Coagulopatia in pazienti affetti da discrasia plasmacellulare: studio caso-controllo.

Disordini del fibrinogeno sono di solito conseguenza di mutazioni genetiche che esitano in ridotti livelli di proteina (ipofibrinogenemia) o in una molecola anomala (disfibrinogenemia). Tuttavia, fattori plasmatici o microambientali possono determinarne un difetto acquisito: ridotta concentrazione o alterata funzione. Ad esempio, anticorpi possono legare fibrinogeno e/o fibrina interferendo con la polimerizzazione ed inibendo la coagulazione. Il nostro obiettivo è quello di individuare la causa di disfibrinogenemia in un uomo di 65 anni, diagnosticata a seguito del rilievo di una discrepanza tra valori di Fg immunologico e valori di Fg coagulativo; nonostante una ridotta concentrazione di fibrinogeno funzionale ed un allungamento del tempo di Trombina (TT), del tempo di tromboplastina parziale attivata (aPTT) e del tempo di protrombina (PT) non potevamo porre diagnosi di deficit ereditario, visti i normali valori dei test emocoagulativi eseguiti dal paziente circa 12 mesi prima. L’individuazione di una banda, riferibile a catena leggera k, in prossimità della banda del fibrinogeno, ci ha indotto ad indagare per una possibile discrasia plasmacellulare. Le indagini hanno evidenziato una monoclonalità k a carico dell’80% delle plasmacellule midollari del paziente. La percentuale delle plasmacellule risultava nella norma sia con studio morfologico del mieloaspirato (4-5% di plasmacellule) che nelle sezioni istologiche di biopsia ossea (4% di plasmacellule). Indagini di biologia molecolare atte ad individuare riarrangiamenti a carico del JH risultavano positive per lo stesso. Veniva fatta diagnosi di FLC-MGUS (gammopatia monoclonale di incerto significato con presenza di CM costituita da sole catene leggere). Test che prevedevano l’utilizzo di pool di plasma normale (PPN) sono stati allestiti per valutare l’interferenza delle catene leggere libere con la molecola del fibrinogeno, sospettata in base al particolare profilo di migrazione all’IF su plasma. Questo caso è particolarmente inusuale perché l’effetto inibitorio era indotto da una singola catena leggera piuttosto che da una molecola anticorpale completa. Il trattamento farmacologico con solo desametasone ha comportato una quasi completa correzione dei parametri emocoagulativi (con la sola eccezione del persistere di un allungamento del TT); a distanza di circa due mesi dal termine della terapia il paziente mostra nuovamente un TT incoagulabile. A fronte di alterazioni laboratoristiche importanti non si sono mai avute manifestazioni emorragiche e/o trombotiche. Poiché le immunoglobuline patologiche di pazienti con Mieloma Multiplo (MM) sappiamo interferire con test emocoagulativi, sono state eseguite indagini anche in un gruppo di pazienti con diagnosi di MM conclamato, per individuare analogie con il quadro emocoagulativo del pz in esame. Nel gruppo di controllo 4 dei 20 pazienti, indipendentemente dal tipo di MM, presentavano un allungamento del TT con normali valori di fibrinogeno coagulabile; nessuno di loro ha avuto esperienza di manifestazioni emorragiche e/o trombotiche conclamate. Due di loro, nel decorso della malattia, hanno effettuato indagini nel sospetto di manifestazione trombotica( 1 - ECD arti inferiori; 2- Scintigrafia polmonare). L’immunofissazione su plasma non evidenziava in nessuno dei 4 pazienti una CM che migrasse alla stessa altezza della banda del Fg, come accadeva invece nel pz in esame. I test con PPN non hanno evidenziato interferenza delle catene leggere con la molecola del Fg

Training verbal working memory in children with mild intellectual disabilities: effects on problem-solving

This multiple case study explores the effects of a cognitive training program in children with mild to borderline intellectual disability. Experimental training effects were evaluated comparing pre-post-test changes after (a) a baseline phase versus a training phase in the same participant, (b) an experimental training versus either a no intervention phase or a control training in two pairs of children matched for cognitive profile. Key elements of the training program included (1) exercises and card games targeting inhibition, switching, and verbal working memory, (2) guided practice emphasizing concrete strategies to engage in exercises, and (3) a variable amount of adult support. The results show that both verbal working memory analyzed with the listening span test and problem-solving tested with the Raven’s matrices were significantly enhanced after the experimental trainin

