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1.
Computers & electrical engineering : an international journal ; 2022.
Article in English | EuropePMC | ID: covidwho-2034105

ABSTRACT

All witnessed the terrible effects of the COVID-19 pandemic on the health and work lives of the population across the world. It is hard to diagnose all infected people in real time since the conventional medical diagnosis of COVID-19 patients takes a couple of days for accurate diagnosis results. In this paper, a novel learning framework is proposed for the early diagnosis of COVID-19 patients using hybrid deep fusion learning models. The proposed framework performs early classification of patients based on collected samples of chest X-ray images and Coswara cough (sound) samples of possibly infected people. The captured cough samples are pre-processed using speech signal processing techniques and Mel frequency cepstral coefficient features are extracted using deep convolutional neural networks. Finally, the proposed system fuses extracted features to provide 98.70% and 82.7% based on Chest-X ray images and cough (audio) samples for early diagnosis using the weighted sum-rule fusion method.

2.
Neural Comput Appl ; : 1-19, 2022 Mar 01.
Article in English | MEDLINE | ID: covidwho-1718730

ABSTRACT

A SARS-CoV-2 virus-specific reverse transcriptase-polymerase chain reaction (RT-PCR) test is usually used to diagnose COVID-19. However, this test requires up to 2 days for completion. Moreover, to avoid false-negative outcomes, serial testing may be essential. The availability of RT-PCR test kits is currently limited, highlighting the need for alternative approaches for the precise and rapid diagnosis of COVID-19. Patients suspected to be infected with SARS-CoV-2 can be assessed using chest CT scan images. However, CT images alone cannot be used for ruling out SARS-CoV-2 infection because individual patients may exhibit normal radiological results in the primary phases of the disease. A machine learning (ML)-based recognition and segmentation system was developed to spontaneously discover and compute infection areas in CT scans of COVID-19 patients. The computable assessment exhibited suitable performance for automatic infection region allocation. The ML models developed were suitable for the direct detection of COVID-19 (+). ML was confirmed to be a complementary diagnostic technique for diagnosing COVID-19(+) by forefront medical specialists. The complete manual delineation of COVID-19 often requires up to 225.5 min; however, the proposed RILML method decreases the delineation time to 7 min after four iterations of model updating.

3.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-317220

ABSTRACT

COVID-19 pandemic in first seven months has led to more than 15 million confirmed infected cases and 600,000 deaths. SARS-CoV-2, the causative agent for COVID-19 has proved a great challenge for its ability to spread in asymptomatic stages and a diverse disease spectrum it has generated. This has created a challenge of unimaginable magnitude not only affecting human health and life but also potentially generating a long-lasting socioeconomic impact. Both medical sciences and biomedical research have also been challenged consequently leading to a large number of clinical trials and vaccine initiatives. While known proteins of pathobiological importance are targets for these therapeutic approaches, it is imperative to explore other factors of viral significance. Accessory proteins are one such trait that have diverse roles in coronavirus pathobiology. Here we analyze certain genomic characteristics of SARS-CoV-2 accessory protein ORF8, predict upon its protein features and review current available literature regarding its function. We have also undertaken review of ORF8 homolog ORF8ab from SARS-CoV with a purpose of developing holistic understanding of these proteins for reason that coronaviruses have been infecting humans repeatedly and might continue to do so. Despite low nucleotide and protein identity and differentiating genome level characteristics, there appears to be significant structural integrity and functional proximity between these proteins pointing towards their high significance. There is further need for comprehensive genomics and structural-functional studies to lead towards definitive conclusions regarding their criticality and that can eventually define their relevance to therapeutics development.

