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  1. Dey A, Chattopadhyay S, Singh PK, Ahmadian A, Ferrara M, Senu N, et al.
    Sci Rep, 2021 Dec 15;11(1):24065.
    PMID: 34911977 DOI: 10.1038/s41598-021-02731-z
    COVID-19 is a respiratory disease that causes infection in both lungs and the upper respiratory tract. The World Health Organization (WHO) has declared it a global pandemic because of its rapid spread across the globe. The most common way for COVID-19 diagnosis is real-time reverse transcription-polymerase chain reaction (RT-PCR) which takes a significant amount of time to get the result. Computer based medical image analysis is more beneficial for the diagnosis of such disease as it can give better results in less time. Computed Tomography (CT) scans are used to monitor lung diseases including COVID-19. In this work, a hybrid model for COVID-19 detection has developed which has two key stages. In the first stage, we have fine-tuned the parameters of the pre-trained convolutional neural networks (CNNs) to extract some features from the COVID-19 affected lungs. As pre-trained CNNs, we have used two standard CNNs namely, GoogleNet and ResNet18. Then, we have proposed a hybrid meta-heuristic feature selection (FS) algorithm, named as Manta Ray Foraging based Golden Ratio Optimizer (MRFGRO) to select the most significant feature subset. The proposed model is implemented over three publicly available datasets, namely, COVID-CT dataset, SARS-COV-2 dataset, and MOSMED dataset, and attains state-of-the-art classification accuracies of 99.15%, 99.42% and 95.57% respectively. Obtained results confirm that the proposed approach is quite efficient when compared to the local texture descriptors used for COVID-19 detection from chest CT-scan images.
  2. Selvakumar M, Srivastava P, Pawar HS, Francis NK, Das B, Sathishkumar G, et al.
    ACS Appl Mater Interfaces, 2016 Feb 17;8(6):4086-100.
    PMID: 26799576 DOI: 10.1021/acsami.5b11723
    Guided bone regeneration (GBR) scaffolds are futile in many clinical applications due to infection problems. In this work, we fabricated GBR with an anti-infective scaffold by ornamenting 2D single crystalline bismuth-doped nanohydroxyapatite (Bi-nHA) rods onto segmented polyurethane (SPU). Bi-nHA with high aspect ratio was prepared without any templates. Subsequently, it was introduced into an unprecedented synthesized SPU matrix based on dual soft segments (PCL-b-PDMS) of poly(ε-caprolactone) (PCL) and poly(dimethylsiloxane) (PDMS), by an in situ technique followed by electrospinning to fabricate scaffolds. For comparison, undoped pristine nHA rods were also ornamented into it. The enzymatic ring-opening polymerization technique was adapted to synthesize soft segments of PCL-b-PDMS copolymers of SPU. Structure elucidation of the synthesized polymers is done by nuclear magnetic resonance spectroscopy. Sparingly, Bi-nHA ornamented scaffolds exhibit tremendous improvement (155%) in the mechanical properties with excellent antimicrobial activity against various human pathogens. After confirmation of high osteoconductivity, improved biodegradation, and excellent biocompatibility against osteoblast cells (in vitro), the scaffolds were implanted in rabbits by subcutaneous and intraosseous (tibial) sites. Various histological sections reveal the signatures of early cartilage formation, endochondral ossification, and rapid bone healing at 4 weeks of the critical defects filled with ornamented scaffold compared to SPU scaffold. This implies osteogenic potential and ability to provide an adequate biomimetic microenvironment for mineralization for GBR of the scaffolds. Organ toxicity studies further confirm that no tissue architecture abnormalities were observed in hepatic, cardiac, and renal tissue sections. This finding manifests the feasibility of fabricating a mechanically adequate nanofibrous SPU scaffold by a biomimetic strategy and the advantages of Bi-nHA ornamentation in promoting osteoblast phenotype progression with microbial protection (on-demand) for GBR applications.
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