Browsing by Author "Summya Rashid"

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A decade's worth of impact: Dox loaded liposomes in anticancer activity
(Elsevier Ltd, 2022) Puja Ghosh; Himja Tiwari; Jaya Lakkakula; Arpita Roy; Talha Bin Emran; Summya Rashid; Saad Alghamdi; Bodour S. Rajab; Mazen Almehmadi; Mamdouh Allahyani; Abdulelah Aljuaid; Ahad Amer Alsaiari; Rohit Sharma; Ahmad O. Babalghith
Clinically approved therapeutics associated with cancer are limited to mostly chemotherapy, surgery and radiotherapy in spite of the advancements in the biomedical field. Due to the cardiotoxicity and uncountable side effects brought by the prevailing treatment strategies, demands are growing for targeted drug delivery using nanomaterials. For this the most commonly used drug, doxorubicin (DOX) is encapsulated within several type of nanovesicles to observe their anticancer activity. Among them, DOX encapsulated liposomes gained popularity because of their clinical success and lower toxicity. To enhance their efficiency and site specific delivery, attempts are made to modify the liposomes by combining them with peptides, aptamers, antibodies etc to develop pH, thermal, UV-sensitive and electro-magnetic liposomal nanocarriers for controlled drug release. The novel strategies for the treatment of Breast, Lung, Liver, Pancreatic, Prostate, Ovarian, Cervical, Blood, Brain and Colon cancer using modified liposomes encapsulating DOX are illustrated in this review. © 2022
A drug design strategy based on molecular docking and molecular dynamics simulations applied to development of inhibitor against triple-negative breast cancer by Scutellarein derivatives
(Public Library of Science, 2023) Shopnil Akash; Farjana Islam Aovi; Md A.K. Azad; Ajoy Kumer; Unesco Chakma; Md Rezaul Islam; Nobendu Mukerjee; Md Mominur Rahman; Imren Bayil; Summya Rashid; Rohit Sharma
Triple-negative breast cancer (TNBC), accounting for 10-15% of all breast malignancies, is more prevalent in women under 40, particularly in those of African descent or carrying the BRCA1 mutation. TNBC is characterized by the absence of estrogen and progesterone receptors (ER, PR) and low or elevated HER2 expression. It represents a particularly aggressive form of breast cancer with limited therapeutic options and a poorer prognosis. In our study, we utilized the protein of TNBC collected from the Protein Data Bank (PDB) with the most stable configuration. We selected Scutellarein, a bioactive molecule renowned for its anti-cancer properties, and used its derivatives to design potential anti-cancer drugs employing computational tools. We applied and modified structural activity relationship methods to these derivatives and evaluated the probability of active (Pa) and inactive (Pi) outcomes using pass prediction scores. Furthermore, we employed in-silico approaches such as the assessment of absorption, distribution, metabolism, excretion, and toxicity (ADMET) parameters, and quantum calculations through density functional theory (DFT). Within the DFT calculations, we analyzed Frontier Molecular Orbitals, specifically the Highest Occupied Molecular Orbital (HOMO) and Lowest Unoccupied Molecular Orbital (LUMO). We then conducted molecular docking and dynamics against TNBC to ascertain binding affinity and stability. Our findings indicated that Scutellarein derivatives, specifically DM03 with a binding energy of -10.7 kcal/mol and DM04 with -11.0 kcal/mol, exhibited the maximum binding tendency against Human CK2 alpha kinase (PDB ID 7L1X). Molecular dynamic simulations were performed for 100 ns, and stability was assessed using rootmean- square deviation (RMSD) and root-mean-square fluctuation (RMSF) parameters, suggesting significant stability for our chosen compounds. Furthermore, these molecules met the pharmacokinetics requirements for potential therapeutic candidates, displaying non-carcinogenicity, minimal aquatic and non-aquatic toxicity, and greater aqueous solubility. Collectively, our computational data suggest that Scutellarein derivatives may serve as potential therapeutic agents for TNBC. However, further experimental investigations are needed to validate these findings. © 2023 Akash et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Amomum subulatum: A treasure trove of anti-cancer compounds targeting TP53 protein using in vitro and in silico techniques
(Frontiers Media S.A., 2023) Sadaqat Ali; Asifa Noreen; Adeem Qamar; Imran Zafar; Quratul Ain; Hiba-Allah Nafidi; Yousef A. Bin Jardan; Mohammed Bourhia; Summya Rashid; Rohit Sharma