Technical Debt Prioritization: State of the Art. A Systematic Literature Review

Background. Software companies need to manage and refactor Technical Debt issues. Therefore, it is necessary to understand if and when refactoring Technical Debt should be prioritized with respect to developing features or fixing bugs. Objective. The goal of this study is to investigate the existing body of knowledge in software engineering to understand what Technical Debt prioritization approaches have been proposed in research and industry. Method. We conducted a Systematic Literature Review among 384 unique papers published until 2018, following a consolidated methodology applied in Software Engineering. We included 38 primary studies. Results. Different approaches have been proposed for Technical Debt prioritization, all having different goals and optimizing on different criteria. The proposed measures capture only a small part of the plethora of factors used to prioritize Technical Debt qualitatively in practice. We report an impact map of such factors. However, there is a lack of empirical and validated set of tools. Conclusion. We observed that technical Debt prioritization research is preliminary and there is no consensus on what are the important factors and how to measure them. Consequently, we cannot consider current research conclusive and in this paper, we outline different directions for necessary future investigations

Evaluation of the relationships between computed tomography features, pathological findings, and rrognostic risk assessment in gastrointestinal stromal tumors

Objectives The aim of this study was to correlate computed tomography (CT) findings with pathology in gastrointestinal stromal tumors (GISTs). Methods A retrospective evaluation of CT images of 44 patients with GISTs was performed. Computed tomography findings analyzed were location, size, margins, degree and pattern of contrast enhancement, angiogenesis, necrosis, signs of invasion, peritoneal effusion, peritoneal implants, surface ulceration, and calcifications. Associations between CT features and mitotic rate, Miettinen classes of risk, lesions size, and among CT features were investigated. χ 2 Test and Fisher test were performed. Results Mitotic rate was associated with margins (P = 0.016) and with adjacent organ invasion (P = 0.043). Pattern of contrast enhancement (P = 0.002), angiogenesis (P = 0.006), necrosis (P = 0.006), invasion of adjacent organs (P = 0.011), and margins (P = 0.006) were associated with classes of risk. Several associations (P < 0.05) between lesion size and CT features and among all the investigated CT features were found. Conclusions Computed tomography features could reflect GIST biology being associated with the mitotic rate and with classes of risk

Single cell analysis reveals the involvement of the long non-coding RNA Pvt1 in the modulation of muscle atrophy and mitochondrial network

Long non-coding RNAs (lncRNAs) are emerging as important players in the regulation of several aspects of cellular biology. For a better comprehension of their function, it is fundamental to determine their tissue or cell specificity and to identify their subcellular localization. In fact, the activity of lncRNAs may vary according to cell and tissue specificity and subcellular compartmentalization. Myofibers are the smallest complete contractile system of skeletal muscle influencing its contraction velocity and metabolism. How lncRNAs are expressed in different myofibers, participate in metabolism regulation and muscle atrophy or how they are compartmentalized within a single myofiber is still unknown. We compiled a comprehensive catalog of lncRNAs expressed in skeletal muscle, associating the fiber-type specificity and subcellular location to each of them, and demonstrating that many lncRNAs can be involved in the biological processes de-regulated during muscle atrophy. We demonstrated that the lncRNA Pvt1, activated early during muscle atrophy, impacts mitochondrial respiration and morphology and affects mito/autophagy, apoptosis and myofiber size in vivo. This work corroborates the importance of lncRNAs in the regulation of metabolism and neuromuscular pathologies and offers a valuable resource to study the metabolism in single cells characterized by pronounced plasticity

Localization and subcellular association of Grapevine Pinot Gris Virus in grapevine leaf tissues

Despite the increasing impact of Grapevine Pinot gris disease (GPG-disease) worldwide, etiology about this disorder is still uncertain. The presence of the putative causal agent, the Grapevine Pinot Gris Virus (GPGV), has been reported in symptomatic grapevines (presenting stunting, chlorotic mottling, and leaf deformation) as well as in symptom-free plants. Moreover, information on virus localization in grapevine tissues and virus-plant interactions at the cytological level is missing at all. Ultrastructural and cytochemical investigations were undertaken to detect virus particles and the associated cytopathic effects in field-grown grapevine showing different symptom severity. Asymptomatic greenhouse-grown grapevines, which tested negative for GPGV by real time RT-PCR, were sampled as controls. Multiplex real-time RT-PCR and ELISA tests excluded the presence of viruses included in the Italian certification program both in field-grown and greenhouse-grown grapevines. Conversely, evidence was found for ubiquitous presence of Grapevine Rupestris Stem Pitting-associated Virus (GRSPaV), Hop Stunt Viroid (HSVd), and Grapevine Yellow Speckle Viroid 1 (GYSVd-1) in both plant groups. Moreover, in every field-grown grapevine, GPGV was detected by real-time RT-PCR. Ultrastructural observations and immunogold labelling assays showed filamentous flexuous viruses in the bundle sheath cells, often located inside membrane-bound organelles. No cytological differences were observed among field-grown grapevine samples showing different symptom severity. GPGV localization and associated ultrastructural modifications are reported and discussed, in the perspective of assisting management and control of the disease. \ua9 2017 The Author(s

