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Researchers and scientists can use computational-based models to turn linked data into useful information, aiding in disease diagnosis, examination, and viral containment due to recent artificial intelligence and machine learning breakthroughs. In this paper, we extensively study the role of artificial intelligence and machine learning in delivering efficient responses to the COVID-19 pandemic almost four years after its start. In this regard, we examine a large number of critical studies conducted by various academic and research communities from multiple disciplines, as well as practical implementations of artificial intelligence algorithms that suggest potential solutions in investigating different COVID-19 decision-making scenarios. We identify numerous areas where artificial intelligence and machine learning can impact this context, including diagnosis (using chest X-ray imaging and CT imaging), severity, tracking, treatment, and the drug industry. Furthermore, we analyse the dilemma's limits, restrictions, and hazards. [ABSTRACT FROM AUTHOR]

Abstract A growing number of scientific contributions suggest that COVID-19 infection can lead to impairment of cognition, mainly in executive functions and memory domains, even in the absence of frank neurological pathologies.The primary objective of this retrospective study is to evaluate the frequency and type of inefficiencies in a selection of cognitive tests administered to a sample of subjects who, following infection, required invasive assisted ventilation and were admitted to rehabilitation wards for the treatment of functional impairment.Fifty-seven subjects were enrolled. The recruited patients undergone an assessment of verbal and visuospatial memory and executive functions, upon entry into the rehabilitation department, after discharge from intensive care. The following tests were administered: Rey Auditory Verbal Learning Test (AVLT) (immediate and delayed recall), Rey-Osterrieth Complex Figure Test (ROCFT) (copy and delayed recall), Stroop Color-Word Test, and Trail Making Test (TMT, A and B).Deficient scores, in beyond 25% of subjects, were found in the copy of the ROCFT (32.1% of subjects), and in the delayed recall of ROCFT (27.2%). Between 10 and 20% of patients presented an abnormal result in delayed recall of AVLT (16.07%), and Stroop Test (time, 15.6%, error, 11.5%). Less than 10% of the sample had abnormal performances on TMT (A, 3.5%, and B, 9.4%), and in AVLT immediate recall (8.9%). Correlations of the performances with age, sex, and education were also found.This paper highlights the high incidence of abnormal cognitive performances in this specific subpopulation of patients with COVID-19 infection. [ABSTRACT FROM AUTHOR]

To report a four-case series of ocular adverse events post an inactivated COVID-19 vaccination in China. The four patients exhibited ocular inflammatory reactions on the same day after receiving an inactivated SARS-CoV-2 vaccine. All patients underwent detailed ophthalmic examinations, with the medical diagnosis of Vogt-Koyanagi-Harada, Ponser-Schlossman, secondary post-inflammatory glaucoma, and iridocyclitis, respectively. No patients had any other underlying medical conditions causing the ocular complications. The ocular inflammatory reactions of these four patients were resolved with the administration of oral or topical corticosteroids. Our cases remind the ophthalmologist that adverse ocular events may happen after the administration of SARS-CoV-2 vaccine. Since the ocular complications could be resolved with the corticosteroid treatment, the events were considered to be inflammatory reactions caused by the SARS-CoV-2 vaccine. [ABSTRACT FROM AUTHOR]

Coronaviridae family consists of many virulent viruses with zoonotic properties that can be transmitted from animals to humans. Different strains of these viruses have caused pandemic in the past such as Severe Respiratory Syndrome Coronavirus (SARS-CoV) in 2002, Middle East respiratory syndrome coronavirus (MERS-CoV) in 2012 and recently Severe Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) also known as COVID-19 in December 2019. Scientists utilised different approaches for the detection and characterisation of CoVs using samples such as serum, throat swabs, nose swabs, nasopharyngeal aspirates and bronchoalveolar lavages. The two common approaches include antigen-based approach and molecular diagnostic approach, which are hindered by limitations such as low sensitivity and requirement for high level of biosafety during isolation of the virus from cell culture. Thus, there is a need for developing a more rapid, sensitive, simple and cheap diagnostic kit for diagnosis of different strains of coronavirus. In this article, we overview 2019 novel coronavirus, pandemic, prior epidemics, diagnosis, treatments, identification of drugs detection based on classification and prediction using artificial intelligence-driven tools. We also overview in-lab molecular testing and on-site testing using CRISPR-based biosensing tools. We also outline limitations of laboratory techniques and open-research issues in the current state of CRISPR-based biosensing applications and artificial intelligence for treatment of Coronaviruses. [ABSTRACT FROM AUTHOR]

