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BackgroundHome blood pressure (BP) monitoring with lifestyle coaching is effective in managing hypertension and reducing cardiovascular risk. However, traditional manual lifestyle coaching models significantly limit availability due to high operating costs and personnel requirements. Furthermore, the lack of patient lifestyle monitoring and clinician time constraints can prevent personalized coaching on lifestyle modifications. ObjectiveThis study assesses the effectiveness of a fully digital, autonomous, and artificial intelligence (AI)–based lifestyle coaching program on achieving BP control among adults with hypertension. MethodsParticipants were enrolled in a single-arm nonrandomized trial in which they received a BP monitor and wearable activity tracker. Data were collected from these devices and a questionnaire mobile app, which were used to train personalized machine learning models that enabled precision lifestyle coaching delivered to participants via SMS text messaging and a mobile app. The primary outcomes included (1) the changes in systolic and diastolic BP from baseline to 12 and 24 weeks and (2) the percentage change of participants in the controlled, stage-1, and stage-2 hypertension categories from baseline to 12 and 24 weeks. Secondary outcomes included (1) the participant engagement rate as measured by data collection consistency and (2) the number of manual clinician outreaches. ResultsIn total, 141 participants were monitored over 24 weeks. At 12 weeks, systolic and diastolic BP decreased by 5.6 mm Hg (95% CI −7.1 to −4.2; P

A magnetic nanodiscs-based magnetomechanical approach is presented that can be used for wireless neuronal stimulation in vitro and untethered deep brain stimulation in vivo without implants or genetic manipulation.

Deep-brain stimulation ameliorates parkinsonian symptoms, but it usually requires permanent implantation of hardware and connectors. Here, the authors show magnetothermal neuromodulation through the activation of TRPV1 can improve locomotor deficits in mouse models of Parkinson’s disease.

Background: Blood pressure (BP) is an essential indicator for human health and is known to be greatly influenced by lifestyle factors, like activity and sleep factors. However, the degree of impact of each lifestyle factor on BP is unknown and may vary between individuals. Our goal is to investigate the relationships between BP and lifestyle factors and provide personalized and precise recommendations to improve BP, as opposed to the current practice of general lifestyle recommendations. Method: Our proposed system consists of automated data collection using home BP monitors and wearable activity trackers and feature engineering techniques to address time-series data and enhance interpretability. We propose Random Forest with Shapley-Value-based Feature Selection to offer personalized BP modeling and top lifestyle factor identification, and subsequent generation of precise recommendations based on the top factors. Result: In collaboration with UC San Diego Health and Altman Clinical and Translational Research Institute, we performed a clinical study, applying our system to 25 patients with elevated BP or stage I hypertension for three consecutive months. Our study results validate our system’s ability to provide accurate personalized BP models and identify the top features which can vary greatly between individuals. We also validate the effectiveness of personalized recommendations in a randomized controlled experiment. After receiving recommendations, the subjects in the experimental group decreased their BPs by 3.8 and 2.3 for systolic and diastolic BP, compared to the decrease of 0.3 and 0.9 for the subjects without recommendations. Conclusion: The study demonstrates the potential of using wearables and machine learning to develop personalized models and precise lifestyle recommendations to improve BP.

Blood pressure (BP) is one of the essential indicators of human health and highly correlated to health behavior (e.g., exercise and sleep). However, the degree of impact of each health behavior on BP is unknown and may vary significantly between individuals. In this paper, we investigate the relationship between BP and health behavior using data collected from off-the-shelf wearable devices and wireless home BP monitors. We propose a personalized BP model based on random forest (RF), which can predict individual's BP using health behavior and historical BP, and identify the most important factors in predicting an individual's BP. The latter can be used to provide personalized health behavior recommendations to improve and manage BP. We propose RF with Feature Selection (RFFS), which performs RF-based feature selection to enhance the prediction. Furthermore, since BP and health behavior data are collected and learned sequentially, the performance of prediction is prone to the existence of concept drifts and anomaly points. To solve this problem, we propose an Online Weighted-Resampling (OWR) technique to enhance RFFS in an online learning scenario. To show the effectiveness of RFFS and OWR, we use existing machine learning methods on the proposed dataset as a comparison. Our experimental results show that the proposed approach achieves the lowest prediction error. We also validate the effectiveness of personalized recommendations based on feature importance, which influences the user to change lifestyle to improve his/her BP.

GABAergic signaling in hippocampal pyramidal neurons undergoes a switch from depolarizing to hyperpolarizing during early neuronal development. Whether such a transformation of GABAergic action occurs in dentate granule cells (DGCs), located at the first stage of the hippocampal trisynaptic circuit, is unclear. Here, we use noninvasive extracellular recording to monitor the effect of synaptically released GABA on the DGC population. We find that GABAergic responses in adolescent and adult rat DGCs are still depolarizing from rest. Using a morphologically realistic DGC model, we show that GABAergic action, depending on its precise timing and location, can have either an excitatory or inhibitory role in signal processing in the dentate gyrus. [ABSTRACT FROM AUTHOR]