Elevated levels of Dickkopf-related protein 3 in seminal plasma of prostate cancer patients

<p>Abstract</p> <p>Background</p> <p>Expression of Dkk-3, a secreted putative tumor suppressor, is altered in age-related proliferative disorders of the human prostate. We now investigated the suitability of Dkk-3 as a diagnostic biomarker for prostate cancer (PCa) in seminal plasma (SP).</p> <p>Methods</p> <p>SP samples were obtained from 81 patients prior to TRUS-guided prostate biopsies on the basis of elevated serum prostate-specific antigen (PSA; > 4 ng/mL) levels and/or abnormal digital rectal examination. A sensitive indirect immunoenzymometric assay for Dkk-3 was developed and characterized in detail. SP Dkk-3 and PSA levels were determined and normalized to total SP protein. The diagnostic accuracies of single markers including serum PSA and multivariate models to discriminate patients with positive (N = 40) and negative (N = 41) biopsy findings were investigated.</p> <p>Results</p> <p>Biopsy-confirmed PCa showed significantly higher SP Dkk-3 levels (100.9 ± 12.3 vs. 69.2 ± 9.4 fmol/mg; <it>p </it>= 0.026). Diagnostic accuracy (AUC) of SP Dkk-3 levels (0.633) was enhanced in multivariate models by including serum PSA (model A; AUC 0.658) or both, serum and SP PSA levels (model B; AUC 0.710). In a subpopulation with clinical follow-up > 3 years post-biopsy to ensure veracity of negative biopsy status (positive biopsy N = 21; negative biopsy N = 25) AUCs for SP Dkk-3, model A and B increased to 0.667, 0.724 and 0.777, respectively.</p> <p>Conclusions</p> <p>In multivariate models to detect PCa, inclusion of SP Dkk-3 levels, which were significantly elevated in biopsy-confirmed PCa patients, improved the diagnostic performance compared with serum PSA only.</p

Epidemics on contact networks: a general stochastic approach

Dynamics on networks is considered from the perspective of Markov stochastic processes. We partially describe the state of the system through network motifs and infer any missing data using the available information. This versatile approach is especially well adapted for modelling spreading processes and/or population dynamics. In particular, the generality of our systematic framework and the fact that its assumptions are explicitly stated suggests that it could be used as a common ground for comparing existing epidemics models too complex for direct comparison, such as agent-based computer simulations. We provide many examples for the special cases of susceptible-infectious-susceptible (SIS) and susceptible-infectious-removed (SIR) dynamics (e.g., epidemics propagation) and we observe multiple situations where accurate results may be obtained at low computational cost. Our perspective reveals a subtle balance between the complex requirements of a realistic model and its basic assumptions.Comment: Main document: 16 pages, 7 figures. Electronic Supplementary Material (included): 6 pages, 1 tabl

Social interaction, noise and antibiotic-mediated switches in the intestinal microbiota

The intestinal microbiota plays important roles in digestion and resistance against entero-pathogens. As with other ecosystems, its species composition is resilient against small disturbances but strong perturbations such as antibiotics can affect the consortium dramatically. Antibiotic cessation does not necessarily restore pre-treatment conditions and disturbed microbiota are often susceptible to pathogen invasion. Here we propose a mathematical model to explain how antibiotic-mediated switches in the microbiota composition can result from simple social interactions between antibiotic-tolerant and antibiotic-sensitive bacterial groups. We build a two-species (e.g. two functional-groups) model and identify regions of domination by antibiotic-sensitive or antibiotic-tolerant bacteria, as well as a region of multistability where domination by either group is possible. Using a new framework that we derived from statistical physics, we calculate the duration of each microbiota composition state. This is shown to depend on the balance between random fluctuations in the bacterial densities and the strength of microbial interactions. The singular value decomposition of recent metagenomic data confirms our assumption of grouping microbes as antibiotic-tolerant or antibiotic-sensitive in response to a single antibiotic. Our methodology can be extended to multiple bacterial groups and thus it provides an ecological formalism to help interpret the present surge in microbiome data.Comment: 20 pages, 5 figures accepted for publication in Plos Comp Bio. Supplementary video and information availabl

Divided attention selectively impairs memory for self-relevant information

Information that is relevant to oneself tends to be remembered more than information that relates to other people, but the role of attention in eliciting this "self-reference effect" is unclear. In the present study, we assessed the importance of attention in self-referential encoding using an ownership paradigm, which required participants to encode items under conditions of imagined ownership by themselves or by another person. Previous work has established that this paradigm elicits a robust self-reference effect, with more "self-owned" items being remembered than "other-owned" items. Access to attentional resources was manipulated using divided-attention tasks at encoding. A significant self-reference effect emerged under full-attention conditions and was related to an increase in episodic recollection for self-owned items, but dividing attention eliminated this memory advantage. These findings are discussed in relation to the nature of self-referential cognition and the importance of attentional resources at encoding in the manifestation of the self-reference effect in memory

Eye Movements Predict Recollective Experience

Previously encountered stimuli can bring to mind a vivid memory of the episodic context in which the stimulus was first experienced ("remembered'' stimuli), or can simply seem familiar ("known'' stimuli). Past studies suggest that more attentional resources are required to encode stimuli that are subsequently remembered than known. However, it is unclear if the attentional resources are distributed differently during encoding and recognition of remembered and known stimuli. Here, we record eye movements while participants encode photos, and later while indicating whether the photos are remembered, known or new. Eye fixations were more clustered during both encoding and recognition of remembered photos relative to known photos. Thus, recognition of photos that bring to mind a vivid memory for the episodic context in which they were experienced is associated with less distributed overt attention during encoding and recognition. The results suggest that remembering is related to encoding of a few distinct details of a photo rather than the photo as a whole. In turn, during recognition remembering may be trigged by enhanced memory for the salient details of the photos

Evaluation of a Bayesian inference network for ligand-based virtual screening

Background Bayesian inference networks enable the computation of the probability that an event will occur. They have been used previously to rank textual documents in order of decreasing relevance to a user-defined query. Here, we modify the approach to enable a Bayesian inference network to be used for chemical similarity searching, where a database is ranked in order of decreasing probability of bioactivity. Results Bayesian inference networks were implemented using two different types of network and four different types of belief function. Experiments with the MDDR and WOMBAT databases show that a Bayesian inference network can be used to provide effective ligand-based screening, especially when the active molecules being sought have a high degree of structural homogeneity; in such cases, the network substantially out-performs a conventional, Tanimoto-based similarity searching system. However, the effectiveness of the network is much less when structurally heterogeneous sets of actives are being sought. Conclusion A Bayesian inference network provides an interesting alternative to existing tools for ligand-based virtual screening