Delayed pain decrease following M1 tDCS in spinal cord injury: A randomized controlled clinical trial

Despite some encouraging findings for the treatment of neuropathic pain in patients with spinal cord injury (SCI), transcranial direct current stimulation (tDCS) directed to the primary motor cortex (M1) has faced some mixed results. Prior to translating this technology to clinical care, consistent results and durable effects need to be found. We, therefore, aimed to assess the direct and long-term effects of tDCS on pain following SCI.We performed a two-phase randomized sham-controlled clinical trial where patients received 5 days of tDCS followed by a 3-month follow-up period (Phase I); then, Phase II consisted of 10 days of tDCS with an 8-week follow-up period. We assessed the level of pain with the Visual Analogue Scale (VAS). Patients' quality of life and life satisfaction were also evaluated.33 patients were enrolled in Phase I and 9 in Phase II. We observed a treatment effect at 1-week follow-up for Phase I and at 4-week follow-up for Phase II. The overall level of pain was significantly lower for the active group, as compared to sham, in Phase II.Our exploratory study shows that tDCS does seem to be a promising tool to manage pain in patients with SCI and repeated stimulation sessions are needed to induce long-lasting effects. Based on our protocol, it appears that adding a second treatment period could induce long-lasting effects.This project was supported by the National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR grant numbers 90DP0035 and H133N110010)

Tandem Mass Spectrometry Measurement of the Collision Products of Carbamate Anions Derived from CO2 Capture Sorbents: Paving the Way for Accurate Quantitation

The reaction between CO2 and aqueous amines to produce a charged carbamate product plays a crucial role in post-combustion capture chemistry when primary and secondary amines are used. In this paper, we report the low energy negative-ion CID results for several anionic carbamates derived from primary and secondary amines commonly used as post-combustion capture solvents. The study was performed using the modern equivalent of a triple quadrupole instrument equipped with a T-wave collision cell. Deuterium labeling of 2-aminoethanol (1,1,2,2,-d4-2-aminoethanol) and computations at the M06-2X/6-311++G(d,p) level were used to confirm the identity of the fragmentation products for 2-hydroxyethylcarbamate (derived from 2-aminoethanol), in particular the ions CN−, NCO− and facile neutral losses of CO2 and water; there is precedent for the latter in condensed phase isocyanate chemistry. The fragmentations of 2-hydroxyethylcarbamate were generalized for carbamate anions derived from other capture amines, including ethylenediamine, diethanolamine, and piperazine. We also report unequivocal evidence for the existence of carbamate anions derived from sterically hindered amines (Tris(2-hydroxymethyl)aminomethane and 2-methyl-2-aminopropanol). For the suite of carbamates investigated, diagnostic losses include the decarboxylation product (−CO2, 44 mass units), loss of 46 mass units and the fragments NCO− (m/z 42) and CN− (m/z 26). We also report low energy CID results for the dicarbamate dianion (−O2CNHC2H4NHCO2−) commonly encountered in CO2 capture solution utilizing ethylenediamine. Finally, we demonstrate a promising ion chromatography-MS based procedure for the separation and quantitation of aqueous anionic carbamates, which is based on the reported CID findings. The availability of accurate quantitation methods for ionic CO2 capture products could lead to dynamic operational tuning of CO2 capture-plants and, thus, cost-savings via real-time manipulation of solvent regeneration energies

A machine-learned analysis of human gene polymorphisms modulating persisting pain points to major roles of neuroimmune processes

Background Human genetic research has implicated functional variants of more than one hundred genes in the modulation of persisting pain. Artificial intelligence and machine-learning techniques may combine this knowledge with results of genetic research gathered in any context, which permits the identification of the key biological processes involved in chronic sensitization to pain. MethodsResultsBased on published evidence, a set of 110 genes carrying variants reported to be associated with modulation of the clinical phenotype of persisting pain in eight different clinical settings was submitted to unsupervised machine-learning aimed at functional clustering. Subsequently, a mathematically supported subset of genes, comprising those most consistently involved in persisting pain, was analysed by means of computational functional genomics in the Gene Ontology knowledgebase. Clustering of genes with evidence for a modulation of persisting pain elucidated a functionally heterogeneous set. The situation cleared when the focus was narrowed to a genetic modulation consistently observed throughout several clinical settings. On this basis, two groups of biological processes, the immune system and nitric oxide signalling, emerged as major players in sensitization to persisting pain, which is biologically highly plausible and in agreement with other lines of pain research. ConclusionsSignificanceThe present computational functional genomics-based approach provided a computational systems-biology perspective on chronic sensitization to pain. Human genetic control of persisting pain points to the immune system as a source of potential future targets for drugs directed against persisting pain. Contemporary machine-learned methods provide innovative approaches to knowledge discovery from previous evidence. We show that knowledge discovery in genetic databases and contemporary machine-learned techniques can identify relevant biological processes involved in Persitent pain.Peer reviewe

