Search

Introduction: The Baveno criteria for assessing advanced liver fibrosis were mainly determined by transient elastography (TE), and its pathology-based validation studies in two-dimensional shear wave elastography (2D-SWE) remain limited. We aimed to validate the Baveno criteria through use of 2D-SWE. Method: Consecutive patients who underwent liver biopsies for various benign liver diseases were prospectively recruited. Liver stiffness measurement (LSM) was simultaneously evaluated by TE and 2D-SWE. The optimal cutoff value to predict advanced liver fibrosis was determined by the Youden Index, and the diagnostic performance was estimated using area under the receiver operating characteristic (AUROC) analysis. Results: A total of 101 patients were enrolled having a median age of 55.0 (IQR: 46.0–63.5) years, with 53 (52.48%) of them being male. Using <9 and >14 kPa as the optimal dual cutoffs, the AUROC values in TE and 2D-SWE were 0.92 (95% CI: 0.83–0.97) and 0.93 (95% CI: 0.84–0.98), respectively (p = 0.61). The sensitivity and specificity of LSM by TE/2D-SWE achieved rates of 94.44%/94.44% and 86.00%/88.00%, respectively. However, using the Baveno criteria, the AUROC values in TE and 2D-SWE could remain achieving 0.91 (95% CI: 0.82–0.97) and 0.93 (95% CI: 0.84–0.98), respectively (p = 0.36). The sensitivity and specificity in TE/2D-SWE were 88.24%/88.24% and 86.79%/90.57%, respectively. Conclusion: This study establishes the compatibility of the Baveno dual cutoff criteria with 2D-SWE, positioning it as an easily used criteria in clinical practice and research. [ABSTRACT FROM AUTHOR]

Background/aim: In this study, we introduce an innovative deep-learning model architecture aimed at enhancing the accuracy of detecting and classifying organizing pneumonia (OP), a condition characterized by the presence of Masson bodies within the alveolar spaces due to lung injury. The variable morphology of Masson bodies and their resemblance to adjacent pulmonary structures pose significant diagnostic challenges, necessitating a model capable of discerning subtle textural and structural differences. Our model incorporates a novel architecture that integrates advancements in three key areas: Semantic segmentation, texture analysis, and structural feature recognition., Materials and Methods: We employed a dataset of whole slide imaging from 20 patients, totaling 100 slides of OP, segmented into training, validation, and testing sets to reflect real-world application scenarios. Our approach utilizes a modified multi-head self-attention mechanism combined with ResUNet for semantic segmentation, enhanced by superpixel concepts. This method facilitates the generation of representative token features through iterative super-token blocks, creating high-resolution token maps that leverage local and high-level feature information for improved accuracy., Results: Benefiting from token features and distribution for enhanced texture alignment with fewer false-positives, the super-token transformer (STT) model achieved a mean intersection over union (mIOU) of 72.42%, with a sensitivity of 47.81%, specificity of 99.83%, positive predictive value of 64.03%, and negative predictive value of 99.94%, highlighting superior efficacy in Masson body segmentation in complex cross-tissue analyses., Conclusion: Our team developed an iterative learning model based on the STT approach, emphasizing token features of super token, including texture and distribution, that enable enhanced alignment with the unique textures of Masson bodies to improve sensitivity and mIOU, The development of this STT model presents a significant advancement in the field of medical image analysis for OP that offers a promising avenue for improving diagnostic precision and patient outcomes in pulmonary pathology., (Copyright © 2024, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)

Among pediatric blood cancers, acute lymphoblastic leukemia (ALL) is the most common hematologic malignancy. Within ALL, T-cell acute lymphoblastic leukemia (T-ALL) accounts for 10 to 15% of all pediatric cases, and ~25% of adult cases. For T-ALL, its recurrence and relapse after treatment remain problematic. Therefore, it is necessary to develop new therapies for T-ALL. Recent studies suggested regulating energy metabolism is a novel approach to inhibit tumor growth, likely a promising treatment. Transketolase (TKT) is an important enzyme for modulating glucose metabolize in the pentose phosphate pathway (PPP). In this study, we treated T-ALL cells with different doses of niclosamide and primary T-ALL PBMCs were analyzed by RNA sequencing. T-ALL cells treated with niclosamide were analyzed with the Western blotting and TKT activity assay. Metabolism of T-ALL cells was evaluated by ATP assay and seahorse analyses. Lastly, we used a T-ALL xenograft murine model to determine effects of TKT knockdown on T-ALL tumor growth. Tumor samples were analyzed by H&E and IHC stainings. We found that niclosamide reduced T-ALL cell viability, and reduced expressions of TKT, Transketolase-Like Protein 1/2 (TKTL1/2) and transaldolase. In addition, niclosamide inhibited TKT enzyme activity, aerobic metabolism and glycolysis, finally leading to lower production of ATP. TKT knockdown inhibited tumor growth of xenograft T-ALL mice. Findings showed that niclosamide inhibits T-ALL cell growth by inhibiting TKT and energy metabolism., Competing Interests: None., (AJCR Copyright © 2024.)