Prediction of pathological stage in patients with prostate cancer: a neuro-fuzzy model

The prediction of cancer staging in prostate cancer is a process for estimating the likelihood that the cancer has spread before treatment is given to the patient. Although important for determining the most suitable treatment and optimal management strategy for patients, staging continues to present significant challenges to clinicians. Clinical test results such as the pre-treatment Prostate-Specific Antigen (PSA) level, the biopsy most common tumor pattern (Primary Gleason pattern) and the second most common tumor pattern (Secondary Gleason pattern) in tissue biopsies, and the clinical T stage can be used by clinicians to predict the pathological stage of cancer. However, not every patient will return abnormal results in all tests. This significantly influences the capacity to effectively predict the stage of prostate cancer. Herein we have developed a neuro-fuzzy computational intelligence model for classifying and predicting the likelihood of a patient having Organ-Confined Disease (OCD) or Extra-Prostatic Disease (ED) using a prostate cancer patient dataset obtained from The Cancer Genome Atlas (TCGA) Research Network. The system input consisted of the following variables: Primary and Secondary Gleason biopsy patterns, PSA levels, age at diagnosis, and clinical T stage. The performance of the neuro-fuzzy system was compared to other computational intelligence based approaches, namely the Artificial Neural Network, Fuzzy C-Means, Support Vector Machine, the Naive Bayes classifiers, and also the AJCC pTNM Staging Nomogram which is commonly used by clinicians. A comparison of the optimal Receiver Operating Characteristic (ROC) points that were identified using these approaches, revealed that the neuro-fuzzy system, at its optimal point, returns the largest Area Under the ROC Curve (AUC), with a low number of false positives (FPR = 0.274, TPR = 0.789, AUC = 0.812). The proposed approach is also an improvement over the AJCC pTNM Staging Nomogram (FPR = 0.032, TPR = 0.197, AUC = 0.582)

Increased Membrane Cholesterol in Lymphocytes Diverts T-Cells toward an Inflammatory Response

Cell signaling for T-cell growth, differentiation, and apoptosis is initiated in the cholesterol-rich microdomains of the plasma membrane known as lipid rafts. Herein, we investigated whether enrichment of membrane cholesterol in lipid rafts affects antigen-specific CD4 T-helper cell functions. Enrichment of membrane cholesterol by 40–50% following squalene administration in mice was paralleled by an increased number of resting CD4 T helper cells in periphery. We also observed sensitization of the Th1 differentiation machinery through co-localization of IL-2Rα, IL-4Rα, and IL-12Rβ2 subunits with GM1 positive lipid rafts, and increased STAT-4 and STAT-5 phosphorylation following membrane cholesterol enrichment. Antigen stimulation or CD3/CD28 polyclonal stimulation of membrane cholesterol-enriched, resting CD4 T-cells followed a path of Th1 differentiation, which was more vigorous in the presence of increased IL-12 secretion by APCs enriched in membrane cholesterol. Enrichment of membrane cholesterol in antigen-specific, autoimmune Th1 cells fostered their organ-specific reactivity, as confirmed in an autoimmune mouse model for diabetes. However, membrane cholesterol enrichment in CD4+ Foxp3+ T-reg cells did not alter their suppressogenic function. These findings revealed a differential regulatory effect of membrane cholesterol on the function of CD4 T-cell subsets. This first suggests that membrane cholesterol could be a new therapeutic target to modulate the immune functions, and second that increased membrane cholesterol in various physiopathological conditions may bias the immune system toward an inflammatory Th1 type response

