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Lung Volume Measurements

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Most cited protocols related to «Lung Volume Measurements»

Deriving and Validating SpO2-PaO2 Ratios

The study was approved by Institutional Review Board at Vanderbilt University Medical Center and involved two phases; Phase 1-derivation phase, followed by Phase 2-validation phase. In Phase 1, matched measurements of oxygen saturation by pulse oximetry (SpO₂) and partial pressure of oxygen in arterial blood (PaO₂) were obtained from 2 groups of patients: Group 1: those undergoing general anesthesia at Vanderbilt University Medical Center from 2002 to 2007 and Group 2: patients from the ARDS network -low versus high tidal volume for the Acute Respiratory Management of ARDS (ARMA) database.(8 (link)) We limited data points to those with SpO₂ ≤ 98% to maximize matched data in the linear range of the sigmoidal association between SpO₂ and PaO₂ in the oxyhemoglobin curve, and at the same time maintain clinical relevance and adequate sample size, given that it is unlikely that patients with higher SpO₂ would have PF ratios of less than 400 and thus impact the SOFA score. SF ratios corresponding to PF ratios of 100, 200, 300 and 400 were then derived. In Phase 2, the SOFA scores calculated by using these SF ratios were validated against outcomes in a 3rd group of surgical and trauma ICU patients.

Pandharipande P.P., Shintani A.K., Hagerman H.E., St Jacques P.J., Rice T.W., Sanders N.W., Ware L.B., Bernard G.R, & Ely E.W. (2009). Derivation and validation of SpO2/FiO2 ratio to impute for PaO2/FiO2 ratio in the respiratory component of the Sequential Organ Failure Assessment (SOFA) Score. Critical care medicine, 37(4), 1317-1321.

Publication 2009

Anesthesia Arteries Ethics Committees, Research Lung Volume Measurements Operative Surgical Procedures Oximetry Oximetry, Pulse Oxygen Oxyhemoglobin Partial Pressure Patients Respiratory Distress Syndrome, Acute Saturation of Peripheral Oxygen Wounds and Injuries

Resting-State fMRI of Healthy Volunteers

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Publication 2010

Cell Respiration ECHO protocol fMRI Head Healthy Volunteers Heart Lung Volume Measurements Males Precipitating Factors Pulse Rate Vaginal Diaphragm

Quantitative CT Analysis of COPD

Except for the additional subjects obtained for longitudinal analysis, all CT data and analysis were performed as part of the COPDGene project. Whole-lung volumetric multi-detector CT acquisition was performed at full inspiration and normal expiration using a standardized previously published protocol.^{3 (link)} Data reconstructed with the standard reconstruction kernel was used for quantitative analysis. All CT data were presented in Hounsfield Units (HU), where stability of CT measurement for each scanner was monitored monthly using a custom COPDGene phantom.^{3 (link)} For reference, air and water attenuation values are −1000 and 0 HU; healthy lung parenchyma is approximately −700 HU. Quantitative analysis of emphysema severity was performed on segmented lung images using Slicer (www.Slicer.org). The percentage of emphysema (% emphysema) was defined as all lung voxels with a CT attenuation value of less than −950 Hounsfield units (HU) divided by the total lung volume at full inflation, multiplied by 100. The total percent gas trapping (GT) was defined as the fraction of lung with a CT attenuation value of less than −856 HU divided by the total lung volume at expiration, multiplied by 100. Automated airway analysis was performed using VIDA Pulmonary Workstation version 2.0 (www.vidadiagnostics.com) using previously validated segmentation methods (details in Supplemental Methods).^{28 (link)} All analytical measurements not pertaining to the discussed PRM approach were performed by COPDGene personnel. Similar data acquisition and post-processing were performed on the additional retrospective longitudinal data sets which were not obtained as part of the COPDGene study.

Galbán C.J., Han M.K., Boes J.L., Chughtai K.A., Meyer C.R., Johnson T.D., Galbán S., Rehemtulla A., Kazerooni E.A., Martinez F.J, & Ross B.D. (2012). CT-based Biomarker Provides Unique Signature for Diagnosis of COPD Phenotypes and Disease Progression. Nature medicine, 18(11), 1711-1715.

