Variations of physiological parameters in newly detected hypothyroidism

Background: Emergence of hypothyroidism as a public health issue apart from a common clinical entity, has gained much attention nowadays. The non-specific features of hypothyroidism lead to incorrect diagnoses, inadequate treatment and complications in the future. The present study was designed to unravel the effects of hypothyroidism on physiological parameters and to highlight the importance of early diagnosis and treatment. This study was done to assess the effects of hypothyroidism on pulse rate, blood pressure and respiratory rate.Methods: This descriptive cross-sectional study was done in 60 hypothyroid patients of 18-45 years age, both males and females, who were either newly detected or on treatment for less than 6 months. Patients with history of other systemic diseases, pregnancy and hyperthyroidism were excluded. After obtaining written consent from the patients, clinical examination was done.Results: Student t-test and ANOVA were used for analysis. Physiological parameters like pulse rate, blood pressure and respiratory rate had variations in the patients and there were changes with age, gender and BMI even though they were not significant. These changes were attributed to increased arterial wall thickness and endothelial dysfunction in blood vessels.Conclusions: The patients showed changes in cardiovascular and respiratory profiles. The variations in systolic and diastolic blood pressures were significant with increase in age. Physiological parameters had variations with gender and BMI also. This proves the cardiovascular and respiratory morbidity in newly detected hypothyroidism, which emphasizes the importance of early diagnosis and treatment in them

EXPOSURE OF CHILDREN TO DEET AND OTHER TOPICALLY APPLIED INSECT REPELLENTS

Use of topical repellents on children is common. Anecdotal reports suggest repellents may be applied inappropriately, but no studies characterizing the actual usage patterns and exposure of children have been reported. In summer 2002, a cross-sectional survey on the use patterns of repellents on children and possible associated effects was conducted in Maryland campgrounds. Information requested included products used, details of applications, post-application practices, and parents' decision-making process. The study yielded 301 respondents. Deet was the most commonly used active ingredient (83.4%); aerosols were the most common formulation (42.5%). Over a third of subjects (38.9%) treated their children's clothing as well as their skin. Over half of the children did not remove the repellent before going to bed. More than a third of parents failed to read or follow label directions. This study provides documentation of practices leading to undesirable exposure. Educational outreach to change parents' usage patterns is required

Adaptive Filtering Using Recurrent Neural Networks

A method for adaptive (or, optionally, nonadaptive) filtering has been developed for estimating the states of complex process systems (e.g., chemical plants, factories, or manufacturing processes at some level of abstraction) from time series of measurements of system inputs and outputs. The method is based partly on the fundamental principles of the Kalman filter and partly on the use of recurrent neural networks. The standard Kalman filter involves an assumption of linearity of the mathematical model used to describe a process system. The extended Kalman filter accommodates a nonlinear process model but still requires linearization about the state estimate. Both the standard and extended Kalman filters involve the often unrealistic assumption that process and measurement noise are zero-mean, Gaussian, and white. In contrast, the present method does not involve any assumptions of linearity of process models or of the nature of process noise; on the contrary, few (if any) assumptions are made about process models, noise models, or the parameters of such models. In this regard, the method can be characterized as one of nonlinear, nonparametric filtering. The method exploits the unique ability of neural networks to approximate nonlinear functions. In a given case, the process model is limited mainly by limitations of the approximation ability of the neural networks chosen for that case. Moreover, despite the lack of assumptions regarding process noise, the method yields minimum- variance filters. In that they do not require statistical models of noise, the neural- network-based state filters of this method are comparable to conventional nonlinear least-squares estimators

An adaptive and flexible brain energized full body exoskeleton with IoT edge for assisting the paralyzed patients

