Dermoscopy and methyl aminolevulinate: A study for detection and evaluation of field cancerization

Actinic keratosis (AK) is a keratinocyte intraepidermal neoplasia UV light – induced that frequently appears in sun-exposed areas of the skin. Although historically AK was de fi ned as “ precancerous ” , actually it is considered as the earliest stage of squamous cell carcinoma (SCC) in situ. Since AKs can progress into invasive SCC, their treatment isrecommended. AKsrarely developasa singlelesion;usually multiplelesions commonly affect anen- tire area of chronically actinic damaged skin. This has led to the concept of “ fi eld cancerization ” , an area chroni- cally sun-exposed that surrounds peripherally visible lesions, in which are individualized subclinical alterations. One of the main principles endpoint in the management of AKs is the evaluation and the treatment of fi eld cancerization. In this view, in order to detect and quantify fi eld cancerization, we employed a method based on the topical application of methyl aminolevulinate (MAL) and the detection of the fl uorescence emitted by its metabolite Protoporphyrin IX (PpIX); then, considering the extension and the intensity of measured fl uores- cence, we create a score of fi eld cancerization. The results show that patients underwent to daylight PDT had a reduction of total score, from T0 to T2. Whereas in the group untreated we observed a stability of total score or a slightly worse. So, the method and the score used allows to evaluate with a good approximation the dimension of fi eld cancerization and show the modi fi cation of it after treatment

The application of artificial intelligence to understand the pathophysiological basis of psychogenic nonepileptic seizures

Abstract Psychogenic nonepileptic seizures (PNES) are episodes of paroxysmal impairment associated with a range of motor, sensory, and mental manifestations, which perfectly mimic epileptic seizures. Several patterns of neural abnormalities have been described without identifying a definite neurobiological substrate. In this multicenter cross-sectional study, we applied a multivariate classification algorithm on morphological brain imaging metrics to extract reliable biomarkers useful to distinguish patients from controls at an individual level. Twenty-three patients with PNES and 21 demographically matched healthy controls (HC) underwent an extensive neuropsychiatric/neuropsychological and neuroimaging assessment. One hundred and fifty morphological brain metrics were used for training a random forest (RF) machine-learning (ML) algorithm. A typical complex psychopathological construct was observed in PNES. Similarly, univariate neuroimaging analysis revealed widespread neuroanatomical changes affecting patients with PNES. Machine-learning approach, after feature selection, was able to perform an individual classification of PNES from controls with a mean accuracy of 74.5%, revealing that brain regions influencing classification accuracy were mainly localized within the limbic (posterior cingulate and insula) and motor inhibition systems (the right inferior frontal cortex (IFC)). This study provides Class II evidence that the considerable clinical and neurobiological heterogeneity observed in individuals with PNES might be overcome by ML algorithms trained on surface-based magnetic resonance imaging (MRI) data

Prediction of vascular invasion using a 7-point scale computed tomography grading system in adrenal tumors in dogs

Background: Previous studies evaluating the accuracy of computed tomography (CT) in detecting caudal vena cava (CVC) invasion by adrenal tumors (AT) used a binary system and did not evaluate for other vessels. Objective: Test a 7-point scale CT grading system for accuracy in predicting vascular invasion and for repeatability among radiologists. Build a decision tree based on CT criteria to predict tumor type. Methods: Retrospective observational cross-sectional case study. Abdominal CT studies were analyzed by 3 radiologists using a 7-point CT grading scale for vascular invasion and by 1 radiologist for CT features of AT. Animals: Dogs with AT that underwent adrenalectomy and had pre- and postcontrast CT. Results: Ninety-one dogs; 45 adrenocortical carcinomas (50%), 36 pheochromocytomas (40%), 9 adrenocortical adenomas (10%) and 1 unknown tumor. Carcinoma and pheochromocytoma differed in pre- and postcontrast attenuation, contralateral adrenal size, tumor thrombus short- and long-axis, and tumor and thrombus mineralization. A decision tree was built based on these differences. Adenoma and malignant tumors differed in contour irregularity. Probability of vascular invasion was dependent on CT grading scale, and a large equivocal zone existed between 3 and 6 scores, lowering CT accuracy to detect vascular invasion. Radiologists' agreement for detecting abnormalities (evaluated by chance-corrected weighted kappa statistics) was excellent for CVC and good to moderate for other vessels. The quality of postcontrast CT study had a negative impact on radiologists' performance and agreement. Conclusions and Clinical Importance: Features of CT may help radiologists predict AT type and provide probabilistic information on vascular invasion

A Machine Learning Approach for Mortality Prediction in COVID-19 Pneumonia: Development and Evaluation of the Piacenza Score

