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The current COVID-19 global pandemic poses immense challenges to global health, largely due to the difficulty to detect infection in the early stages of the disease, as well as the current lack of effective antiviral therapy. Research and understanding of the human immune system can provide important theoretical and technical support for the clinical diagnosis and treatment of COVID-19, the clinical implementations of which include immunoassays and immunotherapy, which play a crucial role in the fight against the pandemic. This review consolidates the current scientific evidence for immunoassay, which includes multiple methods of detecting antigen and antibody against SARS-CoV-2. We compared the characteristics, advantages and disadvantages, and clinical applications of these three detection techniques. In addition to detecting viral infections, knowledge on the body's immunity against the virus is desirable; thus, the immunotherapy-based neutralizing antibody (nAb) detection methods were discussed. We also gave a brief introduction to the new immunoassay technology such as biosensing. This was followed by an in-depth and extensive review on a variety of immunotherapy methods. It includes convalescent plasma therapy, neutralizing antibody-based treatments targeting different regions of SARS-CoV-2, immunotherapy targeted on the host cell including inhibiting the host cell receptor and cytokine storm, as well as cocktail antibodies, cross-neutralizing antibodies, and immunotherapy based on cross-reactivity between viral epitopes and autoepitopes and autoantibody. Despite the development of various immunological testing methods and antibody therapies, the current global situation of COVID-19 is still tense. We need more efficient detection methods and more reliable antibody therapies. The up-to-date knowledge on therapeutic strategies will likely help clinicians worldwide to protect patients from life-threatening viral infections.
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Background: The long-term prognosis of COVID-19 survivors remains poorly understood. It is evidenced that the lung is the main damaged organ in COVID-19 survivors, most notably in impairment of pulmonary diffusion function. Hence, we conducted a meta-analysis of the potential risk factors for impaired diffusing capacity for carbon monoxide (DLCO) in convalescent COVID-19 patients. Methods: We performed a systematic search of PubMed, Web of Science, Embase, and Ovid databases for relevant studies from inception until January 7, 2022, limited to papers involving human subjects. Studies were reviewed for methodological quality. Fix-effects and random-effects models were used to pool results. Heterogeneity was assessed using I2. The publication bias was assessed using the Egger's test. PROSPERO registration: CRD42021265377. Findings: A total of eighteen qualified articles were identified and included in the systematic review, and twelve studies were included in the meta-analysis. Our results showed that female (OR: 4.011; 95% CI: 2.928-5.495), altered chest computerized tomography (CT) (OR: 3.002; 95% CI: 1.319-6.835), age (OR: 1.018; 95% CI: 1.007-1.030), higher D-dimer levels (OR: 1.012; 95% CI: 1.001-1.023) and urea nitrogen (OR: 1.004;95% CI: 1.002-1.007) were identified as risk factors for impaired DLCO. Interpretation: Pulmonary diffusion capacity was the most common impaired lung function in recovered patients with COVID-19. Several risk factors, such as female, altered chest CT, older age, higher D-dimer levels and urea nitrogen are associated with impairment of DLCO. Raising awareness and implementing interventions for possible modifiable risk factors may be valuable for pulmonary rehabilitation. Funding: This work was financially supported by Emergency Key Program of Guangzhou Laboratory (EKPG21-29, EKPG21-31), Incubation Program of National Science Foundation for Distinguished Young Scholars by Guangzhou Medical University (GMU2020-207).
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BACKGROUND: Coronavirus disease 2019 (COVID-19) has caused a large-scale global epidemic, impacting international politics and the economy. At present, there is no particularly effective medicine and treatment plan. Therefore, it is urgent and significant to find new technologies to diagnose early, isolate early, and treat early. Multimodal data drove artificial intelligence (AI) can potentially be the option. During the COVID-19 Pandemic, AI provided cutting-edge applications in disease, medicine, treatment, and target recognition. This paper reviewed the literature on the intersection of AI and medicine to analyze and compare different AI model applications in the COVID-19 Pandemic, evaluate their effectiveness, show their advantages and differences, and introduce the main models and their characteristics. METHODS: We searched PubMed, arXiv, medRxiv, and Google Scholar through February 2020 to identify studies on AI applications in the medical areas for the COVID-19 Pandemic. RESULTS: We summarize the main AI applications in six areas: (I) epidemiology, (II) diagnosis, (III) progression, (IV) treatment, (V) psychological health impact, and (VI) data security. The ongoing development in AI has significantly improved prediction, contact tracing, screening, diagnosis, treatment, medication, and vaccine development for the COVID-19 Pandemic and reducing human intervention in medical practice. DISCUSSION: This paper provides strong advice for using AI-based auxiliary tools for related applications of human diseases. We also discuss the clinicians' role in the further development of AI. They and AI researchers can integrate AI technology with current clinical processes and information systems into applications. In the future, AI personnel and medical workers will further cooperate closely.