Safe discontinuation of nilotinib in a patient with chronic myeloid leukemia: a case report

Case presentation. We report the case of a 64-year-old Caucasian man diagnosed with chronic-phase chronic myeloid leukemia in April 2005. After 4 years of treatment with imatinib, he became intolerant to the drug and was switched to nilotinib. Two years later, he decided to stop nilotinib. Undetectable molecular response persisted for 30 months after discontinuation of the drug. Introduction. Although there is a considerable amount of data in the literature on safe discontinuation of first-generation tyrosine kinase inhibitor therapy in patients with chronic myeloid leukemia, little is known about discontinuation of second-generation tyrosine kinase inhibitor therapy. Most previous studies have been focused on dasatinib, and the few cases of nilotinib withdrawal that have been reported had a median follow-up of 12 months. To the best of our knowledge, the present report is the first to describe nilotinib withdrawal with 30 months of follow-up. Conclusion: Our present case suggests that nilotinib withdrawal is safe for patients with chronic myeloid leukemia who achieve a stable undetectable molecular response. Our patient was homozygous for killer immunoglobulin-like receptor haplotype A, previously reported to be a promising immunogenetic marker for undetectable molecular response. We recommend additional studies to investigate patient immunogenetic profiles and their potential role in complete response to therap

Therapeutic approaches with intravitreal injections in geographic atrophy secondary to age-related macular degeneration: current drugs and potential molecules

The present review focuses on recent clinical trials that analyze the efficacy of intravitreal therapeutic agents for the treatment of dry age-related macular degeneration (AMD), such as neuroprotective drugs, and complement inhibitors, also called immunomodulatory or anti-inflammatory agents. A systematic literature search was performed to identify randomized controlled trials published prior to January 2019. Patients affected by dry AMD treated with intravitreal therapeutic agents were included. Changes in the correct visual acuity and reduction in geographic atrophy progression were evaluated. Several new drugs have shown promising results, including those targeting the complement cascade and neuroprotective agents. The potential action of the two groups of drugs is to block complement cascade upregulation of immunomodulating agents, and to prevent the degeneration and apoptosis of ganglion cells for the neuroprotectors, respectively. Our analysis indicates that finding treatments for dry AMD will require continued collaboration among researchers to identify additional molecular targets and to fully interrogate the utility of pluripotent stem cells for personalized therapy

#Covid-19: A hashtag for examining reactions towards Europe in times of crisis. An analysis of tweets in Italian, Spanish, and French

Hashtag research has established itself as a relevant research field, with various studies having analysed this polysemic collector in crisis and media events. Hashtags are used in social media, most specifically on Twitter. Further, between 2020 and 2021, hashtag studies linked to the COVID-19 pandemic have emerged. Accordingly, this study aimed to analyse the content of tweets during the first phase of the COVID-19 pandemic (March 4-11, 2020) that included the hashtag #Covid-19 in three different languages: Italian, Spanish, and French. For these analyses, we used emotional text mining. The goal of this study was to reconstruct the representation of the pandemic, of containment measures, and of Europe in tweets. We discussed the prevailing attitude towards Europe in times of crisis

Object Recognition and Modeling Using SIFT Features

In this paper we present a technique for object recognition and modelling based on local image features matching. Given a complete set of views of an object the goal of our technique is the recognition of the same object in an image of a cluttered environment containing the object and an estimate of its pose. The method is based on visual modeling of objects from a multi-view representation of the object to recognize. The first step consists of creating object model, selecting a subset of the available views using SIFT descriptors to evaluate image similarity and relevance. The selected views are then assumed as the model of the object and we show that they can effectively be used to visually represent the main aspects of the object. Recognition is done making comparison between the image containing an object in generic position and the views selected as object models. Once an object has been recognized the pose can be estimated searching the complete set of views of the object. Experimental results are very encouraging using both a private dataset we acquired in our lab and a publicly available dataset

Bone marrow homing and engraftment defects of human hematopoietic stem and progenitor cells

Homing of hematopoietic stem cells (HSC) to their microenvironment niches in the bone marrow is a complex process with a critical role in repopulation of the bone marrow after transplantation. This active process allows for migration of HSC from peripheral blood and their successful anchoring in bone marrow before proliferation. The process of engraftment starts with the onset of proliferation and must, therefore, be functionally dissociated from the former process. In this overview, we analyze the characteristics of stem cells (SCs) with particular emphasis on their plasticity and ability to find their way home to the bone marrow. We also address the problem of graft failure which remains a significant contributor to morbidity and mortality after allogeneic hematopoietic stem cell transplantation (HSCT). Within this context, we discuss non-malignant and malignant hematological disorders treated with reduced-intensity conditioning regimens or grafts from human leukocyte antigen (HLA)-mismatched donor

