A foot-and-mouth disease (FMD) recombinant subunit vaccine formulated with a lipid/polymer adjuvant was evaluated in two vaccine efficacy challenge studies in steers. The vaccine active ingredient is a replication-deficient human adenovirus serotype 5 vector encoding the FMD virus (FMDV) A24/Cruzeiro/BRA/55 capsid (AdtA24). In the first study, AdtA24 formulated in ENABL® adjuvant was compared to a fourfold higher dose of AdtA24 without adjuvant. Steers vaccinated with AdtA24 + ENABL® adjuvant developed a significantly higher virus neutralizing test (VNT) antibody titer and an improved clinical response following FMDV A24/Cruzeiro/BRA/55 intradermal lingual challenge at 14 days post-vaccination (dpv) than steers vaccinated with the active ingredient alone. In the second study, vaccination with AdtA24 formulated in ENABL® at the same dose used in the first study, followed by FMDV A24/Cruzeiro/BRA/55 challenge on 7 or 14 dpv, prevented clinical FMD in all steers and conferred 90% protection against viremia. In addition, post-challenge FMDV titers in nasal samples from vaccinated steers compared to unvaccinated steers were significantly reduced. In both studies, none of the AdtA24 vaccinated steers developed antibodies to the FMDV non-structural proteins prior to challenge with FMDV, indicative of the capacity to differentiate infected from vaccinated animals (DIVA). These results demonstrate that administration of AdtA24 formulated in ENABL® adjuvant lowered the protective dose and prevented clinical FMD following exposure of vaccinated steers to virulent FMDV at 7 or 14 dpv.
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http://dx.doi.org/10.1016/j.vaccine.2018.01.026 | DOI Listing |
J Exp Clin Cancer Res
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Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada.
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Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China.
Existing prognostic models are useful for estimating the prognosis of lung adenocarcinoma patients, but there remains room for improvement. In the current study, we developed a deep learning model based on histopathological images to predict the recurrence risk of lung adenocarcinoma patients. The efficiency of the model was then evaluated in independent multicenter cohorts.
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School of Healthy Aging, Aesthetic and Regenerative Medicine, Faculty of Medicine and Health Sciences, UCSI University, 56000, Cheras, Kuala Lumpur, Malaysia.
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Servicio de Oncología Médica, Hospital Universitario Juan Ramón Jiménez, Huelva, Spain.
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