Devashree N. Sakharkar

Detecting urea is crucial for diagnosing related health conditions and ensuring timely medical intervention. The addition of machine learning (ML) technologies has completely changed the field of biochemical sensing, providing enhanced accuracy and reliability. In the present work, an ML-assisted screen-printed, flexible, electrochemical, non-enzymatic biosensor was proposed to quantify urea concentrations. For the detection of urea, the biosensor was modified with a multi-walled carbon nanotube-zinc oxide (MWCNT-ZnO) nanocomposite functionalized with copper oxide (CuO) micro-flowers (MFs). Further, the CuO-MFs were synthesized using a standard sol-gel approach, and the obtained particles were subjected to various characterization techniques, including X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), and Fourier transform infrared (FTIR) spectroscopy. The sensor’s performance for urea detection was evaluated by assessing the dependence of peak currents on analyte concentration using cyclic voltammetry (CV) at different scan rates of 50, 75, and 100 mV/s. The designed non-enzymatic biosensor showed an acceptable linear range of operation of 0.5–8 mM, and the limit of detection (LoD) observed was 78.479 nM, which is well aligned with the urea concentration found in human blood and exhibits a good sensitivity of 117.98 mA mM⁻¹ cm⁻². Additionally, different regression-based ML models were applied to determine CV parameters to predict urea concentrations experimentally. ML significantly improves the accuracy and reliability of screen-printed biosensors, enabling accurate predictions of urea levels. Finally, the combination of ML and biosensor design emphasizes not only the high sensitivity and accuracy of the sensor but also its potential for complex non-enzymatic urea detection applications. Future advancements in accurate biochemical sensing technologies are made possible by this strong and dependable methodology.

Detecting specific cells and molecules in a sample poses a challenge in remote and resource-limited areas, which has resulted in the rising demand for Point-of-Care (POC) systems. Here we demonstrate a design, and testing of Smartphone-based tissue identification system, a valuable tool for preliminary screening or basic tissue identification in resource-constraint settings, remote areas, or Point-of-Care applications. In this work a system Is developed to acquire the images of tissue cells like liver, striated muscle, kidney, nerve, adipose, hyaline cartilage and brain. The device has been calibrated by comparison with microscopic images of different types of tissue cells. The intensity profile shows relative similarity graphs between the images obtained from microscope and designed system. The developed system with up gradation has the potential to solve the problem of availability of expensive facilities at remote regions.