Cancer is a primary global health concern, and researchers seek innovative approaches to combat the disease. Clinical bioinformatics and high-throughput proteomics technologies provide powerful tools to explore cancer biology. Medicinal plants are considered effective therapeutic agents, and computer-aided drug design (CAAD) is used to identify novel drug candidates from plant extracts. The tumour suppressor protein TP53 is an attractive target for drug development, given its crucial role in cancer pathogenesis. This study used a dried extract of Amomum subulatum seeds to identify phytocompounds targeting TP53 in cancer. We apply qualitative tests to determine its phytochemicals (Alkaloid, Tannin, Saponin, Phlobatinin, and Cardic glycoside), and found that alkaloid composed of 9.4% ± 0.04% and Saponin 1.9% ± 0.05% crude chemical constituent. In the results of DPPH Analysis Amomum subulatum Seeds founded antioxidant activity, and then we verified via observing methanol extract (79.82%), BHT (81.73%), and n-hexane extract (51.31%) found to be positive. For Inhibition of oxidation, we observe BHT is 90.25%, and Methanol (83.42%) has the most significant proportion of linoleic acid oxidation suppression. We used diverse bioinformatics approaches to evaluate the effect of A. subulatum seeds and their natural components on TP53. Compound-1 had the best pharmacophore match value (53.92), with others ranging from 50.75 to 53.92. Our docking result shows the top three natural compounds had the highest binding energies (−11.10 to −10.3 kcal/mol). The highest binding energies (−10.9 to −9.2 kcal/mol) compound bonded to significant sections in the target protein’s active domains with TP53. Based on virtual screening, we select top phytocompounds for targets which highly fit based on pharmacophore score and observe these compounds exhibited potent antioxidant activity and inhibited cancer cell inflammation in the TP53 pathway. Molecular Dynamics (MD) simulations indicated that the ligand was bound to the protein with some significant conformational changes in the protein structure. This study provides novel insights into the development of innovative drugs for the treatment of cancer disorders. Copyright © 2023 Ali, Noreen, Qamar, Zafar, Ain, Nafidi, Bin Jardan, Bourhia, Rashid and Sharma.
Bioinformatics approaches in upgrading microalgal oil for advanced biofuel production through hybrid ORF protein construction
(Springer Science and Business Media Deutschland GmbH, 2025) Ihtesham Arshad; Muhammad K. Ahsan; Imran Zafar; Muhammad Sajid; Sheikh Arslan Sehgal; Waqas Yousaf; Amna Noor; Summya Rashid; Somenath Garai; Meivelu Moovendhan; Rohit Sharma
Microalgae are promising for biofuel production due to their high oil content and fast biomass growth, but increasing their oil content is essential for economic viability. In this study, we conducted in silico investigations to identify oil-producing genes in various microalgal species. We selected six genes from different species: ACCD and F751_4275 from Chlorella protothecides, C2E21_7193 and C2E21_2849 from Chlorella sorokiniana, and COO60DRAFT_1295191 and COO60DRAFT_1481410 from Scenedesmus sp. We utilized the NCBI genome database and performed BLASTp analysis to identify these genes’ superfamilies (PLN02349, DUF212, BKR SDR, PRK08591, ACCD, and SET LSMT). The open reading frames (ORFs) of the selected genes were analyzed using the ORF Finder tool to determine their lengths and the locations of their start and stop codons. Based on this analysis, we constructed two hybrid ORFs by combining the ORFs from different genes. Hybrid ORF 1 had a length of 5166 base pairs, while hybrid ORF 2 was 3516 base pairs long. The thermodynamic evaluation was performed on these hybrid ORFs to assess their stability and GC content. We translated the hybrid ORF sequences into protein sequences using the Translate feature of Expasy. Tertiary structure predictions and bioinformatics approaches were employed to analyze the permissible regions for amino acid dihedral angles, providing insights into the potential functionality of these hybrid ORF proteins. The results of this study indicated that both hybrid ORFs have the potential to produce high lipid contents, making them promising candidates for biofuel production. However, it is essential to conduct further in vitro experiments to validate the functionality of these hybrid proteins. Our study contributes to understanding oil-producing genes in microalgae and their potential applications in the biofuel and pharmaceutical industries. The identified genes and hybrid ORFs provide valuable insights into microalgae species’ genetic manipulation and biology, paving the way for advancements in renewable energy and other biotechnological applications. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.