Fine root dynamics across pantropical rainforest ecosystems

Fine roots constitute a significant component of the net primary productivity (NPP) of forest ecosystems but are much less studied than above-ground NPP. Comparisons across sites and regions are also hampered by inconsistent methodologies, especially in tropical areas. Here, we present a novel dataset of fine root biomass, productivity, residence time, and allocation in tropical old-growth rainforest sites worldwide, measured using consistent methods, and examine how these variables are related to consistently determined soil and climatic characteristics. Our pantropical dataset spans intensive monitoring plots in lowland (wet, semi-deciduous, deciduous) and montane tropical forests in South America, Africa, and Southeast Asia (n=47). Large spatial variation in fine root dynamics was observed across montane and lowland forest types. In lowland forests, we found a strong positive linear relationship between fine root productivity and sand content, this relationship was even stronger when we considered the fractional allocation of total NPP to fine roots, demonstrating that understanding allocation adds explanatory power to understanding fine root productivity and total NPP. Fine root residence time was a function of multiple factors: soil sand content, soil pH, and maximum water deficit, with longest residence times in acidic, sandy, and water-stressed soils. In tropical montane forests, on the other hand, a different set of relationships prevailed, highlighting the very different nature of montane and lowland forest biomes. Root productivity was a strong positive linear function of mean annual temperature, root residence time was a strong positive function of soil nitrogen content in montane forests, and lastly decreasing soil P content increased allocation of productivity to fine roots. In contrast to the lowlands, environmental conditions were a better predictor for fine root productivity than for fractional allocation of total NPP to fine roots, suggesting that root productivity is a particularly strong driver of NPP allocation in tropical mountain regions.Output Status: Forthcoming/Available Online Additional co-authors: Christopher E. Doughty, Imma Oliveras, Darcy F. Galiano Cabrera, Liliana Durand Baca, Filio Farfán Amézquita, Javier E. Silva Espejo, Antonio C.L. da Costa, Erick Oblitas Mendoza, Carlos Alberto Quesada, Fidele Evouna Ondo, Josué Edzang Ndong, Vianet Mihindou, Natacha N’ssi Bengone, Forzia Ibrahim, Shalom D. Addo-Danso, Akwasi Duah-Gyamfi, Gloria Djaney Djagbletey, Kennedy Owusu-Afriyie, Lucy Amissah, Armel T. Mbou, Toby R. Marthews, Daniel B. Metcalfe, Luiz E.O. Aragão, Ben H. Marimon-Junior, Beatriz S. Marimon, Noreen Majalap, Stephen Adu-Bredu, Miles Silman, Robert M. Ewers, Patrick Meir, Yadvinder Malh

METACOHORTS for the study of vascular disease and its contribution to cognitive decline and neurodegeneration: an initiative of the Joint Programme for Neurodegenerative Disease Research

Dementia is a global problem and major target for health care providers. Although up to 45% of cases are primarily or partly due to cerebrovascular disease, little is known of these mechanisms or treatments because most dementia research still focuses on pure Alzheimer's disease. An improved understanding of the vascular contributions to neurodegeneration and dementia, particularly by small vessel disease, is hampered by imprecise data, including the incidence and prevalence of symptomatic and clinically “silent” cerebrovascular disease, long-term outcomes (cognitive, stroke, or functional), and risk factors. New large collaborative studies with long follow-up are expensive and time consuming, yet substantial data to advance the field are available. In an initiative funded by the Joint Programme for Neurodegenerative Disease Research, 55 international experts surveyed and assessed available data, starting with European cohorts, to promote data sharing to advance understanding of how vascular disease affects brain structure and function, optimize methods for cerebrovascular disease in neurodegeneration research, and focus future research on gaps in knowledge. Here, we summarize the results and recommendations from this initiative. We identified data from over 90 studies, including over 660,000 participants, many being additional to neurodegeneration data initiatives. The enthusiastic response means that cohorts from North America, Australasia, and the Asia Pacific Region are included, creating a truly global, collaborative, data sharing platform, linked to major national dementia initiatives. Furthermore, the revised World Health Organization International Classification of Diseases version 11 should facilitate recognition of vascular-related brain damage by creating one category for all cerebrovascular disease presentations and thus accelerate identification of targets for dementia prevention