An automatic model of the respiratory control system
Cybernetic feedback connections for automatic model of respiratory control
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Cybernetic feedback connections for automatic model of respiratory control
A system is described for monitoring the respiratory process in which the gas flow rate and the frequency of respiration and expiration cycles can be determined on a real time basis. A face mask is provided with one-way inlet and outlet valves where the gas flow is through independent flowmeters and through a mass spectrometer. The opening and closing of a valve operates an electrical switch, and the combination of the two switches produces a low frequency electrical signal of the respiratory inhalation and exhalation cycles. During the time a switch is operated, the corresponsing flowmeter produces electric pulses representative of the flow rate; the electrical pulses being at a higher frequency than that of the breathing cycle and combined with the low frequency signal. The high frequency pulses are supplied to conventional analyzer computer which also receives temperature and pressure inputs and computes mass flow rate and totalized mass flow of gas. From the mass spectrometer, components of the gas are separately computed as to flow rate. The electrical switches cause operation of up-down inputs of a reversible counter. The respective up and down cycles can be individually monitored and combined for various respiratory measurements.
Mathematical models for transient oxygen at input of human body respiratory control center
Personal portable oxygen respiratory support equipment
Acrolein and trichloroethylene (TCE) are priority hazardous air pollutants due to environmental prevalence and adverse health effects; however, neuroendocrine stress-related systemic effects are not characterized. Comparing acrolein, an airway irritant, and TCE with low irritancy, we hypothesized that airway injury would be linked to neuroendocrine-mediated systemic alterations. Male and female Wistar-Kyoto rats were exposed nose-only to air, acrolein or TCE in incremental concentrations over 30 min, followed by 3.5-hr exposure to the highest concentration (acrolein - 0.0, 0.1, 0.316, 1, 3.16 ppm; TCE - 0.0, 3.16, 10, 31.6, 100 ppm). Real-time head-out plethysmography revealed acrolein decreased minute volume and increased inspiratory-time (males>females), while TCE reduced tidal-volume. Acrolein, but not TCE, inhalation increased nasal-lavage-fluid protein, lactate-dehydrogenase activity, and inflammatory cell influx (males>females). Neither acrolein nor TCE increased bronchoalveolar-lavage-fluid injury markers, although macrophages and neutrophils increased in acrolein-exposed males and females. Systemic neuroendocrine stress response assessment indicated acrolein, but not TCE, increased circulating adrenocorticotrophic hormone, and consequently corticosterone, and caused lymphopenia, but only in males. Acrolein also reduced circulating thyroid-stimulating hormone, prolactin, and testosterone in males. Furthermore, acute acrolein inhalation resulted in sex-specific upper respiratory irritation/inflammation and systemic neuroendocrine alterations linked to hypothalamic-pituitary-adrenal axes activation, which is critical in mediating extra-respiratory effects.
Hydrogen sulfide (H 2 S) has not historically been detected in spacecraft, but the possible evolution of volatile components from polar ice on the lunar surface is a potential concern for NASA’s planned Artemis missions. Numerous case reports and occupational epidemiological studies document that exposure to H 2 S at high concentrations has effects on the respiratory system, potentially leading to unconsciousness followed by debilitating neurological effects. Studies in rodents demonstrate sensitivity of the respiratory system to lower concentrations of H 2 S. Adverse respiratory effects have also been indicated in workers chronically exposed to H 2 S. The objective of the present publication is to develop Spacecraft Maximum Allowable Concentrations (SMACs) for H 2 S for all current standard durations of exposure for spaceflight (1-hour, 24-hours, 7-days, 30-days, 180-days, and 1000-days). Summary sources and literature review were used to identify relevant studies to inform SMAC development. Space flight mission specific activities and most probable exposure scenarios were used to determine the relevant toxicity endpoints and supporting studies. SMACs were established for hydrogen sulfide of 5 ppm for 1-hour, 1.3 ppm for 24-hours, 1.3 ppm for 7-days, 1.3 ppm for 30-days, and 0.3 ppm for 180-days. Data are not sufficient currently to establish a 1000-day SMAC value. SMACs for H 2 S will support development of handling protocols and proper containment for lunar sample collection.
This article presents results from an experimental study to ascertain the transmissibility of the SARS-CoV-2 virus between rooms in a building that are connected by a central ventilation system. Respiratory droplet surrogates made of mucus and virus mimics were released in one room in a test building and measurements of concentration levels were made in other rooms connected via the ventilation system. The paper presents experimental results for different ventilation system configurations, including ventilation rate, filtration level (up to MERV-13), and fractional outdoor air intake. The most important finding is that respiratory droplets can and do transit through central ventilation systems, suggesting a mechanism for viral transmission (and COVID-19 specifically) within the built environment in reasonable agreement with well-mixed models. We also find the deposition of these droplets on room walls to be negligibly small. Furthermore, the energy use impact of the different configuration settings is also quantified to help inform intelligent building operation that can simultaneously address energy use and occupant health.
