Rapid One-Pot Microwave Assisted Green Synthesis Nitrogen Doped Carbon Quantum Dots as Fluorescent Precursor for Estimation of Modafinil as Post-Covid Neurological Drug in Human Plasma with Greenness Assessments.

The neuro-stimulant anti-narcoleptic drug as modafinil (MOD) is used to treatment neurological conditions caused by COVID-19. MOD was used to treatment narcolepsy, shift-work sleep disorder, and obstructive sleep apnea-related sleepiness. So, an innovative, quick, economical, selective, and ecologically friendly procedure was carried out. A highly sensitive N@CQDs technique was created from green Eruca sativa leaves in about 4 min using microwave synthesis at 700 w. The quantum yield of the synthesized N@CQDs was found to be 41.39%. By increasing the concentration of MOD, the quantum dots' fluorescence intensity was gradually quenched. After being excited at 445 nm, the fluorescence reading was recorded at 515 nm. The linear range was found to be in the range 50 - 700 ng mL-1 with lower limit of quantitation (LOQ) equal to 45.00 ng mL-1. The current method was fully validated and bio analytically according to (US-FDA and ICH) guidelines. Full characterization of the N@CQDs has been conducted by high resolution transmission electron microscope (HRTEM), Zeta potential measurement, fluorescence, UV-VIS, and FTIR spectroscopy. Various experimental variables including pH, QDs concentration and the reaction time were optimized. The proposed study is simply implemented for the therapeutic drug monitoring system (TDMS) and various clinical laboratories for further pharmacokinetic research.

Lipid nanoparticles technology in vaccines: Shaping the future of prophylactic medicine.

Throughout decades, the intrinsic power of the immune system to fight pathogens has inspired researchers to develop techniques that enable the prevention or treatment of infections via boosting the immune response against the target pathogens, which has led to the evolution of vaccines. The recruitment of Lipid nanoparticles (LNPs) as either vaccine delivery platforms or immunogenic modalities has witnessed a breakthrough recently, which has been crowned with the development of effective LNPs-based vaccines against COVID-19. In the current article, we discuss some principles of such a technology, with a special focus on the technical aspects from a translational perspective. Representative examples of LNPs-based vaccines against cancer, COVID-19, as well as other infectious diseases, autoimmune diseases, and allergies are highlighted, considering the challenges and promises. Lastly, the key features that can improve the clinical translation of this area of endeavor are inspired.

Deep convolutional forest: a dynamic deep ensemble approach for spam detection in text.

The increase in people's use of mobile messaging services has led to the spread of social engineering attacks like phishing, considering that spam text is one of the main factors in the dissemination of phishing attacks to steal sensitive data such as credit cards and passwords. In addition, rumors and incorrect medical information regarding the COVID-19 pandemic are widely shared on social media leading to people's fear and confusion. Thus, filtering spam content is vital to reduce risks and threats. Previous studies relied on machine learning and deep learning approaches for spam classification, but these approaches have two limitations. Machine learning models require manual feature engineering, whereas deep neural networks require a high computational cost. This paper introduces a dynamic deep ensemble model for spam detection that adjusts its complexity and extracts features automatically. The proposed model utilizes convolutional and pooling layers for feature extraction along with base classifiers such as random forests and extremely randomized trees for classifying texts into spam or legitimate ones. Moreover, the model employs ensemble learning procedures like boosting and bagging. As a result, the model achieved high precision, recall, f1-score and accuracy of 98.38%.

Novel Binding Mechanisms of Fusion Broad Range Anti-Infective Protein Ricin A Chain Mutant-Pokeweed Antiviral Protein 1 (RTAM-PAP1) against SARS-CoV-2 Key Proteins in Silico.

The deadly pandemic named COVID-19, caused by a new coronavirus (SARS-CoV-2), emerged in 2019 and is still spreading globally at a dangerous pace. As of today, there are no proven vaccines, therapies, or even strategies to fight off this virus. Here, we describe the in silico docking results of a novel broad range anti-infective fusion protein RTAM-PAP1 against the various key proteins of SARS-CoV-2 using the latest protein-ligand docking software. RTAM-PAP1 was compared against the SARS-CoV-2 B38 antibody, ricin A chain, a pokeweed antiviral protein from leaves, and the lectin griffithsin using the special CoDockPP COVID-19 version. These experiments revealed novel binding mechanisms of RTAM-PAP1 with a high affinity to numerous SARS-CoV-2 key proteins. RTAM-PAP1 was further characterized in a preliminary toxicity study in mice and was found to be a potential therapeutic candidate. These findings might lead to the discovery of novel SARS-CoV-2 targets and therapeutic protein structures with outstanding functions.