Publications by authors named "Maria Fiorentino"

Article Synopsis
  • Accurate segmentation of intervertebral discs (IVDs) is essential for diagnosing spinal conditions, but traditional deep learning methods require large annotated datasets that are difficult to obtain.
  • This research introduces a self-supervised learning approach that uses unlabeled multi-domain data for effective IVD segmentation in MRI scans, capturing subtle intensity variations characteristic of spinal structures.
  • The model outperformed traditional methods, demonstrating its ability to manage domain shifts and achieve higher accuracy, while also suggesting potential applications in improving generalizability across other medical imaging fields.
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Salmonellosis is an infectious disease caused by bacteria belonging to the Salmonella genus. Bovine salmonellosis is more frequent in young cattle under intensive overcrowd husbandry conditions, and therefore uncommon in adults. We report four outbreaks of clinical salmonellosis due to Salmonella Typhimurium, Salmonella Newport and Salmonella Dublin provoking outbreaks of diarrheic/septicemic disease in adult cattle of Central Argentina.

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Over the past decade, deep-learning (DL) algorithms have become a promising tool to aid clinicians in identifying fetal head standard planes (FHSPs) during ultrasound (US) examination. However, the adoption of these algorithms in clinical settings is still hindered by the lack of large annotated datasets. To overcome this barrier, we introduce FetalBrainAwareNet, an innovative framework designed to synthesize anatomically accurate images of FHSPs.

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Background: To investigate the effectiveness of contrastive learning, in particular SimClr, in reducing the need for large annotated ultrasound (US) image datasets for fetal standard plane identification.

Methods: We explore SimClr advantage in the cases of both low and high inter-class variability, considering at the same time how classification performance varies according to different amounts of labels used. This evaluation is performed by exploiting contrastive learning through different training strategies.

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Article Synopsis
  • In deep learning, models need a lot of data to train effectively, leading to the development of zero-shot learning (ZSL), which tests models on "unseen" classes after training on "seen" ones.
  • This paper investigates how the way data is split influences the performance of ZSL models, specifically looking at the attributes and classes used during training.
  • Experiments show that ZSL models can be improved in terms of generalizability and robustness, particularly on different types of datasets, with dimensionality reduction techniques being effective in enhancing performance for fine-grained datasets.
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Surgical mitral valve repair (SMVR) is performed with various techniques that involve the implantation of non-biological material, such as the prolene of the suture threads, the polytetrafluoroethylene of the neo-chordae or the prosthetic ring for the remodeling of the valve annulus, whose exposure to the bloodstream is capable of triggering the blood coagulation cascade and consequently the development of thrombotic/thromboembolic events. The indications of the literature on the use of antithrombotic drugs after SMVR are weak and not univocal given the absence of randomized data and the availability of only small observational case series, which are generally contaminated by the lack of homogeneity of the populations examined. Indeed in these studies, patients not only undergoing SMVR, but also transcatheter repair of the mitral valve or surgical implantation of a biological valve prosthesis (not only in the mitral position) are included.

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Infectious sporadic abortions in cattle are mainly caused by opportunistic bacteria and fungi usually present in environmental or gastrointestinal and reproductive microbiota of healthy animals. A retrospective analysis was carried out to evaluate the main opportunistic microorganisms involved in bovine abortions recorded at INTA Balcarce (Argentina) from 1997 to 2023, accounting for 2.2% of the total diagnosed etiologies of bovine abortion.

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The identification of fetal-head standard planes (FHSPs) from ultrasound (US) images is of fundamental importance to visualize cerebral structures and diagnose neural anomalies during gestation in a standardized way. To support the activity of healthcare operators, deep-learning algorithms have been proposed to classify these planes. To date, the translation of such algorithms in clinical practice is hampered by several factors, including the lack of large annotated datasets to train robust and generalizable algorithms.

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Vocal folds motility evaluation is paramount in both the assessment of functional deficits and in the accurate staging of neoplastic disease of the glottis. Diagnostic endoscopy, and in particular videoendoscopy, is nowadays the method through which the motility is estimated. The clinical diagnosis, however, relies on the examination of the videoendoscopic frames, which is a subjective and professional-dependent task.

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The intestinal barrier comprises a single layer of epithelial cells tightly joined to form a physical barrier. Disruption or compromise of the intestinal barrier can lead to the inadvertent activation of immune cells, potentially causing an increased risk of chronic inflammation in various tissues. Recent research has suggested that specific dietary components may influence the function of the intestinal barrier, potentially offering a means to prevent or mitigate inflammatory disorders.

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We investigated the occurrence of organochlorine pollutants (OCs) in the muscle of brown trout and evaluated their potential modulation of parasite infection. The toxicological risk for consumer health was assessed, too. Trout were collected from the Sila National Park (Calabria region, South of Italy).

