Rapid adaptation of the intrarenal resistance index after living donor kidney transplantation

Background. Limited data exist concerning changes of renal perfusion directly after kidney transplantation. Colour-coded duplex sonography is the accepted method to assess kidney perfusion after transplantation. A widely used, although unspecific, Doppler parameter is the intrarenal resistance index (RI). The aim of this study was to clarify the influence of different patient- and procedure-related factors on RI before and immediately after living kidney transplantation. Methods. In a prospective study, 80 living kidney transplantation donor-recipient pairs were included. RI was measured in the donor 1 to 3 days before nephrectomy and in the recipient during the first hour after transplantation to examine the influence of age, heart rate, duration of cold and warm ischaemia time and immunosuppressive medications. Results. Mean RI did not differ between donors and recipients. RI correlated with age, both in donors (r = 0.58, P < 0.001) and recipients (r = 0.39, P < 0.001). In recipients, 10 or more years younger than their donors (n = 24), an average decrease of 0.05 in RI compared to the donors' value was observed (P = 0.01). Heart rate, cold and warm ischaemia time and immunosuppressive medications had no influence on the recipient RI. In patients with delayed graft function, a significant increase in RI within 14 days was observed. However, the initial RI was not predictive of graft function. Conclusions. The transplanted kidney seems to be able to adjust its RI within a short time despite several potential harmful factors that can occur during the transplantatio

Effects of dental probing on occlusal surfaces - A scanning electron microscopy evaluation

The aim of this clinical-morphological study was to investigate the effects of dental probing on occlusal surfaces by scanning electron microscopy (SEM). Twenty sound occlusal surfaces of third molars and 20 teeth with initial carious lesions of 17- to 26-year-old patients (n = 18) were involved. Ten molars of each group were probed with a sharp dental probe (No. 23) before extraction; the other molars served as negative controls. After extraction of the teeth, the crowns were separated and prepared for the SEM study. Probing-related surface defects, enlargements and break-offs of occlusal pits and fissures were observed on all occlusal surfaces with initial carious lesions and on 2 sound surfaces, respectively. No traumatic defects whatsoever were visible on unprobed occlusal surfaces. This investigation confirms findings of light-microscopic studies that using a sharp dental probe for occlusal caries detection causes enamel defects. Therefore, dental probing should be considered as an inappropriate procedure and should be replaced by a meticulous visual inspection. Critical views of tactile caries detection methods with a sharp dental probe as a diagnostic tool seem to be inevitable in undergraduate and postgraduate dental education programmes. Copyright (c) 2007 S. Karger AG, Basel

Predicting dementia from primary care records: a systematic review and meta-analysis

Introduction Possible dementia is usually identified in primary care by general practitioners (GPs) who refer to specialists for diagnosis. Only two-thirds of dementia cases are currently recorded in primary care, so increasing the proportion of cases diagnosed is a strategic priority for the UK and internationally. Clinical entities in the primary care record may indicate risk of developing dementia, and could be combined in a predictive model to help find patients who are missing a diagnosis. We conducted a meta-analysis to identify clinical entities with potential for use in such a predictive model for dementia in primary care. Methods and Findings We conducted a systematic search in PubMed, Web of Science and primary care database bibliographies. We included cohort or case-control studies which used routinely collected primary care data, to measure the association between any clinical entity and dementia. Meta-analyses were performed to pool odds ratios. A sensitivity analysis assessed the impact of non-independence of cases between studies. From a sift of 3836 papers, 20 studies, all European, were eligible for inclusion, comprising >1 million patients. 75 clinical entities were assessed as risk factors for all cause dementia, Alzheimer’s (AD) and Vascular dementia (VaD). Data included were unexpectedly heterogeneous, and assumptions were made about definitions of clinical entities and timing as these were not all well described. Meta-analysis showed that neuropsychiatric symptoms including depression, anxiety, and seizures, cognitive symptoms, and history of stroke, were positively associated with dementia. Cardiovascular risk factors such as hypertension, heart disease, dyslipidaemia and diabetes were positively associated with VaD and negatively with AD. Sensitivity analyses showed similar results. Conclusions These findings are of potential value in guiding feature selection for a risk prediction tool for dementia in primary care. Limitations include findings being UK-focussed. Further predictive entities ascertainable from primary care data, such as changes in consulting patterns, were absent from the literature and should be explored in future studies

