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Cornell University

PORTENT

A Center for Point of Care Technologies for Nutrition, Infection and Cancer

Year 2 Projects

Uganda PoC Study for Decentralized HbA1c Testing in Diabetes Management

LumiraDx platform for HbA1C POC Testing
LumiraDx platform for HbA1C POC Testing

PI: Martin Balaba
Infectious Diseases Institute, Uganda

This study aims to validate a point-of-care (PoC) HbA1c testing device to improve diabetes management within Uganda’s decentralized healthcare settings. By enabling accessible HbA1c testing at health centers and pharmacies, the project aligns with the Ugandan Ministry of Health’s objectives to enhance diabetes care, especially in resource-limited and rural areas. The testing will support diabetes monitoring, allowing timely adjustments to care plans that are crucial for managing blood glucose levels and reducing risks of diabetes-related complications.

STI testing and AMR profiling

PhenEXA: Direct-from-Specimen Rapid Multiplexed Diagnostics

PI: Sara Mahshid
McGill University, Canada

Sexually Transmitted Infections (STIs) are one of the most common communicable diseases and a major cause of mortality worldwide, with over 1 million infections every day. The problem is only aggravated by rising antimicrobial resistance in STIs. Neisseria gonorrhea (NG) is one of the most common STI in the world, with significant socio-economic and mortality burden, particularly among low-resource and vulnerable populations. This study aims to have an impact on STI diagnosis and treatment in resource-limited settings. It proposes the point of care device, QolorEX, to detect NG and profile antibiotic resistance with a single visit ‘test and treat’ approach in a primary healthcare center with optimal Target Product Profile characteristics.

POC Rapid Test for Oral Cancer

A non-invasive ELISA-based beta-defensin index detection platform for oral squamous cell carcinoma

PI: Umut Gurkan
Case Western University

Oral squamous cell carcinoma (OSCC), a major subtype of head and neck cancer (HNC), claims the lives of hundreds of thousands across the world annually. The gold standard for diagnosing OSCC, is a biopsy followed by a pathology review, which is painful, invasive, and costly. For these reasons, a non-invasive POC rapid test in the primary care setting to discriminate OSCC lesions from benign ones is an attractive alternative and could be used to determine if a patient requires a biopsy. The proposed project addresses a recognized unmet need for screening and early detection of oral cancer in underserved populations and limited resource settings.

Tentative Project – UTI Antibiotic Susceptibility

Jiddu Analyzer for rapid, accurate diagnosis and antibiotic sensitivity testing for UTIs

PI: Christopher Skipwith
Astek Diagnostics

Astek Diagnostics’ Jiddu Analyzer addresses the critical unmet need for rapid, accurate diagnosis and antibiotic sensitivity testing for urinary tract infections (UTIs), a prevalent condition that significantly contributes to the global health burden of antibiotic resistance. Current diagnostic methods, including slow urine cultures and inaccurate dipstick tests, lead to mismanagement and overuse of antibiotics, exacerbating antibiotic resistance and causing adverse health outcomes. Through this device, the researchers aims to serve vulnerable populations, particularly women who are at a higher risk for UTIs, by facilitating timely and precise treatment, thus reducing the misuse of antibiotics and addressing a global health challenge.

Tentative Project – AI based Oral Cancer Screening 

Overview of Berry.care workflow, an AI-powered mobile app enabling early detection of Oral Cancer

PI: Hari Menon
St. John’s Research Institute, India 

Oral cancer is a significant global health issue, especially in low-resource settings where late detection results in high mortality rates. At present, oral cancer screening relies on visual inspection by medical professionals. However, there is a dearth of trained healthcare professionals in low-resource settings and clinical services are not affordable for widespread screening. Other technological solutions for oral cancer screening require specialized training and/or expensive hardware. This project aims to improve early detection rates of oral cancer and oral premalignant conditions through the deployment of Berrycare, an AI powered mobile application. It screens images captured from mobile phones using a deep learning algorithm.