A chimeric human/dog-DNA vaccine against CSPG4 induces immunity with therapeutic potential in comparative preclinical models of osteosarcoma.

The high mortality rate of osteosarcoma (OSA) patients highlights the requirement of alternative strategies. The young age of patients, as well as the rarity and aggressiveness of the disease, limits opportunities for the robust testing of novel therapies, suggesting the need for valuable preclinical systems. Having previously shown the overexpression of the chondroitin sulfate proteoglycan (CSPG)4 in OSA, herein the functional consequences of its downmodulation in human OSA cells were evaluated in vitro, with a significant impairment of cell proliferation, migration, and osteosphere generation. The potential of a chimeric human/dog (HuDo)-CSPG4 DNA vaccine was explored in translational comparative OSA models, including human xenograft mouse models and canine patients affected by spontaneous OSA. The adoptive transfer of HuDo-CSPG4 vaccine-induced CD8+ T cells and sera in immunodeficient human OSA-bearing mice delayed tumor growth and metastasis development. HuDo-CSPG4 vaccination resulted safe and effective in inducing anti-CSPG4 immunity in OSA-affected dogs, which displayed prolonged survival as compared to controls. Finally, HuDo-CSPG4 was also able to induce a cytotoxic response in a human surrogate setting in vitro. On the basis of these results and the high predictive value of spontaneous OSA in dogs, this study paves the way for a possible translation of this approach to humans.

Exploring the vaccine conversation on TikTok in Italy: beyond classic vaccine stances.

TikTok, a social media platform for creating and sharing short videos, has seen a surge in popularity during the COVID-19 pandemic. To analyse the Italian vaccine conversation on TikTok, we downloaded a sample of videos with a high play count (Top Videos), identified through an unofficial Application Programming Interface (consistent with TikTok's Terms of Service), and collected public videos from vaccine sceptic users through snowball sampling (Vaccine Sceptics' videos). The videos were analysed using qualitative and quantitative methods, in terms of vaccine stance, tone of voice, topic, conformity with TikTok style, and other characteristics. The final datasets consisted of 754 Top Videos (by 510 single users) plus 180 Vaccine Sceptics' videos (by 29 single users), posted between January 2020 and March 2021. In 40.5% of the Top Videos the stance was promotional, 33.9% were indefinite-ironic, 11.3% were neutral, 9.7% were discouraging, and 3.1% were ambiguous (i.e. expressing an ambivalent stance towards vaccines); 43% of promotional videos were from healthcare professionals. More than 95% of the Vaccine Sceptic videos were discouraging. Multiple correspondence analysis showed that, compared to other stances, promotional videos were more frequently created by healthcare professionals and by females, and their most frequent topic was herd immunity. Discouraging videos were associated with a polemical tone of voice and their topics were conspiracy and freedom of choice. Our analysis shows that Italian vaccine-sceptic users on TikTok are limited in number and vocality, and the large proportion of videos with an indefinite-ironic stance might imply that the incidence of affective polarisation could be lower on TikTok, compared to other social media, in the Italian context. Safety is the most frequent concern of users, and we recorded an interesting presence of healthcare professionals among the creators. TikTok should be considered as a medium for vaccine communication and for vaccine promotion campaigns.

Diagnostic Findings and Surgical Management of Three Dogs Affected by Osseous Metaplasia Secondary to a Salivary Mucocele.

Saliva is an irritant of the subcutaneous tissue, thus causing the development of a non-epithelial reactive pseudocapsule. Metaplastic ossification of the pseudocapsule is a condition rarely described in the veterinary literature. The main causes of calcification are trauma, tumours, various chronic inflammatory conditions and fibrodysplasia ossificans progressiva. The aim of the present case series was to describe three dogs affected by a calcified salivary mucocele. The medical records of dogs affected by a cervical sialocele were retrospectively evaluated, and three cases met the inclusion criteria. All the dogs in this study were referred to the Veterinary Teaching Hospital (VTH) of the Department of Veterinary Sciences of the University of Turin (Turin, Italy) for a large solid mass in the intermandibular region. The diagnosis of a mucocele was confirmed clinically by centesis and by radiography or CT. Complete excision of both the pseudocyst and the ipsilateral mandibular/monostomatic sublingual salivary gland was performed in all cases. The histological report showed large areas of bone metaplasia within the pseudocapsule and chronic sialadenitis. Based on this limited case series, complete excision of the pseudocyst and a concurrent sialoadenectomy provided an effective treatment for this rare salivary mucocele disorder.

