Advances in the synthesis of antiviral agents from carbohydrate-derived chiral pools

Carbohydrates are the most abundant natural products and a major component on the cell surface of living beings. They are useful building blocks of various natural products and organic synthesis due to their presence of multiple chiral centers and hydroxy groups. The recent outbreak of COVID-19 and other life-threatening viral infections necessitates the development of potent antiviral drugs. In this review, we focused on the synthesis of antiviral drugs to treat influenza, HIV, herpes, hepatitis, and other diseases, from different monosaccharides such as D-glucose, D-mannose, D-xylose, N-acetyl-D-glucosamine, D-gluconolactone, etc., such as anti-influenza drugs remdesivir, Tamiflu, zanamivir, and so on.

Face Mask Detection: A Deep Learning Approach

The global health is being ravaged by the COVID-19 coronavirus epidemic. The healthcare industry is a complete disaster. As human wants to recover from the outbreak of COVID-19 and seeks to revert to normalcy, there is an increase in unease among those who expect to resume their everyday routines in reality. Numerous preventative measures have been implemented to avoid the transmission of this virus, notably the use of a masks, which the World Health Organization (WHO) significantly and positively instructs. According to multiple research, using a face mask minimizes the likelihood of viral propagation while also providing an impression of security. It is, nevertheless, impossible to independently track the proposal's application. Therefore, technology is incredibly important. The goal of this study is to explain a CNN-based system for recognizing occurrences of a person with or without a mask. It would enable it easy to keep track of safety violations, encourage the use of facial masks, and establish a safe working environment. © 2022 IEEE.

A Machine Learning And Iot Based Contactless Entry System

The COVID-19 pandemic has intensely impacted humanities globally. This scenario has laid down many protocols and procedures to mitigate the risk and thus ensure the safety of individuals. The pandemic has augmented the demand of contactless mechanism especially in the public places.This paper presents novel machine learning and internet of things based solution for contactless entry to premises following mandatory checks at the entry as per COVID-19 protocols. The proposed model senses the body temperature and detects the face mask of an individual prior to entry. The entrance is allowed through contactless opening of the gate only if the body temperature is within prescribed limits and the face mask is properly put on.The current work uses a machine learning model for detection of the face mask which uses the real time image during screening;the algorithm is trained using the data sets with and without mask. The temperature screening is carried out with temperature sensor connected to the Arduino processor.A prototype model using Arduino is prepared based on the inputs received from the temperature sensor and the Machine learning Algorithm. The gate shall open for entry if a person has normal body temperature and wearing a proper face mask, else, will be restricted through a custom alert. Also a track of the number of persons entering is monitored and sent to a web portal on real time basis to account for overcrowding.The model ensures a contactless check at the entry, thus, controlling the outbreak of the Covid situation. © 2022 IEEE.