Thorax

BackgroundCerebrospinal fluid (CSF) circulation in the brain has garnered considerable attention in recent times. In contrast, there have been fewer studies focused on the spine, despite the expected importance of CSF circulation in disorders specific to the spine, including syringomyelia. The driving forces that regulate spinal CSF flow are not well defined and are likely to be different to the brain given the anatomical differences and proximity to the heart and lungs. The aims of this study were to determine the effects of heart rate, blood pressure and respiration on the distribution of CSF tracers in the spinal subarachnoid space, as well as into the spinal cord interstitium.MethodsIn Sprague Dawley rats, physiological parameters were manipulated such that the effects of spontaneous breathing (generating alternating positive and negative intrathoracic pressures), mechanical ventilation (positive intrathoracic pressure only), tachy/bradycardia, as well as hyper/hypotension were separately studied. To investigate spinal CSF hydrodynamics, in vivo near-infrared imaging of intracisternally infused indocyanine green was performed. CSF tracer transport was further characterised with in vivo two-photon intravital imaging. Tracer influx at a microscopic level was quantitatively characterised by ex vivo epifluorescence imaging of fluorescent ovalbumin.ResultsCompared to mechanically ventilated controls, spontaneous breathing animals had significantly greater movement of tracer in the subarachnoid space. There was also greater influx into the spinal cord interstitium. Hypertension and tachycardia had no significant effect on spinal subarachnoid spinal CSF tracer flux and exerted less effect than respiration on tracer influx into the spinal cord.ConclusionsIntrathoracic pressure changes that occur over the respiratory cycle, particularly decreased intrathoracic pressures generated during inspiration, have a profound effect on tracer movement after injection into spinal CSF and increase cord parenchymal tracer influx. Arterial pulsations likely drive fluid transport from perivascular spaces into the surrounding interstitium, but their overall impact is less than that of the respiratory cycle on net tracer influx.

The rapid evolution of the novel coronavirus disease (COVID-19) pandemic has resulted in an urgent need for effective clinical tools to reduce transmission and manage severe illness. Numerous teams are quickly developing artificial intelligence approaches to these problems, including using deep learning to predict COVID-19 diagnosis and prognosis from chest computed tomography (CT) imaging data. In this work, we assess the value of aggregated chest CT data for COVID-19 prognosis compared to clinical metadata alone. We develop a novel patient-level algorithm to aggregate the chest CT volume into a 2D representation that can be easily integrated with clinical metadata to distinguish COVID-19 pneumonia from chest CT volumes from healthy participants and participants with other viral pneumonia. Furthermore, we present a multitask model for joint segmentation of different classes of pulmonary lesions present in COVID-19 infected lungs that can outperform individual segmentation models for each task. We directly compare this multitask segmentation approach to combining feature-agnostic volumetric CT classification feature maps with clinical metadata for predicting mortality. We show that the combination of features derived from the chest CT volumes improve the AUC performance to 0.80 from the 0.52 obtained by using patients' clinical data alone. These approaches enable the automated extraction of clinically relevant features from chest CT volumes for risk stratification of COVID-19 patients.

Obesity is frequently associated with breathing disorders. To investigate if and how the highest levels of obesity impact respiratory function, 17 subjects with obesity (median age: 49 years; BMI: 39.7 kg/m2, 8 females) and 10 normal-weighted subjects (49 years; 23.9 kg/m2, 5 females) were studied. The abdominal volume occupied 41% in the obese group, being higher (p < 0.001) than the normal-weighted group (31%), indicating accumulation of abdominal fat. Restrictive lung defect was present in 17% of subjects with obesity. At rest in the supine position, subjects with obesity breathed with higher minute ventilation (11.9 L/min) and lower ribcage contribution (5.7%) than normal weighted subjects (7.5 L/min, p = 0.001 and 31.1%, p = 0.003, respectively), thus indicating thoracic restriction. Otherwise healthy obesity might not be characterized by a systematic restrictive lung pattern. Despite this, another sign of restriction could be poor thoracic expansion at rest in the supine position, resulting in increased ventilation. Class 3 obesity made respiratory rate further increased. Opto-electronic plethysmography and its thoraco-abdominal analysis of awake breathing add viable and interesting information in subjects with obesity that were complementary to pulmonary function tests. In addition, OEP is able to localize the restrictive effect of obesity.

Implantable medical devices (IMDs) provide effective medical solutions for diverse health care applications. Electrical circuits are crucial for implantable devices due to the requirement of intended functions, such as communication with external devices. Circuits have several risks, such as biocompatibility issues, power limitations, or size constraints. In this work, we propose a passive modulated ultrasound (PMU) principle for IMDs and develop a circuit-free ultrasonic system (CUS) for thoracic pressure monitoring. The PMU principle can passively modulate monitored physiological signals into ultrasound pulses without using electrical circuits or power supply. The size of the developed CUS is only 2.5 millimeters in radius and 850 micrometers in height. Animal experiments demonstrated that the CUS, with a high sensitivity (-22.96 millivolts per kilopascal), can monitor thoracic pressure to assist in diagnosing different heart diseases, including cardiac arrest and myocardial infarction. The PMU provides a human-friendly wireless sensing and communication strategy for IMDs, which promotes advancements in health care applications within the human body.