Covid-19 Detection Using Deep Correlation-Grey Wolf Optimizer

The immediate and quick spread of the coronavirus has become a life-threatening disease around the globe. The widespread illness has dramatically changed almost all sectors, moving from offline to online, resulting in a new normal lifestyle for people. The impact of coronavirus is tremendous in the healthcare sector, which has experienced a decline in the first quarter of 2020. This pandemic has created an urge to use computer-aided diagnosis techniques for classifying the Covid-19 dataset to reduce the burden of clinical results. The current situation motivated me to choose correlationbased development called correlation-based grey wolf optimizer to perform accurate classification. A proposed multistage model helps to identify Covid from Computed Tomography (CT) scan image. The first process uses a convolutional neural network (CNN) for extracting the feature from the CT scans. The Pearson coefficient filter method is applied to remove redundant and irrelevant features. Finally, theGrey wolf optimizer is used to choose optimal features. Experimental analysis proves that this determines the optimal characteristics to detect the deadly disease. The proposed model's accuracy is 14% higher than the krill herd and bacterial foraging optimization for severe accurate respiratory syndrome image (SARS-CoV-2 CT) dataset. The COVID CT image dataset is 22% higher than the existing krill herd and bacterial foraging optimization techniques. The proposed techniques help to increase the classification accuracy of the algorithm in most cases, which marks the stability of the stated result. Comparative analysis reveals that the proposed classification technique to predict COVID-19 withmaximumaccuracy of 98% outperforms other competitive approaches. © 2023 CRL Publishing. All rights reserved.

Nitazoxanide and azithromycin for the early treatment of COVID-19: A multi-spectroscopic, biochemical and computational drug–drug interaction study

Herein, we present an experimental and theoretical drug–drug interaction study between nitazoxanide (NTZ) and azithromycin (AZT) in an aqueous solution. Interaction was studied by using UV/Vis, fluorescence, attenuated total reflectance-fourier transform infrared (ATR-FTIR), and circular dichroism (CD) spectroscopy, while molecular docking studies were performed to establish the interaction computationally. A bright yellow color was observed when the two drugs interacted, giving a hyperchromic band at 420 nm. The rate of absorbance was linearly increased by increasing drug concentrations and in a time-dependent manner. Stability of the interaction complex (i.e., NTZ: AZT) was measured at variable temperatures (25–80°C), pH (5.0–10.0) and ionic strength (0.05–2.0 M NaCl), and not only proved stable but also retained antimicrobial potential with reduced cellular toxicity. Mole ratio and Job's method of continuous variations were used to establish the binding stoichiometry and found to be 2:1. The calculated binding constant (kb = 8,400 M−1) and Gibb's free energy (ΔG° = −22.4 KJ/mol) also suggested an energetically favorable interaction. FTIR spectra of NTZ: AZT complex in comparison with two drugs alone revealed significant interaction which was nicely complemented by molecular docking studies. Interaction was also successfully demonstrated in presence of carrier protein HSA and by spiking the two drugs in real samples of human plasma and urine. © 2023 The Chemical Society Located in Taipei & Wiley-VCH GmbH.

Deep Learning Convolutional Neural Network for SARS-CoV-2 Detection Using Chest X-Ray Images

The COVID-19 coronavirus illness is caused by a newly discovered species of coronavirus known as SARS-CoV-2. Since COVID-19 has now expanded across many nations, the World Health Organization (WHO) has designated it a pandemic. Reverse transcription-polymerase chain reaction (RT-PCR) is often used to screen samples of patients showing signs of COVID-19;however, this method is more expensive and takes at least 24 hours to get a positive or negative response. Thus, an immediate and precise method of diagnosis is needed. In this paper, chest X-rays will be utilized through a deep neural network (DNN), based on a convolutional neural network (CNN), to detect COVID-19 infection. Based on their X-rays, those with COVID-19 indications may be categorized as clean, infected with COVID-19 or suffering from pneumonia, according to the suggested CNN network. Sample pieces from every group are used in experiments, and categorization is performed by a CNN. While experimenting, the CNN-derived features were able to generate the maximum training accuracy of 94.82% and validation accuracy of 94.87%. The F1-scores were 97%, 90% and 96%, in clearly categorizing patients afflicted by COVID-19, normal and having pneumonia, respectively. Meanwhile, the recalls are 95%, 91% and 96% for COVID-19, normal and pneumonia, respectively. © 2023 by the author(s). Licensee Prague University of Economics and Business, Czech Republic.

