Background: To analyze predictive factors of hypertrophy of the nonembolized future remnant liver (FRL) after transhepatic preoperative portal vein embolization (PVE) of the liver to be resected.
Materials And Methods: Age, gender, indocyanin green clearance test, chemotherapy before PVE, type of chemotherapy, operators, extent of PVE, radiofrequency ablation (RFA) associated with PVE, time delay between PVE and surgery, and platelet count were retrospectively evaluated as predictive factors for hypertrophy of FRL in 107 patients with malignant disease in noncirrhotic liver. PVE targeted the right liver lobe [n = 70] or the right liver lobe and segment IV [n = 37] when FRL/total liver volume ratio was below 25% in healthy liver or 40% in altered liver.
Results: After PVE, FRL volume significantly increased by 69%, from 344 +/- 156 cm(3) to 543 +/- 192 cm(3) (P < .0001). The degree of hypertrophy was negatively correlated with FRL volume (correlation coefficient = -0.55, P < .0001) and FRL/TFL ratio (correlation coefficient = -0.52, P < .0001) before PVE. Patients, who have undergone chemotherapy with platin agents prior to PVE, demonstrated lower hypertrophy (P = .048).
Conclusion: Hypertrophy after PVE is inversely correlated to initial FRL volume. Hypertrophy of the liver might be influenced by the systemic chemotherapeutic received before PVE.
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http://dx.doi.org/10.1245/s10434-010-0979-2 | DOI Listing |
Sci Rep
December 2024
School of Engineering and Technology, Sunway University, No. 5, Jalan Universiti, Bandar Sunway, Petaling Jaya, 47500, Selangor Darul Ehsan, Malaysia.
Cervical cancer is a deadly disease in women globally. There is a greater chance of getting rid of cervical cancer in case of earliest diagnosis. But for some patients, there is a chance of recurrence.
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December 2024
Imperial College London, London, UK.
Accurate estimation of the soil resilient modulus (M) is essential for designing and monitoring pavements. However, experimental methods tend to be time-consuming and costly; regression equations and constitutive models usually have limited applications, while the predictive accuracy of some machine learning studies still has room for improvement. To forecast M efficiently and accurately, a new model named black-winged kite algorithm-extreme gradient boosting (BKA-XGBOOST) is proposed.
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December 2024
Department of Applied Mathematics, Faculty of Mathematical Science, Ferdowsi University of Mashhad, Mashhad, Iran.
This study presents a web application for predicting cardiovascular disease (CVD) and hypertension (HTN) among mine workers using machine learning (ML) techniques. The dataset, collected from 699 participants at the Gol-Gohar mine in Iran between 2016 and 2020, includes demographic, occupational, lifestyle, and medical information. After preprocessing and feature engineering, the Random Forest algorithm was identified as the best-performing model, achieving 99% accuracy for HTN prediction and 97% for CVD, outperforming other algorithms such as Logistic Regression and Support Vector Machines.
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December 2024
Department of Medical and Surgical Sciences, Institute of Cardiology, University of Bologna, Policlinico S.Orsola-Malpighi, via Massarenti 9, Bologna, 40138, Italy.
Cardiac implantable electronic devices infections (CIEDI) are associated with poor survival despite the improvement in transvenous lead extraction (TLE). Aetiology and systemic involvement are driving factors of clinical outcomes. The aim of this study was to explore their contribute on overall mortality.
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December 2024
School of Civil & Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Per- and polyfluoroalkyl substances (PFASs) have recently garnered considerable concerns regarding their impacts on human and ecological health. Despite the important roles of polyamide membranes in remediating PFASs-contaminated water, the governing factors influencing PFAS transport across these membranes remain elusive. In this study, we investigate PFAS rejection by polyamide membranes using two machine learning (ML) models, namely XGBoost and multimodal transformer models.
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