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Ghosh S, Dave V, Sharma P, Patel A, Kuila A. Protective face mask: an effective weapon against SARS-CoV-2 with controlled environmental pollution. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:41656-41682. [PMID: 37968481 DOI: 10.1007/s11356-023-30460-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 10/10/2023] [Indexed: 11/17/2023]
Abstract
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.
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Affiliation(s)
- Shovan Ghosh
- Department of Pharmacy, School of Health Science, Central University of South Bihar, Bihar, India
| | - Vivek Dave
- Department of Pharmacy, School of Health Science, Central University of South Bihar, Bihar, India.
| | - Prashansa Sharma
- Department of Home Science, Mahila Maha Vidyalaya, Banaras Hindu University, Varanasi, India
| | - Akash Patel
- Department of Pharmacy, School of Health Science, Central University of South Bihar, Bihar, India
| | - Arindam Kuila
- Department of Bioscience and Biotechnology, Banasthali Vidyapith, Sikar, Rajasthan, 304022, India
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Jamal F, Shafiq S, Aslam M, Khan S, Hussain Z, Abbas Q. Modeling COVID-19 data with a novel neutrosophic Burr-III distribution. Sci Rep 2024; 14:10810. [PMID: 38734768 DOI: 10.1038/s41598-024-61659-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 05/08/2024] [Indexed: 05/13/2024] Open
Abstract
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.
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Affiliation(s)
- Farrukh Jamal
- Department of Statistics, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | - Shakaiba Shafiq
- Department of Statistics, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | - Muhammad Aslam
- Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia.
| | - Sadaf Khan
- Department of Statistics, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | - Zawar Hussain
- Department of Statistics, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | - Qamer Abbas
- Department of Statistics, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
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Abid J, Khalil FMA, Saeed S, Khan SU, Iqbal I, Khan SU, Anthony S, Shahzad R, Koerniati S, Naz F. Nano revolution in cardiovascular health: Nanoparticles (NPs) as tiny titans for diagnosis and therapeutics. Curr Probl Cardiol 2024; 49:102466. [PMID: 38369205 DOI: 10.1016/j.cpcardiol.2024.102466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Accepted: 02/15/2024] [Indexed: 02/20/2024]
Abstract
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.
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Affiliation(s)
- Junaid Abid
- Department of Food Science and Technology, University of Haripur, Pakistan; State Key Laboratory of Food nutrition and Safety, College of Biotechnology, Tianjin University of Science and Technology, Tianjin, China
| | - Fatma Mohamed Ameen Khalil
- King Khalid University, College of Science and Arts, Department of Biology, MohayilAsirAbha, 61421, Saudi Arabia
| | - Sumbul Saeed
- School of Environment and Science, Griffith University, QLD, 4111, Australia
| | - Shahid Ullah Khan
- Women Medical and Dental College, Khyber Medical University, Khyber Pakhtunkhwa, Pakistan; Integrative Science Center of Germplasm Creation in Western China (CHONGQING) Science City and Southwest University, College of Agronomy and Biotechnology, Southwest University, Chongqing, 400715, China
| | - Imran Iqbal
- Department of PLR, Institute of Active Polymers, Helmholtz-Zentrum Hereon, 14513, Teltow, Germany
| | - Safir Ullah Khan
- Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, University of Science and Technology of China, Hefei 230027, China
| | - Stefan Anthony
- Liaoning Provincial Key Laboratory of Cerebral Diseases, Department of Physiology, Dalian Medical University Liaoning Provence China.
| | - Raheel Shahzad
- Research Center for Genetic Engineering, National Research and Innovation Agency (BRIN), KST-Cibinong, JI Raya Bogor KM46, Cibinong 16911, Indonesia
| | - Sri Koerniati
- Research Center for Genetic Engineering, National Research and Innovation Agency (BRIN), KST-Cibinong, JI Raya Bogor KM46, Cibinong, 16911, Indonesia
| | - Farkhanda Naz
- Biological Science Research Center, Academy for Advanced Interdisciplinary Studies, Southwest University, Chongqing, 400715, China
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Sadique MA, Yadav S, Khan R, Srivastava AK. Engineered two-dimensional nanomaterials based diagnostics integrated with internet of medical things (IoMT) for COVID-19. Chem Soc Rev 2024; 53:3774-3828. [PMID: 38433614 DOI: 10.1039/d3cs00719g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2024]
Abstract
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.
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Affiliation(s)
- Mohd Abubakar Sadique
- CSIR - Advanced Materials and Processes Research Institute (AMPRI), Hoshangabad Road, Bhopal 462026, India.
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Shalu Yadav
- CSIR - Advanced Materials and Processes Research Institute (AMPRI), Hoshangabad Road, Bhopal 462026, India.
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Raju Khan
- CSIR - Advanced Materials and Processes Research Institute (AMPRI), Hoshangabad Road, Bhopal 462026, India.
