Left orthotopic lung transplant in rats: the learning process.

INTRODUCTION: The orthotopic left lung transplantation model in rats has been developed to answer a variety of scientific questions in transplant immunology and in the related fields of respiratory diseases. However, its widespread use has been hampered by the complexity of the procedure. AIM OF THE RESEARCH: Our purpose is to provide a detailed description of the procedure of this technique, including the complications and difficulties from the very first microsurgical step until the ultimate successful completion of the transplant procedure. MATERIALS AND METHODS: The transplant procedures were performed by two collaborating transplant surgeons with microsurgical and thoracic surgery skills. A total of 150 left lung transplants in rats were performed. Twenty-seven syngeneic (Lewis to Lewis) and 123 allogeneic (Brown-Norway to Lewis) lung transplants were performed using the cuff technique. RESULTS: In first 50 transplant procedures, post-transplant survival rate was 74% of which 54% reached the end-point of 3 or 7 days post-transplant; whole complication rate was 66%. In the subsequent 50 transplant surgeries (from 51 to 100) post-transplant survival rate increased to 88% of which 56% reached the end-point; whole complication rate was 32 %. In the final 50 transplants (from 101 to 150) post-transplant survival rate was confirmed to be 88% of which 74% reached the end-point; whole complication rate was again 32 %. CONCLUSIONS: One hundred-fifty transplants can represent a reasonable number of procedures to obtain a satisfactory surgical outcome. Training period with simpler animal models is mandatory to develop anesthesiological and microsurgical skills required for successfully develop this model. The collaboration between at least two microsurgeons is mandatory to perform all the simultaneous procedures required for completing the transplant surgery.

Variational and learning models for image and time series inverse problems

Inverse problems are at the core of many challenging applications. Variational and learning models provide estimated solutions of inverse problems as the outcome of specific reconstruction maps. In the variational approach, the result of the reconstruction map is the solution of a regularized minimization problem encoding information on the acquisition process and prior knowledge on the solution. In the learning approach, the reconstruction map is a parametric function whose parameters are identified by solving a minimization problem depending on a large set of data. In this thesis, we go beyond this apparent dichotomy between variational and learning models and we show they can be harmoniously merged in unified hybrid frameworks preserving their main advantages. We develop several highly efficient methods based on both these model-driven and data-driven strategies, for which we provide a detailed convergence analysis. The arising algorithms are applied to solve inverse problems involving images and time series. For each task, we show the proposed schemes improve the performances of many other existing methods in terms of both computational burden and quality of the solution. In the first part, we focus on gradient-based regularized variational models which are shown to be effective for segmentation purposes and thermal and medical image enhancement. We consider gradient sparsity-promoting regularized models for which we develop different strategies to estimate the regularization strength. Furthermore, we introduce a novel gradient-based Plug-and-Play convergent scheme considering a deep learning based denoiser trained on the gradient domain. In the second part, we address the tasks of natural image deblurring, image and video super resolution microscopy and positioning time series prediction, through deep learning based methods. We boost the performances of supervised, such as trained convolutional and recurrent networks, and unsupervised deep learning strategies, such as Deep Image Prior, by penalizing the losses with handcrafted regularization terms.