Endoworm: A new semi-autonomous enteroscopy device

[EN] Using enteroscopes with therapeutic capacity to explore the small intestine entails certain limitations, including long exploration times, patient discomfort, the need for sedation, a high percentage of incomplete explorations and a long learning curve. This article describes the advances and setbacks encountered in designing the new Endoworm enteroscopy system, a semi-autonomous device consisting of a control unit and three cavities that inflate and deflate in such a way that the bowel retracts over the endoscope. The system can be adapted to any commercial enteroscope. Endoworm was tested in different intestine models: a polymethyl methacrylate rigid tube, an in vitro polyester urethane model, an ex vivo pig model and an in vivo animal model. The general behavior of the prototype was evaluated by experienced medical personnel. The mean distance covered through the lumen was measured in each cycle. The system was found to have excellent performance in the rigid tube and in the in vitro model. The ex vivo tests showed that the behavior depended largely on the mechanical properties of the lumen, while the in vivo experiments suggest that the device will require further modifications to improve its performance.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors gratefully acknowledge the financial support from the Spanish Ministry of Economy and Competitiveness through Project PI12/01000 and also from UPV/IIS LA Fe through the Endoworm 3.0 Project. CIBER-BBN is an initiative funded by the VI National R&D&I Plan 2008–2011, Iniciativa Ingenio 2010, Consolider Program and CIBER Actions, and financed by the Instituto de Salud Carlos III with the assistance of the European Regional Development Fund.Sánchez-Diaz, C.; Senent-Cardona, E.; Pons, V.; Santonja Gimeno, AV.; Vidaurre Garayo, AJ. (2018). Endoworm: A new semi-autonomous enteroscopy device. Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine. 232(11):1137-1143. https://doi.org/10.1177/0954411918806330S113711432321

Classification of kinematic and electromyographic signals associated with pathological tremor using machine and deep learning.

Peripheral Electrical Stimulation (PES) of afferent pathways has received increased interest as a solution to reduce pathological tremors with minimal side effects. Closed-loop PES systems might present some advantages in reducing tremors, but further developments are required in order to reliably detect pathological tremors to accurately enable the stimulation only if a tremor is present. This study explores different machine learning (K-Nearest Neighbors, Random Forest and Support Vector Machines) and deep learning (Long Short-Term Memory neural networks) models in order to provide a binary (Tremor; No Tremor) classification of kinematic (angle displacement) and electromyography (EMG) signals recorded from patients diagnosed with essential tremors and healthy subjects. Three types of signal sequences without any feature extraction were used as inputs for the classifiers: kinematics (wrist flexion-extension angle), raw EMG and EMG envelopes from wrist flexor and extensor muscles. All the models showed high classification scores (Tremor vs. No Tremor) for the different input data modalities, ranging from 0.8 to 0.99 for the f1 score. The LSTM models achieved 0.98 f1 scores for the classification of raw EMG signals, showing high potential to detect tremors without any processed features or preliminary information. These models may be explored in real-time closed-loop PES strategies to detect tremors and enable stimulation with minimal signal processing steps

The management of acute venous thromboembolism in clinical practice. Results from the European PREFER in VTE Registry

Venous thromboembolism (VTE) is a significant cause of morbidity and mortality in Europe. Data from real-world registries are necessary, as clinical trials do not represent the full spectrum of VTE patients seen in clinical practice. We aimed to document the epidemiology, management and outcomes of VTE using data from a large, observational database. PREFER in VTE was an international, non-interventional disease registry conducted between January 2013 and July 2015 in primary and secondary care across seven European countries. Consecutive patients with acute VTE were documented and followed up over 12 months. PREFER in VTE included 3,455 patients with a mean age of 60.8 ± 17.0 years. Overall, 53.0 % were male. The majority of patients were assessed in the hospital setting as inpatients or outpatients (78.5 %). The diagnosis was deep-vein thrombosis (DVT) in 59.5 % and pulmonary embolism (PE) in 40.5 %. The most common comorbidities were the various types of cardiovascular disease (excluding hypertension; 45.5 %), hypertension (42.3 %) and dyslipidaemia (21.1 %). Following the index VTE, a large proportion of patients received initial therapy with heparin (73.2 %), almost half received a vitamin K antagonist (48.7 %) and nearly a quarter received a DOAC (24.5 %). Almost a quarter of all presentations were for recurrent VTE, with >80 % of previous episodes having occurred more than 12 months prior to baseline. In conclusion, PREFER in VTE has provided contemporary insights into VTE patients and their real-world management, including their baseline characteristics, risk factors, disease history, symptoms and signs, initial therapy and outcomes

