Remote gestational weight gain monitoring in a large low-risk US population.

Background: Over the past decade there have been rapid advancements in telemedicine and mobile health technology (mHealth) and rapid increases in adoption of these technologies among OB-GYN providers. Mobile technology is routinely used in the general adult population to simplify monitoring of food intake and weight. Studies have demonstrated that weight loss achieved via remote monitoring, through use of wi-fi scales and web applications, is similar to weight loss achieved with in-person support. These technologies also increase flexibility for subjects and providers. However, there has been limited large-scale research to evaluate the use of these technologies to improve adherence to weight-gain recommendations during pregnancy. Objectives: To evaluate gestational weight gain tracking in a large low-risk obstetrical population using remote patient monitoring and a mobile phone app. Methods: Self-reported age, height, estimated due date, and weight data were extracted from low-risk, singleton pregnancies entered from 50,769 participants who were enrolled in the BabyScripts TM phone app between 1 January 2016 and 1 March 2020. After data cleaning, 15,468 participants were included the final analysis. Linear regression and Spearman's correlation were used to examine the relationships between total weight gain, rate of weight gain, body mass index (BMI), postpartum weight loss, and app engagement. Results: The average weight gain in the first, second, and third trimester were 0.09 ± 1.8 kg, 4.2 ± 3.3 kg, and 3.9 ± 3.9 kg, respectively. The average rate of weight gain per week for the second and third trimesters were 0.5 ± 0.4 kg/wk and 0.6 ± 0.8 kg/wk, respectively. Participants with higher initial BMI had slower rate of weight gain than those with lower initial BMI (r = -0.24, r = -0.05, for second and third trimester, respectively). Overall, 21.4% of participants met the Institutes of Medicine (IOM) recommendation for total weight gain during pregnancy. Patients who were highly engaged with the mobile app had increased adherence to the IOM guidelines (29.8% vs. 9.4%, p < 0.001). A larger proportion of highly engaged patients adhered to the IOM guidelines for rate of weight gain in the second and third trimester, compared to the lowest engaged patients (12.7% vs. 6.8%, p < 0.001). On average, participants lost 8.8 ± 3.3 kg over an average of 8.1 ± 4.6 weeks in the immediate postpartum period. This weight loss was positively associated with engagement (r = 0.3, p < 0.001). Comments: Engagement with the mobile app was associated with increased adherence to the IOM gestational weight gain guidelines and with increased postpartum weight loss. Use of remote patient monitoring in conjunction with mHealth technology may be a strategy to improve adherence to IOM guidelines.

Non surgical management of large periapical lesion using mta: report of 2 cases

In the past, cases with open apices were often treated over several appointments, using calcium hydroxide, with the hope of creating a ''calcific'' barrier against which gutta percha could eventually be placed. The treatment could be as long and the prognosis is questionable. These roots were often thinner and, therefore, more brittle; extending treatment over a long period of time without providing a permanent restoration increased the chances of losing these teeth due to fracture. With the favorable histologic response of MTA, this material is the best current choice for this procedure. Completion of these cases in an effective and efficient way allows for permanent restorations to be done in a timelier manner, prolonging the longevity of these teeth. The following case report demonstrates the use of MTA as an obturating material to promote periapical healing with an open apex.

Towards smart prosthetic hand: Adaptive probability based skeletan muscle fatigue model.

Skeletal muscle force can be estimated using surface electromyographic (sEMG) signals. Usually, the surface location for the sensors is near the respective muscle motor unit points. Skeletal muscles generate a spatial EMG signal, which causes cross talk between different sEMG signal sensors. In this study, an array of three sEMG sensors is used to capture the information of muscle dynamics in terms of sEMG signals. The recorded sEMG signals are filtered utilizing optimized nonlinear Half-Gaussian Bayesian filters parameters, and the muscle force signal using a Chebyshev type-II filter. The filter optimization is accomplished using Genetic Algorithms. Three discrete time state-space muscle fatigue models are obtained using system identification and modal transformation for three sets of sensors for single motor unit. The outputs of these three muscle fatigue models are fused with a probabilistic Kullback Information Criterion (KIC) for model selection. The final fused output is estimated with an adaptive probability of KIC, which provides improved force estimates.