Microbiological, histological, immunological, and toxin response to antibiotic treatment in the mouse model of Mycobacterium ulcerans disease.

Mycobacterium ulcerans infection causes a neglected tropical disease known as Buruli ulcer that is now found in poor rural areas of West Africa in numbers that sometimes exceed those reported for another significant mycobacterial disease, leprosy, caused by M. leprae. Unique among mycobacterial diseases, M. ulcerans produces a plasmid-encoded toxin called mycolactone (ML), which is the principal virulence factor and destroys fat cells in subcutaneous tissue. Disease is typically first manifested by the appearance of a nodule that eventually ulcerates and the lesions may continue to spread over limbs or occasionally the trunk. The current standard treatment is 8 weeks of daily rifampin and injections of streptomycin (RS). The treatment kills bacilli and wounds gradually heal. Whether RS treatment actually stops mycolactone production before killing bacilli has been suggested by histopathological analyses of patient lesions. Using a mouse footpad model of M. ulcerans infection where the time of infection and development of lesions can be followed in a controlled manner before and after antibiotic treatment, we have evaluated the progress of infection by assessing bacterial numbers, mycolactone production, the immune response, and lesion histopathology at regular intervals after infection and after antibiotic therapy. We found that RS treatment rapidly reduced gross lesions, bacterial numbers, and ML production as assessed by cytotoxicity assays and mass spectrometric analysis. Histopathological analysis revealed that RS treatment maintained the association of the bacilli with (or within) host cells where they were destroyed whereas lack of treatment resulted in extracellular infection, destruction of host cells, and ultimately lesion ulceration. We propose that RS treatment promotes healing in the host by blocking mycolactone production, which favors the survival of host cells, and by killing M. ulcerans bacilli

A novel multivariate STeady-state index during general ANesthesia (STAN)

The assessment of the adequacy of general anesthesia for surgery, namely the nociception/anti-nociception balance, has received wide attention from the scientific community. Monitoring systems based on the frontal EEG/EMG, or autonomic state reactions (e.g. heart rate and blood pressure) have been developed aiming to objectively assess this balance. In this study a new multivariate indicator of patients' steady-state during anesthesia (STAN) is proposed, based on wavelet analysis of signals linked to noxious activation. A clinical protocol was designed to analyze precise noxious stimuli (laryngoscopy/intubation, tetanic, and incision), under three different analgesic doses; patients were randomized to receive either remifentanil 2.0, 3.0 or 4.0 ng/ml. ECG, PPG, BP, BIS, EMG and [Formula: see text] were continuously recorded. ECG, PPG and BP were processed to extract beat-to-beat information, and [Formula: see text] curve used to estimate the respiration rate. A combined steady-state index based on wavelet analysis of these variables, was applied and compared between the three study groups and stimuli (Wilcoxon signed ranks, Kruskal-Wallis and Mann-Whitney tests). Following institutional approval and signing the informed consent thirty four patients were enrolled in this study (3 excluded due to signal loss during data collection). The BIS index of the EEG, frontal EMG, heart rate, BP, and PPG wave amplitude changed in response to different noxious stimuli. Laryngoscopy/intubation was the stimulus with the more pronounced response [Formula: see text]. These variables were used in the construction of the combined index STAN; STAN responded adequately to noxious stimuli, with a more pronounced response to laryngoscopy/intubation (18.5-43.1 %, [Formula: see text]), and the attenuation provided by the analgesic, detecting steady-state periods in the different physiological signals analyzed (approximately 50 % of the total study time). A new multivariate approach for the assessment of the patient steady-state during general anesthesia was developed. The proposed wavelet based multivariate index responds adequately to different noxious stimuli, and attenuation provided by the analgesic in a dose-dependent manner for each stimulus analyzed in this study.The first author was supported by a scholarship from the Portuguese Foundation for Science and Technology (FCT SFRH/BD/35879/2007). The authors would also like to acknowledge the support of UISPA—System Integration and Process Automation Unit—Part of the LAETA (Associated Laboratory of Energy, Transports and Aeronautics) a I&D Unit of the Foundation for Science and Technology (FCT), Portugal. FCT support under project PEst-OE/EME/LA0022/2013.info:eu-repo/semantics/publishedVersio

Can Score Databanks Help Teaching?