On-line time sharing data processing and computer analysis techniques, and acceleration effects on cardiovascular and respiratory systems
Sampled-data regulator for investigating both steady-state and transient responses of respiratory system of humans to reduced oxygen at fixed levels of carbon dioxide
Abstract Wildfire smoke is associated with short-term respiratory outcomes including asthma exacerbation in children. As investigations into developmental wildfire smoke exposure on children’s longer-term respiratory health are sparse, we investigated associations between developmental wildfire smoke exposure and first use of respiratory medications. Prescription claims from IBM MarketScan Commercial Claims and Encounters database were linked with wildfire smoke plume data from NASA satellites based on Metropolitan Statistical Area (MSA). A retrospective cohort of live infants (2010–2016) born into MSAs in six western states (U.S.A.), having prescription insurance, and whose birthdate was estimable from claims data was constructed ( N = 184,703); of these, gestational age was estimated for 113,154 infants. The residential MSA, gestational age, and birthdate were used to estimate average weekly smoke exposure days ( smoke-day ) for each developmental period: three trimesters, and two sequential 12-week periods post-birth. Medications treating respiratory tract inflammation were classified using active ingredient and mode of administration into three categories:: 'upper respiratory', 'lower respiratory', 'systemic anti-inflammatory'. To evaluate associations between wildfire smoke exposure and medication usage, Cox models associating smoke-days with first observed prescription of each medication category were adjusted for infant sex, birth-season, and birthyear with a random intercept for MSA. Smoke exposure during postnatal periods was associated with earlier first use of upper respiratory medications (1–12 weeks: hazard ratio (HR) = 1.094 per 1-day increase in average weekly smoke-day, 95%CI: (1.005,1.191); 13–24 weeks: HR = 1.108, 95%CI: (1.016,1.209)). Protective associations were observed during gestational windows for both lower respiratory and systemic anti-inflammatory medications; it is possible that these associations may be a consequence of live-birth bias. These findings suggest wildfire smoke exposure during early postnatal developmental periods impact subsequent early life respiratory health.
The popularity of electronic cigarettes and vaping products has launched the outbreak of a condition affecting the respiratory system of users, known as electronic-cigarette/vaping-associated lung injury (EVALI). The build-up of vitamin E acetate (VEA), a diluent of some illicit vaping oils, in the bronchoalveolar lavage of patients with EVALI provided circumstantial evidence as a target for investigation. In this work, we provide a fundamental characterization of the interaction of VEA with lung cells and pulmonary surfactant (PS) models to explore the mechanisms by which vaping-related lung injuries may be present. We first confirm the localization and uptake of VEA in pulmonary epithelial cells. Further, as PS is vitally responsible for the biophysical functions of the lungs, we explore the effect of added VEA on three increasingly complex models of PS: dipalmitoylphosphatidylcholine (DPPC), a lipid-only synthetic PS, and the biologically derived extract Curosurf. Using high-resolution techniques of small-angle X-ray scattering, small-angle neutron scattering, neutron spin–echo spectroscopy, and neutron reflectometry, we compare the molecular-scale behaviors of these membranes to the bulk viscoelastic properties of surfactant monolayer films as studied by Langmuir monolayer techniques. While VEA does not obviously alter the structure or organization of PS membranes, a consistent softening of membrane systems—regardless of compositional complexity—provides a biophysical explanation for the respiratory distress associated with EVALI and yields a new perspective on the behavior of the PS system.
