The sudden outbreak of 2019 novel coronavirus (2019-nCoV, later named SARS-CoV-2) in Wuhan, China, which rapidly grew into a global pandemic, marked the third introduction of a virulent coronavirus into the human society, affecting not only the healthcare system, but also the global economy. Although our understanding of coronaviruses has undergone a huge leap after two precedents, the effective approaches to treatment and epidemiological control are still lacking. In this article, we present a succinct overview of the epidemiology, clinical features, and molecular characteristics of SARS-CoV-2. We summarize the current epidemiological and clinical data from the initial Wuhan studies, and emphasize several features of SARS-CoV-2, which differentiate it from SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV), such as high variability of disease presentation. We systematize the current clinical trials that have been rapidly initiated after the outbreak of COVID-19 pandemic. Whereas the trials on SARS-CoV-2 genome-based specific vaccines and therapeutic antibodies are currently being tested, this solution is more long-term, as they require thorough testing of their safety. On the other hand, the repurposing of the existing therapeutic agents previously designed for other virus infections and pathologies happens to be the only practical approach as a rapid response measure to the emergent pandemic, as most of these agents have already been tested for their safety. These agents can be divided into two broad categories, those that can directly target the virus replication cycle, and those based on immunotherapy approaches either aimed to boost innate antiviral immune responses or alleviate damage induced by dysregulated inflammatory responses. The initial clinical studies revealed the promising therapeutic potential of several of such drugs, including favipiravir, a broad-spectrum antiviral drug that interferes with the viral replication, and hydroxychloroquine, the repurposed antimalarial drug that interferes with the virus endosomal entry pathway. We speculate that the current pandemic emergency will be a trigger for more systematic drug repurposing design approaches based on big data analysis.

Long noncoding RNAs (lncRNAs) are a novel class of RNA molecules defined as transcripts longer than 200 nucleotides that lack protein coding potential. They constitute a major, but still poorly characterized part of human transcriptome, however, evidence is growing that they are important regulatory molecules involved in various cellular processes. It is becoming increasingly clear that many lncRNAs are deregulated in cancer and some of them can be important drivers of malignant transformation. On the one hand, some lncRNAs can have highly specific expression in particular types of cancer making them a promising tool for diagnosis. The expression of other lncRNAs can correlate with different pathophysiological features of tumor growth and with patient survival, thus making them convenient biomarkers for prognosis. In this review we outline the current state of knowledge about the fast growing field of application of lncRNAs as tumor biomarkers.

A sudden outbreak of COVID-19 caused by a novel coronavirus, SARS-CoV-2, in Wuhan, China in December 2019 quickly grew into a global pandemic, putting at risk not only the global healthcare system, but also the world economy. As the disease continues to spread rapidly, the development of prophylactic and therapeutic approaches is urgently required. Although some progress has been made in understanding the viral structure and invasion mechanism of coronaviruses that may cause severe cases of the syndrome, due to the limited understanding of the immune effects caused by SARS-CoV-2, it is difficult for us to prevent patients from developing acute respiratory distress syndrome (ARDS) and pulmonary fibrosis (PF), the major complications of coronavirus infection. Therefore, any potential treatments should focus not only on direct killing of coronaviruses and prevention strategies by vaccine development, but also on keeping in check the acute immune/inflammatory responses, resulting in ARDS and PF. In addition, potential treatments currently under clinical trials focusing on killing coronaviruses or on developing vaccines preventing coronavirus infection largely ignore the host immune response. However, taking care of SARS-CoV-2 infected patients with ARDS and PF is considered to be the major difficulty. Therefore, further understanding of the host immune response to SARS-CoV-2 is extremely important for clinical resolution and saving medication cost. In addition to a breif overview of the structure, infection mechanism, and possible therapeutic approaches, we summarized and compared the hematopathologic effect and immune responses to SARS-CoV, MERS-CoV, and SARS-CoV-2. We also discussed the indirect immune response caused by SARS and direct infection, replication, and destroying of immune cells by MERS-CoV. The molecular mechanisms of SARS-CoV Liang et al. Immune-Pathogenic Responses in SARS-CoV, MERS-CoV, and SARS-Cov-2and MERS-CoV infection-induced lymphopenia or cytokine storm may provide some hint toward fight against SARS-CoV-2, the novel coronavirus. This may provide guidance over using immune therapy as a combined treatment to prevent patients developing severe respiratory syndrome and largely reduce complications.

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.