Basic courses in most medical schools assess students' performance by conferring scores. The objective of this work is to use a large score databank for the early identification of students with low performance and to identify course trends based on the mean of students' grades. METHODOLOGY/PRINCIPAL FINDINGS: We studied scores from 2,398 medical students registered in courses over a period of 10 years. Students in the first semester were grouped into those whose ratings remained in the lower quartile in two or more courses (low-performance) and students who had up to one course in the lower quartile (high-performance). ROC curves were built, aimed at the identification of a cut-off average score in the first semesters that would be able to predict low performances in future semesters. Moreover, to follow the long-term pattern of each course, the mean of all scores conferred in a semester was compared to the overall course mean obtained by averaging 10 years of data. Individuals in the low-performance group had a higher risk of being in the lower quartile of at least one course in the second semester (relative risk 3.907; 95% CI: 3.378-4.519) and in the eighth semester (relative risk 2.873; 95% CI: 2.495-3.308). The prediction analysis revealed that an average score of 7.188 in the first semester could identify students that presented scores below the lower quartiles in both the second and eighth semesters (p<0.0001 for both AUC). When scores conferred by single courses were compared over time, three time-trend patterns emerged: low variation, upward trend and erratic pattern. CONCLUSION/SIGNIFICANCE: An early identification of students with low performance may be useful in promoting pedagogical strategies for these individuals. Evaluation of the time trend of scores conferred by courses may help departments monitoring changes in personnel and methodology that may affect a student's performance

Differential effects of antigens from L. braziliensis isolates from disseminated and cutaneous leishmaniasis on in vitro cytokine production

BACKGROUND: Disseminated leishmaniasis is an emerging infectious disease, mostly due to L. braziliensis, which has clinical and histopathological features distinct from cutaneous leishmaniasis. METHODS: In the current study we evaluated the in vitro production of the cytokines IFN-γ, TNF-α, IL-5 and IL-10 by peripheral blood mononuclear cells (PBMC) from 15 disseminated leishmaniasis and 24 cutaneous leishmaniasis patients upon stimulation with L. braziliensis antigens genotyped as disseminated leishmaniasis or cutaneous leishmaniasis isolates. RESULTS: Regardless of the source of L. braziliensis antigens, PBMC from cutaneous leishmaniasis patients produced significantly higher IFN-γ than PBMC from disseminated leishmaniasis patients. Levels of TNF-α by PBMC from cutaneous leishmaniasis patients were significantly higher than disseminated leishmaniasis patients only when stimulated by genotyped cutaneous leishmaniasis antigens. The levels of IL-5 and IL-10 production by PBMC were very low and similar in PBMCs from both disseminated leishmaniasis and cutaneous leishmaniasis patients. The immune response of each patient evaluated by the two L. braziliensis antigens was assessed in a paired analysis in which we showed that L. braziliensis genotyped as disseminated leishmaniasis isolate was more potent than L. braziliensis genotyped as cutaneous leishmaniasis isolate in triggering IFN-γ and TNF-α production in both diseases and IL-5 only in cutaneous leishmaniasis patients. CONCLUSION: This study provides evidence that antigens prepared from genotypically distinct strains of L. braziliensis induce different degrees of immune response. It also indicates that both parasite and host play a role in the outcome of L. braziliensis infection

A novel pancoronavirus RT-PCR assay: frequent detection of human coronavirus NL63 in children hospitalized with respiratory tract infections in Belgium

BACKGROUND: Four human coronaviruses are currently known to infect the respiratory tract: human coronaviruses OC43 (HCoV-OC43) and 229E (HCoV-229E), SARS associated coronavirus (SARS-CoV) and the recently identified human coronavirus NL63 (HCoV-NL63). In this study we explored the incidence of HCoV-NL63 infection in children diagnosed with respiratory tract infections in Belgium. METHODS: Samples from children hospitalized with respiratory diseases during the winter seasons of 2003 and 2004 were evaluated for the presence of HCoV-NL63 using a optimized pancoronavirus RT-PCR assay. RESULTS: Seven HCoV-NL63 positive samples were identified, six were collected during January/February 2003 and one at the end of February 2004. CONCLUSIONS: Our results support the notation that HCoV-NL63 can cause serious respiratory symptoms in children. Sequence analysis of the S gene showed that our isolates could be classified into two subtypes corresponding to the two prototype HCoV-NL63 sequences isolated in The Netherlands in 1988 and 2003, indicating that these two subtypes may currently be cocirculating