Publication 2012

Inhalation Lung Lung Volume Measurements Lung Volumes Pulmonary Emphysema Reconstructive Surgical Procedures

Improving FMRI Data Analysis with Physiological Modeling

Each time series was realigned to the first volume and co‐registered to a T1‐weighted image acquired in the same scanning session. The unified segmentation algorithm (Ashburner & Friston, 2005), as implemented in SPM12, was used to generate participant‐specific GM masks and to normalize all data to Montreal Neurological Institute (MNI) group space. Volumes were then smoothed with a 6 mm FWHM isotropic Gaussian kernel. The design matrices of all GLMs included regressors for motion, a high‐pass filter (cutoff period 128 s), and the stimulation blocks convolved by the canonical hemodynamic response function. A set of 14 physiological regressors, generated using an in‐house developed Matlab toolbox (Hutton et al., 2011), were based on cardiac and respiratory traces recorded on Spike2 (Cambridge Electronic Design Limited, Cambridge, UK) with a respiration belt and pulse oximeter. Twelve regressors, based on a set of sine and cosine Fourier series components extending to the third harmonic, were built to model the cardiac and respiratory phase (Glover, Li, & Ress, 2000; Josephs, Howseman, Friston, & Turner, 1997). Two additional regressors were included to model the variation in respiratory volume (based on Birn et al., 2006, 2008) and heart rate (based on Chang & Glover, 2009) .
A cohort‐wise GM mask was defined as those voxels that had a GM probability >0.6 in at least half of the cohort. This mask defined the voxels included in the estimation of the GLM parameters. Activation based on viewing scenes or objects was expected in primary visual cortex (V1) and so a further participant‐specific mask of V1 was defined as described in a previous study (Todd et al., 2017).
Temporal autocorrelations were modeled either with a mixture of an AR(1) model + white noise or with the FAST model (SPM12 revision 7203) with varying numbers of components,
p∈1 9. For reference, the data were also analyzed without prewhitening.

Corbin N., Todd N., Friston K.J, & Callaghan M.F. (2018). Accurate modeling of temporal correlations in rapidly sampled fMRI time series. Human Brain Mapping, 39(10), 3884-3897.

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Publication 2018

Heart Hemodynamics Lung Volume Measurements physiology Pulse Rate Rate, Heart Respiration Respiratory Rate Short Interspersed Nucleotide Elements

Identification of Mycobacterium tuberculosis Survival Genes

The experiment was divided into three phases. In each phase, four adult, male, Indian rhesus macaques were infected with aerosols (dose ~ 10,000 cfu) of an identical pool of 100–120 mutants. A total of 326 unique mutants were used over the three phases. Aerosol exposures were performed on anesthetized animals, in an unrestricted breathing configuration. Whole body plethsymography was used to estimate respiratory minute volume prior to exposure [15 , 16 ]. Two of the animals from each phase were euthanized 24 h post-infection. These animals are hereafter referred to as the “input” group (CM77, DG82, BG22, DE81, CB10, BD62; age 8.76±1.31 years, weight 14.55±3.01 Kg). DNA isolated from the CFUs obtained from their lungs was labeled with Cy5 (input). The remaining two animals from each phase were followed for the development of acute and fatal pulmonary TB (time to death = 28.7±4.5 days). These animals (EN36, EK75, DV08, ER12, EI05, DD77; age 6.06±1.24 years, weight 8.19±1.01 Kg) are hereafter referred to as the “output” group. DNA prepared from the CFUs obtained from their lungs was labeled with Cy3 (output). Equimolar quantities of input and output samples were used for DeADMAn. The Mtb mutants that fail to survive during pathogenesis of TB would be under-represented in the output samples and directly reveal the identity of the genes required for the survival and multiplication of Mtb in primate lungs (Figure 1).
The animals were subjected to physical exam, chest X-rays (CXR), primagam IFN-γ (Biocor) and serum CRP assays at scheduled intervals. Primagam assay was performed as previously described, using 3 ml whole lithium-heparin blood [17 (link)]. All procedures were approved by the relevant Institutional Animal Use/Care and Biosafety Committees.

