Trematocine, a Novel Antimicrobial Peptide from the Antarctic Fish Trematomus bernacchii: Identification and Biological Activity

Antimicrobial peptides (AMPs) are short peptides active against a wide range of pathogens and, therefore, they are considered a useful alternative to conventional antibiotics. We have identified a new AMP in a transcriptome derived from the Antarctic fish Trematomus bernacchii. This peptide, named Trematocine, has been investigated for its expression both at the basal level and after in vivo immunization with an endemic Antarctic bacterium (Psychrobacter sp. TAD1). Results agree with the expected behavior of a fish innate immune component, therefore we decided to synthesize the putative mature sequence of Trematocine to determine the structure, the interaction with biological membranes, and the biological activity. We showed that Trematocine folds into a \u3b1-helical structure in the presence of both zwitterionic and anionic charged vesicles. We demonstrated that Trematocine has a highly specific interaction with anionic charged vesicles and that it can kill Gram-negative bacteria, possibly via a carpet like mechanism. Moreover, Trematocine showed minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values against selected Gram-positive and Gram-negative bacteria similar to other AMPs isolated from Antarctic fishes. The peptide is a possible candidate for a new drug as it does not show any haemolytic or cytotoxic activity against mammalian cells at the concentration needed to kill the tested bacteria

Prediction of In Vivo Laser-Induced Thermal Damage with Hyperspectral Imaging Using Deep Learning.

Thermal ablation is an acceptable alternative treatment for primary liver cancer, of which laser ablation (LA) is one of the least invasive approaches, especially for tumors in high-risk locations. Precise control of the LA effect is required to safely destroy the tumor. Although temperature imaging techniques provide an indirect measurement of the thermal damage, a degree of uncertainty remains about the treatment effect. Optical techniques are currently emerging as tools to directly assess tissue thermal damage. Among them, hyperspectral imaging (HSI) has shown promising results in image-guided surgery and in the thermal ablation field. The highly informative data provided by HSI, associated with deep learning, enable the implementation of non-invasive prediction models to be used intraoperatively. Here we show a novel paradigm "peak temperature prediction model" (PTPM), convolutional neural network (CNN)-based, trained with HSI and infrared imaging to predict LA-induced damage in the liver. The PTPM demonstrated an optimal agreement with tissue damage classification providing a consistent threshold (50.6 ± 1.5 °C) for the damage margins with high accuracy (~0.90). The high correlation with the histology score (r = 0.9085) and the comparison with the measured peak temperature confirmed that PTPM preserves temperature information accordingly with the histopathological assessment

Antiangiogenic agents after first line and sorafenib plus chemoembolization: A systematic review

Transarterial chemoembolization (TACE) is the standard treatment for intermediate stage, although the combination of TACE with sorafenib may theoretically benefit HCC patients in intermediate stage. Owing to the significant antiangiogenic effect of sorafenib and the limitation of TACE, it is rational to combine them. Though the strategy of combining TACE and sorafenib has been increasingly used in patients with unresectable HCC but the current evidence is controversial and its clinical role has not been determined yet. In first-line therapy, patients receiving sorafenib had increased overall survival and progression free survival. Therefore several antiangiogenic agents have entered clinical studies on HCC, many with negative results. This review discusses the current drug development for patients with HCC and role of TACE plus sorafenib

Clinical features and outcomes of elderly hospitalised patients with chronic obstructive pulmonary disease, heart failure or both

