Project description:As a follow-up to a previous article, this review provides several in-depth concepts regarding a survival analysis. Also, several codes for specific survival analysis are listed to enhance the understanding of such an analysis and to provide an applicable survival analysis method. A proportional hazard assumption is an important concept in survival analysis. Validation of this assumption is crucial for survival analysis. For this purpose, a graphical analysis method and a goodnessof- fit test are introduced along with detailed codes and examples. In the case of a violated proportional hazard assumption, the extended models of a Cox regression are required. Simplified concepts of a stratified Cox proportional hazard model and time-dependent Cox regression are also described. The source code for an actual analysis using an available statistical package with a detailed interpretation of the results can enable the realization of survival analysis with personal data. To enhance the statistical power of survival analysis, an evaluation of the basic assumptions and the interaction between variables and time is important. In doing so, survival analysis can provide reliable scientific results with a high level of confidence.
Project description:Oncogene addiction provides important therapeutic opportunities for precision oncology treatment strategies. To date the cellular circuitries associated with driving oncoproteins, which eventually establish the phenotypic manifestation of oncogene addiction remain largely unexplored. We employed a targeted mass spectrometry approach to systematically explore alterations in 116 phosphosites related to oncogene signaling and its intersection with the DDR following inhibition of the addicting oncogene alone or in combination with irradiation in MET-, EGFR-, ALK- or BRAF (V600)-positive cancer models and ex vivo non-small cell lung cancer patient organotypic cultures. We identified an ‘oncogene addiction phosphorylation signature’ (OAPS) consisting of 8 protein phosphorylations (ACLY S455, IF4B S422, IF4G1 S1231, LIMA1 S490, MYCN S62, NCBP1 S22, P3C2A S259 and TERF2 S365) that are significantly suppressed upon targeted oncogene inhibition solely in addicted cell line models and patient tissues. We show that the OAPS is present in patient tissues and the OAPS-derived score strongly correlates with the ex vivo responses to targeted treatments.
Project description:BackgroundSerum IGF-I and IGF-II levels decline with age. IGF-I replacement therapy reduces the impact of age in rats. We have recently reported that IGF-II is able to act, in part, as an analogous of IGF-I in aging rats reducing oxidative damage in brain and liver associated with a normalization of antioxidant enzyme activities. Since mitochondria seem to be the most important cellular target of IGF-I, the aim of this work was to investigate whether the cytoprotective actions of IGF-II therapy are mediated by mitochondrial protection.MethodsThree groups of rats were included in the experimental protocol young controls (17 weeks old); untreated old rats (103 weeks old); and aging rats (103 weeks old) treated with IGF-II (2 μg/100 g body weight and day) for 30 days.ResultsCompared with young controls, untreated old rats showed an increase of oxidative damage in isolated mitochondria with a dysfunction characterized by: reduction of mitochondrial membrane potential (MMP) and ATP synthesis and increase of intramitochondrial free radicals production and proton leak rates. In addition, in untreated old rats mitochondrial respiration was not blocked by atractyloside. In accordance, old rats showed an overexpression of the active fragment of caspases 3 and 9 in liver homogenates. IGF-II therapy corrected all of these parameters of mitochondrial dysfunction and reduced activation of caspases.ConclusionsThe cytoprotective effects of IGF-II are related to mitochondrial protection leading to increased ATP production reducing free radical generation, oxidative damage and apoptosis.
Project description:ObjectivesTo develop a proof-of-concept "interpretable" deep learning prototype that justifies aspects of its predictions from a pre-trained hepatic lesion classifier.MethodsA convolutional neural network (CNN) was engineered and trained to classify six hepatic tumor entities using 494 lesions on multi-phasic MRI, described in Part 1. A subset of each lesion class was labeled with up to four key imaging features per lesion. A post hoc algorithm inferred the presence of these features in a test set of 60 lesions by analyzing activation patterns of the pre-trained CNN model. Feature maps were generated that highlight regions in the original image that correspond to particular features. Additionally, relevance scores were assigned to each identified feature, denoting the relative contribution of a feature to the predicted lesion classification.ResultsThe interpretable deep learning system achieved 76.5% positive predictive value and 82.9% sensitivity in identifying the correct radiological features present in each test lesion. The model misclassified 12% of lesions. Incorrect features were found more often in misclassified lesions than correctly identified lesions (60.4% vs. 85.6%). Feature maps were consistent with original image voxels contributing to each imaging feature. Feature relevance scores tended to reflect the most prominent imaging criteria for each class.ConclusionsThis interpretable deep learning system demonstrates proof of principle for illuminating portions of a pre-trained deep neural network's decision-making, by analyzing inner layers and automatically describing features contributing to predictions.Key points• An interpretable deep learning system prototype can explain aspects of its decision-making by identifying relevant imaging features and showing where these features are found on an image, facilitating clinical translation. • By providing feedback on the importance of various radiological features in performing differential diagnosis, interpretable deep learning systems have the potential to interface with standardized reporting systems such as LI-RADS, validating ancillary features and improving clinical practicality. • An interpretable deep learning system could potentially add quantitative data to radiologic reports and serve radiologists with evidence-based decision support.
Project description:Schizophrenia is a common disorder with high heritability and a 10-fold increase in risk to siblings of probands. Replication has been inconsistent for reports of significant genetic linkage. To assess evidence for linkage across studies, rank-based genome scan meta-analysis (GSMA) was applied to data from 20 schizophrenia genome scans. Each marker for each scan was assigned to 1 of 120 30-cM bins, with the bins ranked by linkage scores (1 = most significant) and the ranks averaged across studies (R(avg)) and then weighted for sample size (N(sqrt)[affected casess]). A permutation test was used to compute the probability of observing, by chance, each bin's average rank (P(AvgRnk)) or of observing it for a bin with the same place (first, second, etc.) in the order of average ranks in each permutation (P(ord)). The GSMA produced significant genomewide evidence for linkage on chromosome 2q (PAvgRnk<.000417). Two aggregate criteria for linkage were also met (clusters of nominally significant P values that did not occur in 1,000 replicates of the entire data set with no linkage present): 12 consecutive bins with both P(AvgRnk) and P(ord)<.05, including regions of chromosomes 5q, 3p, 11q, 6p, 1q, 22q, 8p, 20q, and 14p, and 19 consecutive bins with P(ord)<.05, additionally including regions of chromosomes 16q, 18q, 10p, 15q, 6q, and 17q. There is greater consistency of linkage results across studies than has been previously recognized. The results suggest that some or all of these regions contain loci that increase susceptibility to schizophrenia in diverse populations.