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Avanish K Srivastava
- CSIR - Advanced Materials and Processes Research Institute (AMPRI), Hoshangabad Road, Bhopal 462026, India.
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
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Arshad I, Kanwal A, Zafar I, Unar A, Mouada H, Razia IT, Arif S, Ahsan M, Kamal MA, Rashid S, Khan KA, Sharma R. Multifunctional role of nanoparticles for the diagnosis and therapeutics of cardiovascular diseases. ENVIRONMENTAL RESEARCH 2024; 242:117795. [PMID: 38043894 DOI: 10.1016/j.envres.2023.117795] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 10/26/2023] [Accepted: 11/24/2023] [Indexed: 12/05/2023]
Abstract
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.
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Affiliation(s)
- Ihtesham Arshad
- Department of Biotechnology, Faculty of Life Sciences, University of Okara, Okara, 56300, Pakistan.
| | - Ayesha Kanwal
- Department of Biotechnology, Faculty of Life Sciences, University of Okara, Okara, 56300, Pakistan.
| | - Imran Zafar
- Department of Bioinformatics and Computational Biology, Virtual University, Punjab, 54700, Pakistan.
| | - Ahsanullah Unar
- Department of Precision Medicine, University of Campania 'L. Vanvitelli', Naples, Italy.
| | - Hanane Mouada
- Department of Process Engineering, Institute of science University Center of Tipaza, Tipaza, Algeria.
| | | | - Safina Arif
- Medical Lab Technology, University of Lahore, Lahore, 54590, Pakistan.
| | - Muhammad Ahsan
- Institute of Environmental and Agricultural Sciences, University of Okara, Okara, 56300, Pakistan.
| | - Mohammad Amjad Kamal
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, China; King Fahd Medical Research Center, King Abdulaziz University, Saudi Arabia; Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Bangladesh; Enzymoics, 7 Peterlee place, Hebersham, NSW, 2770, Australia; Novel Global Community Educational Foundation, Australia.
| | - Summya Rashid
- Department of Pharmacology and Toxicology, College of Pharmacy, Prince Sattam BinAbdulaziz University, P.O. Box 173, Al-Kharj, 11942, Saudi Arabia.
| | - Khalid Ali Khan
- Unit of Bee Research and Honey Production, Research Center for Advanced Materials Science (RCAMS), King Khalid University, P.O. Box 9004, Abha, 61413, Saudi Arabia; Applied College, King Khalid University, P. O. Box 9004, Abha, 61413, Saudi Arabia.
| | - Rohit Sharma
- Department of Rasa Shastra and Bhaishajya Kalpana, Faculty of Ayurveda, Institute of Medical Sciences, Banaras Hindu University, Varanasi, 221005, Uttar Pradesh, India.
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Viderman D, Kotov A, Popov M, Abdildin Y. Machine and deep learning methods for clinical outcome prediction based on physiological data of COVID-19 patients: a scoping review. Int J Med Inform 2024; 182:105308. [PMID: 38091862 DOI: 10.1016/j.ijmedinf.2023.105308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 11/20/2023] [Accepted: 12/03/2023] [Indexed: 01/07/2024]
Abstract
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.
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Affiliation(s)
- Dmitriy Viderman
- Department of Surgery, School of Medicine, Nazarbayev University, Astana, Kazakhstan; Department of Anesthesiology, Intensive Care, and Pain Medicine, National Research Oncology Center, Astana, Kazakhstan.
| | - Alexander Kotov
- Department of Computer Science, College of Engineering, Wayne State University, Detroit, USA.
| | - Maxim Popov
- Department of Computer Science, School of Engineering and Digital Sciences, Nazarbayev University, Astana, Kazakhstan.
| | - Yerkin Abdildin
- Department of Mechanical and Aerospace Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Astana, Kazakhstan.
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Alsakhen N, Radwan ES, Zafer I, Abed Alfattah H, Shamkh IM, Rehman MT, Shahwan M, Khan KA, Ahmed SA. Computational analysis of bevacizumab binding with protein receptors for its potential anticancer activity. J Biomol Struct Dyn 2024:1-21. [PMID: 38281913 DOI: 10.1080/07391102.2024.2307445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 01/08/2024] [Indexed: 01/30/2024]
Abstract
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.
