Leonardo Sciascia e le immagini della scrittura - Il poliziesco di mafia dalla letteratura al cinema

Il rapporto di Sciascia con il cinema è stato precoce e complesso: il grande schermo offriva la conoscenza e il fascino di immagini che si imprimevano in modo indelebile nella sua memoria di adolescente, incidendo anche sulla sua configurazione del fantastico. Il rapporto è proseguito nel tempo, con un capovolgimento: le idee e le storie di Sciascia hanno costituito un serbatoio per i cineasti e non più viceversa; infine, tuttavia, una profonda distanza si è scavata tra lo scrittore e il cinema, fino a un ritorno nostalgico. La prima parte del lavoro è dedicata alla ricostruzione del pensiero dell’autore in merito al medium, attraverso l’analisi delle riflessioni svolte specialmente in tre diversi saggi, imperniati sul cinema come memoria, come rivoluzione culturale e come veicolo di idee, al fine di illustrare il suo pensiero meno studiato. Nelle parti successive della tesi (seconda, terza e quarta) si realizza un’analisi comparativo-contrastiva dei tre polizieschi di mafia da cui sono state tratte pellicole cinematografiche d’autore. Si tratta dei romanzi Il giorno della civetta (1961), A ciascuno il suo (1966) e Una storia semplice (1989) e delle rispettive trasposizioni filmiche di Damiano Damiani (1968), Elio Petri (1967), ed Emidio Greco (1991). Nel lavoro si evidenziano analogie e differenze tra le diverse rappresentazioni della storia, mediante una ricostruzione che propone il cinema quale strumento di indagine critico-letteraria del testo e il film quale lettura e interpretazione critica. L’indagine si concentra in particolare su alcuni profili: la struttura della narrazione letteraria e filmica; il discorso; la configurazione del tempo e dello spazio; la trattazione di alcuni personaggi selezionati per il carattere emblematico

Radiomic and genomic machine learning method performance for prostate cancer diagnosis : systematic literature review

Background Machine learning algorithms have been drawing attention at the joining of pathology and radiology in prostate cancer research. However, due to their algorithmic learning complexity and the variability of their architecture, there is an ongoing need to analyze their performance. Objective This study assesses the source of heterogeneity and the performance of machine learning applied to radiomic, genomic, and clinical biomarkers for the diagnosis of prostate cancer. One research focus of this study was on clearly identifying problems and issues related to the implementation of machine learning in clinical studies. Methods Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol, 816 titles were identified from the PubMed, Scopus, and OvidSP databases. Studies that used machine learning to detect prostate cancer and provided performance measures were included in our analysis. The quality of the eligible studies was assessed using the QUADAS-2 (quality assessment of diagnostic accuracy studies–version 2) tool. The hierarchical multivariate model was applied to the pooled data in a meta-analysis. To investigate the heterogeneity among studies, I2 statistics were performed along with visual evaluation of coupled forest plots. Due to the internal heterogeneity among machine learning algorithms, subgroup analysis was carried out to investigate the diagnostic capability of machine learning systems in clinical practice. Results In the final analysis, 37 studies were included, of which 29 entered the meta-analysis pooling. The analysis of machine learning methods to detect prostate cancer reveals the limited usage of the methods and the lack of standards that hinder the implementation of machine learning in clinical applications. Conclusions The performance of machine learning for diagnosis of prostate cancer was considered satisfactory for several studies investigating the multiparametric magnetic resonance imaging and urine biomarkers; however, given the limitations indicated in our study, further studies are warranted to extend the potential use of machine learning to clinical settings. Recommendations on the use of machine learning techniques were also provided to help researchers to design robust studies to facilitate evidence generation from the use of radiomic and genomic biomarkers

Monoclinic and Orthorhombic NaMnO2 for Secondary Batteries: A Comparative Study

In this manuscript, we report a detailed physico-chemical comparison between the - and -polymorphs of the NaMnO2 compound, a promising material for application in positive electrodes for secondary aprotic sodium batteries. In particular, the structure and vibrational properties, as well as electrochemical performance in sodium batteries, are compared to highlight differences and similarities. We exploit both laboratory techniques (Raman spectroscopy, electrochemical methods) and synchrotron radiation experiments (Fast-Fourier Transform Infrared spectroscopy, and X-ray diffraction). Notably the vibrational spectra of these phases are here reported for the first time in the literature as well as the detailed structural analysis from diffraction data. DFT+U calculations predict both phases to have similar electronic features, with structural parameters consistent with the experimental counterparts. The experimental evidence of antisite defects in the beta-phase between sodium and manganese ions is noticeable. Both polymorphs have been also tested in aprotic batteries by comparing the impact of different liquid electrolytes on the ability to de-intercalated/intercalate sodium ions. Overall, the monoclinic -NaMnO2 shows larger reversible capacity exceeding 175 mAhg-1 at 10 mAg-1

