Early Predication of Covid-19 by Machine Learning Algorithms

In COVID-19 is the most significant issue for the human community. The virus is easily converted into a new variant, which behaves differently from the previous one. Besides its changing behavior, its transmission and infection rate are very high which causes high death rate. It is a very challenging situation for the healthcare system to early diagnosis of diseases so that predict the transmission growth of virus the number of new, confirmed, recovered, and dead cases can be reduced. To deal with these issues, some prediction tools are required which can help to test and find the cause of existing cases so that it can help the effective and rapid arrangement to overcome the pandemic. To address this issue, we propose a symptom-base Recommendation System which are tested over the dataset by applying the concept of Machine Learning algorithms. In this work, we test our proposed system by suing various machine learning algorithm like LR, SVM, Navie Bays,KNN,Random Forest etc. The experimental results reveal that the proposed system is capable to diagnose the disease accurately approximate 99%. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

Miller Fisher Syndrome in the Last Three Decades: A Systematic Review

Objective: The last retrospective systematic review on Miller Fisher syndrome (MFS) took place in 1992. To understand the evolving nature of the disease and to update the clinical picture, diagnostic testing, treatment, and prognosis, a retrospective systematic review of 174 cases of MFS published in the last three decades was performed. Method(s): We screened 1034 articles on the PUBMED search engine. Out of these articles, 153 met the inclusion criteria of case reports/series published in English after 1992. Each case contained at least two signs of the triad, with the presence or absence of GQ1B antibodies. The Chi-square test or Fisher's exact test was used for data analysis. Assessing the Methodological Quality of Systematic Reviews (AMSTAR 2) was used to assess the quality of the systematic reviews. Finding(s): 174 cases were included, with five case series. Sinopulmonary infection (60%) and gastrointestinal infection (18%) were the most common preceding illnesses, while eight cases occurred after the onset of a COVID-19 infection, and seven had recent exposure to monoclonal antibodies. We found that misdiagnosis was seen in 13.8% of cases, stroke being the most common misnomer. Residual symptoms were reported in 30% of cases, death in three and recurrence in twelve. IVIG was the most frequently used treatment option (51.1%). Severity score was significantly associated with treatment (p=0.0195);however, it was not associated with age (p=0.4255), gender (p=0.7893), GQ1b antibody presence/level (p=0.3870/ p=0.6891), or non-GQ1B (p=0.5426) status. The outcome with residual symptoms was favorable for younger patients (Age 1-18: p=0.0223) and not associated with treatment. Mechanical ventilation (13.8%), feeding tube placement (9.8%), autonomic insufficiency (8.6%), and a patient requiring a cardiac pacemaker were the top three complications. Conclusion(s): Physicians should recognize the protean clinical manifestation of MFS and recognize the various recent preceding factors like COVID-19 and monoclonal antibodies. The benefit provided by the treatment is unclear. Therefore, further studies will be required to identify patients who should be treated and the appropriate treatment to maximize patient outcomes.

Real-time efficient attendance system using face recognition

Across India, offline educational system facilities were forced to collapse and students' access to education was severely curtailed due to the Covid-19 pandemic. During COVID-19 many educational institutes abruptly shifted to virtual classrooms for learning with numerous gaps to maintain the academic activities and provide remote lessons to their students. This has impacted the education system in contrary forces. The huge gap is anonymously costing students by taking away their knowledge growth. On the other hand, faculties are scrambling to adapt to the usability of online learning platforms. Faculties are finding innovative ways to interact with the students, but the loss remains incomparable. Therefore, the concept of a smart attendance system governs a significant role in covering at least one gap by replacing the time-consuming traditional method of taking attendance in the virtual classroom. The system takes attendance by recognizing the face of the student and marking attendance automatically with zero errors. Hence, the goal of an online attendance system is achieved with high accuracy. © 2022 IEEE.

An In Silico investigation for acyclovir and its derivatives to fight the COVID-19: Molecular docking, DFT calculations, ADME and td-Molecular dynamics simulations

In the present work, we have designed three molecules, acyclovir (A), ganciclovir (G) and derivative of hydroxymethyl derivative of ganciclovir (CH2OH of G, that is D) and investigated their biological potential against the Mpro of nCoV via in silico studies. Further, density functional theory (DFT) calculations of A, G and D were performed using Gaussian 16 on applying B3LYP under default condition to collect the information for the delocalization of electron density in their optimized geometry. Authors have also calculated various energies including free energy of A, G and D in Hartree per particle. It can be seen that D has the least free energy. As mentioned, the molecular docking of the A, G and D against the Mpro of nCoV was performed using iGemdock, an acceptable computational tool and the interaction has been studied in the form of physical data, that is, binding energy for A, G and D were calculated in kcal/mol. It can be seen the D showed effective binding, that is, maximum inhibition that A and G. For a better understanding for the inhibition of the Mpro of nCoV by A, G and D, temperature dependent molecular dynamics simulations were performed. Different trajectories like RMSD, RMSF, Rg and hydrogen bond were extracted and analyzed. The results of molecular docking of A, G and D corroborate with the td-MD simulations and hypothesized that D could be a promising candidate to inhibit the activity of Mpro of nCoV. © 2022 Indian Chemical Society