 10 years in the US.

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Low doses of topiramate may also increase the risk of oral clefts but to a lesser extent. Wikimedia Commons
Low doses of topiramate may also increase the risk of oral clefts but to a lesser extent. Wikimedia Commons

The study, published in the journal Neurology, said the risk is particularly high when the drug is used in high doses. “Our results suggest that the increased risk of oral clefts is most pronounced in women taking higher doses of topiramate to treat epilepsy,” said study co-author Elisabetta Patorno of Brigham and Women’s Hospital in Boston, US.

“Low doses of topiramate may also increase the risk of oral clefts but to a lesser extent,” Patorno said. “We hope that this work gives important information to women and their clinicians as they determine the best course of treatment and options available to individuals,” Patorno added. The findings are based on data on more than one million live births over a period of 10 years in the US.

Epilepsy is likely due to the higher doses of topiramate when used for controlling seizures. Wikimedia Commons
Epilepsy is likely due to the higher doses of topiramate when used for controlling seizures. Wikimedia Commons

The team examined the risk of oral clefts including cleft palate or cleft lip among three groups infants born to women who had taken topiramate in their first trimester; infants born to women who had taken the drug lamotrigine (an unrelated drug used to treat bipolar disorder and epilepsy); and infants who had not been exposed to anti-epileptic medications in utero.

The researchers found that the risk of oral clefts was approximately three times higher for the topiramate group than for either the lamotrigine or the unexposed group.

“Our results suggest that women with epilepsy on topiramate have the highest relative risk of giving birth to a baby with cleft lip or cleft palate, likely due to the higher doses of topiramate when used for controlling seizures,” said corresponding author Sonia Hernandez-Diaz of the Harvard T.H. Chan School of Public Health. “The best course may be to avoid prescribing high doses of topiramate to women of childbearing age unless the benefits clearly outweigh the risks,” she added. IANS

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New AI Algorithms Can Predict Drug Response Lung Cancer

AI can better predict drug response to lung cancer therapies

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Lung cancer
With AI, lung cancer imaging can move from an inherently subjective tool to a quantitative and objective asset for precision medicine approaches. Pixabay

Researchers have used Artificial Intelligence (AI) to train algorithms and predict tumour sensitivity in three advanced non-small cell lung cancer therapies which can help predict more accurate treatment efficacy at an early stage of the disease.

The researchers at Columbia University’s Irving Medical Center analyzed CT images from 92 patients receiving drug agent nivolumab in two trials; 50 patients receiving docetaxel in one trial; and 46 patients receiving gefitinib in one trial.

To develop the model, the researchers used the CT images taken at baseline and on first-treatment assessment.

“The purpose of this study was to train cutting-edge AI technologies to predict patients’ responses to treatment, allowing radiologists to deliver more accurate and reproducible predictions of treatment efficacy at an early stage of the disease,” explained Laurent Dercle, associate research scientist at the Columbia University Irving Medical Center.

Lung cancer
Researchers have used Artificial Intelligence (AI) to train algorithms and predict tumour sensitivity in three advanced non-small cell lung cancer therapies. Pixabay

Radiologists currently quantify changes in tumour size and the appearance of new tumour lesions. However, this type of evaluation can be limited, especially in patients treated with immunotherapy, who can display atypical patterns of response and progression.

“Newer systemic therapies prompt the need for alternative metrics for response assessment, which can shape therapeutic decision-making,”
Dercle said in a paper appeared in the journal Clinical Cancer Research.
The researchers used machine learning to develop a model to predict treatment sensitivity in the training cohort.

Each model could predict a score ranging from zero (highest treatment sensitivity) to one (highest treatment insensitivity) based on the change of the largest measurable lung lesion identified at baseline.

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“We observed that similar radiomics features predicted three different drug responses in patients with advanced non-small cell lung cancer (NSCLC) ,” Dercle said.

“With AI, cancer imaging can move from an inherently subjective tool to a quantitative and objective asset for precision medicine approaches,” he added. (IANS)