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

Rafique, Qandeel; Rehman, Ali; Afghan, Muhammad Sher; Ahmad, Hafiz Muhamad; Zafar, Imran; Fayyaz, Kompal; Ain, Quratul; Rayan, Rehab A.; Al-Aidarous, Khadija Mohammed; Rashid, Summya; Mushtaq, Gohar; Sharma, Rohit

doi:10.1016/j.compbiomed.2023.107191

Cited by 15 publications

(6 citation statements)

References 231 publications

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“…To understand this better, we use computer-based in siliconin-silicon studies to predict how NRPs might interact with the oncogenes in breast cancer cells as per the method of ( Kumar et al, 2021 ). Researchers ( Rafique et al, 2023 ; Zafar et al, 2023 ) analyze gene expression data and simulate molecular interactions to see how NRPs could affect cancer pathways. To validate the findings, we conducted in vitro studies in the lab.…”

Section: Discussionmentioning

confidence: 99%

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

Zafar,

Safder,

Imran Afridi

et al. 2023

Front. Chem.

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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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Section: Discussionmentioning

confidence: 99%

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

Zafar,

Safder,

Imran Afridi

et al. 2023

Front. Chem.

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“…394 CRISPR-based techniques face challenges like complexity, lack of standardized testing protocols, sequencing constraints, and limited literature. 395,396 Moreover, additional DNA amplification steps are required to decrease the LOD. In addition to these challenges, expanding genosensing technologies for rapid mass production of COVID-19 tests face challenges such as fabrication time, costly reagents, and limited durability.…”

Section: Perspectives On Healthcare Management Of Futuristic Infectio...mentioning

confidence: 99%

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

Sadique,

Yadav,

Khan

et al. 2024

Chem. Soc. Rev.

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“…Over the past few years, there has been a surge in studies exploring deep learning techniques to diagnose COVID-19 and pneumonia. Systematic reviews of AI-enabled COVID-19 detection can be found in [1][2][3] in 2021, [4][5][6][7][8][9][10] in 2022, and [11][12][13] in 2023. While all reviews delved into deep learning neural networks and medical image databases, there were apparent shifts in focus, from the feasibility of deep learning to this aim and limited databases in 2021, to model performance comparison and database survey in 2022, and to model enhancement/development and real-world applications recently.…”

Section: Introductionmentioning

confidence: 99%

“…Despite exhibiting high accuracies in training and validation, CNN-based transfer learning might underperform during the testing phase [37][38][39][40][41]. This could be due to overfitting, which is a common issue in medical image classification with a limited number of discriminatory image features [7,13,[42][43][44]. Prominent CNN models typically encompass over ten layers, boasting 60+ million trained parameters, and are trained on expansive datasets like ImageJ, which houses 1000 categories.…”

Section: Introductionmentioning

confidence: 99%

Multi-Level Training and Testing of CNN Models in Diagnosing Multi-Center COVID-19 and Pneumonia X-ray Images

Talaat,

Si,

2023

Applied Sciences

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This study aimed to address three questions in AI-assisted COVID-19 diagnostic systems: (1) How does a CNN model trained on one dataset perform on test datasets from disparate medical centers? (2) What accuracy gains can be achieved by enriching the training dataset with new images? (3) How can learned features elucidate classification results, and how do they vary among different models? To achieve these aims, four CNN models—AlexNet, ResNet-50, MobileNet, and VGG-19—were trained in five rounds by incrementally adding new images to a baseline training set comprising 11,538 chest X-ray images. In each round, the models were tested on four datasets with decreasing levels of image similarity. Notably, all models showed performance drops when tested on datasets containing outlier images or sourced from other clinics. In Round 1, 95.2~99.2% accuracy was achieved for the Level 1 testing dataset (i.e., from the same clinic but set apart for testing only), and 94.7~98.3% for Level 2 (i.e., from an external clinic but similar). However, model performance drastically decreased for Level 3 (i.e., outlier images with rotation or deformation), with the mean sensitivity plummeting from 99% to 36%. For the Level 4 testing dataset (i.e., from another clinic), accuracy decreased from 97% to 86%, and sensitivity from 99% to 67%. In Rounds 2 and 3, adding 25% and 50% of the outlier images to the training dataset improved the average Level-3 accuracy by 15% and 23% (i.e., from 56% to 71% to 83%). In Rounds 4 and 5, adding 25% and 50% of the external images increased the average Level-4 accuracy from 81% to 92% and 95%, respectively. Among the models, ResNet-50 demonstrated the most robust performance across the five-round training/testing phases, while VGG-19 persistently underperformed. Heatmaps and intermediate activation features showed visual correlations to COVID-19 and pneumonia X-ray manifestations but were insufficient to explicitly explain the classification. However, heatmaps and activation features at different rounds shed light on the progression of the models’ learning behavior.

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

Cited by 15 publications

References 231 publications

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

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

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

Multi-Level Training and Testing of CNN Models in Diagnosing Multi-Center COVID-19 and Pneumonia X-ray Images

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