Atezolizumab and Bevacizumab Combination Therapy in the Treatment of Advanced Hepatocellular Cancer

Liver cancer, particularly hepatocellular carcinoma, is a global concern. This study focuses on the evaluation of Atezolizumab and Bevacizumab combination therapy as a promising alternative in the treatment of advanced hepatocellular carcinoma. The objectives of this systematic review include evaluating the efficacy of Atezolizumab and Bevacizumab combination therapy compared to conventional therapies with Sorafenib and other conventional therapies, analyzing the associated adverse effects, and exploring prognostic factors in the setting of advanced hepatocellular carcinoma. A systematic literature review was carried out using the PubMed and Web of Science databases. Fifteen related articles were included and evaluated according to their level of evidence and recommendation. Results: The combination therapy of Atezolizumab and Bevacizumab, along with Sorafenib, showed positive results in the treatment of patients with advanced hepatocellular carcinoma. Significant adverse effects were identified, such as gastrointestinal bleeding, arterial hypertension, and proteinuria, which require careful attention. In addition, prognostic factors, such as transforming growth factor beta (TGF-β), alpha-fetoprotein (AFP), and vascular invasion, were highlighted as key indicators of hepatocellular carcinoma progression. Conclusions: The combination of Atezolizumab and Bevacizumab is shown to be effective in the treatment of advanced hepatocellular carcinoma, although it is essential to take into consideration the associated adverse effects. The prognostic factors identified may provide valuable information for the clinical management of this disease. This study provides a comprehensive overview of a promising emerging therapy for liver cancer.Medicin

Temporada de pesca

Treballs de l'alumnat del Grau de Comunicació Audiovisual, Facultat d'Informació i Mitjans Audiovisuals, Universitat de Barcelona, Projectes II. Curs: 2019-2020, Tutor: Francesc Llinares. // Director: Guillem Villalonga i Colomé; Aj. Direcció: Ivette Herrero Serrano i Claudia Turmo Margalef; Direcció dárt: Ivette Herrero Serrano; Productor: Bernat Morros González; Aj. Producció: Marta Millán Jiménez, Lorena Sanchiz Rodríguez i Aina Cruz Arcas; Script i claqueta: Marta Millán Jiménez, Lorena Sanchiz Rodríguez i Claudia Turmo Margalef; Guionista: Guillem Villalonga i Colomé; Dir. Fotografia: Gabriel Alonso Díez; Càmera: Walter Luis Altamirano Castillo; Aj. càmera: Guillem Villalonga i Colomé; Il·luminador: Gabriel Alonso Díez; Storyboard: Claudia Turmo Margalef; Direcció de so: Bernat Morros González; Muntatge: Walter Luis Altamirano Castillo; Música: Roger Albet; Postproducció: Gabriel Alonso Díez. Equip artístic: Irieix Freixas, Aina Cruz Arcas, Elies Villalonga, Pau Rumbo, Claudia Turmo Margalef, Lorena Sanchiz Rodríguez, Marta Millán Jiménez, Gabriel Alonso Díez, Ivette Herrero Serrano, Mariona Fortuny i Guillem Villalonga i Colomé.Sato, un jove turmentat psicològicament per l’educació del seu pare, segresta turistes que no es comporten com ell creu correcte. Una noia que acaba d’arribar al poble on viu es fixa amb ell i s’enamoren. El canvi que la relació estava generant en tots dos es veu aturat quan ella descobreix els segrestos

Discovering HIV related information by means of association rules and machine learning

Acquired immunodeficiency syndrome (AIDS) is still one of the main health problems worldwide. It is therefore essential to keep making progress in improving the prognosis and quality of life of affected patients. One way to advance along this pathway is to uncover connections between other disorders associated with HIV/AIDS-so that they can be anticipated and possibly mitigated. We propose to achieve this by using Association Rules (ARs). They allow us to represent the dependencies between a number of diseases and other specific diseases. However, classical techniques systematically generate every AR meeting some minimal conditions on data frequency, hence generating a vast amount of uninteresting ARs, which need to be filtered out. The lack of manually annotated ARs has favored unsupervised filtering, even though they produce limited results. In this paper, we propose a semi-supervised system, able to identify relevant ARs among HIV-related diseases with a minimal amount of annotated training data. Our system has been able to extract a good number of relationships between HIV-related diseases that have been previously detected in the literature but are scattered and are often little known. Furthermore, a number of plausible new relationships have shown up which deserve further investigation by qualified medical experts