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.

Treatment of knee articular cartilage defects: surgical strategies, results and analysis of factors determining the clinical outcome

Articular cartilage lesions, with their inherent limited healing potential, are hard to treat and remain a challenging problem for orthopedic surgeons. Despite the development of several treatment strategies, the real potential of each procedure in terms of clinical benefit and effects on the joint degeneration processes is not clear. Aim of this PhD project was to evaluate the results, both in terms of clinical and imaging improvement, of new promising procedures developed to address the challenging cartilage pathology. Several studies have been followed in parallel and completed over the 3-year PhD, and are reported in detail in the following pages. In particular, the studies have been focused on the evaluation of the treatment indications of a scaffold based autologous chondrocyte implantation procedure, documenting its results for the classic indication of focal traumatic lesions, as well as its use for the treatment of more challenging patients, older, with degenerative lesions, or even as salvage procedure for more advanced stages of articular degeneration. The second field of study involved the analysis of the results obtained treating lesions of the articular surface with a new biomimetic osteochondral scaffold, which showed promise for the treatment of defects where the entire osteochondral unit is involved. Finally, a new minimally invasive procedure based on the use of growth factors derived from autologous platelets has been explored, showing results and underlining indicatios for the treatment of cartilage lesions and different stages of joint degeneration. These studies shed some light on the potential of the evaluated procedures, underlining good results as well as limits, they give some indications on the most appropriate candidates for their application, and document the current knowledge on cartilage treatment procedures suggesting the limitations that need to be addressed by future studies to improve the management of cartilage lesions.

Applications of metrological techniques for clinical implementation of dosimetry and radiobiology in molecular radiotherapy

Molecular radiotherapy (MRT) is a fast developing and promising treatment for metastasised neuroendocrine tumours. Efficacy of MRT is based on the capability to selectively "deliver" radiation to tumour cells, minimizing administered dose to normal tissues. Outcome of MRT depends on the individual patient characteristics. For that reason, personalized treatment planning is important to improve outcomes of therapy. Dosimetry plays a key role in this setting, as it is the main physical quantity related to radiation effects on cells. Dosimetry in MRT consists in a complex series of procedures ranging from imaging quantification to dose calculation. This doctoral thesis focused on several aspects concerning the clinical implementation of absorbed dose calculations in MRT. Accuracy of SPECT/CT quantification was assessed in order to determine the optimal reconstruction parameters. A model of PVE correction was developed in order to improve the activity quantification in small volume, such us lesions in clinical patterns. Advanced dosimetric methods were compared with the aim of defining the most accurate modality, applicable in clinical routine. Also, for the first time on a large number of clinical cases, the overall uncertainty of tumour dose calculation was assessed. As part of the MRTDosimetry project, protocols for calibration of SPECT/CT systems and implementation of dosimetry were drawn up in order to provide standard guidelines to the clinics offering MRT. To estimate the risk of experiencing radio-toxicity side effects and the chance of inducing damage on neoplastic cells is crucial for patient selection and treatment planning. In this thesis, the NTCP and TCP models were derived based on clinical data as help to clinicians to decide the pharmaceutical dosage in relation to the therapy control and the limitation of damage to healthy tissues. Moreover, a model for tumour response prediction based on Machine Learning analysis was developed.