4.
J Multidiscip Healthc ; 14: 2017-2033, 2021.
Article in English | MEDLINE | ID: covidwho-1346356

ABSTRACT

BACKGROUND: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in Wuhan, China, in late 2019 and created a global pandemic that overwhelmed healthcare systems. COVID-19, as of July 3, 2021, yielded 182 million confirmed cases and 3.9 million deaths globally according to the World Health Organization. Several patients who were initially diagnosed with mild or moderate COVID-19 later deteriorated and were reclassified to severe disease type. OBJECTIVE: The aim is to create a predictive model for COVID-19 ventilatory support and mortality early on from baseline (at the time of diagnosis) and routinely collected data of each patient (CXR, CBC, demographics, and patient history). METHODS: Four common machine learning algorithms, three data balancing techniques, and feature selection are used to build and validate predictive models for COVID-19 mechanical requirement and mortality. Baseline CXR, CBC, demographic, and clinical data were retrospectively collected from April 2, 2020, till June 18, 2020, for 5739 patients with confirmed PCR COVID-19 at King Abdulaziz Medical City in Riyadh. However, of those patients, only 1508 and 1513 have met the inclusion criteria for ventilatory support and mortalilty endpoints, respectively. RESULTS: In an independent test set, ventilation requirement predictive model with top 20 features selected with reliefF algorithm from baseline radiological, laboratory, and clinical data using support vector machines and random undersampling technique attained an AUC of 0.87 and a balanced accuracy of 0.81. For mortality endpoint, the top model yielded an AUC of 0.83 and a balanced accuracy of 0.80 using all features with balanced random forest. This indicates that with only routinely collected data our models can predict the outcome with good performance. The predictive ability of combined data consistently outperformed each data set individually for intubation and mortality. For the ventilator support, chest X-ray severity annotations alone performed better than comorbidity, complete blood count, age, or gender with an AUC of 0.85 and balanced accuracy of 0.79. For mortality, comorbidity alone achieved an AUC of 0.80 and a balanced accuracy of 0.72, which is higher than models that use either chest radiograph, laboratory, or demographic features only. CONCLUSION: The experimental results demonstrate the practicality of the proposed COVID-19 predictive tool for hospital resource planning and patients' prioritization in the current COVID-19 pandemic crisis.

5.
J Healthc Eng ; 2021: 6668985, 2021.
Article in English | MEDLINE | ID: covidwho-1334598

ABSTRACT

Early diagnosis of pandemic diseases such as COVID-19 can prove beneficial in dealing with difficult situations and helping radiologists and other experts manage staffing more effectively. The application of deep learning techniques for genetics, microscopy, and drug discovery has created a global impact. It can enhance and speed up the process of medical research and development of vaccines, which is required for pandemics such as COVID-19. However, current drugs such as remdesivir and clinical trials of other chemical compounds have not shown many impressive results. Therefore, it can take more time to provide effective treatment or drugs. In this paper, a deep learning approach based on logistic regression, SVM, Random Forest, and QSAR modeling is suggested. QSAR modeling is done to find the drug targets with protein interaction along with the calculation of binding affinities. Then deep learning models were used for training the molecular descriptor dataset for the robust discovery of drugs and feature extraction for combating COVID-19. Results have shown more significant binding affinities (greater than -18) for many molecules that can be used to block the multiplication of SARS-CoV-2, responsible for COVID-19.


Subject(s)
COVID-19/drug therapy , Computer Simulation , Drug Discovery/methods , SARS-CoV-2/drug effects , Algorithms , Deep Learning , Humans , Pandemics , Pharmaceutical Preparations
6.
Genomics ; 113(4): 1733-1741, 2021 07.
Article in English | MEDLINE | ID: covidwho-1171554

ABSTRACT

Interferon-induced membrane proteins (IFITM) 3 gene variants are known risk factor for severe viral diseases. We examined whether IFITM3 variant may underlie the heterogeneous clinical outcomes of SARS-CoV-2 infection-induced COVID-19 in large Arab population. We genotyped 880 Saudi patients; 93.8% were PCR-confirmed SARS-CoV-2 infection, encompassing most COVID-19 phenotypes. Mortality at 90 days was 9.1%. IFITM3-SNP, rs12252-G allele was associated with hospital admission (OR = 1.65 [95% CI; 1.01-2.70], P = 0.04]) and mortality (OR = 2.2 [95% CI; 1.16-4.20], P = 0.01). Patients less than 60 years old had a lower survival probability if they harbor this allele (log-rank test P = 0.002). Plasma levels of IFNγ were significantly lower in a subset of patients with AG/GG genotypes than patients with AA genotype (P = 0.00016). Early identification of these individuals at higher risk of death may inform precision public health response.