Inhaled radioactive materials can pose a long-term health concern, as the material can be incorporated into the body’s metabolic pathways and remain in organs and tissues for extended durations. During the retention period, the radioactive material may localize in a source organ and irradiate adjacent target organs and tissues. Distribution of these materials changes over time, requiring biokinetic modeling to evaluate their movement through various tissues and organs. The evolving distribution depends on multiple inputs characterizing the inhaled material, such as particle size and size distribution, particle density, aspect ratio, specific radionuclide, the chemical form, and solubility. In addition, biological parameters such as breathing rate, breathing type (nasal or nasal/oral), respiratory system morphometry, tidal volume, functional residual capacity, and anatomical dead space all influence material transport. These aerosol properties and physiological characteristics of the respiratory tract jointly define a range of initial conditions that influence the time-dependent distribution of radioactive material. To evaluate both uncertainty in the initial conditions of inhalation exposure and the final output (committed effective dose) from biokinetic models, a Python-based software tool, Radiological Exposure Dose Calculator (REDCAL), was developed to propagate uncertainty within the human respiratory tract model. Focusing on deposition fraction uncertainty, the primary objective was to characterize the initial activity distribution across respiratory regions as a function of anticipated particle sizes and distributions. The impact of the deposition fraction uncertainty was propagated to committed effective dose coefficients for selected radionuclides in a companion publication. For each particle size, a lognormal distribution, characterized by its geometric mean as defined within ICRP Publication 66, serves as the basis for introducing uncertainty into the physical processes governing deposition in various lung regions. Finally, this study addresses the deposition process and examines how uncertainty in deposition mechanisms affects activity distribution in the airways, ultimately presenting the expected range and standard deviation of deposited activity as a function of particle size.
All hydrocarbon- (HC-) fueled heat engine exhaust (tailpipe) emissions (<10 to 140 nm) contribute as health hazards, including emissions from transportation vehicles (e.g., aircraft) and other HC-fueled power systems. CO2 emissions are tracked, and when mapped, show outlines of major transportation routes and cities. Particulate pollution affects living tissue and is found to be detrimental to cardiovascular and respiratory systems where ultrafine particulates directly translocate to promote vascular system diseases potentially detectable as organic vapors. This paper discusses aviation emissions, fueling, and certification issues, including heat engine emissions hazards, detection at low levels and tracking of emissions, and alternate energy sources for general aviation.
Determining the fate of inhaled aerosols in the respiratory system is essential in assessing the potential toxicity of inhaled airborne materials, responses to airborne pathogens, or in improving inhaled drug delivery. The availability of high-resolution clinical lung imaging and advances in the reconstruction of lung airways from CT images have led to the development of subject-specific in-silico 3D models of aerosol dosimetry, often referred to as computational fluid-particle-dynamics (CFPD) models. As CFPD models require extensive computing resources, they are typically confined to the upper and large airways. These models can be combined with lower-dimensional models to form multiscale models that predict the transport and deposition of inhaled aerosols in the entire respiratory tract. Understanding where aerosols deposit is only the first of potentially several key events necessary to predict an outcome, being a detrimental health effect or a therapeutic response. To that end, multiscale approaches that combine CFPD with physiologically-based pharmacokinetics (PBPK) models have been developed to evaluate the absorption, distribution, metabolism, and excretion (ADME) of toxic or medicinal chemicals in one or more compartments of the human body. CFPD models can also be combined with host cell dynamics (HCD) models to assess regional immune system responses. Here, this paper reviews the state of the art of these different multiscale approaches and discusses the potential role of personalized or subject-specific modeling in respiratory health.
Assessing the toxicity of airborne particulate matter or the efficacy of inhaled drug depends upon accurate estimates of deposited fraction of inhaled materials. In silico approaches can provide important insights into site- or airway-specific deposition of inhaled aerosols in the respiratory system. In this study, we improved on our recently developed 3D/1D model that simulate aerosol transport and deposition in the whole lung over multiple breath cycles. A subject-specific multiscale lung model of a healthy male subject using computational fluid-particle dynamics (CFPD) in a 3D model of the oral cavity through the large bronchial airways entering each lobe was bidirectionally coupled with a recently improved Multiple Path Particle Dosimetry (MPPD) model to predict aerosol deposition over the entire respiratory tract over multiple breaths for four conditions matching experimental aerosol exposures in the same subject from which the model was developed. These include two particle sizes (1 and 2.9 μm) and two subject-specific breathing rates of ~300 ml/s (slow breathing) and ~750 ml/s (fast breathing) at a target tidal volume of 1 L. In silico predictions of retained fraction were 0.31 and 0.29 for 1 μm and 0.66 and 0.62 for 2.9 μm during slow and fast breathing, respectively, and compared well with experimental data (1 μm: 0.31 ± 0.01 (slow) and 0.27 ± 0.01 (fast), 2.9 μm: 0.63 ± 0.03 (slow) and 0.68 ± 0.02 (fast)). Furthermore, these results provide a great deal of confidence in the validity and reliability of our approach.
