A novel soft tissue prediction methodology for orthognathic surgery based on probabilistic finite element modelling

Repositioning of the maxilla in orthognathic surgery is carried out for functional and aesthetic purposes. Pre-surgical planning tools can predict 3D facial appearance by computing the response of the soft tissue to the changes to the underlying skeleton. The clinical use of commercial prediction software remains controversial, likely due to the deterministic nature of these computational predictions. A novel probabilistic finite element model (FEM) for the prediction of postoperative facial soft tissues is proposed in this paper. A probabilistic FEM was developed and validated on a cohort of eight patients who underwent maxillary repositioning and had pre- and postoperative cone beam computed tomography (CBCT) scans taken. Firstly, a variables correlation assessed various modelling parameters. Secondly, a design of experiments (DOE) provided a range of potential outcomes based on uniformly distributed input parameters, followed by an optimisation. Lastly, the second DOE iteration provided optimised predictions with a probability range. A range of 3D predictions was obtained using the probabilistic FEM and validated using reconstructed soft tissue surfaces from the postoperative CBCT data. The predictions in the nose and upper lip areas accurately include the true postoperative position, whereas the prediction under-estimates the position of the cheeks and lower lip. A probabilistic FEM has been developed and validated for the prediction of the facial appearance following orthognathic surgery. This method shows how inaccuracies in the modelling and uncertainties in executing surgical planning influence the soft tissue prediction and it provides a range of predictions including a minimum and maximum, which may be helpful for patients in understanding the impact of surgery on the face

Studies of optical imaging and fluorescence microscopy with nanoparticles functionalized for brain targeting with NIR

Nowadays the Central Nervous System (CNS) is made object of studying from the international scientific community. The pathologies that affects this system are extremely disabling and many times chronicles therefore represent a big burden for the patient and his family. The study of cerebral diseases is very difficult because this district is characterized by a complex morphology; a very important structure in the CNS is represented by the BBB (blood-brain-barrier), one of the most drawback on the transit of the active principles and contrast agents. One of the strategies for crossing this barrier is the preparation of CNS-targeted nanoparticles (NPs), a nanometrical carrier modified on the surface with specific peptides, that allow the BBB crossing. The aim of this project is the development of poly-lactide-co-glycolide (PLGA) NPs engineered with a simil-opioid glycopeptide (g7) which is able to cross the BBB in order to use for in vivo imaging modifying the NPs with a marker, the near-infrared probe (DY-675). After i.v. administration in mice, a confocal and fluorescence microscopy study allowed to detect the presence of the DY-675 in the brain

STUDI PRELIMINARI SUL BRAIN TARGETING MEDIANTE L’OPTICAL IMAGING E TERAPIA ENZIMATICA PER LA POMPE-DISEASE

Nel campo delle neuroscienze, uno degli obiettivi più ambiti è rappresentato dal direzionamento di principi attivi al Sistema Nervoso Centrale (SNC). Le terapie proposte per la cura delle patologie cerebrali risultano spesso inefficaci; farmaci potenzialmente applicabili, mostrano limitata capacità di superamento della barriera emato-encefalica (BEE) e pertanto concentrazioni non terapeutiche (o permanenza troppo breve) al sito target. In tale ottica, l’utilizzo di sistemi nanoparticellari di rilascio opportunamente modificati, può migliorare l’applicabilità e la targettabilità dei farmaci al SNC [1-3]. Tale progetto di ricerca rappresenta uno studio preliminare sul delivery al SNC. In un primo momento sono state allestite nanoparticelle (Np) derivatizzate sulla solo superficie con un eptapeptide g7 in grado di attraversare la BEE e modificate con un marker fluorescente, il DY-675, allo scopo di visualizzarle in vivo a seguito di somministrazione i.v. in topi. Mediante l’ausilio del microscopio confocale e a fluorescenza è stato possibile visualizzare il DY-675 all’interno del cervello dimostrando l’avvenuto raggiungimento nell’area cerebrale dei nanosistemi.La seconda parte del progetto si è concentrata sugli studi di caricamento e di rilascio di un enzima (Myozime) in Np, successivamente si è proceduto allo studio dell’efficacia farmacologica del sistema su colture di fibroblasti affette dalla Pompe-Disease constatandone una correzione della deficienza enzimatica del 50% a seguito di una singola somministrazione, confermando così il possibile impiego di tali Np nella terapia enzimatica di patologie a carico del SNC.[1] Tosi G, Rivasi F, Gandolfi F, Costantino L, Vandelli M A, Forni F. Biomaterials 26, 4189-4195, 2005.[2] Tosi G, Costantino L, Ruozi B, Forni F, Vandelli M A. Expert Opinion on Drug Delivery 5, 155-174, 2008.[3] Tosi G, Vergoni A V, Ruozi B, Bondioli L, Badiali L, Rivasi F, Costantino L, Forni F, Vandelli M A. Journal of Controlled Release 145, 49–57, 2010

Input parameters, and minimum and maximum values of the material properties.

<p>Input parameters, and minimum and maximum values of the material properties.</p

Patient 1 soft tissue prediction minimum and maximum.

<p>(A) Overall minimum soft tissue prediction displacements with <i>G</i>_<i>soft</i> = 94.4%, <i>E</i>_<i>soft</i> = 0.1 MPa, <i>ν</i>_<i>soft</i> = 0.45, <i>E</i>_<i>cart</i> = 0.5 MPa, <i>x</i>_<i>disp</i> = 3 mm, and (B) maximum soft tissue prediction displacements with <i>G</i>_<i>soft</i> = 30.5%, <i>E</i>_<i>soft</i> = 1 MPa, <i>ν</i>_<i>soft</i> = 0.49, <i>E</i>_<i>cart</i> = 5 MPa, <i>x</i>_<i>disp</i> = 7 mm. Blue depicts no change from the postoperative CBCT (0 mm), bright red depicts a maximum change (6 mm).</p

Design of experiments I: Soft tissue soft prediction ranges in five points for all patients.

<p>Points include nose tip, upper lip, lower lip, right cheek, and left cheek. The vertical bars depict the range of soft tissue prediction; the black dots represent the soft tissue displacement from the postoperative CBCT. The baseline (0 mm) is the preoperative CBCT.</p

Histogram depicting the distribution of material properties following optimisation.

<p>Green bars display all training data from P2, P3, P5, P7 and P8, as well as a fitted Weibull curve (solid black line) and cumulative density (dotted black line). <i>G</i>: viscoelastic scale factor, <i>E</i>: Young’s Modulus, <i>ν</i>: Poisson’s ratio, <i>soft</i>: soft tissue, <i>cart</i>: cartilage.</p

Flowchart of the methodology for the probabilistic finite element analysis.

<p>Colour coding: variable correlation, design of experiments I, optimisation on material properties, and design of experiments II.</p