Dutta N.K., Mehra S., Didier P.J., Roy C.J., Doyle L.A., Alvarez X., Ratterree M., Be N.A., Lamichhane G., Jain S.K., Lacey M.R., Lackner A.A, & Kaushal D. (2010). Genetic Requirements for the Survival of Tubercle Bacilli in Primates. The Journal of infectious diseases, 201(11), 1743-1752.

Publication 2010

Adult Animals Biological Assay BLOOD Heparin Human Body Infection Interferon Type II Lithium Lung Lung Volume Measurements Macaca mulatta Males pathogenesis Physical Examination Primates Radiography, Thoracic Serum Tuberculosis, Pulmonary

Most recents protocols related to «Lung Volume Measurements»

Respiratory Muscle Training for Dialysis Patients

Respiratory muscle training was administered with the aid of an inspiratory expiratory system (Resp-in-out, Medinet, Milan, Italy, Supplementary Material Figure S1). This device consists of a positive pressure and volume exerciser for respiratory training, promoting inhalation and deep exhalation without interruption of the respiratory cycle. The tool is composed of a body into which the air is blown, and which contains a mobile ball; a tube with mouthpiece; and colored connectors with a grid filter, characterized by holes of different sizes (red, yellow and green), changing the resistance to airflow. The subjects, keeping their lips tightly close to the mouthpiece, exhale normally. The rising ball indicates the pressure in cmH₂O. The pressure can increase according to the connector used. At the end of the exhalation, the ball goes down, and without detaching the lips from the mouthpiece, it is possible to complete the breathing exercise, making an inspiration. It is also possible to prolong inhalation and/or exhalation by keeping the ball in place, thus maintaining a certain pressure or a certain volume.
Patients were requested to perform the training on non-dialysis days (at least 4 days per week) two times per day, as follows. Using a metronome set at 30 beats per minute to guide the respiratory rhythm, patients were instructed to perform a complete respiratory cycle with inspiration synchronized with the first beat and expiration corresponding to the second beat and then wait for two consecutive beats before restarting. In one minute, patients performed 8 complete respiratory cycles. Then, the following minute, they rested without doing any training. This 1:1 min train-rest ratio was repeated five times for a total of 10 min. The training device included three different levels of resistance to the air flow, low, medium or hard, according to the dimensions and number of holes present in the connection between the mouthpiece and the device with the ball. During the first week of training, the air resistance was kept at a minimum; then, in week two, it was increased to the intermediate level, and finally, for the last two weeks, it was moved to the hardest level.
During the first week of exercise, an exercise physiologist supervised the training sessions of patients by directly administering them before the dialysis session. The following three weeks were performed at home autonomously by each patient. A daily log was provided to each patient to report the training execution and possible symptoms. Moreover, once per week on a dialysis day, a team member checked the training execution and reinforced the adherence to the program. At the end of the training period, to monitor the long-term adherence, patients of RMT group were allowed to keep the RMT device, but no recommendations were given to the patients in relation to the training continuation.

Lamberti N., Piva G., Battaglia Y., Franchi M., Pizzolato M., Argentoni A., Gandolfi G., Gozzi G., Lembo M., Lavisci P., Storari A., Rinaldo N., Manfredini F, & Cogo A. (2023). Inspiratory–Expiratory Muscle Training Improved Respiratory Muscle Strength in Dialysis Patients: A Pilot Randomised Trial. Advances in Respiratory Medicine, 91(1), 93-102.

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Publication 2023

Breathing Exercises Cocaine Dialysis Human Body Inhalation Lip Lung Volume Measurements Medical Devices Menstruation Disturbances Patients Pressure Respiratory Rate