Background and objective: Chronic obstructive pulmonary disease (COPD) and heart failure (HF) mutually increase the risk of being present in the same patient, especially if older. Whether or not this coexistence may be associated with a worse prognosis is debated. Therefore, employing data derived from the REPOSI register, we evaluated the clinical features and outcomes in a population of elderly patients admitted to internal medicine wards and having COPD, HF or COPD + HF. Methods: We measured socio-demographic and anthropometric characteristics, severity and prevalence of comorbidities, clinical and laboratory features during hospitalization, mood disorders, functional independence, drug prescriptions and discharge destination. The primary study outcome was the risk of death. Results: We considered 2,343 elderly hospitalized patients (median age 81 years), of whom 1,154 (49%) had COPD, 813 (35%) HF, and 376 (16%) COPD + HF. Patients with COPD + HF had different characteristics than those with COPD or HF, such as a higher prevalence of previous hospitalizations, comorbidities (especially chronic kidney disease), higher respiratory rate at admission and number of prescribed drugs. Patients with COPD + HF (hazard ratio HR 1.74, 95% confidence intervals CI 1.16-2.61) and patients with dementia (HR 1.75, 95% CI 1.06-2.90) had a higher risk of death at one year. The Kaplan-Meier curves showed a higher mortality risk in the group of patients with COPD + HF for all causes (p = 0.010), respiratory causes (p = 0.006), cardiovascular causes (p = 0.046) and respiratory plus cardiovascular causes (p = 0.009). Conclusion: In this real-life cohort of hospitalized elderly patients, the coexistence of COPD and HF significantly worsened prognosis at one year. This finding may help to better define the care needs of this population

Hyperspectral Imagery for Assessing Laser-Induced Thermal State Change in Liver

This work presents the potential of hyperspectral imaging (HSI) to monitor the thermal outcome of laser ablation therapy used for minimally invasive tumor removal. Our main goal is the establishment of indicators of the thermal damage of living tissues, which can be used to assess the effect of the procedure. These indicators rely on the spectral variation of temperature-dependent tissue chromophores, i.e., oxyhemoglobin, deoxyhemoglobin, methemoglobin, and water. Laser treatment was performed at specific temperature thresholds (from 60 to 110 °C) on in-vivo animal liver and was assessed with a hyperspectral camera (500–995 nm) during and after the treatment. The indicators were extracted from the hyperspectral images after the following processing steps: the breathing motion compensation and the spectral and spatial filtering, the selection of spectral bands corresponding to specific tissue chromophores, and the analysis of the areas under the curves for each spectral band. Results show that properly combining spectral information related to deoxyhemoglobin, methemoglobin, lipids, and water allows for the segmenting of different zones of the laser-induced thermal damage. This preliminary investigation provides indicators for describing the thermal state of the liver, which can be employed in the future as clinical endpoints of the procedure outcome

Advancing laser ablation assessment in hyperspectral imaging through machine learning

: Hyperspectral imaging (HSI) is gaining increasing relevance in medicine, with an innovative application being the intraoperative assessment of the outcome of laser ablation treatment used for minimally invasive tumor removal. However, the high dimensionality and complexity of HSI data create a need for end-to-end image processing workflows specifically tailored to handle these data. This study addresses this challenge by proposing a multi-stage workflow for the analysis of hyperspectral data and allows investigating the performance of different components and modalities for ablation detection and segmentation. To address dimensionality reduction, we integrated principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) to capture dominant variations and reveal intricate structures, respectively. Additionally, we employed the Faster Region-based Convolutional Neural Network (Faster R-CNN) to accurately localize ablation areas. The two-stage detection process of Faster R-CNN, along with the choice of dimensionality reduction technique and data modality, significantly influenced the performance in detecting ablation areas. The evaluation of the ablation detection on an independent test set demonstrated a mean average precision of approximately 0.74, which validates the generalization ability of the models. In the segmentation component, the Mean Shift algorithm showed high quality segmentation without manual cluster definition. Our results prove that the integration of PCA, t-SNE, and Faster R-CNN enables improved interpretation of hyperspectral data, leading to the development of reliable ablation detection and segmentation systems

Un progetto per la consultazione on-line degli archivi del restauro (1850-1915)

Il progetto di ricerca ha l'obiettivo di studiare, sistematizzare e pubblicare on-line le fonti archivistiche relative al restauro dei monumenti tra il 1850 e il 1915, tramite la loro digitalizzazione e inventariazione. L'importanza della ricerca riguarda la possibilità di divulgare e rendere accessibile la conoscenza della storia edilizia dei monumenti, spesso trascurata sia dal punto di vista della storia dell'arte e dell'architettura, sia da quello tecnico­ professionale: l'accesso diretto ai documenti riguardanti i restauri permetterà infatti un significativo incremento della qualità della progettazione ed esecuzione dei lavori di conservazione dei monumenti e di intervento su tessuti storici della città