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1.
J Thorac Dis ; 12(8): 4299-4306, 2020 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-32944342

RESUMO

BACKGROUND: The Nuss procedure temporarily places intrathoracic bars for repair of pectus excavatum (PE). The bars may impact excursion and compliance of the anterior chest wall while in place. Effective chest compressions during cardiopulmonary resuscitation (CPR) require depressing the anterior chest wall enough to compress the heart between sternum and spine. We assessed the force required to perform the American Heart Association's recommended chest compression depth after Nuss repair. METHODS: A lumped element elastic model was developed to simulate the relationship between chest compression forces and displacement with focus on the amount of force required to achieve a depth of 5 cm in the presence of 1-3 Nuss bars. Literature review was conducted for evidence supporting potential use of active abdominal compressions and decompression (AACD) as an alternative method of CPR. RESULTS: The presence of bars notably lowered compression depth by a minimum of 69% compared to a chest without bar(s). The model also demonstrated a dramatic increase (minimum of 226%) in compressive forces required to achieve recommended 5 cm depth. Literature review suggests AACD could be an alternative CPR in patients with Nuss bar(s). CONCLUSIONS: In our model, Nuss bars limited the ability to perform chest compressions due to increased force required to achieve a 5 cm compression. The greater the number of Nuss bars present the greater the force required. This may prevent effective CPR. Use of active abdominal compressions and decompressions should be studied further as an alternative resuscitation modality for patients after the Nuss procedure.

2.
ACS Sens ; 4(12): 3175-3185, 2019 12 27.
Artigo em Inglês | MEDLINE | ID: mdl-31670508

RESUMO

Wireless implantable neural interfaces can record high-resolution neuropotentials without constraining patient movement. Existing wireless systems often require intracranial wires to connect implanted electrodes to an external head stage or/and deploy an application-specific integrated circuit (ASIC), which is battery-powered or externally power-transferred, raising safety concerns such as infection, electronics failure, or heat-induced tissue damage. This work presents a biocompatible, flexible, implantable neural recorder capable of wireless acquisition of neuropotentials without wires, batteries, energy harvesting units, or active electronics. The recorder, fabricated on a thin polyimide substrate, features a small footprint of 9 mm × 8 mm × 0.3 mm and is composed of passive electronic components. The absence of active electronics on the device leads to near zero power consumption, inherently avoiding the catastrophic failure of active electronics. We performed both in vitro validation in a tissue-simulating phantom and in vivo validation in an epileptic rat. The fully passive wireless recorder was implanted under rat scalp to measure neuropotentials from its contact electrodes. The implanted wireless recorder demonstrated its capability to capture low voltage neuropotentials, including somatosensory evoked potentials (SSEPs), and interictal epileptiform discharges (IEDs). Wirelessly recorded SSEP and IED signals were directly compared to those from wired electrodes to demonstrate the efficacy of the wireless data. In addition, a convoluted neural network-based machine learning algorithm successfully achieved IED signal recognition accuracy as high as 100 and 91% in wired and wireless IED data, respectively. These results strongly support the fully passive wireless neural recorder's capability to measure neuropotentials as low as tens of microvolts. With further improvement, the recorder system presented in this work may find wide applications in future brain machine interface systems.


Assuntos
Eletroencefalografia/métodos , Potenciais Somatossensoriais Evocados , Algoritmos , Animais , Materiais Biocompatíveis/química , Encéfalo/metabolismo , Eletrodos Implantados , Eletroencefalografia/instrumentação , Aprendizado de Máquina , Ratos Wistar , Tecnologia sem Fio/instrumentação
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