Testosterone deficiency causes penile fibrosis and organic erectile dysfunction in aging men. Evaluating association among Age, TDS and ED.

INTRODUCTION: We studied the possible correlation between age, testosterone deficiency, cavernosal fibrosis and erectile dysfunction (ED). METHODS: 47 patients with ED were enrolled between September 2010 and October 2011. IIEF-EF score, NPTR test using the Rigiscan method, total and free testosterone levels, and cavernosum biopsy were carried out on all patients. Patients aged 65 or over were defined as Old Age (OA) while patients under 65 were defined Young age (YA). The strength of the relationships found was estimated by Odds Ratio. RESULTS: 74% of patients with values of over 52% collagen fibers in the corpora cavernosa were found to have organic ED. A significant difference was found in age, percentage of collagen fibers, testosterone levels between patients with Positive Rigiscan (PR) and Negative Rigiscan (NR). Hypotestosteronaemia increased the risk of ED with PR (OR: 21.4, 95% CI: 20.2-22.6) and in both young age patients (OR: 4.3, 95% CI: 2.4-6.2) and old age patients (OR: 15.5, 95% CI: 13.4-17.6). Moreover cavernosal fibrosis increased the risk of ED with PR in both young age patients (OR: 8.2, 95% CI: 6.4-10.0 and old age patients (OR: 24.6, 95% CI: 20.8-28.4). CONCLUSIONS: This study demonstrates a strong association among age, testosterone deficiency, cavernosal fibrosis and ED with PR. Age, testosterone deficiency and cavernosal fibrosis are potentially correctable factors of cavernosal fibrosis and organic ED. Further, prospective studies are needed to evaluate if testosterone treatment, alone or in association with PDE5 inhibitors, may lower the risk of cavernosal fibrosis or decrease the severity the fibrosis in ED patients

Whole organic electronic synapses for dopamine detection

A whole organic artificial synapse has been fabricated by patterning PEDOT:PSS electrodes on PDMS that are biased in frequency to yield a STP response. The timescale of the STP response is shown to be sensitive to the concentration of dopamine, DA, a neurotransmitter relevant for monitoring the development of Parkinson's disease and potential locoregional therapies. The sensitivity of the sensor towards DA has been validated comparing signal variation in the presence of DA and its principal interfering agent, ascorbic acid, AA. The whole organic synapse is biocompatible, soft and flexible, and is attractive for implantable devices aimed to real-time monitoring of DA concentration in bodily fluids. This may open applications in chronic neurodegenerative diseases such as Parkinson's disease

Reconnection surgery in adult post-operative short bowel syndrome < 100 cm: is colonic continuity sufficient to achieve enteral autonomy without autologous gastrointestinal reconstruction? Report from a single center and systematic review of literature

A systematic bibliographic research concerning patients operated on for SBS was performed: inclusion criteria were adult age, reconnection surgery and SBS &lt; 100 cm. Autologous gastrointestinal reconstruction represented an exclusion criteria. The outcomes of interest were the rate of total parenteral nutrition (TPN) independence and the length of follow-up (minimum 1 year) after surgery. We reviewed our experience from 2003 to 2013 with minimum 1-year follow-up, dealing with reconnection surgery in 13 adults affected by &lt; 100 cm SBS after massive small bowel resection: autologous gastrointestinal reconstruction was not feasible. Three (out of 5168 screened papers) non randomized controlled trials with 116 adult patients were analysed showing weaning from TPN (40%, 50% and 90% respectively) after reconnection surgery without autologous gastrointestinal reconstruction. Among our 13 adults, mean age was 54.1 years (53.8 % ASA III): 69.2 % had a high stomal output (&gt; 500 cc/day) and TPN dependence was 100%. We performed a jejuno-colonic anastomosis (SBS type II) in 53.8%, in 46.1% of cases without ileo-cecal valve, leaving a mean residual small bowel length of 75.7 cm. In-hospital mortality was 0%. After a minimum period of 1 year of intestinal rehabilitation, all our patients (100%) went back to oral intake and 69.2% were off TPN (9 patients). No one was listed for transplantation. A residual small bowel length of minimum 75 cm, even if reconnected to part of the colon, seems able to produce a TPN independence without autologous gastrointestinal reconstruction after a minimum period of 1 year of intestinal rehabilitation

Probabilistic predictions of SIS epidemics on networks based on population-level observations

We predict the future course of ongoing susceptible-infected-susceptible (SIS) epidemics on regular, Erdős-Rényi and Barabási-Albert networks. It is known that the contact network influences the spread of an epidemic within a population. Therefore, observations of an epidemic, in this case at the population-level, contain information about the underlying network. This information, in turn, is useful for predicting the future course of an ongoing epidemic. To exploit this in a prediction framework, the exact high-dimensional stochastic model of an SIS epidemic on a network is approximated by a lower-dimensional surrogate model. The surrogate model is based on a birth-and-death process; the effect of the underlying network is described by a parametric model for the birth rates. We demonstrate empirically that the surrogate model captures the intrinsic stochasticity of the epidemic once it reaches a point from which it will not die out. Bayesian parameter inference allows for uncertainty about the model parameters and the class of the underlying network to be incorporated directly into probabilistic predictions. An evaluation of a number of scenarios shows that in most cases the resulting prediction intervals adequately quantify the prediction uncertainty. As long as the population-level data is available over a long-enough period, even if not sampled frequently, the model leads to excellent predictions where the underlying network is correctly identified and prediction uncertainty mainly reflects the intrinsic stochasticity of the spreading epidemic. For predictions inferred from shorter observational periods, uncertainty about parameters and network class dominate prediction uncertainty. The proposed method relies on minimal data at population-level, which is always likely to be available. This, combined with its numerical efficiency, makes the proposed method attractive to be used either as a standalone inference and prediction scheme or in conjunction with other inference and/or predictive models