Background: Several models have been developed to predict mortality in patients with COVID-19 pneumonia, but only a few have demonstrated enough discriminatory capacity. Machine learning algorithms represent a novel approach for the data-driven prediction of clinical outcomes with advantages over statistical modeling.Objective: We aimed to develop a machine learning-based score-the Piacenza score-for 30-day mortality prediction in patients with COVID-19 pneumonia.Methods: The study comprised 852 patients with COVID-19 pneumonia, admitted to the Guglielmo da Saliceto Hospital in Italy from February to November 2020. Patients' medical history, demographics, and clinical data were collected using an electronic health record. The overall patient data set was randomly split into derivation and test cohorts. The score was obtained through the naive Bayes classifier and externally validated on 86 patients admitted to Centro Cardiologico Monzino (Italy) in February 2020. Using a forward-search algorithm, 6 features were identified: age, mean corpuscular hemoglobin concentration, PaO2/FiO(2) ratio, temperature, previous stroke, and gender. The Brier index was used to evaluate the ability of the machine learning model to stratify and predict the observed outcomes. A user-friendly website was designed and developed to enable fast and easy use of the tool by physicians. Regarding the customization properties of the Piacenza score, we added a tailored version of the algorithm to the website, which enables an optimized computation of the mortality risk score for a patient when some of the variables used by the Piacenza score are not available. In this case, the naive Bayes classifier is retrained over the same derivation cohort but using a different set of patient characteristics. We also compared the Piacenza score with the 4C score and with a naive Bayes algorithm with 14 features chosen a priori.Results: The Piacenza score exhibited an area under the receiver operating characteristic curve (AUC) of 0.78 (95% CI 0.74-0.84, Brier score=0.19) in the internal validation cohort and 0.79 (95% CI 0.68-0.89, Brier score=0.16) in the external validation cohort, showing a comparable accuracy with respect to the 4C score and to the naive Bayes model with a priori chosen features; this achieved an AUC of 0.78 (95% CI 0.73-0.83, Brier score=0.26) and 0.80 (95% CI 0.75-0.86, Brier score=0.17), respectively.Conclusions: Our findings demonstrated that a customizable machine learning-based score with a purely data-driven selection of features is feasible and effective for the prediction of mortality among patients with COVID-19 pneumonia

Pseudomonas aeruginosa lectin LecB impairs keratinocyte fitness by abrogating growth factor signalling

Lectins are glycan-binding proteins with no catalytic activity and ubiquitously expressed in nature. Numerous bacteria use lectins to efficiently bind to epithelia, thus facilitating tissue colonisation. Wounded skin is one of the preferred niches for Pseudomonas aeruginosa, which has developed diverse strategies to impair tissue repair processes and promote infection. Here, we analyse the effect of the P. aeruginosa fucose-binding lectin LecB on human keratinocytes and demonstrate that it triggers events in the host, upon binding to fucosylated residues on cell membrane receptors, which extend beyond its role as an adhesion molecule. We found that LecB associates with insulin-like growth factor-1 receptor and dampens its signalling, leading to the arrest of cell cycle. In addition, we describe a novel LecB-triggered mechanism to down-regulate host cell receptors by showing that LecB leads to insulin-like growth factor-1 receptor internalisation and subsequent missorting towards intracellular endosomal compartments, without receptor activation. Overall, these data highlight that LecB is a multitask virulence factor that, through subversion of several host pathways, has a profound impact on keratinocyte proliferation and survival

Next Generation Molecular Diagnosis of Hereditary Spastic Paraplegias: An Italian Cross-Sectional Study

Hereditary spastic paraplegia (HSP) refers to a group of genetically heterogeneous neurodegenerative motor neuron disorders characterized by progressive age-dependent loss of corticospinal motor tract function, lower limb spasticity, and weakness. Recent clinical use of next generation sequencing (NGS) methodologies suggests that they facilitate the diagnostic approach to HSP, but the power of NGS as a first-tier diagnostic procedure is unclear. The larger-than-expected genetic heterogeneity-there are over 80 potential disease-associated genes-and frequent overlap with other clinical conditions affecting the motor system make a molecular diagnosis in HSP cumbersome and time consuming. In a single-center, cross-sectional study, spanning 4 years, 239 subjects with a clinical diagnosis of HSP underwent molecular screening of a large set of genes, using two different customized NGS panels. The latest version of our targeted sequencing panel (SpastiSure3.0) comprises 118 genes known to be associated with HSP. Using an in-house validated bioinformatics pipeline and several in silico tools to predict mutation pathogenicity, we obtained a positive diagnostic yield of 29% (70/239), whereas variants of unknown significance (VUS) were found in 86 patients (36%), and 83 cases remained unsolved. This study is among the largest screenings of consecutive HSP index cases enrolled in real-life clinical-diagnostic settings. Its results corroborate NGS as a modern, first-step procedure for molecular diagnosis of HSP. It also disclosed a significant number of new mutations in ultra-rare genes, expanding the clinical spectrum, and genetic landscape of HSP, at least in Italy

Exploring contested authenticity among speakers of a contested language: the case of ‘Francoprovençal'

This paper explores the notion of speaker authenticity in the context of obsolescent ‘Francoprovençal’: a highly fragmented grouping of Romance varieties spoken in parts of France, Italy, and Switzerland by less than 1% of the total regional population. While Francoprovençal has long been losing ground to the dominant language(s) with which it is in contact, new speakers have begun to emerge within the context of revitalisation movements and activities geared more favourable language planning policies and increased literacy. The emergence of these new speakers has polarised native-speaker communities, and has blurred the lines associated with the traditional view of sociolinguistic authenticity. Through an analysis of qualitative data collected in 2012, this article argues in particular that it may not be sufficient to simply examine contested authenticities from a native–non-native perspective, but rather it is important to consider how new speakers might themselves form a complex spectrum of speaker types with new sets of tensions as has been argued elsewhere

Prolonged higher dose methylprednisolone vs. conventional dexamethasone in COVID-19 pneumonia: a randomised controlled trial (MEDEAS)

Dysregulated systemic inflammation is the primary driver of mortality in severe COVID-19 pneumonia. Current guidelines favor a 7-10-day course of any glucocorticoid equivalent to dexamethasone 6 mg·day-1. A comparative RCT with a higher dose and a longer duration of intervention was lacking