From Eshu to Obatala: animals used in sacrificial rituals at Candomblé "terreiros" in Brazil

<p>Abstract</p> <p>Background</p> <p>The practice of sacrifice has occurred in several cultures and religions throughout history and still exists today. Candomblé, a syncretical Afro-Brazilian religion, practices the sacrificial ritual called "<it>Orô</it>" by its adherents. The present work aims to document the use of animal species in these sacrificial practices in the cities of Caruaru (PE) and Campina Grande (PB) in Norteastern Brazil, and to further understand the symbolism of these rituals.</p> <p>Methods</p> <p>Semi-structured and unstructured interviews and informal discussions were held with 11 Candomblé priests and priestesses between the months of August 2007 and June 2008. We attended rituals performed at "terreiros" where animals were sacrificed, in order to obtain photographic material and observe the procedures and techniques adopted.</p> <p>Results</p> <p>A total of 29 animal species were used during sacrificial rituals according to the priests and priestesses. These species were classified in 5 taxanomic groups: Molluscs (n = 1), Amphibians (n = 2), Reptiles (n = 2), Birds (n = 10) and Mammals (n = 14). According to Candomblé beliefs, animals are sacrificed and offered to their deities, known as orishas, for the prosperity of all life. There is a relationship between the colour, sex and behaviour of the animal to be sacrificed, and the orisha to whom the animal is going to be offered. The many myths that form the cosmogony of Candomblé can often explain the symbolism of the rituals observed and the animal species sacrificed. These myths are conveyed to adherants by the priests and priestesses during the ceremonies, and are essential to the continuation of this religion.</p> <p>Conclusion</p> <p>Candomblé is a sacrificial religion that uses animals for its liturgical purposes. The principal reason for sacrifice is to please supernatural deities known as orishas in order to keep life in harmony. This is accomplished through feeding them in a spiritual sense through sacrifice, maintaining a perfect link between men and the gods, and a connection between the material world (called <it>Aiyê</it>) and the supernatural world (called <it>Orun</it>).</p

Comparing Artificial Neural Networks, General Linear Models and Support Vector Machines in Building Predictive Models for Small Interfering RNAs

Exogenous short interfering RNAs (siRNAs) induce a gene knockdown effect in cells by interacting with naturally occurring RNA processing machinery. However not all siRNAs induce this effect equally. Several heterogeneous kinds of machine learning techniques and feature sets have been applied to modeling siRNAs and their abilities to induce knockdown. There is some growing agreement to which techniques produce maximally predictive models and yet there is little consensus for methods to compare among predictive models. Also, there are few comparative studies that address what the effect of choosing learning technique, feature set or cross validation approach has on finding and discriminating among predictive models.Three learning techniques were used to develop predictive models for effective siRNA sequences including Artificial Neural Networks (ANNs), General Linear Models (GLMs) and Support Vector Machines (SVMs). Five feature mapping methods were also used to generate models of siRNA activities. The 2 factors of learning technique and feature mapping were evaluated by complete 3x5 factorial ANOVA. Overall, both learning techniques and feature mapping contributed significantly to the observed variance in predictive models, but to differing degrees for precision and accuracy as well as across different kinds and levels of model cross-validation.The methods presented here provide a robust statistical framework to compare among models developed under distinct learning techniques and feature sets for siRNAs. Further comparisons among current or future modeling approaches should apply these or other suitable statistically equivalent methods to critically evaluate the performance of proposed models. ANN and GLM techniques tend to be more sensitive to the inclusion of noisy features, but the SVM technique is more robust under large numbers of features for measures of model precision and accuracy. Features found to result in maximally predictive models are not consistent across learning techniques, suggesting care should be taken in the interpretation of feature relevance. In the models developed here, there are statistically differentiable combinations of learning techniques and feature mapping methods where the SVM technique under a specific combination of features significantly outperforms all the best combinations of features within the ANN and GLM techniques