Shoulder Weakness Following Intramuscular COVID- 19 Vaccine

History: Transient and generalized adverse effects are common following COVID-19 vaccination;among other adverse effects, shoulder injuries related to vaccine administration (SIRVA) have been known to occur. In this case, a previously healthy right-hand dominant 62-year-old male presented with left shoulder pain and weakness 3 months after receiving a COVID-19 intramuscular vaccine in the left deltoid. Approximately 2 weeks after the injection, he started experiencing pain and numbness around the injection site along with ipsilateral shoulder weakness. Despite conservative management with Motrin, Medrol Dosepak, gabapentin and physical therapy (PT), the pain and weakness persisted. Physical Exam: Left Shoulder-No calor or erythema;significant atrophy of the anterior and middle deltoid muscle relative to right side;abduction 4/5;external rotation with shoulder adducted 4/5;range of motion for active forward flexion was 150 degrees and passive was 170 degrees;passive range of motion for external rotation was 70 degrees;internal rotation to the level of L5;sensation to light touch was intact. Right Shoulder-Range of motion, strength, and sensation were intact. Cervical Spine-Full ROM;no cervical paraspinal tenderness noted. Negative Spurling's and Lhermitte's tests. Differential Diagnosis: 161. Axillary Nerve Palsy 2/2 Chemical Neurotoxicity 162. Brachial Neuritis 163. Mechanical Axillary Nerve Palsy 2/2 Vaccination 164. Partial-Tear of Left Supraspinatus Tendon 165. Acromioclavicular Osteoarthritis Test Results: Left Shoulder-XR:Mild pseudo-subluxation;MRI w/o contrast: 8x9mmpartial-thickness articular surface tear of the distal supraspinatus tendon (<50%fiber thickness). Minimal subacromial bursitis. Mild acromioclavicular joint osteoarthritis. EMG/NCV: Left and Right Axillary Motor Nerves: prolonged distal onset latency;Left Deltoid: increased insertion activity, moderately increased spontaneous activity, reduced recruitment;Remaining LUE muscles without evidence of electrical instability Final Diagnosis: Axillary Nerve Palsy Secondary To Chemical Neurotoxicity from Intramuscular COVID-19 Vaccine. Discussion(s): We postulate that the neurologic deficits presented in our case may be attributed to chemical neurotoxicity to the axillary nerve following vaccination as the delayed onset of pain and weakness are most consistent with this differential. There are several cases of brachial neuritis following vaccination for the prevention of COVID- 19, however, EMG/NCV results in our patient were not consistent with brachial plexopathy. Additionally, while there have been a handful of reported cases of bursitis following COVID-19 vaccines falling under the SIRVA classification of injuries, this is the first case of reported axillary nerve neurapraxia. Outcome(s): The patient's left shoulder numbness and pain improved with PT and medical management. While mild improvement in strength was noted, weakness and atrophy persisted even on the third follow up visit 6 months after the initial appointment. He was counseled on his injury and was recommended to undergo repeat EMG testing to document recovery after his 6-month follow-up appointment. Follow-Up: The patient did not follow-up for a repeatEMG after his 6-month follow-up appointment. At that time, the patient was clinically stable, tolerating PT, and expecting recovery of his deltoid function.

Private transfers, public transfers, and food insecurity during the time of COVID-19: Evidence from Bangladesh

In the aftermath of the COVID-19 pandemic, interest has grown in what kinds of assistance protect household food security during shocks. We study rural and urban Bangladesh from 2018 to 2019 to late 2021, assessing how pre-pandemic access to social safety net programs and private remittances relate to household food insecurity during the pandemic. Using longitudinal data and estimating differences-in-differences models with household fixed effects, we find that pre-pandemic access to social protection is associated with significant reductions in food insecurity in all rounds collected during the pandemic, particularly in our urban sample. However, pre-pandemic access to remittances shows no similar protective effect.

Expert consensus statement on venovenous extracorporeal membrane oxygenation ECMO for COVID-19 severe ARDS: an international Delphi study.

BACKGROUND: The high-quality evidence on managing COVID-19 patients requiring extracorporeal membrane oxygenation (ECMO) support is insufficient. Furthermore, there is little consensus on allocating ECMO resources when scarce. The paucity of evidence and the need for guidance on controversial topics required an international expert consensus statement to understand the role of ECMO in COVID-19 better. Twenty-two international ECMO experts worldwide work together to interpret the most recent findings of the evolving published research, statement formulation, and voting to achieve consensus. OBJECTIVES: To guide the next generation of ECMO practitioners during future pandemics on tackling controversial topics pertaining to using ECMO for patients with COVID-19-related severe ARDS. METHODS: The scientific committee was assembled of five chairpersons with more than 5 years of ECMO experience and a critical care background. Their roles were modifying and restructuring the panel's questions and, assisting with statement formulation in addition to expert composition and literature review. Experts are identified based on their clinical experience with ECMO (minimum of 5 years) and previous academic activity on a global scale, with a focus on diversity in gender, geography, area of expertise, and level of seniority. We used the modified Delphi technique rounds and the nominal group technique (NGT) through three face-to-face meetings and the voting on the statement was conducted anonymously. The entire process was planned to be carried out in five phases: identifying the gap of knowledge, validation, statement formulation, voting, and drafting, respectively. RESULTS: In phase I, the scientific committee obtained 52 questions on controversial topics in ECMO for COVID-19, further reviewed for duplication and redundancy in phase II, resulting in nine domains with 32 questions with a validation rate exceeding 75% (Fig. 1). In phase III, 25 questions were used to formulate 14 statements, and six questions achieved no consensus on the statements. In phase IV, two voting rounds resulted in 14 statements that reached a consensus are included in four domains which are: patient selection, ECMO clinical management, operational and logistics management, and ethics. CONCLUSION: Three years after the onset of COVID-19, our understanding of the role of ECMO has evolved. However, it is incomplete. Tota14 statements achieved consensus; included in four domains discussing patient selection, clinical ECMO management, operational and logistic ECMO management and ethics to guide next-generation ECMO providers during future pandemic situations.

