Reviewing methods of deep learning for diagnosing COVID-19, its variants and synergistic medicine combinations

Protective face mask: an effective weapon against SARS-CoV-2 with controlled environmental pollution

Masks are face coverings that give protection from infectious agents, airborne pathogens, bacteria, viruses, surgical fog, dust, and other chemical hazards by acting as a barrier between the wearer and the environment. In the COVID-19 pandemic, this major personal protective equipment's became essential part of our daily life. The aim of this review is to analyze and discuss the different types of masks with their pros and cons, manufacturing procedures, evaluation criteria, and application with some of the sterilization process for reuse and smart mask. The review used a thorough examination of the literature to analyze the preventive effects of surgical, N95, smart mask, and potential environmental damage from those masks. Several studies and evidence were also examined to understand the efficiency of different mask on different environment. N95 respirators are capable of filtering out non-oil-based 95% air-born particles, and surgical masks act as a protective barrier between the wearer and the environment. The application of spoon bond and melt blown techniques in the fabrication process of those masks improves their protective nature and makes them lightweight and comfortable. But the high demand and low supply forced users to reuse and extend their use after sterilizations, even though those masks are recommended to be used once. Universal masking in the SARS-COV-2 pandemic increased the chance of environmental pollution, so the application of smart masks became essential because of their high protection power and self-sterilizing and reusing capabilities.

Modeling COVID-19 data with a novel neutrosophic Burr-III distribution

In this study, we have presented a novel probabilistic model called the neutrosophic Burr-III distribution, designed for applications in neutrosophic surface analysis. Neutrosophic analysis allows for the incorporation of vague and imprecise information, reflecting the reality that many real-world problems involve ambiguous data. This ability to handle vagueness can lead to more robust and realistic models especially in situation where classical models fall short. We have also explored the neutrosophic Burr-III distribution in order to deal with the ambiguity and vagueness in the data where the classical Burr-III distribution falls short. This distribution offers valuable insights into various reliability properties, moment expressions, order statistics, and entropy measures, making it a versatile tool for analyzing complex data. To assess the practical relevance of our proposed distribution, we applied it to real-world data sets and compared its performance against the classical Burr-III distribution. The findings revealed that the neutrosophic Burr-III distribution outperformed than the classical Burr-III distribution in capturing the underlying data characteristics, highlighting its potential as a superior modeling toolin various fields.

Nano revolution in cardiovascular health: Nanoparticles (NPs) as tiny titans for diagnosis and therapeutics

Cardiovascular diseases (CVDs) are known as life-threatening illnessescaused by severe abnormalities in the cardiovascular system. They are a leading cause of mortality and morbidity worldwide.Nanotechnology integrated substantialinnovations in cardiovascular diagnostic and therapeutic at the nanoscale. This in-depth analysis explores cutting-edge methods for diagnosing CVDs, including nanotechnological interventions and crucial components for identifying risk factors, developing treatment plans, and monitoring patients' progress with chronic CVDs.Intensive research has gone into making nano-carriers that can image and treat patients. To improve the efficiency of treating CVDs, the presentreview sheds light on a decision-tree-based solution by investigating recent and innovative approaches in CVD diagnosis by utilizing nanoparticles (NPs). Treatment choices for chronic diseases like CVD, whose etiology might take decades to manifest, are very condition-specific and disease-stage-based. Moreover, thisreview alsobenchmarks the changing landscape of employing NPs for targeted and better drug administration while examining the limitations of various NPs in CVD diagnosis, including cost, space, time, and complexity. To better understand and treatment of cardiovascular diseases, the conversation moves on to the nano-cardiovascular possibilities for medical research.We also focus on recent developments in nanoparticle applications, the ways they might be helpful, and the medical fields where they may find future use. Finally, this reviewadds to the continuing conversation on improved diagnosis and treatment approaches for cardiovascular disorders by discussing the obstacles and highlighting the revolutionary effects of nanotechnology.

