Beyond the Concepts of Elder and Marginal in DCD Liver Transplantation: A Prospective Observational Matched-Cohort Study in the Italian Clinical Setting

Donation after circulatory determination of death (DCD) is a valuable strategy to increase the availability of grafts for liver transplantation (LT). As the average age of populations rises, the donor pool is likely to be affected by a potential increase in DCD donor age in the near future. We conducted a prospective cohort study to evaluate post-transplantation outcomes in recipients of grafts from elderly DCD donors compared with younger DCD donors, and elderly donors after brainstem determination of death (DBD). From August 2020 to May 2022, consecutive recipients of deceased donor liver-only transplants were enrolled in the study. DCD recipients were propensity score matched 1:3 to DBD recipients. One-hundred fifty-seven patients were included, 26 of whom (16.6%) were transplanted with a DCD liver graft. After propensity score matching and stratification, three groups were obtained: 15 recipients of DCD donors & GE;75 years, 11 recipients of DCD donors <75 years, and 28 recipients of DBD donors & GE;75 years. Short-term outcomes, as well as 12 months graft survival rates (93.3%, 100%, and 89.3% respectively), were comparable among the groups. LT involving grafts retrieved from very elderly DCD donors was feasible and safe in an experienced high-volume center, with outcomes comparable to LTs from younger DCD donors and age-matched DBD donors

Biparametric versus multiparametric mri with non-endorectal coil at 3t in the detection and localization of prostate cancer

Aim: To assess the sensitivity of biparametric magnetic resonance imaging (bpMRI) with non-endorectal coil in the detection and localization of index (dominant) and nonindex lesions in patients suspected of having prostate cancer. Patients and Methods: We carried-out a retrospective analysis of multiparametric MRI (mpMRI) of 41 patients who underwent radical prostatectomy. Results of MRI for detection and localization of index and non-index lesions were correlated with those of histology. Results: No statistically significant difference in size was seen between tumor lesion at histology and index lesion at MRI. In 41 patients, a total of 131 tumors were identified at histology, while bpMRI (T2-weighted and diffusionweighted MRI) approach detected 181 lesions. bpMRI gave 27.6% false-positives and 3.3% false-negatives. Sensitivity in lesion detection by bpMRI increased with lesion size assuming high values for lesions 10 mm. For bpMRI and mpMRI, the sensitivity for detecting index lesions was the same and equal: 100% in the peripheral zone 97.6% and 94.7% in the entire prostate and transitional zone, respectively. Conclusion: bpMRI can be used alternatively to mpMRI to detect and localize index prostate cancer

575 Heart failure as the cancer for the heart: the prognostic role of the new TNM-like classification

Abstract Aims Heart failure (HF) is the pandemic of the third millennium accounting for the highest mortality rate among general population, second only to lung cancer. Beside heart, HF can affect lungs and peripheral organs, such as kidney, liver, brain, erythropoiesis, leading to multiorgan dysfunction. This is similar to spread of cancer. We proposed a new staging system of HF, named HLM, analogous to TNM classification used in oncology, which refers to heart damage (H), instead of T for tumour, lung involvement (L), instead of N for lymphnodes, and malfunction (M) of peripheral organs, instead of M for metastasis. The aim of this study was a comparison of HLM score with NYHA classes, ACC/AHA stages and HF classification by left ventricular ejection fraction (LVEF), to assess the most accurate prognosis tool for HF patients, in terms of a composite endpoint of all-cause death and hospitalization. Methods and results We performed a multicentre observational, prospective study of consecutive patients admitted for HF, or at risk for HF. All parameters for heart, lungs, and peripheral organ function were collected and examined. Each patient was classified according to HLM, NYHA, ACC/AHA scores and LVEF, at hospital admission and at discharge. The composite endpoint was all-cause death and rehospitalization; the secondary endpoints were all-cause death, cardiac death, and rehospitalization. Patients were followed up at 12 months. We enrolled 2152 patients. Among those, 1720 patients completed the 12-months follow-up. Comparing HLM with other nosologies, the area under the ROC curve (AUC) was greater for HLM score than NYHA, ACC/AHA and LVEF scores regarding the composite endpoint (HLM = 0.644; NYHA = 0.580; ACC/AHA = 0.572; EF = 0.572) and all-cause death (HLM = 0.713; NYHA = 0.596; ACC/AHA = 0.594; EF = 0.565). HLM score related AUC showed statistically significant differences compared to LVEF (P < 0.001), ACC-AHA (P < 0.001), and NYHA (P < 0.001) scores' AUC, in terms of all-cause death and the composite of all-cause death and rehospitalization, at 12 months follow-up. Moreover, the AIC and BIC values to predict the composite of all-cause death and rehospitalization, all-cause death, cardiac death and rehospitalization rate at 12 months follow-up were always lower for HLM model compared with the others. Conclusions According to our results, HLM score has greater prognostic power compared to other nosologies, in terms of composite outcome, rehospitalization, and all-cause death, as well as all-cause death, cardiac death, and rehospitalization, at 12 months follow-up in HF patients. HLM score overcomes the cardiocentric view of HF and it addresses the pathophysiological mechanisms underlining heart abnormalities. Such a multivariable, holistic staging system may be used in HF patients, in order to improve clinical management and to reduce healthcare costs