Bioinformatics approaches in upgrading microalgal oil for advanced biofuel production through hybrid ORF protein construction
(Springer Science and Business Media Deutschland GmbH, 2023) Ihtesham Arshad; Muhammad Ahsan; Imran Zafar; Muhammad Sajid; Sheikh Arslan Sehgal; Waqas Yousaf; Amna Noor; Summya Rashid; Somenath Garai; Meivelu Moovendhan; Rohit Sharma
Microalgae are promising for biofuel production due to their high oil content and fast biomass growth, but increasing their oil content is essential for economic viability. In this study, we conducted in silico investigations to identify oil-producing genes in various microalgal species. We selected six genes from different species: ACCD and F751_4275 from Chlorella protothecides, C2E21_7193 and C2E21_2849 from Chlorella sorokiniana, and COO60DRAFT_1295191 and COO60DRAFT_1481410 from Scenedesmus sp. We utilized the NCBI genome database and performed BLASTp analysis to identify these genes’ superfamilies (PLN02349, DUF212, BKR SDR, PRK08591, ACCD, and SET LSMT). The open reading frames (ORFs) of the selected genes were analyzed using the ORF Finder tool to determine their lengths and the locations of their start and stop codons. Based on this analysis, we constructed two hybrid ORFs by combining the ORFs from different genes. Hybrid ORF 1 had a length of 5166 base pairs, while hybrid ORF 2 was 3516 base pairs long. The thermodynamic evaluation was performed on these hybrid ORFs to assess their stability and GC content. We translated the hybrid ORF sequences into protein sequences using the Translate feature of Expasy. Tertiary structure predictions and bioinformatics approaches were employed to analyze the permissible regions for amino acid dihedral angles, providing insights into the potential functionality of these hybrid ORF proteins. The results of this study indicated that both hybrid ORFs have the potential to produce high lipid contents, making them promising candidates for biofuel production. However, it is essential to conduct further in vitro experiments to validate the functionality of these hybrid proteins. Our study contributes to understanding oil-producing genes in microalgae and their potential applications in the biofuel and pharmaceutical industries. The identified genes and hybrid ORFs provide valuable insights into microalgae species’ genetic manipulation and biology, paving the way for advancements in renewable energy and other biotechnological applications. Graphical Abstract: [Figure not available: see fulltext.] © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Elucidation of novel compounds and epitope-based peptide vaccine design against C30 endopeptidase regions of SARS-CoV-2 using immunoinformatics approaches
(Frontiers Media S.A., 2023) Saigha Marriam; Muhammad Sher Afghan; Mazhar Nadeem; Muhammad Sajid; Muhammad Ahsan; Abdul Basit; Muhammad Wajid; Sabeen Sabri; Imran Zafar; Summya Rashid; Sheikh Arslan Sehgal; Dalal Hussien M. Alkhalifah; Wael N. Hozzein; Kow-Tong Chen; Rohit Sharma
There has been progressive improvement in immunoinformatics approaches for epitope-based peptide design. Computational-based immune-informatics approaches were applied to identify the epitopes of SARS-CoV-2 to develop vaccines. The accessibility of the SARS-CoV-2 protein surface was analyzed, and hexa-peptide sequences (KTPKYK) were observed having a maximum score of 8.254, located between amino acids 97 and 102, whereas the FSVLAC at amino acids 112 to 117 showed the lowest score of 0.114. The surface flexibility of the target protein ranged from 0.864 to 1.099 having amino acid ranges of 159 to 165 and 118 to 124, respectively, harboring the FCYMHHM and YNGSPSG hepta-peptide sequences. The surface flexibility was predicted, and a 0.864 score was observed from amino acids 159 to 165 with the hepta-peptide (FCYMHHM) sequence. Moreover, the highest score of 1.099 was observed between amino acids 118 and 124 against YNGSPSG. B-cell epitopes and cytotoxic T-lymphocyte (CTL) epitopes were also identified against SARS-CoV-2. In molecular docking analyses, -0.54 to -26.21 kcal/mol global energy was observed against the selected CTL epitopes, exhibiting binding solid energies of -3.33 to -26.36 kcal/mol. Based on optimization, eight epitopes (SEDMLNPNY, GSVGFNIDY, LLEDEFTPF, DYDCVSFCY, GTDLEGNFY, QTFSVLACY, TVNVLAWLY, and