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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]

Novel Coronavirus disease (COVID-19) has been widely described as the cause for a proinflammatory and hypercoagulable state. The inflammatory process involving the alveolar vascular endothelium in the respiratory system, is a determining factor for the onset of primary Pulmonary Artery Thrombosis (PAT) even in patients with heparin prophylactic treatment. Little is known about the efficacy of the anticoagulant therapy during the course of PAT caused by COVID-19. In this paper we describe the results obtained in patients with moderate COVID-19 disease, previously threated with prophylactic enoxaparin, who then received full Anticoagulant treatment after diagnosis of PAT. After three months Computed Tomography Pulmonary Angiography demonstrated a complete resolution of the vascular obstructive lesions in all patients, while all the coagulation tests were normal. [ABSTRACT FROM AUTHOR]