E-CIGARETTE OR VAPING USE-ASSOCIATED LUNG INJURY (EVALI): EFFECTS OF VAPING OR COVID-19?

SESSION TITLE: Critical Care in Chest Infections Case Report Posters 2 SESSION TYPE: Case Report Posters PRESENTED ON: 10/17/2022 12:15 pm - 01:15 pm INTRODUCTION: EVALI is an acute lung injury that occurs due to the use of e-cigarettes or vaporizer products that usually contain THC or nicotine. There was an outbreak of EVALI in 2019. This is a diagnosis of exclusion with foamy macrophages with pneumocyte vacuolization being the best diagnostic clues. (1) Vitamin E acetate laced products seem to be the causing factor. CASE PRESENTATION: A 34-year-old female presented to the emergency department due to increasing shortness of breath, fever, pleuritic chest pain, cough, and headaches for the last 9 days. Two days prior she presented to urgent care where she was given an albuterol inhaler and azithromycin. At arrival, the patient was found to have tachycardia with a rate of 120-130, afebrile, SpO2 at 96% on room air, BP at 100/59. Her initial workup was grossly normal except for an elevated WBC and elevated D-Dimer. Chest X-ray revealed opacities in the lower lungs consistent with pneumonia. CTA of the chest revealed patchy pulmonary opacities consistent with COVID pneumonia. She took three separate SARS-CoV-2 PCR tests which all came back negative. The patient underwent a large workup which included infectious disease, pulmonology, and cardiology consults. She was treated with broad-spectrum antibiotics for the presumed diagnosis of pneumonia but her condition quickly deteriorated, eventually requiring 6L of O2 via nasal cannula. Screening for a large array of bacteria, fungus, and viruses all resulted negative. Upon further discussion with the patient, she admitted to smoking a THC vaporizer every night for the last seven months and that she had recently purchased a new fluid for her THC vaporizer through the internet. Bronchoscopy was also acquired but did not show any specific findings, including being negative for eosinophils. Discontinuation of antibiotics and initiation of IV steroids treatment provided rapid improvement of the patient's condition. Based on her history of THC vaping, the clinical presentation of fever, hypoxia, her chest x-ray, and chest CT showing extensive lung infiltrates, infections were ruled out and the most likely diagnosis of EVALI was made which responded well to steroids. DISCUSSION: COVID and EVALI initially can present similarly as respiratory problems, fever, and the need for oxygen. It is important to gather history on the patient as a vaping history is needed to suspect EVALI as imaging can show a wide range from ground-glass opacities to acute hypersensitivity pneumonitis. (2) CONCLUSIONS: There are some distinguishing features of EVALI from COVID one being in EVALI there is a large increase in the white count and lastly the response to steroids is the key (2). Steroids are the primary care for someone with EVALI with most patients recovering in 1-3 days with the use of steroids. (2) Reference #1: Bierwirth, A., Orellana, G., Milazzo, E. and Hamdan, A., 2020. TETRAHYDROCANNABINOL VAPING-ASSOCIATED LUNG INJURY (EVALI): A US EPIDEMIC?. Chestnet Journal. Reference #2: MacMurdo, M., Lin, C., Saeedan, M., Doxtader, E., Mukhopadhyay, S., Arrossi, V., Reynolds, J., Ghosh, S. and Choi, H., 2020. e-Cigarette or Vaping Product Use-Associated Lung Injury. Chestnet Journal. DISCLOSURES: No relevant relationships by Narden Gorgy No relevant relationships by Matheus Moreira Sanches Peraci No relevant relationships by George Walbridge No relevant relationships by John Zakhary

Two distant helicases in one mycovirus: evidence of horizontal gene transfer between mycoviruses, coronaviruses and other nidoviruses.

Nidovirales, which accommodates viruses with the largest RNA genomes, includes the notorious coronaviruses; however, the evolutionary route for nidoviruses is not well understood. We have characterized a positive-sense (+) single-stranded (ss) RNA mycovirus, Rhizoctonia solani hypovirus 2 (RsHV2), from the phytopathogenic fungus Rhizoctonia solani. RsHV2 has the largest RNA genome size of 22,219 nucleotides, excluding the poly(A) tail, in all known mycoviruses, and contains two open reading frames (ORF1 and ORF2). ORF1 encodes a protein of 2,009 amino acid (aa) that includes a conserved helicase domain belonging to helicase superfamily I (SFI). In contrast, ORF2 encodes a polyprotein of 4459 aa containing the hallmark genes of hypoviruses. The latter includes a helicase belonging to SFII. Following phylogenetic analysis, the ORF1-encoded helicase (Hel1) unexpectedly clustered in an independent evolutionary branch together with nidovirus helicases, including coronaviruses, and bacteria helicases. Thus, Hel1 presence indicates the occurrence of horizontal gene transfer between viruses and bacteria. These findings also suggest that RsHV2 is most likely a recombinant arising between hypoviruses and nidoviruses.

Deep learning approaches for natural product discovery from plant endophytic microbiomes.

Plant microbiomes are not only diverse, but also appear to host a vast pool of secondary metabolites holding great promise for bioactive natural products and drug discovery. Yet, most microbes within plants appear to be uncultivable, and for those that can be cultivated, their metabolic potential lies largely hidden through regulatory silencing of biosynthetic genes. The recent explosion of powerful interdisciplinary approaches, including multi-omics methods to address multi-trophic interactions and artificial intelligence-based computational approaches to infer distribution of function, together present a paradigm shift in high-throughput approaches to natural product discovery from plant-associated microbes. Arguably, the key to characterizing and harnessing this biochemical capacity depends on a novel, systematic approach to characterize the triggers that turn on secondary metabolite biosynthesis through molecular or genetic signals from the host plant, members of the rich 'in planta' community, or from the environment. This review explores breakthrough approaches for natural product discovery from plant microbiomes, emphasizing the promise of deep learning as a tool for endophyte bioprospecting, endophyte biochemical novelty prediction, and endophyte regulatory control. It concludes with a proposed pipeline to harness global databases (genomic, metabolomic, regulomic, and chemical) to uncover and unsilence desirable natural products. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40793-021-00375-0.