Video Object Recognition and Modeling by SIFT Matching Optimization

In this paper we present a novel technique for object modeling and object recognition in video. Given a set of videos containing 360 degrees views of objects we compute a model for each object, then we analyze short videos to determine if the object depicted in the video is one of the modeled objects. The object model is built from a video spanning a 360 degree view of the object taken against a uniform background. In order to create the object model, the proposed techniques selects a few representative frames from each video and local features of such frames. The object recognition is performed selecting a few frames from the query video, extracting local features from each frame and looking for matches in all the representative frames constituting the models of all the objects. If the number of matches exceed a fixed threshold the corresponding object is considered the recognized objects .To evaluate our approach we acquired a dataset of 25 videos representing 25 different objects and used these videos to build the objects model. Then we took 25 test videos containing only one of the known objects and 5 videos containing only unknown objects. Experiments showed that, despite a significant compression in the model, recognition results are satisfactory

The construction of the meanings of #coronavirus on Twitter: An analysis of the initial reactions of the Italian people

The first months of 2020 saw the coronavirus pandemic explode. Moving from China, it arrived in Europe and hit Italy. The place where the debate around it exploded was the media ecosystem. In a short time, it was an explosion of tweets related to the hashtag #coronavirus on Twitter. With the aim of reconstructing the meanings of the hashtag and the content, in terms of sentiment and opinions, of the reactions of the Italians, we collected in a large size corpus, the hundred thousand Italian tweets containing the #coronavirus produced during the media hype period from the Twitter repository (February 24th - 28th, 2020). Media hype period was discovered by digging in the online articles of ‘la Repubblica', based on the presence of the words: coronavirus and Italy. The media hype is February 26th. The corpus underwent Emotional Text Mining (ETM), an unsupervised methodology, which allows social profiling based on communication. The study of the word chosen to talk about a topic and their co-occurrence allows the understanding of people’s symbolizations, representations, and sentiment, about the coronavirus. In a retrospective logic, this mechanism allows us to reconstruct the sensemaking and nuances of meaning attributed by users to the coronavirus hashtag

Therapeutic Effectiveness of Nutrition Therapy in Pediatric Patients with Chronic Liver Diseases Awaiting Liver Transplantation

Abstract It is important to prevent protein/calorie malnutrition in children with end stage liver diseases prior to transplantation. This study involved 34 patients between the ages of 10 and 156 months (mean value 25.69 months 32.2) (13 females and 21 males) on the liver transplant waiting list. Data collected as of three months before transplant and up to ten months after the procedure concerned gender, age, weight, height, Pediatric End Stage Liver Disease Score, baseline pathology, type of nutrition, type of transplant, immunosuppression, pulse steroid therapy, length of stay, and post transplant complications. Linear regression analysis showed that the length of hospital stay was 24.5 days more for females than for males, but also that intensive nutrition therapy shortens this stay for both female patients (P = 0.085) and younger patients (P = 0.023). The study population was divided into two groups according to the different nutritional therapies adopted. The Student’s t-test and Mann-Whitney test evidenced that the group receiving intensive nutrition therapy grew taller compared with the group following an oral diet (mean -1.37 and Prob = 0.043); that females grew taller compared to males (mean -1.65 +/- 0.56); and that there was an increase in height among the children in the group receiving intensive nutrition therapy despite the presence (-1.37 +/- 0.56) or absence (-14.8 +/- 5.44 and Prob = 0.035) of complications, and despite the administration (-1.03 +/- 0.33) or non administration (-1.48 +/- 0.55 and Prob = 0.019) of steroids. Intensive nutrition therapy enhances the velocity of growth in height and shortens the length of hospital stay, thus optimizing the final prognosis of the baseline pathology

DANTE at GeoLingIt: Dialect-Aware Multi-Granularity Pre-training for Locating Tweets within Italy

This paper presents an NLP research system designed to geolocate tweets within Italy, a country renowned for its diverse linguistic landscape. Our methodology consists of a two-step process involving pre-training and fine-tuning phases. In the pre-training step, we take a semi-supervised approach and introduce two additional tasks. The primary objective of these tasks is to provide the language model with comprehensive knowledge of language varieties, focusing on both the sentence and token levels. Subsequently, during the fine-tuning phase, the model is adapted explicitly for two subtasks: coarse- and fine-grained variety geolocation. To evaluate the effectiveness of our methodology, we participate in the GeoLingIt 2023 shared task and assess our model’s performance using standard metrics. Ablation studies demonstrate the crucial role of thepre-training step in enhancing the model’s performance on both task

The Topics-scape of the Pandemic Crisis: The Italian Sentiment on Political Leaders

The aim of the article is to identify themes, actors and mood of the tweets shared by users in the period from March 25 to April 3, 2020 in Italy. It seems an extremely delicate and complex period, because it corresponds to the first phase of the lockdown, introduced following the Covid-19 pandemic. It was a period characterized by emergency and crisis, with nuances related to fear and uncertainty. We assumed that this situation could have influenced and produced effects on the ideologically oriented digital language practice. Taking this background into consideration, we have scraped the messages containing the surnames of the Italian Premier and the one of the opposition leader from Twitter, in order to identify the debate connected to them and to the crisis. To achieve this goal, we performed a computational linguistic technique, Emotinal Text Mining. The first result reconstructs the landscape of the debate. Arising topics-scape drawn by: the leader, the players, the economy, the entertainment, the politic, the skill, and the guilt. Then, representations were identified and sentiments measured