Evaluation of circulating insulin-like growth factor-1, heart-type fatty acid-binding protein, and endotrophin levels as prognostic markers of COVID-19 infection severity.

BACKGROUND: Coronavirus Disease 2019 (COVID-19) is a worldwide pandemic challenge spreading enormously within a few months. COVID-19 is characterized by the over-activation of the immune system causing cytokine storm. Insulin-like growth factor-1 (IGF-1) pathway can regulate the immune response via interaction with various implicated cytokines. Heart-type fatty acid-binding protein (H-FABP) has been shown to promote inflammation. Given the fact that coronavirus infections induce cytokines secretion leading to inflammatory lung injury, it has been suggested that H-FABP levels are affected by COVID-19 severity. Moreover, endotrophin (ETP), the cleavage product of collagen VI, may be an indicator of an overactive repair process and fibrosis, considering that viral infection may predispose or exacerbate existing respiratory conditions, including pulmonary fibrosis. This study aims to assess the prognostic capacity of circulating IGF-1, HFABP, and ETP, levels for COVID-19 severity progression in Egyptian patients. METHODS: The study cohort included 107 viral RNA-positive patients and an equivalent number of control individuals with no clinical signs of infection. Clinical assessments included profiling of CBC; serum iron; liver and kidney functions; inflammatory markers. Circulating levels of IGF-1; H-FABP, and ETP were estimated using the corresponding ELISA kits. RESULTS: No statistical difference in the body mass index was detected between the healthy and control groups, while the mean age of infected patients was significantly higher (P = 0.0162) than the control. Patients generally showed elevated levels of inflammatory markers including CRP and ESR concomitant with elevated serum ferritin; D dimer and procalcitonin levels, besides the COVID-19 characteristic lymphopenia and hypoxemia were also frequent. Logistic regression analysis revealed that oxygen saturation; serum IGF-1, and H-FABP can significantly predict the infection progression (P < 0.001 each). Both serum IGF-1 and H-FABP as well as O2 saturation showed remarkable prognostic potentials in terms of large AUC values, high sensitivity/specificity values, and wide confidence interval. The calculated threshold for severity prognosis was 25.5 ng/mL; 19.5 ng/mL, 94.5, % and for IGF-1, H-FABP, and O2 saturation; respectively. The calculated thresholds of serum IGF-1; H-FABP, and O2 saturation showed positive and negative value ranges of 79-91% and 72-97%; respectively, with 66-95%, 83-94% sensitivity, and specificity; respectively. CONCLUSION: The calculated cut-off values of serum IGF-1 and H-FABP represent a promising non-invasive prognostic tool that would facilitate the risk stratification in COVID-19 patients, and control the morbidity/mortality associated with progressive infection.

Evaluating the ability of some natural phenolic acids to target the main protease and AAK1 in SARS COV-2.

Researchers are constantly searching for drugs to combat the coronavirus pandemic caused by SARS-CoV-2, which has lasted for over two years. Natural compounds such as phenolic acids are being tested against Mpro and AAK1, which are key players in the SARS-CoV-2 life cycle. This research work aims to study the ability of a panel of natural phenolic acids to inhibit the virus's multiplication directly through Mpro and indirectly by affecting the adaptor-associated protein kinase-1 (AAK1). Pharmacophore mapping, molecular docking, and dynamic studies were conducted over 50 ns and 100 ns on a panel of 39 natural phenolic acids. Rosmarinic acid (16) on the Mpro receptor (- 16.33 kcal/mol) and tannic acid (17) on the AAK1 receptor (- 17.15 kcal/mol) exhibited the best docking energy against both receptors. These favourable docking score values were found to be superior to those of the co-crystallized ligands. Preclinical and clinical research is required before using them simultaneously to halt the COVID-19 life cycle in a synergistic manner.

The artificial intelligence (AI) role for tackling against COVID-19 pandemic.

COVID-19 gains from the research and technology component's establishment of information science, artificial intelligence, and computer understanding. The article aims to discuss the numerous facets of today's modern technology utilized to combat COVID-19 emergencies on various scales, such as medicinal picture handling, illness tracking, expected outcomes, computational science, and medications. Techniques: A complex search of the knowledge base associated with existing COVID-19 innovation is conducted. Furthermore, a concise survey of the excluded data is conducted, analyzing the various aspects of current developments for dealing with the COVID-19 pandemic. The below are the outcomes: We have a window of musings on the audit of the tech propellers used to mitigate and mask the significant impact of the upheaval. Even though several investigations into current innovation in COVID-19 have surfaced, there are still required implementations and contributions of innovation in this war. Consequently, a thorough presentation of the available data is given, and several modern technology implementations for combating the pandemic of COVID-19. Continuous advancements of advanced technologies have aided in improving the public's lives, and there is a strong belief that proven study plans utilizing AI would be of great benefit in assisting people in combating this infection.