Engineered two-dimensional nanomaterials based diagnostics integrated with internet of medical things (IoMT) for COVID-19

More than four years have passed since an inimitable coronavirus disease (COVID-19) pandemic hit the globe in 2019 after an uncontrolled transmission of the severe acute respiratory syndrome (SARS-CoV-2) infection. The occurrence of this highly contagious respiratory infectious disease led to chaos and mortality all over the world. The peak paradigm shift of the researchers was inclined towards the accurate and rapid detection of diseases. Since 2019, there has been a boost in the diagnostics of COVID-19 via numerous conventional diagnostic tools like RT-PCR, ELISA, etc., and advanced biosensing kits like LFIA, etc. For the same reason, the use of nanotechnology and two-dimensional nanomaterials (2DNMs) has aided in the fabrication of efficient diagnostic tools to combat COVID-19. This article discusses the engineering techniques utilized for fabricating chemically active E2DNMs that are exceptionally thin and irregular. The techniques encompass the introduction of heteroatoms, intercalation of ions, and the design of strain and defects. E2DNMs possess unique characteristics, including a substantial surface area and controllable electrical, optical, and bioactive properties. These characteristics enable the development of sophisticated diagnostic platforms for real-time biosensors with exceptional sensitivity in detecting SARS-CoV-2. Integrating the Internet of Medical Things (IoMT) with these E2DNMs-based advanced diagnostics has led to the development of portable, real-time, scalable, more accurate, and cost-effective SARS-CoV-2 diagnostic platforms. These diagnostic platforms have the potential to revolutionize SARS-CoV-2 diagnosis by making it faster, easier, and more accessible to people worldwide, thus making them ideal for resource-limited settings. These advanced IoMT diagnostic platforms may help with combating SARS-CoV-2 as well as tracking and predicting the spread of future pandemics, ultimately saving lives and mitigating their impact on global health systems.

Multifunctional role of nanoparticles for the diagnosis and therapeutics of cardiovascular diseases

The increasing burden of cardiovascular disease (CVD) remains responsible for morbidity and mortality worldwide; their effective diagnostic or treatment methods are of great interest to researchers. The use of NPs and nanocarriers in cardiology has drawn much interest. The present comprehensive review provides deep insights into the use of current and innovative approaches in CVD diagnostics to offer practical ways to utilize nanotechnological interventions and the critical elements in the CVD diagnosis, associated risk factors, and management strategies of patients with chronic CVDs. We proposed a decision tree-based solution by discussing the emerging applications of NPs for the higher number of rules to increase efficiency in treating CVDs. This review-based study explores the screening methods, tests, and toxicity to provide a unique way of creating a multi-parametric feature that includes cutting-edge techniques for identifying cardiovascular problems and their treatments. We discussed the benefits and drawbacks of various NPs in the context of cost, space, time and complexity that have been previously suggested in the literature for the diagnosis of CVDs risk factors. Also, we highlighted the advances in using NPs for targeted and improved drug delivery and discussed the evolution toward the nano-cardiovascular potential for medical science. Finally, we also examined the mixed-based diagnostic approaches crucial for treating cardiovascular disorders, broad applications and the potential future applications of nanotechnology in medical sciences.

Machine and deep learning methods for clinical outcome prediction based on physiological data of COVID-19 patients: a scoping review

INTRODUCTION

Since the beginning of the COVID-19 pandemic, numerous machine and deep learning (MDL) methods have been proposed in the literature to analyze patient physiological data. The objective of this review is to summarize various aspects of these methods and assess their practical utility for predicting various clinical outcomes.

METHODS

We searched PubMed, Scopus, and Cochrane Library, screened and selected the studies matching the inclusion criteria. The clinical analysis focused on the characteristics of the patient cohorts in the studies included in this review, the specific tasks in the context of the COVID-19 pandemic that machine and deep learning methods were used for, and their practical limitations. The technical analysis focused on the details of specific MDL methods and their performance.

RESULTS

Analysis of the 48 selected studies revealed that the majority (∼54 %) of them examined the application of MDL methods for the prediction of survival/mortality-related patient outcomes, while a smaller fraction (∼13 %) of studies also examined applications to the prediction of patients' physiological outcomes and hospital resource utilization. 21 % of the studies examined the application of MDL methods to multiple clinical tasks. Machine and deep learning methods have been shown to be effective at predicting several outcomes of COVID-19 patients, such as disease severity, complications, intensive care unit (ICU) transfer, and mortality. MDL methods also achieved high accuracy in predicting the required number of ICU beds and ventilators.