TANPKTPKY) showed reliable findings. The study calculated the associated HLA alleles with MHC-I and MHC-II and found that MHC-I epitopes had higher population coverage (0.9019% and 0.5639%) than MHC-II epitopes, which ranged from 58.49% to 34.71% in Italy and China, respectively. The CTL epitopes were docked with antigenic sites and analyzed with MHC-I HLA protein. In addition, virtual screening was conducted using the ZINC database library, which contained 3,447 compounds. The 10 top-ranked scrutinized molecules (ZINC222731806, ZINC077293241, ZINC014880001, ZINC003830427, ZINC030731133, ZINC003932831, ZINC003816514, ZINC004245650, ZINC000057255, and ZINC011592639) exhibited the least binding energy (-8.8 to -7.5 kcal/mol). The molecular dynamics (MD) and immune simulation data suggest that these epitopes could be used to design an effective SARS-CoV-2 vaccine in the form of a peptide-based vaccine. Our identified CTL epitopes have the potential to inhibit SARS-CoV-2 replication. Copyright © 2023 Marriam, Afghan, Nadeem, Sajid, Ahsan, Basit, Wajid, Sabri, Sajid, Zafar, Rashid, Sehgal, Alkhalifah, Hozzein, Chen and Sharma.
Exploring the multifunctional roles of quantum dots for unlocking the future of biology and medicine
(Academic Press Inc., 2023) Muhammad Kashif Ali; Saher Javaid; Haseeb Afzal; Imran Zafar; Kompal Fayyaz; Qurat ul Ain; Mohd Ashraf Rather; Md. Jamal Hossain; Summya Rashid; Khalid Ali Khan; Rohit Sharma
With recent advancements in nanomedicines and their associated research with biological fields, their translation into clinically-applicable products is still below promises. Quantum dots (QDs) have received immense research attention and investment in the four decades since their discovery. We explored the extensive biomedical applications of QDs, viz. Bio-imaging, drug research, drug delivery, immune assays, biosensors, gene therapy, diagnostics, their toxic effects, and bio-compatibility. We unravelled the possibility of using emerging data-driven methodologies (bigdata, artificial intelligence, machine learning, high-throughput experimentation, computational automation) as excellent sources for time, space, and complexity optimization. We also discussed ongoing clinical trials, related challenges, and the technical aspects that should be considered to improve the clinical fate of QDs and promising future research directions. © 2023 Elsevier Inc.
Formulation of pH-responsive highly swellable hydrogel scaffolds for controlled release of tramadol HCl: characterization and biocompatibility evaluation
(Frontiers Media S.A., 2023) Zainab Abdullah; Muhammad Umer Ashraf; Kashif Barkat; Syed Faisal Badshah; Umaira Rehman; Asma Razzaq; Asif Mahmood; Farid Ulhaq; Hitesh Chopra; Summya Rashid; Marian Valko; Suliman Alomar; Kamil Kuca; Rohit Sharma
Introduction: The objective of current project was to formulate a system for controlled delivery of Tramadol HCl (TRD), an opioid analgesic used in the treatment of moderate to severe pain. Methods: For this purpose, a pH responsive AvT-co-poly hydrogel network was formulated through free radical polymerization by incorporating natural polymers i.e., aloe vera gel and tamarind gum, monomer and crosslinker. Formulated hydrogels were loaded with Tramadol HCl (TRD) and evaluated for percent drug loading, sol-gel fraction, dynamic and equilibrium swelling, morphological characteristics, structural features and in-vitro release of Tramadol HCl. Results and Discussions: Hydrogels were proved to be pH sensitive as remarkable dynamic swelling response ranging within 2.94g/g-10.81g/g was noticed at pH 7.4 as compared to pH 1.2. Percent drug loading was in the range of 70.28%-90.64% for all formulations. Thermal stability and compatibility of hydrogel components were validated by DSC analysis and FTIR spectroscopy. Controlled release pattern of Tramadol HCl from the polymeric network was confirmed as maximum release of 92.22% was observed for over a period of 24 hours at pH 7.4. Moreover, oral toxicity studies were also conducted in rabbits to investigate the safety of hydrogels. No evidence of any toxicity, lesions and degeneration was reported, confirming the biocompatibility and safety of grafted system. Copyright © 2023 Abdullah, Ashraf, Barkat, Badshah, Rehman, Razzaq, Mahmood, Ulhaq, Chopra, Rashid, Valko, Alomar, Kuca and Sharma.
