A candidate multi-epitope vaccine against porcine reproductive and respiratory syndrome virus and Mycoplasma hyopneumoniae induces robust humoral and cellular response in mice

Porcine reproductive and respiratory syndrome virus (PRRSV) and Mycoplasma hyopneumoniae (M. hyopneumoniae, Mhp) are two of the most common pathogens involved in the porcine respiratory disease complex (PRDC) resulting in significant economic losses worldwide. Vaccination is the most effective approach to disease prevention. Since PRRSV and Mhp co-infections are very common, an efficient dual vaccine against these pathogens is required for the global swine industry. Compared with traditional vaccines, multi-epitope vaccines have several advantages, they are comparatively easy to produce and construct, are chemically stable, and do not have an infectious potential. In this study, to develop a safe and effective vaccine, B cell and T cell epitopes of PRRSV-GP5, PRRSV-M, Mhp-P46, and Mhp-P65 protein had been screened to construct a recombinant epitope protein rEP-PM that has good hydrophilicity, strong antigenicity, and high surface accessibility, and each epitope is independent and complete. After immunization in mice, rEP-PM could induce the production of high levels of antibodies, and it had good immunoreactivity with anti-rEP-PM, anti-PRRSV, and anti-Mhp antibodies. The anti-rEP-PM antibody specifically recognizes proteins from PRRSV and Mhp. Moreover, rEP-PM induced a Th1-dominant cellular immune response in mice. Our results showed that the rEP-PM protein could be a potential candidate for the development of a safe and effective multi-epitope peptide combined vaccine to control PRRSV and Mhp infections.     

Vaccine innovation model: A technology transfer perspective in pandemic contexts

This work identifies the innovations that made it possible for the Bio-Manguinhos/Fiocruz Immunobiological Technology Institute to engage in the entire production of the Oxford/AstraZeneca vaccine (ChAdOx1 nCov-19) in Brazil, just 1.8 years after the COVID-19 pandemic was declared. The results were summarized in a case-based innovation model composed of 11 workstreams, 32 stages, 22 gates, 11 innovations, and 38 events. In terms of research contributions, three were found: (i) the identification of firm and government-level innovations allowing the substantial reduction in the COVID-19 vaccine time-to-market in Brazil; (ii) the presentation of empirical evidence supporting the new Outbreak Paradigm for vaccine research, development, and production; and (iii) the proposition of a conceptual model for describing innovations through the vaccine value chain in pandemic contexts, particularly when technology transfer is involved.     

What is the Potential Interplay between Microbiome and Tumor Microenvironment in Oral Squamous Cell Carcinomas?

Oral cancer, with an around 50% mortality rate, is one of the most common malignancies world-wide. It is often detected in advanced or terminal stage and has a poor prognosis, although substantial progress in cancer management. Microbiome has become an increasingly recognized factor that may contribute to the cancerous development. Oral microbiological population comprising more than 700 bacterial species, varies since saliva and different habitats of oral cavity. A shift of composition of oral microbiome from usual condition to functional inflammation to pathological state has been discovered amongst patients with premalignant disorders and oral carcinoma, with evidence suggesting the tumor microenvironment (TME) could strongly exacerbate the influence of oral microorganisms. The complex interactions taking place in either cancer formation or progression have been evaluated in several publications, however given their results’ heterogeneity, a review is needed to correctly untangle the potential correlation in this group of pro-carcinogenesis. In this review, we briefly summarize our current knowledge of the role of oral microbiome, focusing on its potential crosstalk with TME in oral squamous cell carcinomas (OSCC) more precisely, and pave the way for manipulating oral microbiome to deal with OSCC in the future.     

Multiomic analysis reveals conservation of cancer-associated fibroblast phenotypes across species and tissue of origin

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Preoperative comprehensive malignancy risk estimation for thyroid nodules: Development and verification of a network-based prediction model

BackgroundIn order to avoid excessive treatment of thyroid nodules in the clinic, it is necessary to find a simple and practical analysis method to comprehensively and accurately reflect benign or malignant thyroid nodules. This study aimed to construct and validate a comprehensive and reliable network-based predictive model using a variety of imaging and laboratory criteria for thyroid nodules to stratify the risk of malignancy prior to surgery.MethodsWe retrospectively analyzed data from patients who underwent surgical treatment for thyroid nodules at the Thyroid and Breast Diagnosis and Treatment Center of Weifang Hospital of Traditional Chinese Medicine between January 2018 and December 2020. Binary logical regression analysis was performed to predict whether nodules were malignant or benign. The developmental dataset included 457 patients (January 2018–December 2020). The validation set included separate data points (n = 225, January 2018–December 2020).ResultsIn this study, criteria that showed significant predictive value for malignant nodules included TI-RADS: 4b (p = 0.065); Bethesda IV, Bethesda V, Bethesda VI (P < 0.0001); BRAF^V600E mutation (P < 0.0001); Calcitonin>5 pg/ml (p = 0.0037); and FNA-Tg>30 ng/ml (p = 0.0003). A 10-grade risk scoring system was developed. The risk of malignancy risk ranged from 2.06% to 100% and was positively associated with increasing risk grade. The areas under the receiver-operating characteristic curve of the development and validation sets were 0.972 and 0.946, respectively.ConclusionA simple, comprehensive and reliable web-based predictive model was designed using a variety of imaging and laboratory criteria to stratify thyroid nodules by probability of malignancy.