Pediatric Cardiopulmonary Exercise Testing

18 patients performed a symptom-limited cardiopulmonary exercise test (CPET) on a bicycle ergometer (VIAsprint^® ergoselect 100 ergometer, ergoline GmbH, Bitz, Germany). The test implementation was adapted to the methods previously described by Pasquali et al. (14 (link)) Pedal length was adjusted according to the recommendations of the German society for pediatric cardiology (24 , 25 ). As recommended for children and adolescents, a weight-based protocol was used with a starting load of 1.0 W/kg body weight and an increase of 0.5 W/kg load every two minutes (24 , 26 (link)). The test was continued until limiting symptoms for exercise termination occurred, according to criteria previously described (27 (link)). For overweight children and adolescents, the workload was calculated corresponding to their height to avoid overloading (26 (link)). Respiratory gas exchange measurements were obtained breath by breath by a commercially available system (Vyntus^® CPX, Vyaire medical Inc., Mettawa, IL-USA). Peak oxygen uptake (VO2) was recorded as the mean value of VO2 during the last 20 s of the test. In addition, the respiratory volume per minute, the breathing rate per minute and the heart rate per minute were measured. All parameters were recorded at the start of the test (resting conditions) and continuously during the test. When the maximum workload was reached, the system was immediately resetted to the individual starting conditions and measurements were obtained for another two minutes. As recommended by the German society for pediatric cardiology, individual z-scores for participants ages 6 – 18 were calculated based on the reported data by Klemt et al. (28 , 29 ) For participants >18 years, the reference values reported by Gläser et al. from the SHIP-Study (30 (link)) were used.

Spiekermann J., Sinningen K., Hanusch B., Kleber M., Schündeln M.M., Kiewert C., Siggelkow H., Höppner J, & Grasemann C. (2023). Cardiorespiratory fitness in adolescents and young adults with Klinefelter syndrome – a pilot study. Frontiers in Endocrinology, 14, 1106118.

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Publication 2023

Adolescent Body Weight Cardiopulmonary Exercise Test Cardiovascular System Child Ergoline Foot Lung Volume Measurements Oxygen Patients Rate, Heart Respiratory Rate

Quantifying Lung Lesions in COVID-19

The data was collected from City of Hope patients during the period of 01 January 2020 till 30 June 2021. Volumetric quantification of lung and pneumonia lesions was carried out using the deep-learning segmentation model of lung and lesions on the 3DQI platform (https://3dqi.mgh.harvard.edu, accessed on 30 June 2021) [18 (link)]. After automated segmentation of lung and lesions, an image analyst, who was blinded to clinical data, reviewed and confirmed the segmentation results. A 3D radiometric analysis of the data was performed on the 3DQI Radiometrics tool based on extracted image features and clinical data.

Rahmanuddin S., Jamil A., Chaudhry A., Seto T., Brase J., Motarjem P., Khan M., Tomasetti C., Farwa U., Boswell W., Ali H., Guidaben D., Haseeb R., Luo G., Marcucci G., Rosen S.T, & Cai W. (2023). COVID and Cancer: A Complete 3D Advanced Radiological CT-Based Analysis to Predict the Outcome. Cancers, 15(3), 651.

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Publication 2023

Lung Lung Volume Measurements Patients Pneumonia Radiometry

Cardiovascular Variability During Breathing Paced

Participants were asked to refrain from smoking and drinking coffee or tea for 24 h before the study. The experimental session took place in the early afternoon, with the subjects supine in a quiet room at an environmental temperature between 20 and 23 °C. The recordings started after 20–30 min of rest. Participants were allowed to breathe spontaneously during the first 15 min of the recording and then were asked to breathe following a metronome. Two paced frequencies, fast and slow, were set and allocated in random order. Aim of the fast-paced breathing was to remove the spontaneous breath-by-breath variability during the breathing cycle while preserving the physiological average breathing rate: thus, the paced respiratory frequency was set at a constant rate between 15 and 12 bpm (according to the patient's preference), for 5 min. Aim of the slow-paced breathing was to evaluate the influence of a 10-s breathing cycle on cardiovascular variability: thus, the respiratory rate was paced at 6 bpm for 4 min. In both patients and controls the recorded signals included the continuous noninvasive finger blood pressure (Finapres, Ohmeda Inc., Englewood, Colorado, USA) and one-lead EKG, sampled at 200 Hz. The recorded signals also included the uncalibrated respiratory volume (RSP) by induction plethysmography (Respitrace, Ambulatory Monitoring, Ardsley, NY)^{15 (link)} but due to a technical problem, this device was available for the experimental sessions of all the 10 controls and 17 HF patients. Oxygen saturation (SaO₂) was measured at the end of each (free, fast-paced, and slow-paced) breathing condition in all participants.

