Coronavirus disease 2019 (COVID-19) is mainly an infectious disease of the respiratory system transmitted through air droplets, and pulmonary symptoms constitute main presentations of this disease. However, COVID-19 demonstrates a clinically diverse manifestation ranging from asymptomatic presentation to critically illness with severe pneumonia, acute respiratory distress syndrome, respiratory failure, or multiple organ failure. Accumulating evidences demonstrated that COVID-19 has extrapulmonary involvement, including neurological, smelling sensation, cardiovascular, digestive, hepatobiliary, renal, endocrinologic, dermatologic system, and others. Over a third of COVID-19 patients manifest a wide range of neurological symptoms involving the central/peripheral nervous system. Underlying cardiovascular comorbidities were associated with detrimental outcomes, meanwhile the occurrence of cardiovascular complications correlate to poor survival. Gastrointestinal symptoms frequently occur and have been associated with a longer period of illness. Impaired hepatic functions were associated with the severity of the disease. Higher rate of acute kidney injury was reported in critically ill patients with COVID-19. Endocrinologic presentations of COVID-19 include exacerbating hyperglycemia, euglycemic ketosis, and diabetic ketoacidosis. The most common cutaneous manifestation was acro-cutaneous (pernio or chilblain-like) lesions, and other skin lesions consist of maculopapular rash, vesicular lesions, livedoid/necrotic lesions, exanthematous rashes, and petechiae. This review article summarized the general clinical signs and symptoms, radiologic features, and disease manifestation with progression in patients with COVID-19.

Artificial intelligence (AI) based on convolutional neural networks (CNNs) has a great potential to enhance medical workflow and improve health care quality. Of particular interest is practical implementation of such AI-based software as a cloud-based tool aimed for telemedicine, the practice of providing medical care from a distance using electronic interfaces.Methods: In this study, we used a dataset of labeled 35,900 optical coherence tomography (OCT) images obtained from age-related macular degeneration (AMD) patients and used them to train three types of CNNs to perform AMD diagnosis.Results: Here, we present an AI- and cloud-based telemedicine interaction tool for diagnosis and proposed treatment of AMD. Through deep learning process based on the analysis of preprocessed optical coherence tomography (OCT) imaging data, our AI-based system achieved the same image discrimination rate as that of retinal specialists in our hospital. The AI platform's detection accuracy was generally higher than 90% and was significantly superior (p < 0.001) to that of medical students (69.4% and 68.9%) and equal (p = 0.99) to that of retinal specialists (92.73% and 91.90%). Furthermore, it provided appropriate treatment recommendations comparable to those of retinal specialists.Conclusions: We therefore developed a website for realistic cloud computing based on this AI platform, available at https://www.ym.edu.tw/~AI-OCT/. Patients can upload their OCT images to the website to verify whether they have AMD and require treatment. Using an AI-based cloud service represents a real solution for medical imaging diagnostics and telemedicine.

Mitochondrial DNA gene expression is coordinately regulated both pre- and post-transcriptionally, and its perturbation can lead to human pathologies. Mitochondrial rRNAs (mt-rRNAs) undergo a series of nucleotide modifications after release from polycistronic mitochondrial RNA precursors, which is essential for mitochondrial ribosomal biogenesis. Cytosine N4-methylation (m4C) at position 839 (m4C839) of the 12S small subunit mt-rRNA was identified decades ago; however, its biogenesis and function have not been elucidated in detail. Here, using several approaches, including immunofluorescence, RNA immunoprecipitation and methylation assays, and bisulfite mapping, we demonstrate that human methyltransferase-like 15 (METTL15), encoded by a nuclear gene, is responsible for 12S mt-rRNA methylation at m4C839 both in vivo and in vitro. We tracked the evolutionary history of RNA m4C methyltransferases and identified a difference in substrate preference between METTL15 and its bacterial ortholog rsmH. Additionally, unlike the very modest impact of a loss of m4C methylation in bacterial small subunit rRNA on the ribosome, we found that METTL15 depletion results in impaired translation of mitochondrial protein-coding mRNAs and decreases mitochondrial respiration capacity. Our findings reveal that human METTL15 is required for mitochondrial function, delineate the evolution of methyltransferase substrate specificities and modification patterns in rRNA, and highlight a differential impact of m4C methylation on prokaryotic ribosomes and eukaryotic mitochondrial ribosomes.

Background: Since COVID-19 outbreak, hydroxychloroquine (HCQ) has been tested for effective therapies, and the relevant researches have shown controversial results. Methods: Systematic review and meta-analysis were conducted after a thorough search of relevant studies from databases. Trials that have evaluated HCQ for COVID-19 treatment were recruited for statistical analysis with fixed- and random-effect models. Results: Nine trials involving 4112 patients were included in present meta-analysis. It was seen that HCQ-azithromycin (HCQ-AZI) combination regimen increased the mortality rate in COVID-19 (odds ratio [OR], 2.34; 95% confidence interval [CI], 1.63–3.36) patients; however, it also showed benefits associated with the viral clearance in patients (OR, 27.18; 95% CI, 1.29–574.32). HCQ-alone when used as a therapy in COVID-19 did not reveal significant changes in mortality rate, clinical progression, viral clearance, and cardiac QT prolongation. Subsequent subgroup analysis showed that HCQ treatment could decrease mortality rate and progression to severe illness in severely infected COVID-19 patients (OR, 0.27; 95% CI, 0.13–0.58). A lower risk of mortality rate was also noted in the stratified group of >14 days follow-up period (OR, 0.27; 95% CI, 0.13–0.58) compared to ≤14 days follow-up period group that conversely showed an increased mortality rate (OR, 2.09; 95% CI, 1.41–3.10). Conclusion: Our results indicated that HCQ-AZI combination treatment increased mortality rate in patients with COVID-19, but it also showed benefits associated with viral clearance in patients. HCQ-alone used for treatment has revealed benefits in decreasing the mortality rate among severely infected COVID-19 group and showed potential to be used for COVID-19 treatment in long-term follow-up period group. Accordingly, more rigorous, large-scale, and long follow-up period studies in patients with COVID-19 are needed.