Thyroid papillary carcinoma arising in ectopic thyroid tissue within a neck branchial cyst

BACKGROUND: Thyroid gland derives from one median anlage at the base of the tongue, and from the two fourth branchial pouches. A number of anomalies may occur during their migration. These can be in form of ectopic tissues, which are frequently found along the course of thyroglossal duct and rarely in other sites, many of these may develop same diseases as the thyroid gland. CASE PRESENTATION: A 36-years-old female presented with a 3 month history of left side neck mass. The mass disappeared following aspiration of brown colored fluid, which on cytological examination showed cells with nuclear irregularities that warranted the resection of the lesion. The histology demonstrated a thyroid papillary carcinoma arising within the branchial cyst. Thereafter, the patient underwent a total thyroidectomy with central lymph nodes dissection. Histology showed a multifocal papillary carcinoma with central lymph nodes metastases. Only four cases of primary thyroid carcinomas in neck branchial cyst have been described so far. CONCLUSION: In a lateral cystic neck mass, although rare, occurrence of ectopic thyroid tissue and presence of a papillary thyroid carcinoma should be kept in mind

Prognostic and Predictive Role of Body Composition in Metastatic Neuroendocrine Tumor Patients Treated with Everolimus: A Real-World Data Analysis

Neuroendocrine tumors (NETs) are rare neoplasms frequently characterized by an up- regulation of the mammalian rapamycin targeting (mTOR) pathway resulting in uncontrolled cell proliferation. The mTOR pathway is also involved in skeletal muscle protein synthesis and in adipose tissue metabolism. Everolimus inhibits the mTOR pathway, resulting in blockade of cell growth and tumor progression. The aim of this study is to investigate the role of body composition in- dexes in patients with metastatic NETs treated with everolimus. The study population included 30 patients with well-differentiated (G1-G2), metastatic NETs treated with everolimus at the IRCCS Romagnolo Institute for the Study of Tumors (IRST) “Dino Amadori”, Meldola (FC), Italy. The body composition indexes (skeletal muscle index [SMI] and adipose tissue indexes) were assessed by measuring on a computed tomography (CT) scan the cross-sectional area at L3 at baseline and at the first radiological assessment after the start of treatment. The body mass index (BMI) was assessed at baseline. The median progression-free survival (PFS) was 8.9 months (95% confidence interval [CI]: 3.4–13.7 months). The PFS stratified by tertiles was 3.2 months (95% CI: 0.9–10.1 months) in patients with low SMI (tertile 1), 14.2 months (95% CI: 2.3 months-not estimable [NE]) in patients with intermediate SMI (tertile 2), and 9.1 months (95% CI: 2.7 months-NE) in patients with high SMI (tertile 3) (p = 0.039). Similarly, the other body composition indexes also showed a statistically significant difference in the three groups on the basis of tertiles. The median PFS was 3.2 months (95% CI: 0.9–6.7 months) in underweight patients (BMI 18.49 kg/m2) and 10.1 months (95% CI: 3.7–28.4 months) in normal-weight patients (p = 0.011). There were no significant differences in terms of overall survival. The study showed a correlation between PFS and the body composition indexes in patients with NETs treated with everolimus, underlining the role of adipose and muscle tissue in these patients

A Complex Radiomic Signature in Luminal Breast Cancer from a Weighted Statistical Framework: A Pilot Study

Radiomics is rapidly advancing in precision diagnostics and cancer treatment. However, there are several challenges that need to be addressed before translation to clinical use. This study presents an ad-hoc weighted statistical framework to explore radiomic biomarkers for a better characterization of the radiogenomic phenotypes in breast cancer. Thirty-six female patients with breast cancer were enrolled in this study. Radiomic features were extracted from MRI and PET imaging techniques for malignant and healthy lesions in each patient. To reduce within-subject bias, the ratio of radiomic features extracted from both lesions was calculated for each patient. Radiomic features were further normalized, comparing the z-score, quantile, and whitening normalization methods to reduce between-subjects bias. After feature reduction by Spearman’s correlation, a methodological approach based on a principal component analysis (PCA) was applied. The results were compared and validated on twenty-seven patients to investigate the tumor grade, Ki-67 index, and molecular cancer subtypes using classification methods (LogitBoost, random forest, and linear discriminant analysis). The classification techniques achieved high area-under-the-curve values with one PC that was calculated by normalizing the radiomic features via the quantile method. This pilot study helped us to establish a robust framework of analysis to generate a combined radiomic signature, which may lead to more precise breast cancer prognosis