Medicinal and therapeutic properties of garlic, garlic essential oil, and garlic-based snack food: An updated review
(Frontiers Media S.A., 2023) Tarun Verma; Ankur Aggarwal; Priya Dey; Anil Kumar Chauhan; Summya Rashid; Kow-Tong Chen; Rohit Sharma
Garlic (Allium sativum) is an edible tuber belonging to the family Liliaceae. It has been used since ancient times as a spice to enhance the sensory characteristics of food and as a household remedy for the treatment of a variety of ailments. Garlic has been studied for its medicinal and therapeutic effects in the treatment of various human diseases for a long time. Health benefits associated with the consumption of garlic are attributed to the various sulfur compounds present in it such as allicin, ajoene, vinyl-dithiin, and other volatile organosulfur compounds which are all metabolized from alliin. Several researches in the literature have shown evidence that garlic exhibits antioxidant, antiviral, anti-microbial, anti-fungal, antihypertensive, anti-anemic, anti-hyperlipidemic, anticarcinogenic, antiaggregant, and immunomodulatory properties. The present review identifies and discusses the various health benefits associated with the consumption of garlic, its essential oil, and bioactive constituents, along with exploring the various snack-food products developed by incorporating garlic. Copyright © 2023 Verma, Aggarwal, Dey, Chauhan, Rashid, Chen and Sharma.
Multifunctional role of nanoparticles for the diagnosis and therapeutics of cardiovascular diseases
(Academic Press Inc., 2024) Ihtesham Arshad; Ayesha Kanwal; Imran Zafar; Ahsanullah Unar; Hanane Mouada; Iashia Tur Razia; Safina Arif; Muhammad Ahsan; Mohammad Amjad Kamal; Summya Rashid; Khalid Ali Khan; Rohit Sharma
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. © 2023 Elsevier Inc.
Nutritional, Nutraceutical, and Medicinal Potential of Cantharellus cibarius Fr.: A Comprehensive Review
(John Wiley and Sons Inc, 2025) Ajay Kumar; Reema Umilla Devi; Rajni Dhalaria; Ashwani Tapwal; Rachna Verma; Summya Rashid; Gehan M. Elossaily; Khalid Ali Khan; Kowtong Chen; Tarun S. Verma
Mushrooms are considered as nutraceutical foods that can effectively prevent diseases such as cancer and other serious life-threatening conditions include neurodegeneration, hypertension, diabetes, and hypercholesterolemia. The Cantharellus cibarius, also known as the “Golden chanterelle” or “Golden girolle,” is a significant wild edible ectomycorrhizal mushroom. It is renowned for its delicious, apricot-like aroma and is highly valued in various culinary traditions worldwide. It is well known for its nutritional, nutraceutical, and therapeutic properties. The high nutritional value of C. cibarius is attributed to its abundant carbohydrates, proteins, β-glucans, dietary fiber, and low-fat content. It also contains medicinal polysaccharides (β-glucans), proteins (lectins and selenoproteins), important fatty acids (linoleic and omega-6), vitamins, and minerals (N, P, K, Ca, Zn, Ag, Se, etc.). The sporocarp of C. cibarius contains a diverse array of bioactive metabolites, including flavonoids, phenolics, sterols, fatty acids, organic acids, indole groups, carbohydrates, vitamins (tocopherols), amino acids, enzymes, bioelements, carotenoids, and 5ˊ-nucleotides. C. cibarius has a wide array of biological properties, such as antioxidant, anticancer, anti-inflammatory, antifungal, antibacterial, anthelmintic, insecticidal, antihypoxia, antihyperglycemic, wound-healing, cytotoxic, and iron-chelating activity. Thus, the present review gives an overview of C. cibarius, covering its chemical composition, ecological significance, postharvest preservation strategies, and potential applications in dietary supplements, nutraceuticals, and pharmaceuticals. It also dives into the etymology, taxonomy, and global distribution of the renowned “Golden Chanterelle.” Furthermore, there is a need to valorize waste materials created during production and processing, as well as to acquire a thorough understanding of the mechanisms of action of bioactive compounds in mushrooms. © 2024 The Author(s). Food Science & Nutrition published by Wiley Periodicals LLC.