The paralyzed population is increasing worldwide due to stroke, spinal code injury, post-polio, and other related diseases. Different assistive technologies are used to improve the physical and mental health of the affected patients. Exoskeletons have emerged as one of the most promising technology to provide movement and rehabilitation for the paralyzed. But exoskeletons are limited by the constraints of weight, flexibility, and adaptability. To resolve these issues, we propose an adaptive and flexible Brain Energized Full Body Exoskeleton (BFBE) for assisting the paralyzed people. This paper describes the design, control, and testing of BFBE with 15 degrees of freedom (DoF) for assisting the users in their daily activities. The flexibility is incorporated into the system by a modular design approach. The brain signals captured by the Electroencephalogram (EEG) sensors are used for controlling the movements of BFBE. The processing happens at the edge, reducing delay in decision making and the system is further integrated with an IoT module that helps to send an alert message to multiple caregivers in case of an emergency. The potential energy harvesting is used in the system to solve the power issues related to the exoskeleton. The stability in the gait cycle is ensured by using adaptive sensory feedback. The system validation is done by using six natural movements on ten different paralyzed persons. The system recognizes human intensions with an accuracy of 85%. The result shows that BFBE can be an efficient method for providing assistance and rehabilitation for paralyzed patients. © 2013 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramanian” is provided in this record*

Self-organized Pattern Formation in Motor-Microtubule Mixtures

We propose and study a hydrodynamic model for pattern formation in mixtures of molecular motors and microtubules. The steady state patterns we obtain in different regimes of parameter space include arrangements of vortices and asters separately as well as aster-vortex mixtures and fully disordered states. Such stable steady states are observed in experiments in vitro. The sequence of patterns obtained in the experiments can be associated with smooth trajectories in a non-equilibrium phase diagram for our model.Comment: 11 pages Latex file, 2 figures include

Validation of Visual Estimation of Portion Size Consumed as a Method for Estimating Food Intake by Young Indian Children

In this observational study, estimation of food intake was evaluated using recording of portion size consumed, instead of post-weighing, as a method. In total, 930 feeding episodes were observed among 128 children aged 12–24 months in which actual intake was available by pre- and post-weighing. For each offering and feeding episode, portion size consumed was recorded by an independent nutritionist—as none, less than half, half or more, and all. Using the pre-weighed offering, available intake was estimated by multiplying portion sizes by the estimated weight. The estimated mean intake was 510.4 kilojoules compared to actual intake of 510.7 kilojoules by weighing. Similar results were found with nestum (52.0 vs 56.2 g), bread (3.8 vs 3.7 g), puffed rice (1.7 vs 1.9 g), banana (31.3 vs 24.4 g), and milk (41.6 vs 44.2 mL). Recording portion size consumed and estimating food intake from that provides a good alternative to the time-consuming and often culturally-unacceptable method of post-weighing food each time after a feeding episode

Zinc Deficiency: Descriptive Epidemiology and Morbidity among Preschool Children in Peri-urban Population in Delhi, India

Community-based data relating to factors influencing zinc deficiency among preschool children in India are inadequate. Data of a large, double-blinded, randomized, controlled zinc-supplementation trial were used for assessing the descriptive epidemiology of zinc deficiency among children aged 6–35 months (n=940). In total, 609 children were followed up for 120 days for information on morbidity. Of these children, 116 from the control group belonging to the upper and the lower 25th quartile of plasma zinc status at baseline were selected for assessing the association of zinc deficiency with prospective morbidity. At baseline, demographic, socioeconomic and dietary information was collected, and anthropometric measurements and levels of plasma zinc were assessed. At baseline, 73.3% of the children were zinc-deficient (plasma zinc <70 µg/dL), of which 33.8% had levels of plasma zinc below 60 µg/dL. A significantly higher risk of morbidity was prevalent among the subjects with lower plasma zinc compared to those with higher levels of plasma zinc

Algorithm for Training a Recurrent Multilayer Perceptron

An improved algorithm has been devised for training a recurrent multilayer perceptron (RMLP) for optimal performance in predicting the behavior of a complex, dynamic, and noisy system multiple time steps into the future. [An RMLP is a computational neural network with self-feedback and cross-talk (both delayed by one time step) among neurons in hidden layers]. Like other neural-network-training algorithms, this algorithm adjusts network biases and synaptic-connection weights according to a gradient-descent rule. The distinguishing feature of this algorithm is a combination of global feedback (the use of predictions as well as the current output value in computing the gradient at each time step) and recursiveness. The recursive aspect of the algorithm lies in the inclusion of the gradient of predictions at each time step with respect to the predictions at the preceding time step; this recursion enables the RMLP to learn the dynamics. It has been conjectured that carrying the recursion to even earlier time steps would enable the RMLP to represent a noisier, more complex system