Articles

<b>Background:</b> <i>Gallibacterium</i><i> </i><i>anatis</i> is an opportunistic pathogen, mainly associated with infections in poultry. Infections in humans have only been described four times (in France and China). <b>Case presentation:</b> We report the case of a 62-year-old non-immunocompromised male patient with a gastric bypass in his medical history, presenting with cholecystitis in whom <i>Gallibacterium</i><i> </i><i>anatis</i> was isolated from the blood, besides an <i>Escherichia coli</i>. Identification of the <i>G. </i><i>anatis</i> was achieved using MALDI-TOF MS and confirmatory 16s RNA sequencing. The patient was successfully treated with piperacillin-tazobactam, resulting in clinical improvement. In the context of impaired host defenses due to acute cholecystitis with an <i>E. coli</i> bacteremia, it is conceivable that <i>G. </i><i>anatis</i> entered the bloodstream through bacterial translocation from the gastrointestinal tract. <b>Conclusion:</b><b> </b>This case highlights the importance of advanced identification techniques and awareness of rare pathogens in clinical practice. <i>Gallibacterium</i><i> </i><i>anatis</i>, particularly drug-resistant strains, may represent a potential infectious agent and should therefore be considered in diagnostic work-up.

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.

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].