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Background And Aim: Although sorafenib is the first systemic therapy to show survival benefit for advanced hepatocellular carcinoma (HCC), its survival benefit is variable for HCC. Systemic inflammation may be associated with survival in HCC. We investigated the use of systemic inflammation markers, including neutrophil-to-lymphocyte ratio (NLR), in the prognosis of sorafenib-treated HCC patients.
Patients And Methods: We retrospectively analyzed data of 82 patients with advanced HCC who received sorafenib as the first-line treatment. Data on pretreatment and post-treatment (2-3 months after initiating sorafenib therapy, first tumor response evaluation day) clinical, laboratory, and tumor characteristics were collected. Survival-related prognostic factors were analyzed.
Results: Patients were mostly in the intermediate (12.2%) or advanced (87.8%) Barcelona Clinic Liver Cancer stages. Fifty-six (68.3%) patients had vascular invasion and 34 (41.5%) patients had extrahepatic disease. The median progression-free survival (PFS) and overall survival (OS) were 4.7 months [95% confidence interval (CI): 2.8-6.5 months] and 4.7 months (95% CI: 2.8-6.5 months). In multivariate analysis for OS, diarrhea (hazard ratio: 0.588; 95% CI: 0.348-0.993) and NLR decline (decreased compared with pretreatment) (hazard ratio: 0.479; 95% CI: 0.300-0.765) were independent factors of good OS. In the NLR decline group, the median PFS and OS were 7.1 and 7.3 months, respectively. In the NLR nondecline group, the median PFS and OS were 3.0 and 3.2 months, respectively. The difference in OS between the two groups was significant (P = 0.004).
Conclusion: A change in NLR after sorafenib therapy was associated with a better prognosis in patients with advanced HCC.
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http://dx.doi.org/10.1097/MEG.0000000000001405 | DOI Listing |
J Electrocardiol
August 2025
Computational Physics Laboratory, Tampere University, P.O. Box 600, FI-33014 Tampere, Finland. Electronic address:
The QT interval is a key indicator in assessing arrhythmia risk, evaluating drug safety, and supporting clinical diagnosis in cardiology. The QT interval is significantly influenced by heart rate so it must be accurately corrected to ensure reliable clinical interpretation. Conventional correction formulas, such as Bazett's formula, are widely utilized but often criticized for inaccuracies, either under- or overcorrecting QT intervals in different physiological conditions.
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September 2025
Instituto Mexicano de Infertilidad, Zapopan, Jalisco, Mexico. Electronic address:
This study examines uterine transplantation (UTx) in 2024. It focuses on various aspects, such as animal models, clinical outcomes, surgical procedures, patient perspectives, and ethical and legal considerations. The study particularly considers the context of Mexico.
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Heart Failure Clinic, Division of Cardiology, Alessandro Manzoni Hospital, ASST Lecco, Lecco, Italy.
Introduction: Ischemic heart disease (IHD) constitutes the most prevalent form of cardiac disease in the general population. Although current therapeutic interventions have significantly improved both quality of life and survival rates, no available treatment can reverse the loss of cardiomyocytes resulting from ischemic injury. Existing therapies are limited to attenuating myocardial damage, reducing its extent, and mitigating its clinical consequences.
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Department of Nephrology, Daping Hospital, Army Medical University, Chongqing, China.
Chronic kidney disease (CKD) substantially increases cardiovascular risk, with endothelial dysfunction as its central pathological mechanism. This review summarises the molecular regulatory mechanisms underlying endothelial dysfunction in CKD and highlights recent advances in treatment strategies. The pathophysiology of endothelial injuries involves a complex network of multiple factors and mechanisms, including oxidative stress, inflammation, glycocalyx damage, ischaemia, hypoxia, cellular senescence and endothelial-mesenchymal transition (EndMT).
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September 2025
Department of Medical Oncology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China.
Background: Immune checkpoint inhibitors (ICIs) play a pivotal role in the treatment of advanced gastric cancer (GC). However, the biomarkers used to predict ICI efficacy are limited due to their reliance on single or static tumor characteristics. This study aims to develop a machine learning (ML) model that incorporates dynamic changes in clinlabomics data to optimize the predictive accuracy of ICI efficacy.
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