Predicting the effects of rare genetic variants on oncogenic signaling pathways: A computational analysis of HRAS protein function
(Frontiers Media S.A., 2023) Sadaqat Ali; Usman Ali; Adeem Qamar; Imran Zafar; Muhammad Yaqoob; Qurat ul Ain; Summya Rashid; Rohit Sharma; Hiba-Allah Nafidi; Yousef A. Bin Jardan; Mohammed Bourhia
The HRAS gene plays a crucial role in regulating essential cellular processes for life, and this gene's misregulation is linked to the development of various types of cancers. Nonsynonymous single nucleotide polymorphisms (nsSNPs) within the coding region of HRAS can cause detrimental mutations that disrupt wild-type protein function. In the current investigation, we have employed in-silico methodologies to anticipate the consequences of infrequent genetic variations on the functional properties of the HRAS protein. We have discovered a total of 50 nsSNPs, of which 23 were located in the exon region of the HRAS gene and denoting that they were expected to cause harm or be deleterious. Out of these 23, 10 nsSNPs ([G60V], [G60D], [R123P], [D38H], [I46T], [G115R], [R123G], [P11OL], [A59L], and [G13R]) were identified as having the most delterious effect based on results of SIFT analysis and PolyPhen2 scores ranging from 0.53 to 69. The DDG values −3.21 kcal/mol to 0.87 kcal/mol represent the free energy change associated with protein stability upon mutation. Interestingly, we identified that the three mutations (Y4C, T58I, and Y12E) were found to improve the structural stability of the protein. We performed molecular dynamics (MD) simulations to investigate the structural and dynamic effects of HRAS mutations. Our results showed that the stable model of HRAS had a significantly lower energy value of −18756 kj/mol compared to the initial model of −108915 kj/mol. The RMSD value for the wild-type complex was 4.40 Å, and the binding energies for the G60V, G60D, and D38H mutants were −107.09 kcal/mol, −109.42 kcal/mol, and −107.18 kcal/mol, respectively as compared to wild-type HRAS protein had −105.85 kcal/mol. The result of our investigation presents convincing corroboration for the potential functional significance of nsSNPs in augmenting HRAS expression and adding to the activation of malignant oncogenic signalling pathways. Copyright © 2023 Ali, Ali, Qamar, Zafar, Yaqoob, Ain, Rashid, Sharma, Nafidi, Bin Jardan and Bourhia.
Reviewing methods of deep learning for diagnosing COVID-19, its variants and synergistic medicine combinations
(Elsevier Ltd, 2023) Qandeel Rafique; Ali Rehman; Muhammad Sher Afghan; Hafiz Muhamad Ahmad; Imran Zafar; Kompal Fayyaz; Quratul Ain; Rehab A. Rayan; Khadija Mohammed Al-Aidarous; Summya Rashid; Gohar Mushtaq; Rohit Sharma
The COVID-19 pandemic has necessitated the development of reliable diagnostic methods for accurately detecting the novel coronavirus and its variants. Deep learning (DL) techniques have shown promising potential as screening tools for COVID-19 detection. In this study, we explore the realistic development of DL-driven COVID-19 detection methods and focus on the fully automatic framework using available resources, which can effectively investigate various coronavirus variants through modalities. We conducted an exploration and comparison of several diagnostic techniques that are widely used and globally validated for the detection of COVID-19. Furthermore, we explore review-based studies that provide detailed information on synergistic medicine combinations for the treatment of COVID-19. We recommend DL methods that effectively reduce time, cost, and complexity, providing valuable guidance for utilizing available synergistic combinations in clinical and research settings. This study also highlights the implication of innovative diagnostic technical and instrumental strategies, exploring public datasets, and investigating synergistic medicines using optimised DL rules. By summarizing these findings, we aim to assist future researchers in their endeavours by providing a comprehensive overview of the implication of DL techniques in COVID-19 detection and treatment. Integrating DL methods with various diagnostic approaches holds great promise in improving the accuracy and efficiency of COVID-19 diagnostics, thus contributing to effective control and management of the ongoing pandemic. © 2023 Elsevier Ltd
Reviewing methods of deep learning for intelligent healthcare systems in genomics and biomedicine