Extracting information from the text of electronic medical records to improve case detection: a systematic review

Background: Electronic medical records (EMRs) are revolutionizing health-related research. One key issue for study quality is the accurate identification of patients with the condition of interest. Information in EMRs can be entered as structured codes or unstructured free text. The majority of research studies have used only coded parts of EMRs for case-detection, which may bias findings, miss cases, and reduce study quality. This review examines whether incorporating information from text into case-detection algorithms can improve research quality. Methods: A systematic search returned 9659 papers, 67 of which reported on the extraction of information from free text of EMRs with the stated purpose of detecting cases of a named clinical condition. Methods for extracting information from text and the technical accuracy of case-detection algorithms were reviewed. Results: Studies mainly used US hospital-based EMRs, and extracted information from text for 41 conditions using keyword searches, rule-based algorithms, and machine learning methods. There was no clear difference in case-detection algorithm accuracy between rule-based and machine learning methods of extraction. Inclusion of information from text resulted in a significant improvement in algorithm sensitivity and area under the receiver operating characteristic in comparison to codes alone (median sensitivity 78% (codes + text) vs 62% (codes), P = .03; median area under the receiver operating characteristic 95% (codes + text) vs 88% (codes), P = .025). Conclusions: Text in EMRs is accessible, especially with open source information extraction algorithms, and significantly improves case detection when combined with codes. More harmonization of reporting within EMR studies is needed, particularly standardized reporting of algorithm accuracy metrics like positive predictive value (precision) and sensitivity (recall)

Selection of diazotrophic bacterial communities in biological sand filter mesocosms used for the treatment of phenolic-laden wastewater

Agri effluents such as winery or olive mill waste-waters are characterized by high phenolic concentrations. These compounds are highly toxic and generally refractory to biodegradation. Biological sand filters (BSFs) represent inexpensive, environmentally friendly, and sustainable wastewater treatment systems which rely vastly on microbial catabolic processes. Using denaturing gradient gel electrophoresis and terminal-restriction fragment length polymorphism, this study aimed to assess the impact of increasing concentrations of synthetic phenolic-rich wastewater, ranging from 96 mg L−1 gallic acid and138 mg L−1 vanillin (i.e., a total chemical oxygen demand (COD) of 234 mg L−1) to 2,400mg L−1 gallic acid and 3,442 mg L−1 vanillin (5,842 mg COD L−1), on bacterialcommunities and the specific functional diazotrophic community from BSF mesocosms. This amendment procedure instigated efficient BSF phenolic removal, significant modifications of the bacterial communities, and notably led to the selection of a phenolic-resistant and less diverse diazotrophic community. This suggests that bioavailable N is crucial in the functioning of biological treatment processes involving microbial communities, and thus that functional alterations in the bacterial communities in BSFs ensure provision of sufficient bioavailable nitrogen for the degradation of wastewater with a high C/N ratio.Web of Scienc

On the way to large-scale and high-resolution brain-chip interfacing

Brain-chip-interfaces (BCHIs) are hybrid entities where chips and nerve cells establish a close physical interaction allowing the transfer of information in one or both directions. Typical examples are represented by multi-site-recording chips interfaced to cultured neurons, cultured/acute brain slices, or implanted “in vivo”. This paper provides an overview on recent achievements in our laboratory in the field of BCHIs leading to enhancement of signals transmission from nerve cells to chip or from chip to nerve cells with an emphasis on in vivo interfacing, either in terms of signal-to-noise ratio or of spatiotemporal resolution. Oxide-insulated chips featuring large-scale and high-resolution arrays of stimulation and recording elements are presented as a promising technology for high spatiotemporal resolution interfacing, as recently demonstrated by recordings obtained from hippocampal slices and brain cortex in implanted animals. Finally, we report on an automated tool for processing and analysis of acquired signals by BCHIs

