A non-invasive ultrasensitive diagnostic approach for COVID-19 infection using salivary label-free SERS fingerprinting and artificial intelligence.

Clinical diagnostics for SARS-CoV-2 infection usually comprises the sampling of throat or nasopharyngeal swabs that are invasive and create patient discomfort. Hence, saliva is attempted as a sample of choice for the management of COVID-19 outbreaks that cripples the global healthcare system. Although limited by the risk of eliciting false-negative and positive results, tedious test procedures, requirement of specialized laboratories, and expensive reagents, nucleic acid-based tests remain the gold standard for COVID-19 diagnostics. However, genetic diversity of the virus due to rapid mutations limits the efficiency of nucleic acid-based tests. Herein, we have demonstrated the simplest screening modality based on label-free surface enhanced Raman scattering (LF-SERS) for scrutinizing the SARS-CoV-2-mediated molecular-level changes of the saliva samples among healthy, COVID-19 infected and COVID-19 recovered subjects. Moreover, our LF-SERS technique enabled to differentiate the three classes of corona virus spike protein derived from SARS-CoV-2, SARS-CoV and MERS-CoV. Raman spectral data was further decoded, segregated and effectively managed with the aid of machine learning algorithms. The classification models built upon biochemical signature-based discrimination method of the COVID-19 condition from the patient saliva ensured high accuracy, specificity, and sensitivity. The trained support vector machine (SVM) classifier achieved a prediction accuracy of 95% and F1-score of 94.73%, and 95.28% for healthy and COVID-19 infected patients respectively. The current approach not only differentiate SARS-CoV-2 infection with healthy controls but also predicted a distinct fingerprint for different stages of patient recovery. Employing portable hand-held Raman spectrophotometer as the instrument and saliva as the sample of choice will guarantee a rapid and non-invasive diagnostic strategy to warrant or assure patient comfort and large-scale population screening for SARS-CoV-2 infection and monitoring the recovery process.

Fabrication, Characterization, and Modeling of an Aluminum Oxide-Gate Ion-Sensitive Field-Effect Transistor-Based pH Sensor.

The ion-sensitive field-effect transistor (ISFET) is a popular technology utilized for pH sensing applications. In this work, we have presented the fabrication, characterization, and electrochemical modeling of an aluminum oxide (Al2O3)-gate ISFET-based pH sensor. The sensor is fabricated using well-established metal-oxide-semiconductor (MOS) unit processes with five steps of photolithography, and the sensing film is patterned using the lift-off process. The Al2O3 sensing film is deposited over the gate area using pulsed-DC magnetron-assisted reactive sputtering technique in order to improve the sensor performance. The material characterization of sensing film has been done using x-ray diffraction, field-emission scanning electron microscopy, energy-dispersive spectroscopy, and x-ray photoelectron spectroscopy techniques. The sensor has been packaged using thick-film technology and encapsulated by a dam-and-fill approach. The packaged device has been tested in various pH buffer solutions, and a sensitivity of nearly 42.1 mV/pH has been achieved. A simulation program with integrated circuit emphasis (SPICE) macromodel of the Al2O3-gate ISFET is empirically derived from the experimental results, and the extracted electrochemical parameters have been reported. The drift and hysteresis characteristics of the Al2O3-gate ISFET were also studied, and the obtained drift rates for different pH buffer solutions of 4, 7, and 10 are 0.136 µA/min, 0.124 µA/min, and 0.108 µA/min, respectively. A hysteresis of nearly 5.806 µA has been obtained. The developed sensor has high sensitivity along with low drift and hysteresis.