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The microbiota-gut-brain axis (MGBA) involves bidirectional communication between intestinal microbiota and the gastrointestinal (GI) tract, central nervous system (CNS), neuroendocrine/neuroimmune systems, hypothalamic-pituitary-adrenal (HPA) axis, and enteric nervous system (ENS). The intestinal microbiota can influence host physiology and pathology. Dysbiosis involves the loss of beneficial microbial input or signal, diversity, and expansion of pathobionts, which can lead to loss of barrier function and increased intestinal permeability (IP).

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Zonulin is a physiologic epithelial and endothelial permeability modulator. Zonulin increases antigen trafficking from the gut lumen into the bloodstream and in between body compartments, a mechanism linked to many chronic inflammatory diseases. Upon its initial discovery, it was noted that zonulin was not a single protein, but rather a family of structurally and functionally related proteins referred to as the zonulin family proteins (ZFPs).

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The purpose of this report is to provide information about the different presentations of cardiac and extra-cardiac histophilosis and, to assess the antimicrobial (ATM) susceptibility of Histophilus somni isolated from these cardiac lesions to different ATM agents commonly used for treating bovine bacterial respiratory pathogens. Eight feedlot calves, which died after suffering from food rejection, apathy, hyperthermia, cough and nasal mucous discharge, and lack of response to ATM therapy, were studied. Cardiac lesions observed at necropsy included valvular/mural endocarditis, myocardial infarction, and necrotizing myocarditis, miliar non-suppurative myocarditis, myocardic necrotic sequestrum, and/or pericarditis.

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Deep-learning (DL) algorithms are becoming the standard for processing ultrasound (US) fetal images. A number of survey papers in the field is today available, but most of them are focusing on a broader area of medical-image analysis or not covering all fetal US DL applications. This paper surveys the most recent work in the field, with a total of 153 research papers published after 2017.

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Article Synopsis
  • * The study introduces a deep learning approach for automatic segmentation of the median nerve in ultrasound images, utilizing a dataset of 246 images from 103 rheumatic patients.
  • * The proposed model showed high accuracy in detecting and segmenting the median nerve, with performance metrics comparable to manual measurements, though it faced challenges with rare anatomical variations; future work aims to enhance the dataset for better support to sonographers.
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Blackleg is an infectious disease caused by . Cardiac blackleg has been reported in ruminants as an uncommon presentation of the disease; its pathogenesis is not understood completely. We include here a literature review of cardiac blackleg and a description of 2 cases in 12-15-mo-old feedlot steers in Argentina.

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The aims of this work are, firstly, to provide the geolocalization of cases of bovine abortion with definitive diagnosis and, secondly, to estimate the economic losses due to the most frequent abortifacients diagnosed agents in cattle in Buenos Aires province, Argentina. The total beef and dairy cattle population at risk of abortion is 8,358,186 and 538,076, respectively. In beef cattle, the overall risk of abortion was estimated at 4.

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Bovine respiratory disease (BRD) is one of the most frequent clinical concerns in weaned calves after their arrival at the feedlot. This work reports the first local isolation of Mycoplasma bovis from feedlot calves with pneumonia and polyarthritis in Argentina. Twenty four out of 545 calves showed progressive, subacute to chronic respiratory distress, coughing, and fever.

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This study describes for the first time an abortion outbreak caused by Neospora caninum in farmed red deer. During a 5-year period, farmed hinds, naturally mated, were regularly ultrasound monitored to detect reproductive losses over their gestation. During the 4 years previous to the outbreak, abortion rates ranged from 4.

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The aim of this study is to provide preliminary data about the occurrence of and -related abortions and perinatal deaths in sheep from Argentina. Thirty ovine aborted foetuses and 33 perinatal deaths were submitted to the Veterinary Diagnostic Service at INTA EEA Balcarce (Argentina) during 2017-2019. A complete necropsy was performed on all specimens submitted, and foetal and placental tissues were examined.

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Background: Deep learning applied to ultrasound (US) can provide a feedback to the sonographer about the correct identification of scanned tissues and allows for faster and standardized measurements. The most frequently adopted parameter for US diagnosis of carpal tunnel syndrome is the increasing of the cross-sectional area (CSA) of the median nerve. Our aim was to develop a deep learning algorithm, relying on convolutional neural networks (CNNs), for the localization and segmentation of the median nerve and the automatic measurement of its CSA on US images acquired at the proximal inlet of the carpal tunnel.

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Objective: Rheumatoid arthritis (RA) is a chronic disease characterized by erosive symmetrical polyarthritis. Bone and cartilage are the main joint targets of this disease. Cartilage damage is one of the most relevant determinants of physical disability in RA patients.

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We examined the relationship between zonulin and gastric motility in critical care patients and a translational mouse model of systemic inflammation. Gastric motility and haptoglobin (HP) 2 isoform quantification, proxy for zonulin, were examined in patients. Inflammation was triggered by lipopolysaccharide (LPS) injection in C57Bl/6 zonulin transgenic mouse (Ztm) and wildtype (WT) mice as controls, and gastro-duodenal transit was examined by fluorescein-isothiocyanate, 6 and 12 h after LPS-injection.

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