ARIANNA: A research environment for neuroimaging studies in autism spectrum disorders

The complexity and heterogeneity of Autism Spectrum Disorders (ASD) require the implementation of dedicated analysis techniques to obtain the maximum from the interrelationship among many variables that describe affected individuals, spanning from clinical phenotypic characterization and genetic profile to structural and functional brain images. The ARIANNA project has developed a collaborative interdisciplinary research environment that is easily accessible to the community of researchers working on ASD (https://arianna.pi.infn.it). The main goals of the project are: to analyze neuroimaging data acquired in multiple sites with multivariate approaches based on machine learning; to detect structural and functional brain characteristics that allow the distinguishing of individuals with ASD from control subjects; to identify neuroimaging-based criteria to stratify the population with ASD to support the future development of personalized treatments. Secure data handling and storage are guaranteed within the project, as well as the access to fast grid/cloud-based computational resources. This paper outlines the web-based architecture, the computing infrastructure and the collaborative analysis workflows at the basis of the ARIANNA interdisciplinary working environment. It also demonstrates the full functionality of the research platform. The availability of this innovative working environment for analyzing clinical and neuroimaging information of individuals with ASD is expected to support researchers in disentangling complex data thus facilitating their interpretation

Development and Validation of a New Risk Score for Infection with Coronavirus (Ri.S.I.Co) Obtained from Treating Coronavirus Disease (COVID-19) Patients on the Field

Background: The Coronavirus Disease 2019 (COVID-19) pandemic has necessitated the alteration of the organization of entire hospitals to try to prevent them from becoming epidemiological clusters. The adopted diagnostic tools lack sensitivity or specificity. Objectives: The aim of the study was to create an easy-to-get risk score (Ri.S.I.Co., risk score for infection with the new coronavirus) developed on the field to stratify patients admitted to hospitals according to their risk of COVID-19 infection. Methods: In this prospective study, we included all patients who were consecutively admitted to the suspected COVID-19 department of the Bufalini Hospital, Cesena (Italy). All clinical, radiological, and laboratory predictors were included in the multivariate logistic regression model to create a risk model. A simplified model was internally and externally validated, and two score thresholds for stratifying the probability of COVID-19 infection were introduced. Results: From 11th March to 5th April 2020, 200 patients were consecutively admitted. A Ri.S.I.Co lower than 2 showed a higher sensitivity than SARS-Cov-2 nucleic acid detection (96.2% vs. 65.4%; P < 0.001). The presence of ground-glass pattern on the lung-CT scan had a lower sensitivity than a Ri.S.I.Co lower than 2 (88.5% vs. 96.2%; P < 0.001) and a lower specificity than a Ri.S.I.Co higher than 6 (75.0% vs. 96.9%; P < 0.001). Conclusions: We believe that the Ri.S.I.Co could allow to stratify admitted patients according to their risk, preventing hospitals from becoming the main COVID-19 carriers themselves. Furthermore, it could guide clinicians in starting therapies early in severe-onset cases with a high probability of COVID-19, before molecular SARS-CoV-2 infection is confirmed

Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach

Background: To investigate machine learning methods, ranging from simpler interpretable techniques to complex (non-linear) “black-box” approaches, for automated diagnosis of Age-related Macular Degeneration (AMD). Methods: Data from healthy subjects and patients diagnosed with AMD or other retinal diseases were collected during routine visits via an Electronic Health Record (EHR) system. Patients’ attributes included demographics and, for each eye, presence/absence of major AMD-related clinical signs (soft drusen, retinal pigment epitelium, defects/ pigment mottling, depigmentation area, subretinal haemorrhage, subretinal fluid, macula thickness, macular scar, subretinal fibrosis). Interpretable techniques known as white box methods including logistic regression and decision trees as well as less interpreitable techniques known as black box methods, such as support vector machines (SVM), random forests and AdaBoost, were used to develop models (trained and validated on unseen data) to diagnose AMD. The gold standard was confirmed diagnosis of AMD by physicians. Sensitivity, specificity and area under the receiver operating characteristic (AUC) were used to assess performance. Results: Study population included 487 patients (912 eyes). In terms of AUC, random forests, logistic regression and adaboost showed a mean performance of (0.92), followed by SVM and decision trees (0.90). All machine learning models identified soft drusen and age as the most discriminating variables in clinicians’ decision pathways to diagnose AMD. C Conclusions: Both black-box and white box methods performed well in identifying diagnoses of AMD and their decision pathways. Machine learning models developed through the proposed approach, relying on clinical signs identified by retinal specialists, could be embedded into EHR to provide physicians with real time (interpretable) support

Applications of Next-Generation Sequencing Technologies to Diagnostic Virology

Novel DNA sequencing techniques, referred to as “next-generation” sequencing (NGS), provide high speed and throughput that can produce an enormous volume of sequences with many possible applications in research and diagnostic settings. In this article, we provide an overview of the many applications of NGS in diagnostic virology. NGS techniques have been used for high-throughput whole viral genome sequencing, such as sequencing of new influenza viruses, for detection of viral genome variability and evolution within the host, such as investigation of human immunodeficiency virus and human hepatitis C virus quasispecies, and monitoring of low-abundance antiviral drug-resistance mutations. NGS techniques have been applied to metagenomics-based strategies for the detection of unexpected disease-associated viruses and for the discovery of novel human viruses, including cancer-related viruses. Finally, the human virome in healthy and disease conditions has been described by NGS-based metagenomics

International Asteroid Warning Network Timing Campaign : 2019 XS

Peer reviewe

Semantics and Ideology During the Renaissance: Confessional Translations of the Greek Word ἐπίσκοπος

During the sixteenth century the disputes between Catholics and Protestants became the battleground to determine and shape authentic Christianity and the Church. Humanism played a key role in this process conditioned by cultural and theological diversity, justifying doctrinal positions and legitimizing the existence of respective institutions with an appeal to history. Translations from church historical sources illustrate how they often derived from theological preconceptions. Starting with the ‘episcopacy issue’ opened initially by Luther and Calvin inter al., this article analyzes the translations of the Greek word episkopos in the Magdeburg Centuries, Cesare Baronio’s Ecclesiastical Annals, in contemporary vernacular versions of Eusebius’s Ecclesiastical History, in J. C. Dietrich’s Lexicon and in some English Bibles. The material gathered and also compared with the position of the Council of Trent shows how these confessionally conditioned translations impacted on the scholarly world, and how they influenced church law with religio-political consequences, thereby having a striking significance

Outcomes from elective colorectal cancer surgery during the SARS-CoV-2 pandemic

This study aimed to describe the change in surgical practice and the impact of SARS-CoV-2 on mortality after surgical resection of colorectal cancer during the initial phases of the SARS-CoV-2 pandemic