Since pandemic declaration, the vulnerability of countries with serious economic challenges and limited health resources became evident. This vulnerability has been put to the test once again with the appearance of Omicron as another variant of concern. Although great efforts have been made to develop effective and safe vaccines, they need to be made available globally at an affordable price to all governments and distributed equitably to maximize immediate and long-term efforts to contain the virus and advance global public health. Potential sources of unfair allocation of COVID-19 vaccines are not hard to find. The COVID-19 Vaccine Global Access Facility (COVAX) has so far shipped over 406 million COVID-19 vaccines to 144 eligible participants. From that batches, about 115 million doses (28%) were allocated to 49 African countries. If proactive measures are not undertaken, Nigeria, pointed here as a case study, and Sub-Saharan Africa countries may not be self-reliant for COVID-19 vaccines. This report raises a discussion on the difficulties in accessing vaccines and diagnostics in sub-Saharan Africa, compared to high- and middle-income countries. Now more than ever, it is crucial to note that there is no overcoming a pandemic without coordinated action for actions that go beyond borders. The coordinated effort to raise vaccination rates in the African continent is not a humanitarian action aimed exclusively at Africa, but more than that, it is an effort for the benefit of global public health. [ABSTRACT FROM AUTHOR]

With a global COVID-19 pandemic, the number of confirmed patients increases rapidly, leaving the world with very few medical resources. Therefore, the fast diagnosis and monitoring of COVID-19 are one of the world's most critical challenges today. Artificial intelligence-based CT image classification models can quickly and accurately distinguish infected patients from healthy populations. Our research proposes a deep learning model (WE-SAJ) using wavelet entropy for feature extraction, two-layer FNNs for classification and the adaptive Jaya algorithm as a training algorithm. It achieves superior performance compared to the Jaya-based model. The model has a sensitivity of 85.47±1.84, specificity of 87.23±1.67 precision of 87.03±1.34, an accuracy of 86.35±0.70, and F1 score of 86.23±0.77, Matthews correlation coefficient of 72.75±1.38, and feature mutual information of 86.24±0.76. Our experiments demonstrate the potential of artificial intelligence techniques for COVID-19 diagnosis and the effectiveness of the Self-adaptive Jaya algorithm compared to the Jaya algorithm for medical image classification tasks. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic in India has resulted in mass destruction in the form of second wave.Indian citizens and government were badly affected and were left constrained with basic medical facilities in treating the heavy load of patients. The possibility of reemergence of virulent strains of SARS-CoV-2 still exists. In this article, we tend to discuss why India is at high risk to be to be affected by the other waves and what strategies could be implemented to contain the viral spread. It provides these insights with reference to the gaps and the lessons learnt from the second wave along with the possible solutions to tackle these problems that were not effectively handled during the previous episodes of viral spread. We propose strategies for implementing effective vaccination programs, focus on speeding up different ways of diagnosis, management of essential hospital aids, prevention from vaccine escape mutant strains and implementation of COVID-appropriate behavior in rural areas of India. This article has significance to the researchers and government officials in understanding the gaps that led to COVID-19 second wave in India and provides an opportunity to improve on certain areas for handling the future waves with more vigilance. Abbreviations: COVID-19 – Coronavirus disease 2019; SARS-COV-2 – severe acute respiratory syndrome – Coronavirus 2; δ – Delta variant; δ+ – Delta plus variant; WHO – World Health Organization; RT –PCR – Reverse transcription PCR; IFN-1 – Interferon 1; VOCs – Variants of concern; HCWs – healthcare workers [ABSTRACT FROM AUTHOR]

Seven coronavirus species have been identified that can infect humans. While human coronavirus infections had been historically associated with only mild respiratory symptoms similar to the common cold, three coronaviruses identified since 2003, Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV), Middle East Respiratory Syndrome Coronavirus (MERS-CoV), and SARS-CoV-2, cause life-threatening severe respiratory syndromes. The coronavirus disease 2019 (COVID-19) caused by the highly transmissible SARS-CoV-2 has triggered a worldwide health emergency. Due to the lack of effective drugs and vaccination, rapid and reliable detection is of vital importance to control coronavirus epidemics/pandemics. A literature search was performed in Pubmed covering the detections and diagnostics of SARS, MERS and SARS-CoV-2. This review summarized the current knowledge of established and emerging methods for coronavirus detection. The characteristics of different diagnostic approaches were described, and the strengths and weaknesses of each method were analyzed and compared. In addition, future trends in the field of coronavirus detection were also discussed. Nucleic acid–based RT-PCR is the current golden-standard of coronavirus detection, while immunoassays provide history of coronavirus infection besides diagnostic information. Integrated high-throughput system holds the great potential and is the trend of future detection and diagnosis of virus infection. [ABSTRACT FROM AUTHOR]

A speedy response made a significant difference to the number of infections and deaths due to COVID-19. Did legal philosophies matter for policy responses? We find that when 100 cases had been diagnosed (and 7–14 days thereafter), common law countries had implemented weaker measures than civil law countries. However, no significant difference is found for COVID-19 related deaths. Lower vulnerability is also associated with weaker policies. [ABSTRACT FROM AUTHOR]