Neonatal Near Miss: A Systematic Review.

The concept of neonatal near miss has been proposed as a tool for assessment of quality of care in neonates who suffered any life-threatening condition. However, there are no internationally agreed concepts or criteria for defining or identifying neonatal near miss. The purpose of this study was to perform a systematic review of studies and markers that are able to identify neonatal near miss cases and predict neonatal mortality. Electronic searches were performed in the Medline, Embase and Scielo databases, with no time or language restriction, until December 2014. The term neonatal near miss was used alone or in combination with terms related to neonatal morbidity/mortality and neonatal severity scores. Study selection criteria involved three steps: title, abstract and full text of the articles. Two researchers performed study selection and data extraction independently. Heterogeneity of study results did not permit the performance of meta-analysis. Following the inclusion and exclusion criteria adopted, only four articles were selected. Preterm and perinatal asphyxia were used as near miss markers in all studies. Health indicators on neonatal morbidity and mortality were extracted or estimated. The neonatal near miss rate was 2.6 to 8 times higher than the neonatal mortality rate. Pragmatic and management criteria are used to help develop the neonatal near miss concept. The most severe cases are identified and mortality is predicted with these criteria. Furthermore, the near miss concept can be used as a tool for evaluating neonatal care. It is the first step in building management strategies to reduce mortality and long-term sequelae.1532

Co-authorship Network Analysis: A Powerful Tool for Strategic Planning of Research, Development and Capacity Building Programs on Neglected Diseases

The selection and prioritization of research proposals is always a challenge, particularly when addressing neglected tropical diseases, as the scientific communities are relatively small, funding is usually limited and the disparity between the science and technology capacity of different countries and regions is enormous. When the Ministry of Health and the Ministry of Science and Technology of Brazil decided to launch an R&D program on neglected diseases for which at least 30% of the Program's resources were supposed to be invested in institutions and authors from the poorest regions of Brazil, it became clear to us that new strategies and approaches would be required. Social network analysis of co-authorship networks is one of the new approaches we are exploring to develop new tools to help policy-/decision-makers and academia jointly plan, implement, monitor and evaluate investments in this area. Publications retrieved from international databases provide the starting material. After standardization of names and addresses of authors and institutions with text mining tools, networks are assembled and visualized using social network analysis software. This study enabled the development of innovative criteria and parameters, allowing better strategic planning, smooth implementation and strong support and endorsement of the Program by key stakeholders

A simulation study on the effects of neuronal ensemble properties on decoding algorithms for intracortical brain-machine interfaces

Background: Intracortical brain-machine interfaces (BMIs) harness movement information by sensing neuronal activities using chronic microelectrode implants to restore lost functions to patients with paralysis. However, neuronal signals often vary over time, even within a day, forcing one to rebuild a BMI every time they operate it. The term "rebuild" means overall procedures for operating a BMI, such as decoder selection, decoder training, and decoder testing. It gives rise to a practical issue of what decoder should be built for a given neuronal ensemble. This study aims to address it by exploring how decoders' performance varies with the neuronal properties. To extensively explore a range of neuronal properties, we conduct a simulation study. Methods: Focusing on movement direction, we examine several basic neuronal properties, including the signal-to-noise ratio of neurons, the proportion of well-tuned neurons, the uniformity of their preferred directions (PDs), and the non-stationarity of PDs. We investigate the performance of three popular BMI decoders: Kalman filter, optimal linear estimator, and population vector algorithm. Results: Our simulation results showed that decoding performance of all the decoders was affected more by the proportion of well-tuned neurons that their uniformity. Conclusions: Our study suggests a simulated scenario of how to choose a decoder for intracortical BMIs in various neuronal conditions

Closing the Mind's Eye: Incoming Luminance Signals Disrupt Visual Imagery

Mental imagery has been associated with many cognitive functions, both high and low-level. Despite recent scientific advances, the contextual and environmental conditions that most affect the mechanisms of visual imagery remain unclear. It has been previously shown that the greater the level of background luminance the weaker the effect of imagery on subsequent perception. However, in these experiments it was unclear whether the luminance was affecting imagery generation or storage of a memory trace. Here, we report that background luminance can attenuate both mental imagery generation and imagery storage during an unrelated cognitive task. However, imagery generation was more sensitive to the degree of luminance. In addition, we show that these findings were not due to differential dark adaptation. These results suggest that afferent visual signals can interfere with both the formation and priming-memory effects associated with visual imagery. It follows that background luminance may be a valuable tool for investigating imagery and its role in various cognitive and sensory processes

Toward a model-based predictive controller design in brain-computer interfaces

A first step in designing a robust and optimal model-based predictive controller (MPC) for brain-computer interface (BCI) applications is presented in this article. An MPC has the potential to achieve improved BCI performance compared to the performance achieved by current ad hoc, nonmodel-based filter applications. The parameters in designing the controller were extracted as model-based features from motor imagery task-related human scalp electroencephalography. Although the parameters can be generated from any model-linear or non-linear, we here adopted a simple autoregressive model that has well-established applications in BCI task discriminations. It was shown that the parameters generated for the controller design can as well be used for motor imagery task discriminations with performance (with 8-23% task discrimination errors) comparable to the discrimination performance of the commonly used features such as frequency specific band powers and the AR model parameters directly used. An optimal MPC has significant implications for high performance BCI applications.Grants K25NS061001 (MK) and K02MH01493 (SJS) from the National Institute of Neurological Disorders And Stroke (NINDS) and the National Institute of Mental Health (NIMH), the Portuguese Foundation for Science and Technology (FCT) Grant SFRH/BD/21529/2005 (NSD), the Pennsylvania Department of Community and Economic Development Keystone Innovation Zone Program Fund (SJS), and the Pennsylvania Department of Health using Tobacco Settlement Fund (SJS)

Associated use of infrared thermography and ozone therapy for diagnosis and treatment of an inflammatory process in an equine: case report.

This study aims to was a clinical case report relating the use of infrared thermography (IR) associated to the ozone therapy as complementary tools to diagnose and treat a non-infectious inflammatory process in the locomotor system of an athlete-horse of the Amazon. The heart rate (HR), respiratory rate (RR), and rectal temperature (RT) were measured both rest and after walking. Radiographic evaluation, complete hemogram and creatine phosphokinase dosage (CPK) were primarily conducted. The IR examination was additionally undertaken, and the thermograms were analyzed using Flir Tools. Ozone therapy was performed via intramuscular in the scapular area. All comparisons were done using ANOVA and Tukey test (5%). The animal presented HR, RR, and RT all within normal ranges. When the animal was made to walk it demonstrated pain, and HR (48 beats.min-1) and RR (60 breaths.min-1). The creatine phosphokinase dosage was 79 UL-1 and the IR showed that the thoracic region had a surface temperature of up to 39.1ºC, indicating of an inflammatory process. After the ozone therapy was a reduction in the white color pattern from 39.1ºC to 37.2ºC. The infrared thermography is an efficient technique that can be used for the diagnosis of inflammation, and ozone therapy is an innovative treatment