Filamentous sieve element proteins are able to limit phloem mass flow, but not phytoplasma spread

In Fabaceae, dispersion of forisomes\u2014highly ordered aggregates of sieve element proteins\u2014in response to phytoplasma infection was proposed to limit phloem mass flow and, hence, prevent pathogen spread. In this study, the involvement of filamentous sieve element proteins in the containment of phytoplasmas was investigated in non-Fabaceae plants. Healthy and infected Arabidopsis plants lacking one or two genes related to sieve element filament formation\u2014AtSEOR1 (At3g01680), AtSEOR2 (At3g01670), and AtPP2-A1 (At4g19840)\u2014were analysed. TEM images revealed that phytoplasma infection induces phloem protein filament formation in both the wild-type and mutant lines. This result suggests that, in contrast to previous hypotheses, sieve element filaments can be produced independently of AtSEOR1 and AtSEOR2 genes. Filament presence was accompanied by a compensatory overexpression of sieve element protein genes in infected mutant lines in comparison with wild-type lines. No correlation was found between phloem mass flow limitation and phytoplasma titre, which suggests that sieve element proteins are involved in defence mechanisms other than mechanical limitation of the pathogen

Fine-tuning and data augmentation techniques for semantic segmentation of heritage point clouds

This topic of this contribution falls within the broader debate on Digital Humanities. Experiencing and testing an approach that combines geomatics and its production of three-dimensional data of the built cultural heritage (CH) with information technology is the core point. In the digital CH domain, the ever-increasing availability of three-dimensional data, provides the opportunity to rapidly generate detailed 3D scenes to support restoration and conservation activities of built heritage. Concurrently, the recent research trends in geomatics are facing the issue of managing these heritage data to enrich the geometrical representation of the asset, creating a complete informative data collector. HBIM (Historic Building Information Modeling) constitutes a reference, and they typically rely on point clouds to perform the scan-to-BIM processes. These processes are still mostly manually carried out by domain experts, making the workflow very time-consuming, not fully exploiting the potential of point clouds and wasting an uncountable amount of data. In fact, parametric objects can be described through a few relevant points or contours. The use of Artificial Intelligence algorithms, in particular Deep Learning (DL) techniques, for the automatic recognition of architectural elements from point clouds can therefore provide valuable support through the semantic segmentation task. A proposal to tackle this framework was outlined in previous works, and the methodology here proposed constitutes a development of their results. Starting from those former tests obtained with the Dynamic Graph Convolutional Neural Network (DGCNN), close attention is paid to: i) transfer learning techniques, ii) the combination with external classifiers, such as Random Forest (RF), iii) the evaluation of data augmentation techniques on a domain-specific dataset (ArCH dataset). Besides, an investigation on how to make the whole workflow more functional and "friendly" for external users is carried out too. With regard to transfer learning techniques, the fine-tuning approach is proposed to understand if, also in the CH domain, it can lead to performances improvement, introducing a new scene in a pre-trained network. In fact, the peculiarities of each scene do not guarantee certain and definite results, as for other domains. This section is divided into two subsections: a classic fine-tuning and a fine-tuning with the addition of the RF in the final part of the prediction. In the latter case, the choice of adding the RF is due to the results obtained in some stateof-the-art works, where this classifier provides excellent results in a short time and even in the presence of relatively limited data. In this hybrid approach, the network weights are employed as well as in the classic fine-tuning technique. Then, the final part of the DGCNN performing the segmentation of the points is excluded, leading the network to be used as a feature extractor method; afterwards, a scene of the dataset never seen by the network is chosen and divided into one part for training and one for the test. Finally, the features of both parts are extracted, using the feature extractor, and exploited as input for training the RF classifier. Tests conducted on data augmentation show that it does not significantly affect overall performances, but still provide proper support for those categories with fewer points. On the other side, the tests on the fine-tuning have given rise to manifold considerations. Firstly, the standard fine-tuning can achieve performances almost equal to those where only the DGCNN is used, considerably improving some categories. Thus, they confirm that, once the DNN is pre-trained, data processing and prediction times can be significantly reduced (from ca. 48 to 0.5 h), in the case of heritage point clouds too. Then, performances similar to the reference tests are obtained also with the use of the DGCNN as a feature extractor and the RF as a classifier, demonstrating that the final classifier does not affect the prediction

