Modeling autosomal dominant Alzheimer's disease with machine learning

INTRODUCTION: Machine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease. METHODS: Longitudinal structural magnetic resonance imaging, amyloid positron emission tomography (PET), and fluorodeoxyglucose PET were acquired in 131 mutation carriers and 74 non-carriers from the Dominantly Inherited Alzheimer Network; the groups were matched for age, education, sex, and apolipoprotein ε4 (APOE ε4). A deep neural network was trained to predict disease progression for each modality. Relief algorithms identified the strongest predictors of mutation status. RESULTS: The Relief algorithm identified the caudate, cingulate, and precuneus as the strongest predictors among all modalities. The model yielded accurate results for predicting future Pittsburgh compound B (R2  = 0.95), fluorodeoxyglucose (R2  = 0.93), and atrophy (R2  = 0.95) in mutation carriers compared to non-carriers. DISCUSSION: Results suggest a sigmoidal trajectory for amyloid, a biphasic response for metabolism, and a gradual decrease in volume, with disease progression primarily in subcortical, middle frontal, and posterior parietal regions

Pirt, a TRPV1 Modulator, Is Required for Histamine-Dependent and -Independent Itch

Itch, or pruritus, is an important clinical problem whose molecular basis has yet to be understood. Recent work has begun to identify genes that contribute to detecting itch at the molecular level. Here we show that Pirt, known to play a vital part in sensing pain through modulation of the transient receptor potential vanilloid 1 (TRPV1) channel, is also necessary for proper itch sensation. Pirt−/− mice exhibit deficits in cellular and behavioral responses to various itch-inducing compounds, or pruritogens. Pirt contributes to both histaminergic and nonhistaminergic itch and, crucially, is involved in forms of itch that are both TRPV1-dependent and -independent. Our findings demonstrate that the function of Pirt extends beyond nociception via TRPV1 regulation to its role as a critical component in several itch signaling pathways

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases

Reactive oxygen species and male reproductive hormones

Reports of the increasing incidence of male infertility paired with decreasing semen quality have triggered studies on the effects of lifestyle and environmental factors on the male reproductive potential. There are numerous exogenous and endogenous factors that are able to induce excessive production of reactive oxygen species (ROS) beyond that of cellular antioxidant capacity, thus causing oxidative stress. In turn, oxidative stress negatively affects male reproductive functions and may induce infertility either directly or indirectly by affecting the hypothalamus-pituitary-gonadal (HPG) axis and/or disrupting its crosstalk with other hormonal axes. This review discusses the important exogenous and endogenous factors leading to the generation of ROS in different parts of the male reproductive tract. It also highlights the negative impact of oxidative stress on the regulation and cross-talk between the reproductive hormones. It further describes the mechanism of ROS-induced derangement of male reproductive hormonal profiles that could ultimately lead to male infertility. An understanding of the disruptive effects of ROS on male reproductive hormones would encourage further investigations directed towards the prevention of ROS-mediated hormonal imbalances, which in turn could help in the management of male infertility