Measuring congestion with a non-invasive monitoring device in heart failure and haemodialysis: CONGEST-HF.

AIMS: We examined the effectiveness of a novel cardiopulmonary management wearable sensor (worn for less than 5 mins) at measuring congestion and correlated the device findings with established clinical measures of congestion. METHODS AND RESULTS: We enrolled three cohorts of patients: (1) patients with heart failure (HF) receiving intravenous diuretics in hospital; (2) patients established on haemodialysis, and (3) HF patients undergoing right heart catheterization (RHC). The primary outcomes in the respective cohorts were a Spearman correlation between (1) change in weight and change in thoracic impedance (TI) (from enrolment, 24 h after admission to discharge) in patients hospitalized for HF; (2) lung ultrasound B-lines and volume removed during dialysis with device measured TI, and (3) pulmonary capillary wedge pressure (PCWP) and sub-acoustic diastolic, third heart sound (S3) in the patients undergoing RHC. A total of 66 patients were enrolled. In HF patients (n = 25), change in weight was correlated with both change in device TI (Spearman correlation [rsp] = -0.64, p = 0.002) and change in device S3 (rsp = -0.53, p = 0.014). In the haemodialysis cohort (n = 21), B-lines and TI were strongly correlated before (rsp = -0.71, p < 0.001) and after (rsp = -0.77, p < 0.001) dialysis. Volume of fluid removed by dialysis was correlated with change in device TI (rsp = 0.49, p = 0.024). In the RHC cohort (n = 20), PCWP measured at one time point and device S3 were not significantly correlated (rsp = 0.230, p = 0.204). There were no device-related adverse events. CONCLUSIONS: A non-invasive device was able to detect changes in congestion in patients with HF receiving decongestion therapy and patients having fluid removed at haemodialysis. The cardiopulmonary management device, which measures multiple parameters, is a potentially useful tool to monitor patients with HF to prevent hospitalizations.

Sparse and Specific Coding during Information Transmission between Co-cultured Dentate Gyrus and CA3 Hippocampal Networks.

To better understand encoding and decoding of stimulus information in two specific hippocampal sub-regions, we isolated and co-cultured rat primary dentate gyrus (DG) and CA3 neurons within a two-chamber device with axonal connectivity via micro-tunnels. We tested the hypothesis that, in these engineered networks, decoding performance of stimulus site information would be more accurate when stimuli and information flow occur in anatomically correct feed-forward DG to CA3 vs. CA3 back to DG. In particular, we characterized the neural code of these sub-regions by measuring sparseness and uniqueness of the responses evoked by specific paired-pulse stimuli. We used the evoked responses in CA3 to decode the stimulation sites in DG (and vice-versa) by means of learning algorithms for classification (support vector machine, SVM). The device was placed over an 8 × 8 grid of extracellular electrodes (micro-electrode array, MEA) in order to provide a platform for monitoring development, self-organization, and improved access to stimulation and recording at multiple sites. The micro-tunnels were designed with dimensions 3 × 10 × 400 µm allowing axonal growth but not migration of cell bodies and long enough to exclude traversal by dendrites. Paired-pulse stimulation (inter-pulse interval 50 ms) was applied at 22 different sites and repeated 25 times in each chamber for each sub-region to evoke time-locked activity. DG-DG and CA3-CA3 networks were used as controls. Stimulation in DG drove signals through the axons in the tunnels to activate a relatively small set of specific electrodes in CA3 (sparse code). CA3-CA3 and DG-DG controls were less sparse in coding than CA3 in DG-CA3 networks. Using all target electrodes with the three highest spike rates (14%), the evoked responses in CA3 specified each stimulation site in DG with optimum uniqueness of 64%. Finally, by SVM learning, these evoked responses in CA3 correctly decoded the stimulation sites in DG for 43% of the trials, significantly higher than the reverse, i.e., how well-recording in DG could predict the stimulation site in CA3. In conclusion, our co-cultured model for the in vivo DG-CA3 hippocampal network showed sparse and specific responses in CA3, selectively evoked by each stimulation site in DG.