The emergence of a pandemic affecting the respiratory system can result in a significant demand for face masks. This includes the use of cloth masks by large sections of the public, as can be seen during the current global spread of COVID-19. However, there is limited knowledge available on the performance of various commonly available fabrics used in cloth masks. Importantly, there is a need to evaluate filtration efficiencies as a function of aerosol particulate sizes in the 10 nm – 10 µm range, which is particularly relevant for respiratory virus transmission. We have carried out these studies for several common fabrics including cotton, silk, chiffon, flannel, various synthetics, and their combinations. While the filtration efficiencies for various fabrics when a single layer was used ranged from 5-80% and 15-95% for particle sizes <300 nm and >300 nm respectively, the efficiencies improved when multiple layers were used, and when using a specific combination of different fabrics. Filtration efficiencies of the hybrids (such as cotton-silk, cotton-chiffon, cotton-flannel) was >80 % (for particles <300 nm) and >90 % (for particles >300 nm). We speculate that the enhanced performance of the hybrids is likely due to the combined effect of mechanical and electrostatic-based filtration. Cotton, the most widely used material for cloth masks performs better at higher weave densities (i.e., threads per inch) and can make a significant difference in filtration efficiencies. Our studies also imply that gaps (as caused by an improper fit of the mask) can result in over a 60% decrease in the filtration efficiency, implying the need for future cloth mask design studies to take into account issues of “fit” and leakage, while allowing the exhaled air to vent efficiently. Overall, we find that combinations of various commonly available fabrics used in cloth masks can potentially provide significant protection against the transmission of aerosol particles.
The tremendous global health burden related to COVID-19 means that identifying determinants of COVID-19 severity is important for prevention and intervention. We aimed to explore long-term exposure to ambient air pollution as a potential contributor to COVID-19 severity, given its known impact on the respiratory system. Methods: We used a cohort of all people with confirmed SARS-CoV-2 infection, aged 20 years and older and not residing in a long-term care facility in Ontario, Canada, during 2020. We evaluated the association between long-term exposure to fine particulate matter (PM 2.5 ), nitrogen dioxide (NO 2 ) and ground-level ozone (O 3 ), and risk of COVID-19-related hospital admission, intensive care unit (ICU) admission and death. We ascertained individuals’ longterm exposures to each air pollutant based on their residence from 2015 to 2019. We used logistic regression and adjusted for confounders and selection bias using various individual and contextual covariates obtained through data linkage. Results: Among the 151105 people with confirmed SARS-CoV-2 infection in Ontario in 2020, we observed 8630 hospital admissions, 1912 ICU admissions and 2137 deaths related to COVID-19. For each interquartile range increase in exposure to PM 2.5 (1.70 µg/m 3 ), we estimated odds ratios of 1.06 (95% confidence interval [CI] 1.01–1.12), 1.09 (95% CI 0.98–1.21) and 1.00 (95% CI 0.90–1.11) for hospital admission, ICU admission and death, respectively. Estimates were smaller for NO 2 . We also estimated odds ratios of 1.15 (95% CI 1.06–1.23), 1.30 (95% CI 1.12–1.50) and 1.18 (95% CI 1.02–1.36) per interquartile range increase of 5.14 ppb in O 3 for hospital admission, ICU admission and death, respectively.
Exhaled breath condensate (EBC) represents a low-cost and non-invasive means of examining respiratory health. EBC has been used to discover and validate exhaled volatile and non-volatile biomarkers of disease related to the respiratory system distress such as asthma, COPD, lung cancer, and secondary infections. One newly emerging utilization of EBC, is proteomics analysis, which can provide an unbiased snapshot into ongoing biological processes in the airway. Fully characterizing the biological landscape of EBC collections is challenging though, due to sample variability, and low detection sensitivity. EBC is primarily composed of condensed water, causing technical challenges with detecting key macromolecules from the dilute sample matrix; therefore, high sensitivity techniques are required to unlock the full capability of EBC as a method for non-invasive biomarker detection. To overcome some of these technical challenges for proteomic analyses, we applied our recently developed microscale proteomic techniques and developed a novel TMT based approach which enabled reliable, relative quantification with significantly improved detection of low abundance peptides/proteins across multiple healthy volunteer EBC samples. Our EBC collection design includes longitudinal EBC collections from five individual healthy volunteers on three separate days of the week with triplicate, back-to-back donations each day. Here, we report a total of 235 quantifiable proteins corresponding to 1,877 non-redundant peptides for evaluating sample collection reproducibility and establishing a healthy (human host) baseline EBC biomarker proteome studies. This work will pave the way for further investigations of EBC protein expression profiles and showcase the value of using non-invasive collection method techniques for clinically relevant biomarker discovery. This research was supported by the LDRD Biomedical Resilience And Readiness in AdVerse Operating Environments (BRAVE) Project (73748), and was conducted at Pacific Northwest National Laboratory (PNNL) in Richland, WA. PNNL is a multiprogram national laboratory operated by Battelle for the Department of Energy (DOE) under Contract DE-AC05-76RLO 1830.