J Ricardo McFaline-Figueroa

Fluorescence loss in photobleaching experiments and analysis of mitochondrial function using superoxide and redox potential biosensors revealed that mitochondria within individual yeast cells are physically and functionally distinct. Mitochondria that are retained in mother cells during yeast cell division have a significantly more oxidizing redox potential and higher superoxide levels compared to mitochondria in buds. Retention of mitochondria with more oxidizing redox potential in mother cells occurs to the same extent in young and older cells and can account for the age-associated decline in total cellular mitochondrial redox potential in yeast as they age from 0 to 5 generations. Deletion of Mmr1p, a member of the DSL1 family of tethering proteins that localizes to mitochondria at the bud tip and is required for normal mitochondrial inheritance, produces defects in mitochondrial quality control and heterogeneity in replicative lifespan (RLS). Long-lived mmr1Δ cells exhibit prolonged RLS, reduced mean generation times, more reducing mitochondrial redox potential and lower mitochondrial superoxide levels compared to wild-type cells. Short-lived mmr1Δ cells exhibit the opposite phenotypes. Moreover, short-lived cells give rise exclusively to short-lived cells, while the majority of daughters of long-lived cells are long lived. These findings support the model that the mitochondrial inheritance machinery promotes retention of lower-functioning mitochondria in mother cells and that this process contributes to both mother-daughter age asymmetry and age-associated declines in cellular fitness.

BACKGROUND: Approximately one-fourth of all cancer metastases are found in the brain. MRI is the primary technique for detection of brain metastasis, planning of radiotherapy, and the monitoring of treatment response. Progress in tumor treatment now requires detection of new or growing metastases at the small subcentimeter size, when these therapies are most effective. PURPOSE: To develop a deep-learning-based approach for finding brain metastasis on MRI. STUDY TYPE: Retrospective. SEQUENCE: -weighted imaging. FIELD STRENGTH: 1.5T and 3T. POPULATION: A total of 361 scans of 121 patients were used to train and test the Faster region-based convolutional neural network (Faster R-CNN): 1565 lesions in 270 scans of 73 patients for training; 488 lesions in 91 scans of 48 patients for testing. From the 48 outputs of Faster R-CNN, 212 lesions in 46 scans of 18 patients were used for training the RUSBoost algorithm (MatLab) and 276 lesions in 45 scans of 30 patients for testing. ASSESSMENT: Two radiologists diagnosed and supervised annotation of metastases on brain MRI as ground truth. This data were used to produce a 2-step pipeline consisting of a Faster R-CNN for detecting abnormal hyperintensity that may represent brain metastasis and a RUSBoost classifier to reduce the number of false-positive foci detected. STATISTICAL TESTS: The performance of the algorithm was evaluated by using sensitivity, false-positive rate, and receiver's operating characteristic (ROC) curves. The detection performance was assessed both per-metastases and per-slice. RESULTS: Testing on held-out brain MRI data demonstrated 96% sensitivity and 20 false-positive metastases per scan. The results showed an 87.1% sensitivity and 0.24 false-positive metastases per slice. The area under the ROC curve was 0.79. CONCLUSION: Our results showed that deep-learning-based computer-aided detection (CAD) had the potential of detecting brain metastases with high sensitivity and reasonable specificity. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:1227-1236.