Metformin Prevents Nigrostriatal Dopamine Degeneration Independent of AMPK Activation in Dopamine Neurons

Metformin is a widely prescribed drug used to treat type-2 diabetes, although recent studies show it has wide ranging effects to treat other diseases. Animal and retrospective human studies indicate that Metformin treatment is neuroprotective in Parkinson’s Disease (PD), although the neuroprotective mechanism is unknown, numerous studies suggest the beneficial effects on glucose homeostasis may be through AMPK activation. In this study we tested whether or not AMPK activation in dopamine neurons was required for the neuroprotective effects of Metformin in PD. We generated transgenic mice in which AMPK activity in dopamine neurons was ablated by removing AMPK beta 1 and beta 2 subunits from dopamine transporter expressing neurons. These AMPK WT and KO mice were then chronically exposed to Metformin in the drinking water then exposed to MPTP, the mouse model of PD. Chronic Metformin treatment significantly attenuated the MPTP-induced loss of Tyrosine Hydroxylase (TH) neuronal number and volume and TH protein concentration in the nigrostriatal pathway. Additionally, Metformin treatment prevented the MPTP-induced elevation of the DOPAC:DA ratio regardless of genotype. Metformin also prevented MPTP induced gliosis in the Substantia Nigra. These neuroprotective actions were independent of genotype and occurred in both AMPK WT and AMPK KO mice. Overall, our studies suggest that Metformin’s neuroprotective effects are not due to AMPK activation in dopaminergic neurons and that more research is required to determine how metformin acts to restrict the development of PD

What can MaxEnt reveal about high-density recordings and what can high-density recordings reveal about MaxEnt?

Recent advances in neural recording techniques open exciting possibilities of better understanding whole populations of neurons. Devices such as APS MEA (Active Pixel Sensor Microelectrode Array) [1,2] allow for simultaneous recordings from 4096 channels (64x64 grid) at near-cellular resolution (electrode size: 21μm, electrode spacing: 42μm) and constitute a potentially very rich and detailed source of information on the dynamics of neural systems. Such volumes of data are however difficult to analyse: simple measures such as mean firing rates and correlations are often insufficient to capture interesting phenomena, while more sophisticated approaches can be computationally intensive and hard to interpret. Here we examine the applicability of pairwise maximum entropy (MaxEnt) [3-5] modelling to describe APS MEA data.Pairwise maximum entropy model (equivalent to Ising model in physics), when fit to the data, yields a minimally structured probability distribution of network states that respects first and second order interactions. It is a convex, parsimonious and readily interpretable model that has been shown to characterize spiking patterns surprisingly robustly in many cases [3,4]. Additionally, it can provide a sensitive tool in detecting higher-order interactions. As reported in [5], the significant failure of the Ising model in close range (&lt;300 μm) uncovers a high-order processing mode in local clusters of neurons, a mode of processing absent on larger scale (&gt;600 μm) and undetectable with correlations.In present work we examine the results and performance of the MaxEnt model fitting in different preparation types and parameter regimes; owing to high resolution recordings we can specifically focus on varying spatial scales. As can be seen in Fig.1, indeed even in cultured tissue data there are indicators of certain discrepancies between local populations and populations further apart. Firstly (panel A), it is in local populations where the advantage of Ising model over the independent model is most prominent. Secondly (panel B), the interactions within local populations reveal a different structure than those among groups of neurons spread further apart (Kolmogorov-Smirnov test, p&lt;0.05); and, importantly, this is not a feature that can be shown by correlation analysis

PRACTISE Survey-PhaRmAcist-led CogniTIve Services in Europe: first results

Poster presented at the 10th PCNE Working Conference, 1-4 February 2017, Bled, SloveniaN/

PRACTISE - PhaRmAcist-led CogniTIve Services in Europe: preliminary results

Communication presented at the 2nd International Congress of CiiEM: Translational Research and Innovation in Human and Health Sciences. 11-13 June 2017, Campus Egas Moniz, Caparica, PortugalN/