(Elsevier Ltd, 2023) Imran Zafar; Shakila Anwar; Faheem kanwal; Waqas Yousaf; Fakhar Un Nisa; Tanzeela Kausar; Qurat ul Ain; Ahsanullah Unar; Mohammad Amjad Kamal; Summya Rashid; Khalid Ali Khan; Rohit Sharma
The advancements in genomics and biomedical technologies have generated vast amounts of biological and physiological data, which present opportunities for understanding human health. Deep learning (DL) and machine learning (ML) are frontiers and interdisciplinary fields of computer science that consider comprehensive computational models and provide integral roles for disease diagnosis and therapy investigation. DL-based algorithms can discover the intrinsic hierarchies in the training data to show great promise for extracting features and learning patterns from complex datasets and performing various analytical tasks. This review comprehensively discusses the wide-ranging DL approaches for intelligent healthcare systems (IHS) in genomics and biomedicine. This paper explores advanced concepts in deep learning (DL) and discusses the workflow of utilizing role-based algorithms in genomics and biomedicine to integrate intelligent healthcare systems (IHS). The aim is to overcome biomedical obstacles like patient disease classification, core biomedical processes, and empowering patient-disease integration. The paper also highlights how DL approaches are well-suited for addressing critical challenges in these domains, offering promising solutions for improved healthcare outcomes. We also provided a concise concept of DL architectures and model optimization in genomics and bioinformatics at the molecular level to deal with biomedicine classification, genomic sequence analysis, protein structure classification, and prediction. Finally, we discussed DL's current challenges and future perspectives in genomics and biomedicine for future directions. © 2023 Elsevier Ltd
Synthesis and characterization of copper oxide nanoparticles: its influence on corn (Z. mays) and wheat (Triticum aestivum) plants by inoculation of Bacillus subtilis
(Springer Science and Business Media Deutschland GmbH, 2023) Hafiz Imran Haider; Imran Zafar; Qurat ul Ain; Asifa Noreen; Aamna Nazir; Rida Javed; Sheikh Arslan Sehgal; Azmat Ali Khan; Md. Mominur Rahman; Summya Rashid; Somenath Garai; Rohit Sharma
Nanotechnology is now playing an emerging role in green synthesis in agriculture as nanoparticles (NPs) are used for various applications in plant growth and development. Copper is a plant micronutrient; the amount of copper oxide nanoparticles (CuONPs) in the soil determines whether it has positive or adverse effects. CuONPs can be used to grow corn and wheat plants by combining Bacillus subtilis. In this research, CuONPs were synthesized by precipitation method using different precursors such as sodium hydroxide (0.1 M) and copper nitrate (Cu(NO3)2) having 0.1 M concentration with a post-annealing method. The NPs were characterized through X-ray diffraction (XRD), scanning electron microscope (SEM), and ultraviolet (UV) visible spectroscopy. Bacillus subtilis is used as a potential growth promoter for microbial inoculation due to its prototrophic nature. The JAR experiment was conducted, and the growth parameter of corn (Z. mays) and wheat (Triticum aestivum) was recorded after 5 days. The lab assay evaluated the germination in JARs with and without microbial inoculation under CuONP stress at different concentrations (25 and 50 mg). The present study aimed to synthesize CuONPs and systematically investigate the particle size effects of copper (II) oxide (CuONPs) (< 50 nm) on Triticum aestivum and Z. mays. In our results, the XRD pattern of CuONPs at 500 °C calcination temperature with monoclinic phase is observed, with XRD peak intensity slightly increasing. The XRD patterns showed that the prepared CuONPs were extremely natural, crystal-like, and nano-shaped. We used Scherrer’s formula to calculate the average size of the particle, indicated as 23 nm. The X-ray diffraction spectrum of synthesized materials and SEM analysis show that the particles of CuONPs were spherical in nature. The results revealed that the synthesized CuONPs combined with Bacillus subtilis used in a field study provided an excellent result, where growth parameters of Z. Mays and Triticum aestivum such as root length, shoot length, and plant biomass was improved as compared to the control group. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.