Euclid preparation. TBD. The effect of linear redshift-space distortions in photometric galaxy clustering and its cross-correlation with cosmic shear

Cosmological surveys planned for the current decade will provide us with unparalleled observations of the distribution of galaxies on cosmic scales, by means of which we can probe the underlying large-scale structure (LSS) of the Universe. This will allow us to test the concordance cosmological model and its extensions. However, precision pushes us to high levels of accuracy in the theoretical modelling of the LSS observables, in order not to introduce biases in the estimation of cosmological parameters. In particular, effects such as redshift-space distortions (RSD) can become relevant in the computation of harmonic-space power spectra even for the clustering of the photometrically selected galaxies, as it has been previously shown in literature studies. In this work, we investigate the contribution of linear RSD, as formulated in the Limber approximation by arXiv:1902.07226, in forecast cosmological analyses with the photometric galaxy sample of the Euclid survey, in order to assess their impact and quantify the bias on the measurement of cosmological parameters that neglecting such an effect would cause. We perform this task by producing mock power spectra for photometric galaxy clustering and weak lensing, as expected to be obtained from the Euclid survey. We then use a Markov chain Monte Carlo approach to obtain the posterior distributions of cosmological parameters from such simulated observations. We find that neglecting the linear RSD leads to significant biases both when using galaxy correlations alone and when these are combined with cosmic shear, in the so-called 3$\times$2pt approach. Such biases can be as large as $5\,\sigma$-equivalent when assuming an underlying $\Lambda$CDM cosmology. When extending the cosmological model to include the equation-of-state parameters of dark energy, we find that the extension parameters can be shifted by more than $1\,\sigma$.Comment: 15 pages, 5 figures. To be submitted in A&

Potential mechanisms supporting the value of motor cortex stimulation to treat chronic pain syndromes

Throughout the first years of the twenty-first century, neurotechnologies such as motor cortex stimulation (MCS), transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) have attracted scientific attention and been considered as potential tools to centrally modulate chronic pain, especially for those conditions more difficult to manage and refractory to all types of available pharmacological therapies. Interestingly, although the role of the motor cortex in pain has not been fully clarified, it is one of the cortical areas most commonly targeted by invasive and non-invasive neuromodulation technologies. Recent studies have provided significant advances concerning the establishment of the clinical effectiveness of primary motor cortex stimulation to treat different chronic pain syndromes. Concurrently, the neuromechanisms related to each method of primary motor cortex (M1) modulation have been unveiled. In this respect, the most consistent scientific evidence originates from MCS studies, which indicate the activation of top-down controls driven by M1 stimulation. This concept has also been applied to explain M1-TMS mechanisms. Nevertheless, activation of remote areas in the brain, including cortical and subcortical structures, has been reported with both invasive and non-invasive methods and the participation of major neurotransmitters (e.g. glutamate, GABA and serotonin) as well as the release of endogenous opioids has been demonstrated. In this critical review, the putative mechanisms underlying the use of motor cortex stimulation to provide relief from chronic migraine and other types of chronic pain are discussed. Emphasis is placed on the most recent scientific evidence obtained from chronic pain research studies involving MCS and non-invasive neuromodulation methods (e.g. tDCS and TMS), which are analyzed comparatively

Gingival proliferative lesions in children and adolescents in Brazil: A 15-year-period cross-sectional study

Background: Studies assessing the prevalence of oral lesions in children and adolescents, particularly in gingiva are scarce in the literature. The aim of the study was to describe the distribution of gingival proliferative lesions based on clinical and histopathological diagnoses in children and adolescents. Materials and Methods: A review of clinical charts of children and adolescents aged between 0 and 18 years old, admitted to the Oral Medicine Outpatient Unit, of Universidade Federal do Paraná, for 15 years (1994–2009) was performed. Results: Six hundred and sixty-nine out of 5,129 patients treated during this period were aged between 0 and 18 years old, and 45 of these had gingival lesions. The largest number of lesions was observed between 11 and 16 years old. The majority of the patients were referred by Curitiba's public health system. Pyogenic granuloma was the most frequent lesion (19 = 42.2%), followed by peripheral giant cell lesion (11 = 24.4%), gingival fibromatosis (10 = 22.2%), and peripheral ossifying fibroma (5 = 11.1%). Conclusion: Gingival proliferative lesions can show similar clinical characteristics. Appropriate clinical and histopathological diagnoses are necessary to guide the healthcare professional to establish the adequate treatment and to estimate the risk of recurrence