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BackgroundAdenoid cystic carcinoma (ACC) is the second most common malignancy of the salivary glands, accounting for ~ 1% of malignant tumors of the head and neck region and 10% of salivary gland neoplasms. Predicting the long-term outcomes of patients with ACC is still challenging, as reliable prognostic biomarkers are not available. Among salivary gland tumors, Myb overexpression is highly specific for ACC. In addition, the MYB-NF1B fusion translocation is a hallmark of ACC, and although the detection of this translocation does not appear to impact prognosis, the MYB-NF1B fusion is also implicated in MYB upregulation. Myb has recently been identified as an activator of the Wnt/β-catenin signaling pathway, and aberrant cytoplasmic expression of β-catenin has been observed in many salivary gland malignancies. In this study, we aim to analyze the impact of Myb and β-catenin expression on prognosis in ACC.MethodsA tissue microarray constructed from archival tissue from 64 patients with ACC was stained for Myb and β-catenin; both localization and intensity were evaluated. In parallel, we abstracted demographic data, tumor characteristics, survival data, and outcomes, including local recurrence, regional recurrence, and distant metastasis from the medical record. Statistical analysis was performed.ResultsOur analysis supports that ACC patients negative for Myb by immunohistochemical methods have a higher risk of developing metastasis than patients with Myb staining (HR: 4.06, 95% CI: 1.02-14.96, p-value: 0.03). Although not statistically significant, cytoplasmic localization of β-catenin is may suggest a diminished rate of relapse-free survival (HR 2.45, 95%CI: 0.9-6.7, p = 0.08). Furthermore, Myb expression correlated with β-catenin expression, increasing 1.69 in staining intensity units with each increase in β-catenin staining intensity (p-value: 0.04).ConclusionsOur study suggests that Myb expression is protective; Myb positive patients have diminished risk of distant metastasis. In contrast, there is a trend towards increased hazard of death in ACC patients with cytoplasmic β-catenin expression. Additional analyses will be necessary to establish Myb and β-catenin as independent protective and adverse biomarkers, respectively.

We aimed to develop efficient data labeling strategies for ground truth segmentation in lower-leg magnetic resonance imaging (MRI) of patients with Charcot-Marie-Tooth disease (CMT) and to develop an automated muscle segmentation model using different labeling approaches. The impact of using unlabeled data on model performance was further examined. Using axial T1-weighted MRIs of 120 patients with CMT (60 each with mild and severe intramuscular fat infiltration), we compared the performance of segmentation models obtained using several different labeling strategies. The effect of leveraging unlabeled data on segmentation performance was evaluated by comparing the performances of few-supervised, semi-supervised (mean teacher model), and fully-supervised learning models. We employed a 2D U-Net architecture and assessed its performance by comparing the average Dice coefficients (ADC) using paired t-tests with Bonferroni correction. Among few-supervised models utilizing 10% labeled data, labeling three slices (the uppermost, central, and lowermost slices) per subject exhibited a significantly higher ADC (90.84±3.46%) compared with other strategies using a single image slice per subject (uppermost, 87.79±4.41%; central, 89.42±4.07%; lowermost, 89.29±4.71%, p < 0.0001) or all slices per subject (85.97±9.82%, p < 0.0001). Moreover, semi-supervised learning significantly enhanced the segmentation performance. The semi-supervised model using the three-slices strategy showed the highest segmentation performance (91.03±3.67%) among 10% labeled set models. Fully-supervised model showed an ADC of 91.39±3.76. A three-slice-based labeling strategy for ground truth segmentation is the most efficient method for developing automated muscle segmentation models of CMT lower leg MRI. Additionally, semi-supervised learning with unlabeled data significantly enhances segmentation performance. [ABSTRACT FROM AUTHOR]

This work suggests an engineering methodology for catalytic 2D materials to achieve more efficient catalytic systems. Zinc gallate (ZnG), a Lewis acidic material with a thin 2D morphology, can easily form aggregates through layer–layer packing. Inner parts of ZnG aggregates would have difficulty in interacting with substrates. By coating hollow microporous organic polymer (H-MOP) supports with ZnG, the catalytic performance of ZnG can be enhanced due to facilitated contact of substrates with Zn species in shells. H-MOP@ZnG-2 with an optimal ZnG coating showed a much enhanced catalytic performance for the reductive carbon dioxide fixation with amines to formamides compared with ZnG. Various amines can be utilized in the reductive carbon dioxide fixation to formamides. In addition, H-MOP@ZnG-2 showed recyclability, maintaining its catalytic performance for five successive reactions. [ABSTRACT FROM AUTHOR]