Transcription enhanced associate domain factor 1 (TEAD1) predicts liver regeneration outcome of ALPPS-treated patients.

Background: The two-stage surgical technique of associated liver partition and portal vein ligation for staged hepatectomy (ALPPS) enables extensive liver resection and promotes future liver remnant regeneration (FLR), in part by inhibiting the Hippo signalling pathway. Its main effector, Yes-associated protein (YAP), has low intrinsic transcriptional activity and requires the transcription enhanced associated domain factor (TEAD) family members as cofactors for target gene transcription. We evaluated the intracellular localization and expression of TEAD1-4, hypothesized to regulate the activity of YAP and, consequently, liver regeneration.

Patient-centric comparative analysis of experiences in open upright and conventional closed MRI scanners.

Introduction: MRI often induces anxiety, leading to incomplete scans and claustrophobia-related distress. Open MRI systems aim to enhance patient comfort. This study examines how prior MRI experiences impact subsequent encounters in an open upright MRI scanner.

Venous vascular closure system vs. figure-of-eight suture following atrial fibrillation ablation: the STYLE-AF Study.

Aims: Simplified ablation technologies for pulmonary vein isolation (PVI) are increasingly performed worldwide. One of the most common complications following PVI are vascular access-related complications. Lately, venous closure systems (VCSs) were introduced into clinical practice, aiming to reduce the time of bed rest, to increase the patients' comfort, and to reduce vascular access-related complications.

Financial toxicity in people with chronic kidney disease undergoing hemodialysis treatment.

Objective: to assess the financial toxicity of people with chronic kidney disease undergoing hemodialysis treatment.

Method: a descriptive analytical cross-sectional study, carried out with 214 people, between February and May 2022. For data collection, a sociodemographic and clinical instrument and the COmprehensive Score for financial Toxicity were used.

Deep Learning-based Assessment of Oncologic Outcomes from Natural Language Processing of Structured Radiology Reports.

Purpose: To train a deep natural language processing (NLP) model, using data mined structured oncology reports (SOR), for rapid tumor response category (TRC) classification from free-text oncology reports (FTOR) and to compare its performance with human readers and conventional NLP algorithms.

Materials And Methods: In this retrospective study, databases of three independent radiology departments were queried for SOR and FTOR dated from March 2018 to August 2021. An automated data mining and curation pipeline was developed to extract Response Evaluation Criteria in Solid Tumors-related TRCs for SOR for ground truth definition.