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Affiliation(s)
- Nada Alsakhen
- Department of Chemistry, Faculty of Science, The Hashemite University, Zarqa, Jordan
| | | | - Imran Zafer
- Department of Bioinformatics and Computational Biology, Virtual University, Lahore, Pakistan
| | | | - Israa M Shamkh
- Botany and Microbiology department, Faculty of Science, Cairo University, Giza, Egypt
| | - Md Tabish Rehman
- College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
- Center for Medical and Bio-Allied Health Sciences Research, Ajman University, Ajman, United Arab Emirates
| | - Moayad Shahwan
- Center for Medical and Bio-Allied Health Sciences Research, Ajman University, Ajman, United Arab Emirates
- Department of Clinical Sciences, College of Pharmacy and Health Sciences, Ajman University, Ajman, United Arab Emirates
| | - Khalid Ali Khan
- Applied College, Center of Bee Research and its Products, Unit of Bee Research and Honey Production, and Research Center for Advanced Materials Science (RCAMS), King Khalid University, Abha, Saudi Arabia
| | - Shimaa A Ahmed
- Department of Chemistry, Faculty of Science, Beni-Suef University, Beni Suef, Egypt
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Baygül Eden A, Bakir Kayi A, Erdem MG, Demirci M. COVID-19 studies involving machine learning methods: A bibliometric study. Medicine (Baltimore) 2023; 102:e35564. [PMID: 37904407 PMCID: PMC10615482 DOI: 10.1097/md.0000000000035564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 09/18/2023] [Indexed: 11/01/2023] Open
Abstract
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.
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Affiliation(s)
- Arzu Baygül Eden
- Koç University, School of Medicine, Department of Biostatistics, Istanbul, Turkey
| | - Alev Bakir Kayi
- Istanbul University, Institute of Child Health, Department of Social Pediatrics, Istanbul, Türkiye
| | - Mustafa Genco Erdem
- Department of Internal Medicine, Faculty of Medicine, Beykent University, Istanbul, Turkey
| | - Mehmet Demirci
- Department of Medical Microbiology, Faculty of Medicine, Kirklareli University, Kirklareli, Turkey
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Zafar I, Safder A, Imran Afridi H, Riaz S, -ur-Rehman R, Unar A, Un Nisa F, Gaafar ARZ, Bourhia M, Wondmie GF, Sharma R, Kumar D. In silico and in vitro study of bioactive compounds of Nigella sativa for targeting neuropilins in breast cancer. Front Chem 2023; 11:1273149. [PMID: 37885828 PMCID: PMC10598785 DOI: 10.3389/fchem.2023.1273149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 09/25/2023] [Indexed: 10/28/2023] Open
Abstract
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.
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Affiliation(s)
- Imran Zafar
- Department of Bioinformatics and Computational Biology, Virtual University Pakistan, Lahore, Pakistan
| | - Arfa Safder
- Institute of Molecular Biology and Biotechnology, The University of Lahore, Punjab, Pakistan
| | - Hassan Imran Afridi
- National Center of Excellence in Analytical Chemistry, University of Sindh, Jamshoro, Pakistan
| | - Sania Riaz
- Faculty of Health and Life Sciences, Department of Bioinformatics and Biosciences, Capital University of Science and Technology, Islamabad, Pakistan
| | - Rizwan -ur-Rehman
- Faculty of Health and Life Sciences, Department of Bioinformatics and Biosciences, Capital University of Science and Technology, Islamabad, Pakistan
| | - Ahsanullah Unar
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, Naples, Italy
| | - Fakhar Un Nisa
- Depatment of Molecular Biology, Virtual University of Pakistan, Lahore, Pakistan
| | - Abdel-Rhman Z. Gaafar
- Department of Botany and Microbiology, College of Science, King Saud University, Riyadh, Saudi Arabia
| | - Mohammed Bourhia
- Department of Chemistry and Biochemistry, Faculty of Medicine and Pharmacy, Ibn Zohr University, Laayoune, Morocco
| | | | - Rohit Sharma
- Department of Rasa Shastra and Bhaishajya Kalpana, Faculty of Ayurveda, Institute of Medical Science, Banaras Hindu University, Varanasi, India
| | - Dileep Kumar
- UC Davis Comprehensive Cancer Center, University of California, Davis, Davis, CA, United States
- Poona College of Pharmacy, Bharati Vidyapeeth (Deemed to be) University, Pune, India
- Centre for Advanced Research in Pharmaceutical Sciences, Poona College of Pharmacy, Pune, India
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Dawood H, Celik I, Ibrahim RS. Computational biology and in vitro studies for anticipating cancer-related molecular targets of sweet wormwood (Artemisia annua). BMC Complement Med Ther 2023; 23:312. [PMID: 37684586 PMCID: PMC10492370 DOI: 10.1186/s12906-023-04135-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 08/21/2023] [Indexed: 09/10/2023] Open
Abstract
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.
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Affiliation(s)
- Hend Dawood
- Department of Pharmacognosy, Faculty of Pharmacy, Alexandria University, Alexandria, 21521, Egypt
| | - Ismail Celik
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Erciyes University, Kayseri, 38039, Turkey
| | - Reham S Ibrahim
- Department of Pharmacognosy, Faculty of Pharmacy, Alexandria University, Alexandria, 21521, Egypt.
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