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Neha Chaudhary on Muck Rack

Neha Chaudhary

San Francisco
As seen in: CNN, MSN Canada, The New York Times, Yahoo Life, Nature, The Independent (UK), The Times of India, The Washington Post, Wiley Online Library, Yahoo Canada and
Chief Medical Officer @ Headway |Child Psychiatrist @HarvardMed MGH @StanfordMHiLab |BL NYT,WaPo,CNN,ABC |Advise @madeofmillions_ @thinkkids @mindsitenews
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Articles

Enhancing clinical skills and critical management strategies for Neonatal Perinatal Medicine senior fellows through simulation boot camp: an 8-year experience

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By Neha Chaudhary, Romal K. Jassar, Indrani Bhattacharjee, Rachana Singh, Jaclyn Boulais, Michael Zujkowski, Brooke Krbec
| Nature Verified
Abstract Neonatal-Perinatal Medicine (NPM) Fellowship programs have the responsibility of providing realistic exposure to challenging and complex scenarios to trainees, to help develop sound knowledge, skills, and attitudes to be successful neonatologists. Changing ICU exposure time and work hour restrictions has made the role of simulation-based training even more essential.
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Non-invasive biomarkers for early oral cancer detection through multiomics approach

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By Tarun Verma, Siddharth Singh Verified, Neha Chaudhary, Anjali Chaudhary, Rajesh Chandramohanadas, Deepak Agrawal, Hem Chandra Jha, Sreenath Meloth, Paniki Nad
| Nature Verified
Abstract Non-invasive biomarker-based approaches are urgently needed to support early identification and monitoring of Oral squamous cell carcinoma (OSCC) and its precursor, oral submucous fibrosis (OSMF). We employed combined metabolomic and proteomic analyses to profile saliva and lesion surface swabs from clinically diagnosed OSCC (n = 15), OSMF (n = 20), and healthy controls (n = 27).
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Enhancing spectral analysis of ghee adulteration via a deep learning-based multimodal attention mechanism

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By Aditya Upadhyay, Neha Chaudhary
| Nature Verified
Abstract Expensive dairy products like ghee are at high risk of hazardous adulteration with cheaper fats, and traditional testing approaches such as FTIR device and conventional machine learning are too slow, complex, and lack the interpretability to be useful for practical food safety. To address this, we propose a new deep learning framework which is robust and highly interpretable.
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