CONCLUSION

Machine and deep learning methods have been shown to be valuable tools for predicting disease severity, organ dysfunction and failure, patient outcomes, and hospital resource utilization during the COVID-19 pandemic. The discovered knowledge and our conclusions and recommendations can also be useful to healthcare professionals and artificial intelligence researchers in managing future pandemics.

Computational analysis of bevacizumab binding with protein receptors for its potential anticancer activity

Breast cancer poses a significant global challenge, prompting researchers to explore novel approaches for potential treatments. In this study, we investigated the binding free energy (ΔG) of bevacizumab, an anti-cancer therapy targeting angiogenesis through the inhibition of vascular endothelial growth factor (VEGF), with various proto-oncogenes including CDK4, EGFR, frizzled, IGFR, OmoMYC, and KIT. Our in-silico investigation revealed that hydrogen bonding is pivotal in inducing conformational changes within the DNA structure, impeding its replication and preventing cell death. Molecular docking results revealed the presence of crucial hydrogen bonds and supported the formation of stable bevacizumab complexes. The molecular docking scores for the tested complexes were CDK4 (Score = -7.2 kcal/mol), EGFR (Score = -8.5 kcal/mol), frizzled (Score = -6.9 kcal/mol), IGFR (Score = -7.8 kcal/mol), KIT (Score = -6.5 kcal/mol), and MYC (Score = -8.3 kcal/mol). The binding mode demonstrated vital hydrogen bonds correlated with the observed energy gap. Notably, the calculated binding free energies of the tested compounds are as follows: CDK4 (ΔG = 24275.195 ± 6411.293 kJ/mol), EGFR (ΔG = 363273.625 ± 8731.466 kJ/mol), frizzled (ΔG = 181751.990 ± 28438.515 kJ/mol), IGFR (ΔG = 162414.725 ± 10728.367 kJ/mol), KIT (ΔG = 40162.585 ± 4331.017 kJ/mol), and MYC (ΔG = 434783.463 ± 53989.676 kJ/mol). Furthermore, through extensive 100 ns MD simulations, we observed the formation of a stable bevacizumab complex structure. The simulations confirmed the stability of the bevacizumab complex with the proto-oncogenes. The results of this study highlight the potential of bevacizumab complex as a promising candidate for anticancer treatment. The identification of hydrogen bonding, along with the calculated binding free energies and molecular docking scores, provides valuable insights into the molecular interactions and stability of the bevacizumab complexes. These findings and the extensive MD simulations open new avenues for future research and development of bevacizumab as a targeted therapy for breast cancer and other related malignancies.Communicated by Ramaswamy H. Sarma.

COVID-19 studies involving machine learning methods: A bibliometric study

BACKGROUND

Machine learning (ML) and artificial intelligence (AI) techniques are gaining popularity as effective tools for coronavirus disease of 2019 (COVID-19) research. These strategies can be used in diagnosis, prognosis, therapy, and public health management. Bibliometric analysis quantifies the quality and impact of scholarly publications. ML in COVID-19 research is the focus of this bibliometric analysis.

METHODS

A comprehensive literature study found ML-based COVID-19 research. Web of Science (WoS) was used for the study. The searches included "machine learning," "artificial intelligence," and COVID-19. To find all relevant studies, 2 reviewers searched independently. The network visualization was analyzed using VOSviewer 1.6.19.

RESULTS

In the WoS Core, the average citation count was 13.6 ± 41.3. The main research areas were computer science, engineering, and science and technology. According to document count, Tao Huang wrote 14 studies, Fadi Al-Turjman wrote 11, and Imran Ashraf wrote 11. The US, China, and India produced the most studies and citations. The most prolific research institutions were Harvard Medical School, Huazhong University of Science and Technology, and King Abdulaziz University. In contrast, Nankai University, Oxford, and Imperial College London were the most mentioned organizations, reflecting their significant research contributions. First, "Covid-19" appeared 1983 times, followed by "machine learning" and "deep learning." The US Department of Health and Human Services funded this topic most heavily. Huang Tao, Feng Kaiyan, and Ashraf Imran pioneered bibliographic coupling.

CONCLUSION

This study provides useful insights for academics and clinicians studying COVID-19 using ML. Through bibliometric data analysis, scholars can learn about highly recognized and productive authors and countries, as well as the publications with the most citations and keywords. New data and methodologies from the pandemic are expected to advance ML and AI modeling. It is crucial to recognize that these studies will pioneer this subject.

