Identifying Gut Microbiome Features that Predict Responsiveness Toward a Prebiotic Capable of Increasing Calcium Absorption: A Pilot Study.

Previously, we demonstrated that prebiotics may provide a complementary strategy for increasing calcium (Ca) absorption in adolescents which may improve long-term bone health. However, not all children responded to prebiotic intervention. We determine if certain baseline characteristics of gut microbiome composition predict prebiotic responsiveness. In this secondary analysis, we compared differences in relative microbiota taxa abundance between responders (greater than or equal to 3% increase in Ca absorption) and non-responders (less than 3% increase). Dual stable isotope methodologies were used to assess fractional Ca absorption at the end of crossover treatments with placebo, 10, and 20 g/day of soluble corn fiber (SCF). Microbial DNA was obtained from stool samples collected before and after each intervention. Sequencing of the 16S rRNA gene was used to taxonomically characterize the gut microbiome. Machine learning techniques were used to build a predictive model for identifying responders based on baseline relative taxa abundances. Model output was used to infer which features contributed most to prediction accuracy. We identified 19 microbial features out of the 221 observed that predicted responsiveness with 96.0% average accuracy. The results suggest a simplified prescreening can be performed to determine if a subject's bone health may benefit from a prebiotic. Additionally, the findings provide insight and prompt further investigation into the metabolic and genetic underpinnings affecting calcium absorption during pubertal bone development.

Anticipating Postoperative Delirium During Burst Suppression Using Electroencephalography.

OBJECTIVE: This study develops an electro-encephalography-based method for predicting postoperative delirium early during cardiac surgeries involving deep hypothermia circulatory arrest (DHCA), potentially providing an opportunity to intervene and minimize poor surgical outcome. DHCA is a surgical technique used during cardiac surgeries to facilitate repairs. Deep hypothermia is induced and supplemented by perfusion techniques to protect the brain during circulatory arrest, but concern for cerebral injury still remains. METHODS: This research studies whether or not monitoring burst suppression, an electrophysiological phenomenon observed during patient cooling and warming, helps in predicting postoperative delirium, a correlate of poor prognosis. A metric called the burst suppression duty cycle (BSDC), akin to burst suppression ratio, is formulated to characterize this electrophysiological activity. RESULTS: Assuming no complications occur prior to circulatory arrest, delirium diagnoses are correlated with the time elapsed until suppression activity ceases since resuming cardiopulmonary bypass. By comparing against a benchmark the times when BSDC reaches 100%, 15 of 16 cases can be correctly predicted. Similar accuracy can be achieved when querying BSDC progress earlier during warming. CONCLUSION: Our results show that our BSDC metric is a promising candidate for early detection of postoperative delirium, and motivates further analysis of the causal relationship between postoperative delirium and the procedure transitioning out of circulatory arrest. SIGNIFICANCE: The developed methodology anticipates incidences of postoperative delirium during rewarming, which potentially provides an opportunity to intervene and avert it.

Identifying Free-Living Physical Activities Using Lab-Based Models with Wearable Accelerometers.

The purpose of this study was to classify, and model various physical activities performed by a diverse group of participants in a supervised lab-based protocol and utilize the model to identify physical activity in a free-living setting. Wrist-worn accelerometer data were collected from ( N = 152 ) adult participants; age 18⁻64 years, and processed the data to identify and model unique physical activities performed by the participants in controlled settings. The Gaussian mixture model (GMM) and the hidden Markov model (HMM) algorithms were used to model the physical activities with time and frequency-based accelerometer features. An overall model accuracy of 92.7% and 94.7% were achieved to classify 24 physical activities using GMM and HMM, respectively. The most accurate model was then used to identify physical activities performed by 20 participants, each recorded for two free-living sessions of approximately six hours each. The free-living activity intensities were estimated with 80% accuracy and showed the dominance of stationary and light intensity activities in 36 out of 40 recorded sessions. This work proposes a novel activity recognition process to identify unsupervised free-living activities using lab-based classification models. In summary, this study contributes to the use of wearable sensors to identify physical activities and estimate energy expenditure in free-living settings.