PDE limits of stochastic SIS epidemics on networks

Stochastic epidemic models on networks are inherently high-dimensional and the resulting exact models are intractable numerically even for modest network sizes. Mean-field models provide an alternative but can only capture average quantities, thus offering little or no information about variability in the outcome of the exact process. In this article, we conjecture and numerically demonstrate that it is possible to construct partial differential equation (PDE)-limits of the exact stochastic susceptible-infected-susceptible epidemics on Regular, Erdős–Rényi, Barabási–Albert networks and lattices. To do this, we first approximate the exact stochastic process at population level by a Birth-and-Death process (BD) (with a state space of O(N) rather than O(2N)⁠) whose coefficients are determined numerically from Gillespie simulations of the exact epidemic on explicit networks. We numerically demonstrate that the coefficients of the resulting BD process are density-dependent, a crucial condition for the existence of a PDE limit. Extensive numerical tests for Regular, Erdős–Rényi, Barabási–Albert networks and lattices show excellent agreement between the outcome of simulations and the numerical solution of the Fokker–Planck equations. Apart from a significant reduction in dimensionality, the PDE also provides the means to derive the epidemic outbreak threshold linking network and disease dynamics parameters, albeit in an implicit way. Perhaps more importantly, it enables the formulation and numerical evaluation of likelihoods for epidemic and network inference as illustrated in a fully worked out example

Multifunctionally-doped PEDOT for organic electrochemical transistors

Organic Electrochemical Transistors (OECTs) are suitable for developing ultra-sensitive bioelectronic sensors. In the organic electrochemical transistors architecture, the source-drain channel is made of a conductive polymer film either cast from a formulated dispersion or electrodeposited from a monomer solution. The commercial poly(3,4-ethylenedioxidethiophene)/poly(styrene sulfonate) (PEDOT:PSS) water dispersion is the workhorse of organic bioelectronics for its high conductance, low impact and ease of processability. In this study, a hybrid organic electrochemical transistors channel fabrication strategy is presented, where electrochemical deposition of a PEDOT/X (with X indicating the counterion) is performed on a dispersion-cast PEDOT:PSS film. Six different counterions where used: X = PSS, Nafion, Hyaluronate, Dextran sulfate, Dexamethasone phosphate and tauroursodeoxycholic acid, each potentially endowing organic electrochemical transistors with additional functions such as ion exchange and pharmacological activity upon release of X. The PEDOT/X-PEDOT:PSS bilayers were characterized by means of electrochemical impedance spectroscopy (EIS), atomic force microscopy (AFM), X-ray photoelectron spectroscopy (XPS) and focused ion beam tomography combined with scanning electron microscopy (FIB-SEM). In addition, their respective organic electrochemical transistorss were characterized and compared to PEDOT:PSS organic electrochemical transistors. Our results show that the hybrid bilayer strategy is viable to fabricate multifunctional organic electrochemical transistorss with biologically-relevant function, thereby retaining the outstanding figures of merit of commercial PEDOT:PSS

Analysis of the ATR-Chk1 and ATM-Chk2 pathways in male breast cancer revealed the prognostic significance of ATR expression

The ATR-Chk1 and ATM-Chk2 pathways are central in DNA damage repair (DDR) and their over-activation may confer aggressive molecular features, being an adaptive response to endogenous DNA damage and oncogene-induced replication stress. Herein we investigated the ATR-Chk1 and ATM-Chk2 signalings in male breast cancer (MBC). The expression of DDR kinases (pATR, pATM, pChk1, pChk2, and pWee1) and DNA damage markers (pRPA32 and γ-H2AX) was evaluated by immunohistochemistry in 289 MBC samples to assess their association. Survival analyses were carried out in 112 patients. Survival curves were estimated with the Kaplan-Meier method and compared by log-rank test. Cox proportional regression models were generated to identify variables impacting survival outcomes. The expression of pATR conferred poorer survival outcomes (log rank p = 0.013, p = 0.007 and p = 0.010 for overall, 15- and 10-year survival, respectively). Multivariate Cox models of 10-year survival and overall indicated that pATR expression, alone or combined with pChk2, was an independent predictor of adverse outcomes (10-year survival: pATR: HR 2.74, 95% CI: 1.23–6.10; pATR/pChk2: HR 2.92, 95% CI: 1.35–6.33; overall survival: pATR: HR 2.58, 95% CI: 1.20–5.53; pATR/pChk2: HR 2.89, 95% CI: 1.37–6.12). Overall, the ATR/ATM-initiated molecular cascade seems to be active in a fraction of MBC patients and may represent a negative prognostic factor

Network inference from population-level observation of epidemics

Using the continuous-time susceptible-infected-susceptible (SIS) model on networks, we investigate the problem of inferring the class of the underlying network when epidemic data is only available at population-level (i.e., the number of infected individuals at a finite set of discrete times of a single realisation of the epidemic), the only information likely to be available in real world settings. To tackle this, epidemics on networks are approximated by a Birth-and-Death process which keeps track of the number of infected nodes at population level. The rates of this surrogate model encode both the structure of the underlying network and disease dynamics. We use extensive simulations over Regular, Erdős–Rényi and Barabási–Albert networks to build network class-specific priors for these rates. We then use Bayesian model selection to recover the most likely underlying network class, based only on a single realisation of the epidemic. We show that the proposed methodology yields good results on both synthetic and real-world networks