Donors risk assessment in transplantation: From the guidelines to their real-world application.

Transplantation of an organ from a donor carries an unavoidable risk of tumor transmission. The need to extend the donor pool increases the use of organs from donors with malignancies and potential disease transmission is a constant tension influencing donor suitability decisions. Current classification systems for the assessment of donor malignancy transmission risk have evolved from reports of potential transmission events in recipients to national donation and transplant surveillance agencies. Although the risk of malignancy transmission is very low in the general transplant setting it must constantly be balanced with the transplant benefits. Guidelines are mainly based on large registries and sparse case reports of transmission, so they cannot cover all the possible situations. For this reason, in 2004 in Italy, the National Transplant Center gave rise to the Second Opinion Service, charged by the Ministry of Health, by structuring expertise in diagnostic oncology and risk transmission and making it available to the Italian Transplant Centers. In this paper the registry of the Italian Oncological Second Opinion was reviewed, from 2016 to 2018, to detail the most frequent and problematic neoplastic topics addressed, those are separately reported and discussed. Furthermore, a review of the most recent strategies and risk stratification is provided, according to the most recent literature evidence and to the European Guidelines.

Non-uniform adaptive angular spectrum method and its application to neural network assisted coherent beam combining.

Increasing the number of laser beams that can be coherently combined requires accurate and fast algorithms for compensating phase and alignment errors. The paper proposes to use a Fully Connected Artificial Neural Network (FCANN) to correct the beam positioning perturbations by evaluating the beam shifts and tilts from two images taken at slightly different locations. Then, since it is practically impossible to have a large enough experimental dataset to train the neural network, this approach required developing an accurate and fast simulation method to evaluate the beam propagation in arbitrary directions, overcoming the limitations occurring when the computation must be repeated a large number of times. The numerical approach is a variant of the Angular Spectrum (AS) method, called Non Uniform ADaptive Angular Spectrum (NUADAS) method, which relies on the combination of non-uniform and adaptive Fourier transform algorithms to allow the computation of an arbitrary field distribution in a plane that is shifted and tilted with respect to the source. The parallel implementation of the NUADAS method is discussed and the numerical and experimental validations are presented. Then, an FCANN is trained using the synthetic dataset generated with the NUADAS method and the results are discussed, demonstrating the viability of the proposed approach not only for coherent beam combing, but also in other beam alignment applications.

BRAF mutations in papillary thyroid carcinoma and emerging targeted therapies (review).

Papillary thyroid carcinoma (PTC) is the most common histotype among the thyroid cancer types. Although PTC is a curable malignancy, many patients relapse after treatment. Thus, there is a need to identify novel factors involved in the pathogenesis of PTC that may be used as targets for new therapies. The MAPK pathway has been implicated in the pathogenesis of PTC. Therefore, in this review, we summarize the role of the BRAF V600E mutation in the development and progression of thyroid cancer. The cinical implication of this molecular abnormality is also discussed. It is evident that the detection of the BRAF V600E mutation is crucial in order to identify novel avenues for thyroid cancer treatment.

microRNAs and thyroid cancer: biological and clinical significance (Review).

Despite recent advances in the management of thyroid cancer, the survival rate of this tumor may still be improved. Therefore, the identification of biological and molecular features of indolent and aggressive disease would be critical to define clinically useful predictors of high-risk lesions. microRNAs (miRNAs) are small RNA molecules with regulatory function and marked tissue specificity that modulate multiple targets belonging to several pathways. They are frequently deregulated in cancer and constitute a new class of blood-based biomarkers useful for cancer detection and prognosis definition, including thyroid cancer. In this review, the role of miRNAs in thyroid cancer development is described. The most common miRNAs detected in thyroid cancer along with their clinical significance are also discussed. Further studies aimed to detect plasma-based miRNA biomarkers in thyroid cancer patients may provide further insight into the management of thyroid cancer.