Subject(s)
COVID-19/genetics , Genetic Predisposition to Disease , Membrane Proteins/genetics , RNA-Binding Proteins/genetics , SARS-CoV-2/genetics , Adult , Aged , Aged, 80 and over , COVID-19/mortality , COVID-19/virology , Female , Genetic Association Studies , Genotype , Humans , Interferons/genetics , Male , Middle Aged , Polymorphism, Single Nucleotide/genetics , SARS-CoV-2/pathogenicity
7.
BMC Surg ; 21(1): 120, 2021 Mar 08.
Article in English | MEDLINE | ID: covidwho-1123653

ABSTRACT

BACKGROUND: Most of the head and neck cancers are time-critical and need urgent surgical treatment. Our unit is one of the departments in the region, at the forefront in treating head and neck cancers in Pakistan. We have continued treating these patients in the COVID-19 pandemic with certain modified protocols. The objective of this study is to share our experience and approach towards head and neck reconstruction during the COVID-19 pandemic. RESULTS: There were a total of 31 patients, 20 (64.5%) were males and 11 (35.4%) patients were females. The mean age of patients was 52 years. Patients presented with different pathologies, i.e. Squamous cell carcinoma n = 26 (83.8%), mucoepidermoid carcinoma n = 2 (6.4%), adenoid cystic carcinoma n = 2 (6.4%) and mucormycosis n = 1 (3%). The reconstruction was done with loco-regional flaps like temporalis muscle flap n = 12 (38.7%), Pectoralis major myocutaneous flap n = 8 (25.8%), supraclavicular artery flap n = 10 (32.2%) and combination of fore-head, temporalis major and cheek rotation flaps n = 1 (3%). Defects involved different regions like maxilla n = 11 (35.4%), buccal mucosa n = 6 (19.3%), tongue with floor of mouth n = 6 (19.3%), mandible n = 4 (12.9%), parotid gland, mastoid n = 3 (9.6%) and combination of defects n = 1 (3%). Metal reconstruction plate was used in 3 (9.6%) patients with mandibular defects. All flaps survived, with the maximum follow-up of 8 months and minimum follow-up of 6 months. CONCLUSION: Pedicled flaps are proving as the workhorse for head and neck reconstruction in unique global health crisis. Vigilant use of proper PPE and adherence to the ethical principles proves to be the only shield that will benefit patients, HCW and health system.


Subject(s)
COVID-19 , Head and Neck Neoplasms , Reconstructive Surgical Procedures , Female , Head and Neck Neoplasms/surgery , Humans , Male , Middle Aged , Reconstructive Surgical Procedures/methods , Surgical Flaps
8.
Pathogens ; 9(9)2020 Aug 20.
Article in English | MEDLINE | ID: covidwho-725117

ABSTRACT

The COVID-19 pandemic, in the first seven months, has led to more than 15 million confirmed infected cases and 600,000 deaths. SARS-CoV-2, the causative agent for COVID-19, has proved to be a great challenge for its ability to spread in asymptomatic stages and the diverse disease spectrum it has generated. This has created a challenge of unimaginable magnitude, not only affecting human health and life but also potentially generating a long-lasting socioeconomic impact. Both medical sciences and biomedical research have also been challenged, consequently leading to a large number of clinical trials and vaccine initiatives. While known proteins of pathobiological importance are targets for these therapeutic approaches, it is imperative to explore other factors of viral significance. Accessory proteins are one such trait that have diverse roles in coronavirus pathobiology. Here, we analyze certain genomic characteristics of SARS-CoV-2 accessory protein ORF8 and predict its protein features. We have further reviewed current available literature regarding its function and comparatively evaluated these and other features of ORF8 and ORF8ab, its homolog from SARS-CoV. Because coronaviruses have been infecting humans repeatedly and might continue to do so, we therefore expect this study to aid in the development of holistic understanding of these proteins. Despite low nucleotide and protein identity and differentiating genome level characteristics, there appears to be significant structural integrity and functional proximity between these proteins pointing towards their high significance. There is further need for comprehensive genomics and structural-functional studies to lead towards definitive conclusions regarding their criticality and that can eventually define their relevance to therapeutics development.

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