Bleeding in the upper gastrointestinal tract is a common and potentially life-threatening emergency that requires rapid and highly precise endoscopic intervention. As supervised hands-on training is limited and often restricted to living animal models, an immersive virtual reality (VR) simulation offers a risk-free and accessible alternative. A novel open-source VR training simulator, OPTESK, was developed for the treatment of upper gastrointestinal (GI) bleeding. The system consists of a combination of conventional endoscopes used in routine clinical practice, which can be operated in conjunction with commercially available head-mounted displays and the in-house developed OPTESK hardware. The simulator covers two scenarios: the treatment of a non-bleeding angiodysplasia with argon plasma coagulation (APC) and the treatment of a bleeding angiodysplasia starting with the injection of adrenaline followed by APC therapy. The simulator was evaluated in a multicenter user study in terms of workload, usability, immersion, cybersickness, and face, content, and construct validity. User performance was assessed via scores on time, amount of treated area, and correctly applied APC intensity. In total, 44 participants from more than 20 centers were included in the study. Successful hemostatic control in the second scenario was achieved by 97.4
Background There is emerging evidence that the nervous system regulates immune and metabolic alterations mediating Metabolic syndrome (MetS) pathogenesis via the vagus nerve. This study evaluated the effects of transcutaneous auricular vagus nerve stimulation (TAVNS) on key cardiovascular and inflammatory components of MetS. Methods We conducted an open label, randomized (2:1), two-arm, parallel-group controlled trial in MetS patients. Subjects in the treatment group ( n = 20) received 30 min of TAVNS with a NEMOS® device placed on the cymba conchae of the left ear, once weekly. Patients in the control group ( n = 10) received no stimulation. Hemodynamic, heart rate variability (HRV), biochemical parameters, and monocytes, progenitor endothelial cells, circulating endothelial cells, and endothelial micro particles were evaluated at randomization, after the first TAVNS treatment, and again after 8 weeks of follow-up. Results An improvement in sympathovagal balance (HRV analysis) was observed after the first TAVNS session. Only patients treated with TAVNS for 8 weeks had a significant decrease in office BP and HR, a further improvement in sympathovagal balance, with a shift of circulating monocytes towards an anti-inflammatory phenotype and endothelial cells to a reparative vascular profile. Conclusion These results are of interest for further study of TAVNS as treatment of MetS.
Chest radiographs (CXRs) are the most widely available radiographic imaging modality used to detect respiratory diseases that result in lung opacities. CXR reports often use non-standardized language that result in subjective, qualitative, and non-reproducible opacity estimates. Our goal was to develop a robust deep transfer learning framework and adapt it to estimate the degree of lung opacity from CXRs. Following CXR data selection based on exclusion criteria, segmentation schemes were used for ROI (Region Of Interest) extraction, and all combinations of segmentation, data balancing, and classification methods were tested to pick the top performing models. Multifold cross validation was used to determine the best model from the initial selected top models, based on appropriate performance metrics, as well as a novel Macro-Averaged Heatmap Concordance Score (MA HCS). Performance of the best model is compared against that of expert physician annotators, and heatmaps were produced. Finally, model performance sensitivity analysis across patient populations of interest was performed. The proposed framework was adapted to the specific use case of estimation of degree of CXR lung opacity using ordinal multiclass classification. Acquired between March 24, 2020, and May 22, 2020, 38,365 prospectively annotated CXRs from 17,418 patients were used. We tested three neural network architectures (ResNet-50, VGG-16, and ChexNet), three segmentation schemes (no segmentation, lung segmentation, and lateral segmentation based on spine detection), and three data balancing strategies (undersampling, double-stage sampling, and synthetic minority oversampling) using 38,079 CXR images for training, and validation with 286 images as the out-of-the-box dataset that underwent expert radiologist adjudication. Based on the results of these experiments, the ResNet-50 model with undersampling and no ROI segmentation is recommended for lung opacity classification, based on optimal values for the MAE metric and HCS (Heatmap Concordance Score). The degree of agreement between the opacity scores predicted by this model with respect to the two sets of radiologist scores (OR or Original Reader and OOBTR or Out Of Box Reader) in terms of performance metrics is superior to the inter-radiologist opacity score agreement.
BACKGROUND:Reduced heart rate (HR) during vagus nerve stimulation (VNS) is associated with therapy for heart failure, but stimulation frequency and amplitude are limited by patient tolerance. An understanding of physiological responses to parameter adjustments would allow differential control of therapeutic and side effects. To investigate selective modulation of the physiological responses to VNS, we quantified the effects and interactions of parameter selection on two physiological outcomes: one related to therapy (reduced HR) and one related to side effects (laryngeal muscle EMG). METHODS:We applied a broad range of stimulation parameters (mean pulse rates (MPR), intra-burst frequencies, and amplitudes) to the vagus nerve of anesthetized mice. We leveraged the in vivo recordings to parameterize and validate computational models of HR and laryngeal muscle activity across amplitudes and temporal patterns of VNS. We constructed a finite element model of excitation of fibers within the mouse cervical vagus nerve. RESULTS:HR decreased with increased amplitude, increased MPR, and decreased intra-burst frequency. EMG increased with increased MPR. Preferential HR effects over laryngeal EMG effects required combined adjustments of amplitude and MPR. The model of HR responses highlighted contributions of ganglionic filtering to VNS-evoked changes in HR at high stimulation frequencies. Overlap in activation thresholds between small and large modeled fibers was consistent with the overlap in dynamic ranges of related physiological measures (HR and EMG). CONCLUSION:The present study provides insights into physiological responses to VNS required for informed parameter adjustment to modulate selectively therapeutic effects and side effects.
