ABSTRACT
The current epidemic of severe coronavirus illness has led to global medical issues. Furthermore, the ongoing and protracted post-COVID-19 or chronic COVID imposes a significant burden on health care professionals because of inadequate medical facilities. Heart disease has been identified as the most prevalent enduring post-COVID-19 consequence of coronavirus persistence. COVID-19's undesirable psychological repercussions, such as anxiety and depression, have been generally anticipated but have yet to be fully assessed. COVID-19 has several physiological conditions and diseases. However, it is unknown whether there are possible psychological causes. The severe acute respiratory pandemic was also related to a rise in PTSD, tension, and emotional anguish among victims and doctors. In the context of certain occurrences, the effects might be rapid and then last for a significant period. COVID-19 may induce severe psychological issues like disorientation, restlessness, and dementia. Patients who have had psychological, cognitive, or medication abuse issues are significantly more susceptible to SARS-CoV-2 infection and may have a higher risk of devastating outcomes, even fatality. During one year post-acute, victims of the devastating disease have ongoing psychological disturbance, with considerable psychological distress, sadness, and chronic posttraumatic depression. Most people with symptomatic respiratory failure have neuropsychological problems, such as poor concentration, cognition, remembering, and intellectual information processing. This chapter addressed the post-COVID psychological issues and their consequences. © 2022 Nova Science Publishers, Inc. All rights reserved.
ABSTRACT
As innovative solutions and analysis methodologies became accessible, data science gained a more vital role in health. Data science can assist us in improving solutions for psychological difficulties. Several patients attempt a variety of psychologists, therapeutic strategies, and drugs before discovering what works for people. Locating suitable treatment is especially troubling considering how long most patients have to suffer before receiving treatment and the expense and mental strain of attempting many choices. As a result, it is critical to determine whether or not a specific medicine is effective. Healthcare data science has been expanding rapidly, but using objectives to analyse and address psychiatric problems has regressed the rest of the community. Psychological wellness is well adapted for data science strategies: the impact of psychological illness is enormous, primarily neglected, and remains unclear, generating immense promise for computation investigation and therapies. This chapter discussed various data science applications of post-COVID-19 psychological effects and consequences. © 2022 Nova Science Publishers, Inc. All rights reserved.
ABSTRACT
The most dangerous and infectious disease, COVID-19, affecting millions of people is by an enveloped RNA virus known as SARS-COV-2 or Coronavirus, and the disease is unknown before the epidemic commenced in Wuhan, China, in December 2019. Many researchers are busy finding the vaccine for the pandemic. Here, we analyze the diagnostic methods by using mathematical modeling. The majority probable corona patient category with an enhanced AUC characterizes the SVM’s optimal diagnostics model in this chapter. Experimental and computational analyses demonstrate that the diagnosis of potentially COVID-19 can be supported by adopting ML algorithms that learn linguistic diagnostics from the interpretation of elderly persons. Highlight the collection of significant semantic, lexical, and top n-gram properties with the better ML method to estimate diseases. But diagnostics methods must be trained on massive datasets, leading to improved AUC and medical diagnoses of COVID-19 probability. A significant use resulting from mathematical modeling is that it claims transparency and accurateness about our model. These techniques can help in decision-making by useful predictions about substantial issues such as treatment protocols and interfere and minimize the spread of COVID-19. © 2022 Elsevier Inc. All rights reserved.
ABSTRACT
We investigate electropolymerized molecularly imprinted polymers (E-MIPs) for the selective recognition of SARS-CoV-2 whole virus. E-MIPs imprinted with SARS-CoV-2 pseudoparticles (pps) were electrochemically deposited onto screen printed electrodes by reductive electropolymerization, using the water-soluble N-hydroxmethylacrylamide (NHMA) as functional monomer and crosslinked with N,N'-methylenebisacrylamide (MBAm). E-MIPs for SARS-CoV-2 showed selectivity for template SARS-CoV-2 pps, with an imprinting factor of 3:1, and specificity (significance = 0.06) when cross-reacted with other respiratory viruses. E-MIPs detected the presence of SARS-CoV-2 pps in <10 min with a limit of detection of 4.9 log10 pfu/mL, suggesting their suitability for detection of SARS-CoV-2 with minimal sample preparation. Using electrochemical impedance spectroscopy (EIS) and principal component analysis (PCA), the capture of SARS-CoV-2 from real patient saliva samples was also evaluated. Fifteen confirmed COVID-19 positive and nine COVID-19 negative saliva samples were compared against the established loop-mediated isothermal nucleic acid amplification (LAMP) technique used by the UK National Health Service. EIS data demonstrated a PCA discrimination between positive and negative LAMP samples. A threshold real impedance signal (ZRe) â« 4000 Ω and a corresponding charge transfer resistance (RCT) â« 6000 Ω was indicative of absence of virus (COVID-19 negative) in agreement with values obtained for our control non-imprinted polymer control. A ZRe at or below a threshold value of 600 Ω with a corresponding RCT of <1200 Ω was indicative of a COVID-19 positive sample. The presence of virus was confirmed by treatment of E-MIPs with a SARS-CoV-2 specific monoclonal antibody.