A canine leishmaniasis pilot survey in an emerging focus of visceral leishmaniasis: Posadas (Misiones, Argentina)

<p>Abstract</p> <p>Background</p> <p>An increasing number of reports are calling our attention to the worldwide spread of leishmaniasis. The urbanization of zoonotic visceral leishmaniasis (VL) has been observed in different South American countries, due to changes in demographic and ecological factors. In May 2006, VL was detected for the first time in the city of Posadas (Misiones, Argentina). This event encouraged us to conduct a clinical and parasitological pilot survey on domestic dogs from Posadas to identify their potential role as reservoirs for the disease.</p> <p>Methods</p> <p>One hundred and ten dogs from the city of Posadas were included in the study. They were selected based on convenience and availability. All dogs underwent clinical examination. Symptomatology related to canine leishmaniasis was recorded, and peripheral blood and lymph node aspirates were collected. Anti-<it>Leishmania </it>antibodies were detected using rK39-immunocromatographic tests and IFAT. Parasite detection was based on peripheral blood and lymph node aspirate PCR targeting the <it>SSUrRNA </it>gene. Molecular typing was addressed by DNA sequence analysis of the PCR products obtained by <it>SSUrRNA </it>and ITS-1 PCR.</p> <p>Results</p> <p>According to clinical examination, 69.1% (76/110) of the dogs presented symptoms compatible with canine leishmaniasis. Serological analyses were positive for 43.6% (48/110) of the dogs and parasite DNA was detected in 47.3% (52/110). A total of 63 dogs (57.3%) were positive by serology and/or PCR. Molecular typing identified <it>Leishmania infantum </it>(syn. <it>Leishmania chagasi</it>) as the causative agent.</p> <p>Conclusions</p> <p>This work confirms recent findings which revealed the presence of <it>Lutzomyia longipalpis</it>, the vector of <it>L. infantum </it>in this area of South America. This new VL focus could be well established, and further work is needed to ascertain its magnitude and to prevent further human VL cases.</p

HERV-K and HERV-W transcriptional activity in myalgic encephalomyelitis/chronic fatigue syndrome.

BACKGROUND: Chronic fatigue syndrome/myalgic encephalomyelitis (CFS/MS) is an incapacitating chronic disease that dramatically compromise the life quality. The CFS/ME pathogenesis is multifactorial, and it is believed that immunological, metabolic and environmental factors play a role. It is well documented an increased activity of Human endogenous retroviruses (HERVs) from different families in autoimmune and neurological diseases, making these elements good candidates for biomarkers or even triggers for such diseases. METHODS: Here the expression of Endogenous retroviruses K and W (HERV-K and HERV-W) was determined in blood from moderately and severely affected ME/CFS patients through real time PCR. RESULTS: HERV-K was overexpressed only in moderately affected individuals but HERV-W showed no difference. CONCLUSIONS: This is the first report about HERV-K differential expression in moderate ME/CFS. Although the relationship between HERVs and ME/CFS has yet to be proven, the observation of this phenomenon deserves further attention

Never Resting Brain: Simultaneous Representation of Two Alpha Related Processes in Humans

Brain activity is continuously modulated, even at “rest”. The alpha rhythm (8–12 Hz) has been known as the hallmark of the brain's idle-state. However, it is still debated if the alpha rhythm reflects synchronization in a distributed network or focal generator and whether it occurs spontaneously or is driven by a stimulus. This EEG/fMRI study aimed to explore the source of alpha modulations and their distribution in the resting brain. By serendipity, while computing the individually defined power modulations of the alpha-band, two simultaneously occurring components of these modulations were found. An ‘induced alpha’ that was correlated with the paradigm (eyes open/ eyes closed), and a ‘spontaneous alpha’ that was on-going and unrelated to the paradigm. These alpha components when used as regressors for BOLD activation revealed two segregated activation maps: the ‘induced map’ included left lateral temporal cortical regions and the hippocampus; the ‘spontaneous map’ included prefrontal cortical regions and the thalamus. Our combined fMRI/EEG approach allowed to computationally untangle two parallel patterns of alpha modulations and underpin their anatomical basis in the human brain. These findings suggest that the human alpha rhythm represents at least two simultaneously occurring processes which characterize the ‘resting brain’; one is related to expected change in sensory information, while the other is endogenous and independent of stimulus change

Interpretable machine learning models for classifying low back pain status using functional physiological variables.

PURPOSE:To evaluate the predictive performance of statistical models which distinguishes different low back pain (LBP) sub-types and healthy controls, using as input predictors the time-varying signals of electromyographic and kinematic variables, collected during low-load lifting. METHODS:Motion capture with electromyography (EMG) assessment was performed on 49 participants [healthy control (con) = 16, remission LBP (rmLBP) = 16, current LBP (LBP) = 17], whilst performing a low-load lifting task, to extract a total of 40 predictors (kinematic and electromyographic variables). Three statistical models were developed using functional data boosting (FDboost), for binary classification of LBP statuses (model 1: con vs. LBP; model 2: con vs. rmLBP; model 3: rmLBP vs. LBP). After removing collinear predictors (i.e. a correlation of > 0.7 with other predictors) and inclusion of the covariate sex, 31 predictors were included for fitting model 1, 31 predictors for model 2, and 32 predictors for model 3. RESULTS:Seven EMG predictors were selected in model 1 (area under the receiver operator curve [AUC] of 90.4%), nine predictors in model 2 (AUC of 91.2%), and seven predictors in model 3 (AUC of 96.7%). The most influential predictor was the biceps femoris muscle (peak [Formula: see text]  = 0.047) in model 1, the deltoid muscle (peak [Formula: see text] =  0.052) in model 2, and the iliocostalis muscle (peak [Formula: see text] =  0.16) in model 3. CONCLUSION:The ability to transform time-varying physiological differences into clinical differences could be used in future prospective prognostic research to identify the dominant movement impairments that drive the increased risk. These slides can be retrieved under Electronic Supplementary Material