Influence of application mode of universal adhesives on bond strength performances and enzymatic activity of coronal and radicular dentin

Objective: The aims of this thesis were to analyze the application mode of the universal adhesives (UA) and to give instructions for clinical procedures. The etching mode of UA on the bond strength to dentin and on the risk of retention, marginal discoloration, marginal adaptation and post-operative sensitivity (POS) was analyzed by two systematic reviews. Three in vitro studies were conducted: 1) evaporation mode of a UA on coronal dentin; 2) cementation approach on radicular dentin; 3) adhesion of metal brackets to enamel. Materials and methods: Two systematic review were conducted firstly, then in vitro study to investigate the evaporation mode in presence or not of pulpal pressure by means of μTBS, and the enzymatic activity using in situ zymography, at T0 and T6. The cementation of a fiber into radicular dentin with different resin-cements was studied, by push-out bond strength evaluation. Orthodontic brackets were cemented according to 4 adhesive protocols and shear bond strength test was conducted. Two adhesive removal techniques were evaluated, and spectrophotometry was used. Results: The probability of POS occurrence was less in SE. SEE approach seems to perform better than SE. Air-drying resulted in higher μTBS. Suction-evaporation, aging and ER mode increased MMPs activity. Differences in NL expression were present at T0 for fiber post study, and the aging produced an increase in marginal infiltration. Brackets cemented with new universal cement with previous etchant application showed good μTBS values. Conclusion: SEE performed better than SE and TE with UA in terms of uTBS. Evaporating with air-drying is better for UA in terms of uTBS and enzymatic activity. Aging and choice of resin cement for cementation of fiber posts influenced the PBS. Brackets cementation with a new resin- cement seems to offer the highest bond strength and leaves more cement remnants after the bracket removal.

Movement analysis on the field in young football players: biomechanical patterns of performance and injury prevention

The integration of quantitative data from movement analysis technologies is reshaping the analysis of athletes’ performances and injury mitigation, e.g., anterior cruciate ligament (ACL) rupture. Most of the movement assessments are performed in laboratory environments. Recent progress provides the chance to shift the paradigm to a more ecological approach with sport-specific elements and a closer examination of “real” movement patterns associated with performance and (ACL) injury risk. The present PhD thesis aimed at investigating the on-field motion patterns related to performance and injury prevention in young football players. The objectives of the thesis were: (I) in-lab measures of high-dynamics movements were used to validate wearable inertial sensors technology; (II) in-laboratory and on-field agility movement tasks were compared to inspect the effect of football-specific environment; (III) on-field analysis was conducted to challenge wearable sensors technology in the assessment of dangerous movement patterns towards the ACL rupture; (IV) an overview of technologies that could shape present and future assessment of ACL injury risk in daily practice was presented. The validity of wearables in the assessment of high-dynamics movements was confirmed. Relevant differences emerged between the movements performed in a laboratory setting and on the football pitch, supporting the inclusion of an ecological dynamics approach in preventive protocols. The on-field analysis of football-specific movement tasks demonstrated good reliability of wearable sensors and the presence of residual dangerous patterns in the injured players. A tool to inspect at-risk movement patterns on the field through objective measurements was presented. It discussed how potential alternatives to wearable inertial sensors embrace artificial intelligence and closer collaboration between clinical and technical expertise. The present thesis was meant to contribute to setting the basis for data-driven prevention protocols. A deeper comprehension of injury-related principles and counteractions will contribute to preserving athletes’ careers and health over time.

Advancing digital healthcare research and machine learning-aided knowledge extraction amidst the COVID-19 pandemic

The rapid progression of biomedical research coupled with the explosion of scientific literature has generated an exigent need for efficient and reliable systems of knowledge extraction. This dissertation contends with this challenge through a concentrated investigation of digital health, Artificial Intelligence, and specifically Machine Learning and Natural Language Processing's (NLP) potential to expedite systematic literature reviews and refine the knowledge extraction process. The surge of COVID-19 complicated the efforts of scientists, policymakers, and medical professionals in identifying pertinent articles and assessing their scientific validity. This thesis presents a substantial solution in the form of the COKE Project, an initiative that interlaces machine reading with the rigorous protocols of Evidence-Based Medicine to streamline knowledge extraction. In the framework of the COKE (“COVID-19 Knowledge Extraction framework for next-generation discovery science”) Project, this thesis aims to underscore the capacity of machine reading to create knowledge graphs from scientific texts. The project is remarkable for its innovative use of NLP techniques such as a BERT + bi-LSTM language model. This combination is employed to detect and categorize elements within medical abstracts, thereby enhancing the systematic literature review process. The COKE project's outcomes show that NLP, when used in a judiciously structured manner, can significantly reduce the time and effort required to produce medical guidelines. These findings are particularly salient during times of medical emergency, like the COVID-19 pandemic, when quick and accurate research results are critical.