Over het ontstaan van het zure milieu in de vagina

De bekleding van de vagina bestaat uit p!aveiselepitheel, dat opgebouwd is uit een basale, intermediaire en superficiële laag; de laatste bevat het meeste glycogeen (Rakoff e.a. 1944, Matter 1955, Gregoire e.a. 1971 ). De fluor vaginalis bevat glycogeen door exfoliatie van de oppervlakkig cellen. Döderlein (1892), die met een lakmoespapiertje vaststelde dat het vaginale milieu zuur reageert, legde verband tussen het voorkomen in de vagina van Jactobacillen en melkzuur. Hij veronderstelde, dat het melkzuur door de lactobacil!en wordt gevormd uit het glycogeen van de afgeschilferde epitheelcellen. Zijn theorie leek ondersteund door de klinische waarneming dat juist of vooral bij rijkelijke aanwezigheid van lacto bacillen de fluor vaginalis zuur reageert. Als nuttige funktie van het zure karakter van het vaginale milieu werd gezien het voorkomen van opstijgende infekties. Deze theorie vond zo algemeen ingang dat deze zich tot heden als een vast gegeven in de vaginale fysiologie heeft gehandhaafd

Atmospheric Channel Characteristics for Quantum Communication with Continuous Polarization Variables

We investigate the properties of an atmospheric channel for free space quantum communication with continuous polarization variables. In our prepare-and-measure setup, coherent polarization states are transmitted through an atmospheric quantum channel of 100m length on the roof of our institute's building. The signal states are measured by homodyne detection with the help of a local oscillator (LO) which propagates in the same spatial mode as the signal, orthogonally polarized to it. Thus the interference of signal and LO is excellent and atmospheric fluctuations are autocompensated. The LO also acts as spatial and spectral filter, which allows for unrestrained daylight operation. Important characteristics for our system are atmospheric channel influences that could cause polarization, intensity and position excess noise. Therefore we study these influences in detail. Our results indicate that the channel is suitable for our quantum communication system in most weather conditions.Comment: 6 pages, 4 figures, submitted to Applied Physics B following an invitation for the special issue "Selected Papers Presented at the 2009 Spring Meeting of the Quantum Optics and Photonics Section of the German Physical Society

Two types of liposomal formulations improve the therapeutic ratio of prednisolone phosphate in a zebrafish model for inflammation

Glucocorticoids (GCs) are effective anti-inflammatory drugs, but their clinical use is limited by their side effects. Using liposomes to target GCs to inflammatory sites is a promising approach to improve their therapeutic ratio. We used zebrafish embryos to visualize the biodistribution of liposomes and to determine the anti-inflammatory and adverse effects of the GC prednisolone phosphate (PLP) encapsulated in these liposomes. Our results showed that PEGylated liposomes remained in circulation for long periods of time, whereas a novel type of liposomes (which we named AmbiMACs) selectively targeted macrophages. Upon laser wounding of the tail, both types of liposomes were shown to accumulate near the wounding site. Encapsulation of PLP in the PEGylated liposomes and AmbiMACs increased its potency to inhibit the inflammatory response. However, encapsulation of PLP in either type of liposome reduced its inhibitory effect on tissue regeneration, and encapsulation in PEGylated liposomes attenuated the activation of glucocorticoid-responsive gene expression throughout the body. Thus, by exploiting the unique possibilities of the zebrafish animal model to study the biodistribution as well as the anti-inflammatory and adverse effects of liposomal formulations of PLP, we showed that PEGylated liposomes and AmbiMACs increase the therapeutic ratio of this GC drug.Supramolecular & Biomaterials ChemistryAnimal science

Myocardial perfusion imaging: Lessons learned and work to be done-update

Cardiolog

Differences in Characteristics and Outcome of Patients with Penetrating Injuries in the USA and the Netherlands: A Multi-institutional Comparison

Introduction: The incidence and nature of penetrating injuries differ between countries. The aim of this study was to analyze characteristics and clinical outcomes of patients with penetrating injuries treated at urban Level-1 trauma centers in the USA (USTC) and the Netherlands (NLTC). Methods: In this retrospective cohort study, 1331 adult patients (470 from five NLTC and 861 from three USTC) with truncal penetrating injuries admitted between July 2011 and December 2014 were included. In-hospital mortality was the primary outcome. Outcome comparisons were adjusted for differences in population characteristics in multivariable analyses. Results: In USTC, gunshot wound injuries (36.1 vs. 17.4%, p < 0.001) and assaults were more frequent (91.2 vs. 77.7%, p < 0.001). ISS was higher in USTC, but the Revised Trauma Score (RTS) was comparable. In-hospital mortality was similar (5.0 vs. 3.6% in NLTC, p = 0.25). The adjusted odds ratio for mortality in USTC compared to NLTC was 0.95 (95% confidence interval 0.35–2.54). Hospital stay length of stay was shorter in USTC (difference 0.17 days, 95% CI −0.29 to −0.05, p = 0.005), ICU admission rate was comparable (OR 0.96, 95% CI 0.71–1.31, p = 0.80), and ICU length of stay was longer in USTC (difference of 0.39 days, 95% CI 0.18–0.60, p < 0.0001). More USTC patients were discharged to home (86.9 vs. 80.6%, p < 0.001). Readmission rates were similar (5.6 vs. 3.8%, p = 0.17). Conclusion: Despite the higher incidence of penetrating trauma, particularly firearm-related injuries, and higher hospital volumes in the USTC compared to the NLTC, the in-hospital mortality was similar. In this study, outcome of care was not significantly influenced by differences in incidence of firearm-related injuries

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Neuroimaging-based classification of PTSD using data-driven computational approaches: a multisite big data study from the ENIGMA-PGC PTSD consortium

Background: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. Results: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for D-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Conclusion: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.Stress-related psychiatric disorders across the life spa