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The treatment of chronic lymphocytic leukemia (CLL) consists of the continuous use of Bruton tyrosine kinase inhibitors (BTKis) such as ibrutinib, acalabrutinib, zanubrutinib and pirtobrutinib, or Bcl-2 inhibitors, such as venetoclax. Overall survival (OS) and progression-free survival (PFS) of CLL patients are significantly improved with the use of these therapies. Adverse effects (AEs) that can occur during treatment and the presence of pre-existing comorbidities in patients can influence subsequent treatment outcomes and, consequently, OS and PFS.

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Acute fulminant myocarditis is a rare event in children, accounting for about 10% of all cases of acute myocarditis. Its lack of specific onset patterns and unpredictable evolution make diagnosis and prompt treatment challenging. We observed six cases of fulminant myocarditis admitted to our Pediatric Emergency Unit (Campania region, Sothern Italy) within a very short timeframe (50 days, from July to September 2024).

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Effect of Digoxin vs Beta-Blockers on Left Atrial Strain for Heart Rate-Controlled Atrial Fibrillation: The DIGOBET-AF Randomized Clinical Trial.

Am J Cardiovasc Drugs

December 2024

Cardiology A Department, Research Laboratory LR12 SP 16 Fattouma Bourguiba University Hospital, University of Monastir, Monastir University, Rue du 1er juin 1955, 5000, Monastir, Tunisia.

Unlabelled: BACKGROUND AND OBJECTIVE: Left atrial strain (LAS) has prognostic value in patients with atrial fibrillation (AF). Consequently, therapies that improve LAS may help reduce AF-related adverse cardiac events. We aimed to compare how digoxin and bisoprolol modulate LAS in patients with AF being treated with rate control.

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Introduction: Ovarian cancer remains a leading cause of mortality among gynecological malignancies, often diagnosed at advanced stages due to nonspecific symptoms and limited screening tools. Standard treatment, including cytoreductive surgery and chemotherapy, can cause fatigue, physical dysfunction, and psychological distress, impacting quality of life. Exercise interventions have shown potential to mitigate these effects, but inconsistent methodologies in randomized controlled trials (RCTs) limit reliable conclusions and clinical integration.

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This study aimed to evaluate the accuracy of machine learning (ML) techniques in classifying pediatric individuals-cardiological patients, healthy participants, and athletes-based on cardiorespiratory features from short-term static measurements. It also examined the impact of cardiorespiratory coupling (CRC)-related features (from causal and information domains) on the modeling accuracy to identify a preferred cardiorespiratory feature set that could be further explored for specialized tasks, such as monitoring training progress or diagnosing health conditions. We utilized six self-prepared datasets that comprised various subsets of cardiorespiratory parameters and applied several ML algorithms to classify subjects into three distinct groups.

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