Mucosa associated lymphoid tissue lymphoma of the thyroid gland: a case report and literature review = MALT linfoma della tiroide: caso clinico e revisione della letteratura

Mucosa associated lymphoid tissue (MALT) lymphomas are low-grade, non-Hodgkin’s B cell lymphomas, mainly occurring in the gastrointestinal tract, but also in other tissues. We describe the management of a patient with hypothyroidism, tracheoesophageal compressive symptoms and chest tightness affected by a thyroid MALT lymphoma. The patient underwent debulking thyroidectomy and temporary tracheostomy in order to reduce dysphonia and dysphagia, followed by adjuvant chemotherapy and subsequently radiation therapy. A CT scan performed at the end of radiotherapy 6 months after surgery revealed remnants of residual tissue from the thyroidectomy without any pathological findings. I linfomi MALT sono dei linfomi non-Hodgkin a cellule B a basso grado che in genere insorgono a livello del tratto gastrointestinale, ma anche in altri tessuti. Descriviamo in questo articolo il management clinico-chirurgico di un paziente con ipotiroidismo, sintomi da compressione tracheo-esofagea e senso di oppressione toracica, affetto da linfoma MALT della tiroide. Il paziente è stato sottoposto a parziale asportazione della massa tiroidea e tracheostomia allo scopo di ridurre i sintomi compressivi ed in seguito a trattamento chemioterapico e radioterapico. L’esame TC effettuato una volta conclusa la radioterapia, circa 6 mesi dopo l’intervento, ha evidenziato gli esiti della tiroidectomia parziale in assenza di altri reperti patologici

Analyse von Infektionskrankheiten in konditionalen Maus-Mutanten

This thesis comprises two parts. The first part concerns the generation of an IL-12p40 conditional knock-out mouse. ET cloning was employed to modify the endogenous locus of the IL-12p40 murine gene contained in a BAC vector. The resulting targeting construct was capable of recombining homologously within ES cells, which were subsequently injected into blastocysts in order to generate a conditional knock-out mouse line. None of the generated chimeras could be tested for germ-line transmission due to early lethality and sterility. In part two, macrophage and/or neutrophil specific MnSOD knock-out mice were obtained through the breeding of MnSODfl/fl to LysM-Cre mice. Initially, the LysMCre-driven deletion of the loxP-flanked exon3 of MnSOD was shown to be specific and efficient. Subsequently, three different infection models, namely Streptococcus pyogenes, Staphylococcus aureus and Listeria monocytogenes, were tested in order to investigate the role of MnSOD during both extra-cellular and intra-cellular bacterial infections. The MnSODfl/fl x LysMCre breeding was not on a pure C57BL/6 genetic background. Thus, the results concerning the streptococcal infection model are not interpretable at this point. MnSOD deficiency was found to be irrelevant for the outcome of the infection during Staphylococcus aureus intravenous infections. However, a major protective role of MnSOD in Listeriosis models was established. Macrophage and/or neutrophil specific MnSOD knock-out mice displayed a significantly higher susceptibility to intravenous Listeria monocytogenes infection compared to controls, as implied by a lower survival rate and higher bacterial burden in liver and spleen. Moreover, in-vitro experiments showed a higher bacterial up-take and a lower efficiency in killing bacteria.Die vorliegende Arbeit gliedert sich in zwei Teile. Im ersten Teil wird die Generierung einer konditionalen IL-12p40 Mausmutante beschrieben. Für diesen Zweck wurde die Methodik des ET Klonierens genutzt, um den endogenen Lokus des murinen IL-12p40 Gens zu modifizieren. Das daraus resultierende Targeting Konstrukt wurde in murine ES Zellen eingebracht, um die konditionale Mutation im Il-12p40 Gen mittels Gen Targeting in vitro zu etablieren. Die daraus resultierenden homolog rekombinierten ES Zelklone wurden anschließend in murine Blastozysten injiziert, um eine konditionale Mausmutantenlinie zu generieren. Bis zum Abschluss dieser Dissertation konnte bei keiner der chimären Nachkommen eine Besiedelung der Keimbahn nachgewiesen werden. Im zweiten Teil dieser Arbeit wurde durch gezielte Verpaarung von MnSODfl/fl Mäusen mit LysM-Cre Mäusen eine Deletion von MnSOD in Makrophagen bzw. Neutrophilen erzielt. Hierbei konnte als erstes die spezifische und effiziente Deletion von MnSOD in Makrophagen bzw. Neutrophilen mittels Southern Blot nachgewiesen werden. Diese konditionalen Mausmutanten wurden schließlich in drei verschiedenen Infektionsmodellen getestet, um eine mögliche Rolle von MnSOD während bakterieller Infektionen zu untersuchen. Als Infektionsorganismen wurden Streptococcus pyogenes, Staphylococcus aureus sowie Listeria monocytogenes ausgewählt. Es konnte gezeigt werden, dass die Deletion von MnSOD keine Auswirkungen auf den Infektionsverlauf mit dem Erreger Staphylococcus aureus in der Maus zu haben scheint. Jedoch zeigte sich im Infektionsmodell mit dem Pathogen Listeria monocytogenes eine protektive Rolle für MnSOD. Die spezifische Deletion von MnSOD in Makrophagen bzw. Neutrophilen führt zu einer signifikant höheren Suszeptibilität der konditionalen Mausmutanten gegenüber Listeria monocytogenes, was sich in einer geringeren Überlebensrate der Tiere sowie einer höheren Anzahl von Listerien in den Organen widerspiegelt

Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy

The Nocturnal Frontal Lobe Epilepsy (NFLE) is a form of epilepsy in which seizures occur predominantly during sleep. In other forms of epilepsy, the commonly used clinical approach mainly involves manual inspection of encephalography (EEG) signals, a laborious and time-consuming process which often requires the contribution of more than one experienced neurologist. In the last decades, numerous approaches to automate this detection have been proposed and, more recently, machine learning has shown very promising performance. In this paper, an original Convolutional Neural Network (CNN) architecture is proposed to develop patient-specific seizure detection models for three patients affected by NFLE. The performances, in terms of accuracy, sensitivity, and specificity, exceed by several percentage points those in the most recent literature. The capability of the patient-specific models has been also tested to compare the obtained seizure onset times with those provided by the neurologists, with encouraging results. Moreover, the same CNN architecture has been used to develop a cross-patient seizure detection system, resorting to the transfer-learning paradigm. Starting from a patient-specific model, few data from a new patient are enough to customize his model. This contribution aims to alleviate the task of neurologists, who may have a robust indication to corroborate their clinical conclusions

A polarization modulator unit for the mid- and high-frequency telescopes of the LiteBIRD mission

The LiteBIRD mission is a JAXA strategic L-class mission for all sky CMB surveys which will be launched in the 2020s. The main target of the mission is the detection of primordial gravitational waves with a sensitivity of the tensor-to-scalar ratio δr<0.001. The polarization modulator unit (PMU) represents a critical and powerful component to suppress 1/f noise contribution and mitigate systematic uncertainties induced by detector gain drift, both for the high-frequency telescope (HFT) and for the mid-frequency telescope (MFT). Each PMU is based on a continuously-rotating transmissive half-wave plate (HWP) held by a superconducting magnetic bearing in a 5K environment. In this contribution we will present the design and expected performance of the LiteBIRD PMUs and testing performed on every PMU subsystem with a room-temperature rotating disk used as a stand-in for the cryogenic HWP rotor

The need for new "patient-related" guidelines for the treatment of acute cholecystitis

Heterogeneity of patients affected by acute cholecystitis, and their co-morbidities make very difficult to standardize the therapy for this very common condition. The staging system suggested in the recent "Tokyo guidelines", did not show a relevant impact on the management of patients and on the outcome of the disease. The relation among local pathological picture, patient clinical status and treatment algorithm, has to be better studied

Performance Comparison of Machine Learning Disruption Predictors at JET

Reliable disruption prediction (DP) and disruption mitigation systems are considered unavoidable during international thermonuclear experimental reactor (ITER) operations and in the view of the next fusion reactors such as the DEMOnstration Power Plant (DEMO) and China Fusion Engineering Test Reactor (CFETR). In the last two decades, a great number of DP systems have been developed using data-driven methods. The performance of the DP models has been improved over the years both for a more appropriate choice of diagnostics and input features and for the availability of increasingly powerful data-driven modelling techniques. However, a direct comparison among the proposals has not yet been conducted. Such a comparison is mandatory, at least for the same device, to learn lessons from all these efforts and finally choose the best set of diagnostic signals and the best modelling approach. A first effort towards this goal is made in this paper, where different DP models will be compared using the same performance indices and the same device. In particular, the performance of a conventional Multilayer Perceptron Neural Network (MLP-NN) model is compared with those of two more sophisticated models, based on Generative Topographic Mapping (GTM) and Convolutional Neural Networks (CNN), on the same real time diagnostic signals from several experiments at the JET tokamak. The most common performance indices have been used to compare the different DP models and the results are deeply discussed. The comparison confirms the soundness of all the investigated machine learning approaches and the chosen diagnostics, enables us to highlight the pros and cons of each model, and helps to consciously choose the approach that best matches with the plasma protection needs

Decision trees to evaluate the risk of developing multiple sclerosis

Introduction: Multiple sclerosis (MS) is a persistent neurological condition impacting the central nervous system (CNS). The precise cause of multiple sclerosis is still uncertain; however, it is thought to arise from a blend of genetic and environmental factors. MS diagnosis includes assessing medical history, conducting neurological exams, performing magnetic resonance imaging (MRI) scans, and analyzing cerebrospinal fluid. While there is currently no cure for MS, numerous treatments exist to address symptoms, decelerate disease progression, and enhance the quality of life for individuals with MS. Methods: This paper introduces a novel machine learning (ML) algorithm utilizing decision trees to address a key objective: creating a predictive tool for assessing the likelihood of MS development. It achieves this by combining prevalent demographic risk factors, specifically gender, with crucial immunogenetic risk markers, such as the alleles responsible for human leukocyte antigen (HLA) class I molecules and the killer immunoglobulin-like receptors (KIR) genes responsible for natural killer lymphocyte receptors. Results: The study included 619 healthy controls and 299 patients affected by MS, all of whom originated from Sardinia. The gender feature has been disregarded due to its substantial bias in influencing the classification outcomes. By solely considering immunogenetic risk markers, the algorithm demonstrates an ability to accurately identify 73.24% of MS patients and 66.07% of individuals without the disease. Discussion: Given its notable performance, this system has the potential to support clinicians in monitoring the relatives of MS patients and identifying individuals who are at an increased risk of developing the disease

Risk of aortic dissection in patients with ascending aorta aneurysm: a new biological, morphological, and biomechanical network behind the aortic diameter

Thoracic aortic aneurysm represents a deadly condition, particularly when it evolves into rupture and dissection. Proper surgical timing is the key to positively influencing the survival of patients with this pathology. According to the most recent guidelines, ascending aorta size ≥ 55 mm and a rate of growth ≥ 0.5 cm per year are the most important factors for surgical indication. Nevertheless, a lot of evidence show that aortic ruptures and dissections might occur also in small size ascending aorta. In this review, we sought to analyze a new biological and morphological network behind the aortic diameter that need to be considered in order to identify the portion of patients with thoracic aortic aneurysm who are at increased risk of aortic complications, despite current aortic guidelines not advising surgical intervention in this group