In silico and in vitro study of bioactive compounds of Nigella sativa for targeting neuropilins in breast cancer

Introduction: Breast cancer poses a significant global challenge, prompting researchers to explore novel approaches for potential treatments. Material and Methods: For in vitro study we used thin layer chromatography (TAC) for phytochemical screening, total antioxidant capacity (TLC) assay for antioxidant capacity, and hemolytic activity test for toxicity of Neuropilins (NRPs). We performed bioinformatic analyses to predict protein structures, molecular docking, pharmacophore modeling, and virtual screening to reveal interactions with oncogenes. We conducted 200 ns Molecular Dynamics (MD) simulations and MMGBSA calculations to assess the complex dynamics and stability. Results: We identified phytochemical constituents in Nigella sativa leaves, including tannins, saponins, steroids, and cardiac glycosides, while phlobatannins and terpenoids were absent. The leaves contained 9.4% ± 0.04% alkaloids and 1.9% ± 0.05% saponins. Methanol extract exhibited the highest yield and antioxidant capacity, with Total Flavonoid Content at 127.51 ± 0.76 mg/100 g and Total Phenolic Content at 134.39 ± 0.589 mg GAE/100 g. Hemolysis testing showed varying degrees of hemolysis for different extracts. In-silico analysis indicated stable Neuropilin complexes with key signaling pathways relevant for anti-cancer therapy. Molecular docking scores at different possesses (0, C-50, C -80, C-120,C -150, C -200 ns) revealed strong hydrogen bonding in the complexes and showed -12.9, -11.6, and -11.2 binding Affinities (kcal/mol) to support their stability. Our MD simulations analysis at 200ns confirmed the stability of Neuropilin complexes with the signaling pathways protein PI3K. The calculated binding free energies using MMGBSA provided valuable quantitative information on ligand potency on different time steps. These findings highlight the potential health benefits of N. sativa leaves and their possible role in anti-cancer treatments targeting angiogenesis. Conclusion: Nigella sativa leaves have shown significant medical potential due to their bioactive compounds, which exhibit strong properties in supporting organogenic processes related to cancer. Furthermore, studies have highlighted the promising role of neuropilins in anticancer treatment, demonstrating stable interactions and potential as targeted therapy specifically for breast cancer.

Computational biology and in vitro studies for anticipating cancer-related molecular targets of sweet wormwood (Artemisia annua)

BACKGROUND

Cancer is one of the leading causes of death worldwide. Recently, it was shown that many natural extracts have positive effects against cancer, compared with chemotherapy or recent hormonal treatments. A. annua is an annual medicinal herb used in the traditional Chinese medicine. It has also been shown to inhibit the proliferation of various cancer cell lines.

METHODS

Multi-level modes of action of A. annua constituents in cancer therapy were investigated using an integrated approach of network pharmacology, molecular docking, dynamic simulations and in-vitro cytotoxicity testing on both healthy and cancer cells.

RESULTS

Network pharmacology-based analysis showed that the hit Artemisia annua constituents related to cancer targets were 3-(2-methylpropanoyl)-4-cadinene-3,11-diol, artemisinin G, O-(2-propenal) coniferaldehyde, (2-glyceryl)-O-coniferaldehyde and arteamisinin III, whereas the main cancer allied targets were NFKB1, MAP2K1 and AR. Sixty-eight significant signaling KEGG pathways with p < 0.01 were recognized, the most enriched of which were prostate cancer, breast cancer, melanoma and pancreatic cancer. Thirty-five biological processes were mainly regulated by cancer, involving cellular response to mechanical stimulus, positive regulation of gene expression and transcription. Molecular docking analysis of the top hit compounds against the most enriched target proteins showed that 3-(2-methylpropanoyl)-4-cadinene-3,11-diol and O-(2-propenal) coniferaldehyde exhibited the most stabilized interactions. Molecular dynamics simulations were performed to explain the stability of these two compounds in their protein-ligand complexes. Finally, confirmation of the potential anticancer activity was attained by in-vitro cytotoxicity testing of the extract on human prostate (PC-3), breast (MDA-MB-231), pancreatic (PANC-1) and melanoma (A375) cancerous cell lines.

CONCLUSION

This study presents deeper insights into A. annua molecular mechanisms of action in cancer for the first time using an integrated approaches verifying the herb's value.