Back close

40 Plus- A Paper Based Microfluidic Chip for the Monitoring of Women Health After 40

Project Incharge:Dr. Satheesh Babu T. G.
40 Plus- A Paper Based Microfluidic Chip for the Monitoring of Women Health After 40

The project titled ”Fabrication of a Lab-on-a-Chip Device for the Point of Care Testing of Hemoglobin” is funded by DST and Dr. T. G. Satheesh Babu, Associate Professor, Department of Sciences, School of Engineering, Coimbatore,  is the Principal Investigator.

The onset of menopause can invite life threatening diseases like osteoporosis. By regular monitoring of a panel of biomarkers, health of women can be monitored and morbidity levels associated with menopause can be reduced. The proposed work envisages the fabrication of a paper based microfluidic module for optical detection of bone turnover markers and bone health indicators. The paper platform helps in keeping the required sample volume very low. With the help of the module, bone turnover markers and bone health indicators can be analysed using a single device. These analytes exists in trace quantities, and hence will be detected through reactions which yields chemiluminescence. The resulting emissions will be detected with a light sensor, and the intensity will be converted as concentration with the help of electronic module and also will be sent to a remote computer via Bluetooth.

Related Projects

Reliability based soft decision decoding of Turbo codes for satellite communication
Reliability based soft decision decoding of Turbo codes for satellite communication
Predictive Modeling of Complex IT systems
Predictive Modeling of Complex IT systems
Impacts of recent El-Nino Southern Oscillation (ENSO) on the Water-Food-Energy Nexus in South Asia (Indian PI)
Impacts of recent El-Nino Southern Oscillation (ENSO) on the Water-Food-Energy Nexus in South Asia (Indian PI)
Computer-Aided Study of Numerical Methods and Graph Labelling Problems
Computer-Aided Study of Numerical Methods and Graph Labelling Problems
A Machine Learning Approach for Early Prediction of Blood Culture Positivity in Neutropenia Patients Using Medical History and Hematological Parameters
A Machine Learning Approach for Early Prediction of Blood Culture Positivity in Neutropenia Patients Using Medical History and Hematological Parameters
Admissions Apply Now