Avian influenza is a very lethal disease caused by influenza viruses that normally circulate among birds, and contains well-known subtypes are A(H5N1) and A(H7N9). Human infection is rare and occurs through close contact with infected poultry, therefore, confirming a history of such contact is important. The same treatment as for seasonal influenza is recommended, such as the neuraminidase inhibitors, cap endonuclease inhibitors, and RNA polymerase inhibitor. The avian influenza patients are regulated by the Infectious Diseases Control Law in Japan, we should manage the avian influenza appropriately.

Since the first outbreak of SARS-CoV2, Severe Acute Respiratory Syndrome Coronavirus-2 in Wuhan, China in the December 2019, the world continues to be in state of fear, uncertainty, tension and anxiety till date and this state might continue because of emerging mutants. The vaccination drive all over the world is continuing, still the new variants are cause of concern, with the aim to prevent the spread of the virus, focus should be on techniques and processes which helps in strengthening our defense mechanism, the immune system. It has becoming clear from several studies that the immune system is greatly impacted by the COVID-19 infection through several inflammatory reactions. The role of balanced diet full of nutrients cannot be overlooked in controlling infectious diseases. A balanced diet which should include plant foods, vitamins and micronutrients and this review wants to emphasize on the preventive role played by the plant foods, vitamins and minerals in COVID-19 infections, to reduce the mortality rate as well as the morbidity in patients who are infected.

This paper leverages gene and cell therapy research in diverse disorders ranging from monogenic to infectious diseases to cancer and emerging breakthroughs, where one can harness individual genes or a synthetic gene sequence designed based on a shared molecular pattern in infected cells to better fight various disorders [1]. A pivotal task is to predict the performances of candidate gene therapies to guide clinical translational research using methods such as retrospective bioinformatic analyses. Implementing them to a large-scale gene therapy database reveals that it is feasible to construct and apply well-performing interpretable, supervised learning models [2]. Preliminary evidence of machine learning approaches' statistical significance helps clinicians and biomedical researchers, market participants, and regulatory and economic experts derive relevant, practical applications, thereby enhancing the deployment of gene therapy and genomics to achieve positive, long-term growth for humanity while alleviating the ongoing worldwide economic burden precipitated by prolonged and recurring diseases. Deploying machine learning techniques to accelerate gene and cell therapy drug development and trials shall also mitigate the existing obstacle of limited patient access to emerging, transformative medical innovations such as gene therapy due to skyrocketing prices, which often herald gene therapy products as the world's most expensive medicines [3]. Moreover, in preventing patients from accessing effective, life-saving genetic medicines, there commonly exists a multidimensional access gap encompassing the availability, affordability, and quality or acceptability of these clinical treatments. The ensuing substantial gap has repeatedly been documented and mainly emanates from differential institutional and socio-political choices around resource allocation at international and domestic levels [4]. Particularly, it is also due to the stringent licensure and regulatory approval processes underpinned by insufficient evidence for novel safety and clinical efficacy profiles for genetic therapies in multiple micro-local diagnoses and subpopulations. We believe that a higher likelihood of gene therapy adoption shall result when the clinical evidence path contains adequate representation from the most diverse and relevant patient populations [5].