Mujer de 19 años con cambios de coloración, debilidad y disestesias en extremidades superiores

Se presenta el caso de una mujer de 19 años que ingresa por un acrosíndrome vascular agudo grave en extremidades superiores que asociaba clínica neurológica en la que aunque el cuadro clínico no era típico se quería descartar la sospecha inicial de fenómeno de Raynaud secundario a conectivopatía/vasculitis. El estudio efectuado permitió el diagnóstico de hemangioblastoma medular cervical que precisó tratamiento quirúrgico tras el que se resolvió el cuadro vasomotor pero quedaron secuelas neurológicas

Mujer de 36 años con síndrome febril, rash cutáneo y dolor en miembros superiores

Se presenta el caso de una paciente de 36 años de edad que ingresa procedente de urgencias por cuadro de 3 semanas de evolución consistente en síndrome febril, rash cutáneo pruriginoso y dolor en MMSS. En la exploración física destacó la presencia de lesiones eritematosas con descamación superficial distribuidas por el tronco y MMSS, adenopatías subcentrimétricas en cuello y limitación dolorosa sin sinovitis en hombro y muñeca derechas. Se describe el diagnóstico diferencial y los hallazgos de la biopsia de una adenopatía cervical, que finalmente permitieron el diagnóstico de una enfermedad de Kikuchi

Classification of Kinematic and Electromyographic Signals Associated with Pathological Tremor Using Machine and Deep Learning

Peripheral Electrical Stimulation (PES) of afferent pathways has received increased interest as a solution to reduce pathological tremors with minimal side effects. Closed-loop PES systems might present some advantages in reducing tremors, but further developments are required in order to reliably detect pathological tremors to accurately enable the stimulation only if a tremor is present. This study explores different machine learning (K-Nearest Neighbors, Random Forest and Support Vector Machines) and deep learning (Long Short-Term Memory neural networks) models in order to provide a binary (Tremor; No Tremor) classification of kinematic (angle displacement) and electromyography (EMG) signals recorded from patients diagnosed with essential tremors and healthy subjects. Three types of signal sequences without any feature extraction were used as inputs for the classifiers: kinematics (wrist flexion–extension angle), raw EMG and EMG envelopes from wrist flexor and extensor muscles. All the models showed high classification scores (Tremor vs. No Tremor) for the different input data modalities, ranging from 0.8 to 0.99 for the f score. The LSTM models achieved 0.98 f scores for the classification of raw EMG signals, showing high potential to detect tremors without any processed features or preliminary information. These models may be explored in real-time closed-loop PES strategies to detect tremors and enable stimulation with minimal signal processing steps.This study was funded by the European Union’s Horizon2020 research and innovation program (Project EXTEND—Bidirectional Hyper-Connected Neural System) under grant agreement No 779982.This work was also funded by the Spanish Ministry of Science and Innovation (Project NETremor: Development of a digital platform for the remote data management of patients with movement disorders), Project TED2021-130174B-C32, funded by MCIN/AEI/10.13039/501100011033 and the European Union Next Generation EU/PRTR. This work was also partially funded by the Spanish MCIN/AEI/10.13039/501100011033 and by the “European Union Next Generation EU/PRTR” under Grant agreement IJC2020-044467-I