Chennai: Capsaicin, the compound responsible for chillies’ heat and also used in creams sold to relieve pain if taken in high doses can kill prostate cancer cells. Researchers from the Indian Institute of Technology, Madras, have worked out a process wherein the compound responsible for chillies’ heat can be put to yet another effective use.
In this study, researchers Ashok Kumar Mishra and Jitendriya Swain found that, in high doses, the compound causes cell membranes to come apart.
About 10 years ago, researchers reported that capsaicin can kill prostate cancer cells in mice while leaving healthy cells unharmed. But translating that dose for humans would require them to eat a huge number of chili peppers per day.
So the researchers tried to gain a deeper understanding of capsaicin’s effects so it might be harnessed in the future for new medicines.
The scientists were able to detect how the compound interacts with cell membranes by monitoring its natural fluorescence.
The study showed that capsaicin lodges in the membranes near the surface. Add enough of it, and the capsaicin essentially causes the membranes to come apart.
The findings appeared in The Journal of Physical Chemistry B.
The Indian Institute of Technology, IIT-Madras on Monday said its researchers have developed algorithms that enable novel applications for artificial intelligence (AI), machine learning and deep learning to solve engineering problems.
The researchers are going to establish a start-up to deploy their AI Software called ‘AISoft’ to develop solutions to engineering problems in varied fields such as in thermal management, semiconductors, automobile, aerospace and electronic cooling applications.
“We tested AIsoft and used it to solve such thermal management problems. We found it to be nearly million-fold faster compared to existing solutions currently used in the field,” said Vishal Nandigana, Assistant Professor, Fluid Systems Laboratory, Department of Mechanical Engineering.
“Our AI works on any generalised rectilinear and curvilinear input geometry. Our research saves the computational time, which is the bottleneck to solve most engineering problems, Nandigana added.
The researchers utilised a data-driven AI and a deep learning model to arrive at solutions for engineering problems after training the AI with data sets.
These prior data sets can be from existing big data in the relevant engineering industry where there are lots of experimental data available.
Also, if data is not available for training the AI, it can be generated using commercially-available CFD (Computational Fluid Dynamics) software on small independent pieces of the full-blown problem.
This idea is new and is only now being looked at by a few research groups across the world. Most of these research groups use Convolutional Neural Networks (CNN) or C-GAN (conditional generative adversarial network) to solve engineering problems.
They have also developed hardware products using graphics processing unit (GPU) and multi-threading processing to solve thermal management problems in thermal and electronic cooling industries.