Nissan Motor to infuse over Rs 2 crore to boost Tamil Nadu's health infrastructure
In partnership with the non-governmental organization Hand-in-Hand (HiH India), the auto-major renovated the Medical Out-Patient Department (MOPD) and would also take up the construction of a new waiting hall at the hospital.
Chennai: Japanese car maker Nissan Motor has committed an investment of over Rs two crore to upgrade the infrastructure at the medical college and hospital in the neighbouring Chengalpet district, the company said on Wednesday.
In partnership with the non-governmental organization Hand-in-Hand (HiH India), the auto-major renovated the Medical Out-Patient Department (MOPD) and would also take up the construction of a new waiting hall at the hospital campus.
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"Guided by our global vision of People First, Nissan India has been working on numerous corporate social responsibility initiatives in the areas of community development, road safety, health and well-being, promoting equity by benefiting the most disadvantaged and marginalised groups," Nissan Motor Managing Director Rakesh Srivastava said in a statement here.
"The new and renovated infrastructure at the hospital will serve a large number of Tamil Nadu residents and is a significant step forward in strengthening healthcare access..," he said.
The Medical Out-Patient Department renovated recently and was inaugurated by Tamil Nadu Minister for Micro, Small and Medium Enterprises Thamo' Anbarasan in the presence of senior government officials including Chengalpet district collect Ragul Nath, the statement said.
Medical Dialogues team had earlier reported that the Indian Institute of Technology Madras (IIT Madras) Researchers have developed an Artificial Intelligence-based tool, 'PIVOT', that can predict the cancer-causing genes in an individual. This tool will ultimately help in devising personalized cancer treatment strategies.
'PIVOT,' developed by IIT Madras researchers, is designed to predict genes that are responsible for causing cancer in an individual. The prediction is based on a model that utilizes information on mutations, expression of genes, and copy number variation in genes and perturbations in the biological network due to an altered gene expression.
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