3D printed optical sensor for highly sensitive detection of picric acid using perovskite nanocrystals and mechanism of photo-electron transfer.

Hand-held, compact and portable sensors for on-site detection of environmental contaminants are in high demand for industry 4.0. Here, we have developed a sensor based on luminescent organic-inorganic metal halide hybrid perovskites nanocrystals (CH3NH3PbBr3) with p-xylylenediamine as an additional capping agent for highly sensitive and selective detection of picric acid (PA), with a good linear range of 1.8 µM-14.3 µM achieving detection of limit (LOD) of 0.3 µM. The electrostatic interaction between PA and the capping ligand of perovskite nanocrystals resulted in significant fluorescence quenching, as revealed by the steady-state and time-resolved spectroscopy. The applicability of the developed sensor for PA detection was validated with a 3D printed device integrating surface mounting device (SMD) and paper microfluidics. This prototype device was successfully applied as a fluorescence turn-off sensor to detect PA, showing great potential for on-site detection. This 3D-printed paper-based microfluidic optical sensor proved very efficient for naked-eye detection of PA with an inbuilt excitation source, avoiding the requirement of expensive and complex instrumentation.

Highly sensitive and selective detection of dopamine with boron and sulfur co-doped graphene quantum dots.

In this work, we report, the synthesis of Boron and Sulfur co-doped graphene quantum dots (BS-GQDs) and its applicability as a label-free fluorescence sensing probe for the highly sensitive and selective detection of dopamine (DA). Upon addition of DA, the fluorescence intensity of BS-GQDs were effectively quenched over a wide concentration range of DA (0-340 µM) with an ultra-low detection limit of 3.6 µM. The quenching mechanism involved photoinduced electron transfer process from BS-GQDs to dopamine-quinone, produced by the oxidization of DA under alkaline conditions. The proposed sensing mechanism was probed using a detailed study of UV-Vis absorbance, steady state and time resolved fluorescence spectroscopy. The high selectivity of the fluorescent sensor towards DA is established. Our study opens up the possibility of designing a low-cost biosensor which will be suitable for detecting DA in real samples.

Repurposing therapeutics for COVID-19: Rapid prediction of commercially available drugs through machine learning and docking.

BACKGROUND: The outbreak of the novel coronavirus disease COVID-19, caused by the SARS-CoV-2 virus has spread rapidly around the globe during the past 3 months. As the virus infected cases and mortality rate of this disease is increasing exponentially, scientists and researchers all over the world are relentlessly working to understand this new virus along with possible treatment regimens by discovering active therapeutic agents and vaccines. So, there is an urgent requirement of new and effective medications that can treat the disease caused by SARS-CoV-2. METHODS AND FINDINGS: We perform the study of drugs that are already available in the market and being used for other diseases to accelerate clinical recovery, in other words repurposing of existing drugs. The vast complexity in drug design and protocols regarding clinical trials often prohibit developing various new drug combinations for this epidemic disease in a limited time. Recently, remarkable improvements in computational power coupled with advancements in Machine Learning (ML) technology have been utilized to revolutionize the drug development process. Consequently, a detailed study using ML for the repurposing of therapeutic agents is urgently required. Here, we report the ML model based on the Naive Bayes algorithm, which has an accuracy of around 73% to predict the drugs that could be used for the treatment of COVID-19. Our study predicts around ten FDA approved commercial drugs that can be used for repurposing. Among all, we found that 3 of the drugs fulfils the criterions well among which the antiretroviral drug Amprenavir (DrugBank ID-DB00701) would probably be the most effective drug based on the selected criterions. CONCLUSIONS: Our study can help clinical scientists in being more selective in identifying and testing the therapeutic agents for COVID-19 treatment. The ML based approach for drug discovery as reported here can be a futuristic smart drug designing strategy for community applications.