Understanding the vaccine stance of Italian tweets and addressing language changes through the COVID-19 pandemic: Development and validation of a machine learning model.

Social media is increasingly being used to express opinions and attitudes toward vaccines. The vaccine stance of social media posts can be classified in almost real-time using machine learning. We describe the use of a Transformer-based machine learning model for analyzing vaccine stance of Italian tweets, and demonstrate the need to address changes over time in vaccine-related language, through periodic model retraining. Vaccine-related tweets were collected through a platform developed for the European Joint Action on Vaccination. Two datasets were collected, the first between November 2019 and June 2020, the second from April to September 2021. The tweets were manually categorized by three independent annotators. After cleaning, the total dataset consisted of 1,736 tweets with 3 categories (promotional, neutral, and discouraging). The manually classified tweets were used to train and test various machine learning models. The model that classified the data most similarly to humans was XLM-Roberta-large, a multilingual version of the Transformer-based model RoBERTa. The model hyper-parameters were tuned and then the model ran five times. The fine-tuned model with the best F-score over the validation dataset was selected. Running the selected fine-tuned model on just the first test dataset resulted in an accuracy of 72.8% (F-score 0.713). Using this model on the second test dataset resulted in a 10% drop in accuracy to 62.1% (F-score 0.617), indicating that the model recognized a difference in language between the datasets. On the combined test datasets the accuracy was 70.1% (F-score 0.689). Retraining the model using data from the first and second datasets increased the accuracy over the second test dataset to 71.3% (F-score 0.713), a 9% improvement from when using just the first dataset for training. The accuracy over the first test dataset remained the same at 72.8% (F-score 0.721). The accuracy over the combined test datasets was then 72.4% (F-score 0.720), a 2% improvement. Through fine-tuning a machine-learning model on task-specific data, the accuracy achieved in categorizing tweets was close to that expected by a single human annotator. Regular training of machine-learning models with recent data is advisable to maximize accuracy.

How the Italian Twitter Conversation on Vaccines Changed During the First Phase of the Pandemic: A Mixed-Method Analysis.

In the context of the European Joint Action on Vaccination, we analyzed, through quantitative and qualitative methods, a random sample of vaccine-related tweets published in Italy between November 2019 and June 2020, with the aim of understanding how the Twitter conversation on vaccines changed during the first phase of the pandemic, compared to the pre-pandemic months. Tweets were analyzed by a multidisciplinary team in terms of kind of vaccine, vaccine stance, tone of voice, population target, mentioned source of information. Multiple correspondence analysis was used to identify variables associated with vaccine stance. We analyzed 2,473 tweets. 58.2% mentioned the COVID-19 vaccine. Most had a discouraging stance (38.1%), followed by promotional (32.5%), neutral (22%) and ambiguous (2.5%). The discouraging stance was the most represented before the pandemic (69.6%). In February and March 2020, discouraging tweets decreased intensely and promotional and neutral tweets dominated the conversation. Between April and June 2020, promotional tweets remained more represented (36.5%), followed by discouraging (30%) and neutral (24.3%). The tweets' tone of voice was mainly polemical/complaining, both for promotional and for discouraging tweets. The multiple correspondence analysis identified a definite profile for discouraging and neutral tweets, compared to promotional and ambiguous tweets. In conclusion, the emergence of SARS-CoV-2 caused a deep change in the vaccination discourse on Twitter in Italy, with an increase of promotional and ambiguous tweets. Systematic monitoring of Twitter and other social media, ideally combined with traditional surveys, would enable us to better understand Italian vaccine hesitancy and plan tailored, data-based communication strategies.