Transferencia de técnicas de aprendizaje y mejora del rendimiento en la segmentación semántica profunda de nubes de puntos del patrimonio construido

[EN] The growing availability of three-dimensional (3D) data, such as point clouds, coming from Light Detection and Ranging (LiDAR), Mobile Mapping Systems (MMSs) or Unmanned Aerial Vehicles (UAVs), provides the opportunity to rapidly generate 3D models to support the restoration, conservation, and safeguarding activities of cultural heritage (CH). The so-called scan-to-BIM process can, in fact, benefit from such data, and they can themselves be a source for further analyses or activities on the archaeological and built heritage. There are several ways to exploit this type of data, such as Historic Building Information Modelling (HBIM), mesh creation, rasterisation, classification, and semantic segmentation. The latter, referring to point clouds, is a trending topic not only in the CH domain but also in other fields like autonomous navigation, medicine or retail. Precisely in these sectors, the task of semantic segmentation has been mainly exploited and developed with artificial intelligence techniques. In particular, machine learning (ML) algorithms, and their deep learning (DL) subset, are increasingly applied and have established a solid state-of-the-art in the last half-decade. However, applications of DL techniques on heritage point clouds are still scarce; therefore, we propose to tackle this framework within the built heritage field. Starting from some previous tests with the Dynamic Graph Convolutional Neural Network (DGCNN), in this contribution close attention is paid to: i) the investigation of fine-tuned models, used as a transfer learning technique, ii) the combination of external classifiers, such as Random Forest (RF), with the artificial neural network, and iii) the evaluation of the data augmentation results for the domain-specific ArCH dataset. Finally, after taking into account the main advantages and criticalities, considerations are made on the possibility to profit by this methodology also for non-programming or domain experts.[ES] La creciente disponibilidad de datos tridimensionales (3D), como nubes de puntos, provenientes de la detección de la luz y distancia (LiDAR), sistemas de mapeado móvil (MMS) o vehículos aéreos no tripulados (UAV), brinda la oportunidad de generar rápidamente modelos 3D para apoyar las actividades de restauración, conservación y salvaguardia del patrimonio cultural (CH). El llamado proceso de escaneado-a-BIM puede, de hecho, beneficiarse de dichos datos, y ellos mismos pueden ser una fuente para futuros análisis o actividades sobre el patrimonio arqueológico y el construido. Hay varias formas de explotar este tipo de datos, como el modelado de información de edificios históricos (HBIM), la creación de mallas, la rasterización, la clasificación y la segmentación semántica. Este último, referido a las nubes de puntos, es un tema de máxima actualidad no solo en el dominio del PC sino también en otros campos como la navegación autónoma, la medicina o el comercio minorista. Precisamente en estos sectores, la tarea de la segmentación semántica se ha explotado y desarrollado principalmente con técnicas de inteligencia artificial. En particular, los algoritmos de aprendizaje automático (AA) y su subconjunto de aprendizaje profundo (AP) se aplican cada vez más y han establecido un sólido estado de la técnica en la última media década. Sin embargo, las aplicaciones de las técnicas de AP en las nubes de puntos tradicionales son todavía escasas; por tanto, nos proponemos abordar este marco dentro del ámbito del patrimonio construido. Partiendo de algunas pruebas anteriores con la Red Neural Convolucional de Gráfico Dinámico (DGCNN), en esta contribución se presta atención a: i) la investigación de modelos afinados, utilizados como técnica de aprendizaje por transferencia, ii) la combinación de clasificadores externos, como Random Forest (RF), con la red neuronal artificial, y iii) la evaluación de los resultados de aumentación de datos para el conjunto de datos específico del dominio ArCH. Finalmente, después de tener en cuenta las principales ventajas y criticidades, se hace una consideración sobre la posibilidad de beneficiarse de esta metodología también a expertos no programadores o del campo.Matrone, F.; Martini, M. (2021). Transfer learning and performance enhancement techniques for deep semantic segmentation of built heritage point clouds. Virtual Archaeology Review. 12(25):73-84. https://doi.org/10.4995/var.2021.15318OJS73841225Armeni, I., Sener, O., Zamir, A. 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