Radaelli A., Mancia G., Balestri G., Bonfanti D, & Castiglioni P. (2023). Respiratory patterns and baroreflex function in heart failure. Scientific Reports, 13, 2220.

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Publication 2023

Blood Pressure Cardiovascular System Coffee Fingers Lung Volume Measurements Medical Devices Oxygen Saturation Patients physiology Plethysmography Respiratory Rate

Fetal lung MRI acquisition protocol

Fetal MRI data from clinically indicated scans were retrospectively identified. Repeated standardised axial T2-weighted acquisitions of the lungs were routinely performed for e.g. lung volumetry or data collection for super-resolution body imaging according to the clinical indication and in accordance with ISUOG guidelines [16 (link)]. One 1.5 T MRI scanner (Ingenia, Philips Healthcare, Best, The Netherlands) was used for all exams. Axial T2-weighted acquisitions were acquired in a standardised fashion using a body coil and the following parameters: 200 to 300 mm field of view, 3 to 4 mm slice thickness (thinner slices used in early gestation), 0.3 to 0.4 mm gap, 256 × 256 matrix, shortest (7536.2 to 31,575 ms) repetition time, 100 ms echo time, and 90° flip angle. Specific absorption rates were less than 2W/kg for all cases. Fetal MRI scans were performed without administration of sedation or contrast medium. The time in minutes between initial and repeat axial T2-weighted acquisitions was calculated for each case. Gestational age in weeks plus days (GW) post menstruationem at the time of the fetal MRI scan was calculated based on the first fetal ultrasound examination.

Watzenboeck M.L., Heidinger B.H., Rainer J., Schmidbauer V., Ulm B., Rubesova E., Prayer D., Kasprian G, & Prayer F. (2023). Reproducibility of 2D versus 3D radiomics for quantitative assessment of fetal lung development: a retrospective fetal MRI study. Insights into Imaging, 14, 31.

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Publication 2023

Care, Prenatal Contrast Media ECHO protocol Fetal Ultrasonography Gestational Age Human Body Lung Lung Volume Measurements MRI Scans Pregnancy Sedatives

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What are the different types of lung volume measurements?

Lung volume measurements can be categorized into several main types, including: 1. Spirometry: Measures the volume of air inhaled and exhaled during breathing. 2. Body plethysmography: Determines lung volumes by measuring changes in body volume during breathing. 3. Helium dilution: Assesses lung volumes by measuring the dilution of an inhaled helium gas mixture. 4. Nitrogen washout: Evaluates lung volumes by monitoring the clearance of nitrogen from the lungs. 5. Radiologic imaging: Uses CT scans or MRI to visually assess lung volumes. Each method has its own advantages and applications, so the choice depends on the specific research goals and patient characteristics.

What are some common challenges in performing accurate lung volume measurements?

Some key challenges in obtaining reliable lung volume measurements include: 1. Patient cooperation: Accurate results require the patient to follow breathing instructions precisely during the test. 2. Anatomical variations: Lung volumes can vary significantly based on factors like age, sex, height, and body composition. 3. Respiratory conditions: Diseases like COPD, asthma, and fibrosis can alter lung mechanics and affect measurement accuracy. 4. Equipment calibration: Proper calibration and maintenance of measurement devices is crucial for consistent results. 5. Interpretation nuances: Interpreting the measurements requires expertise to account for normal variations and identify abnormalities. These challenges highlight the need for optimized protocols and thorough data analysis to ensure the most accurate and meaningful lung volume measurements.

How can PubCompare.ai help with lung volume measurement research?

PubCompare.ai can assist lung volume measurement research in several key ways: 1. Efficient literature screening: The platform allows you to quickly sreen a large volume of published protocols, pre-prints, and patents related to lung volume measurement techniques. 2. AI-driven protocol comparison: PubCompare.ai's AI analysis can identify critical insights by comparing the effectiveness, reproducibility, and accuracy of different lung volume measurement protocols. 3. Optimized protocol selection: By highlighting the most effective protocols, the platform helps you choose the best approach for your specific research goals and sample characteristics. 4. Streamlined research process: Access to the most relevant and reliable lung volume measurement methods can significantly improve the efficiency and quality of your research.

What are some specific applications of lung volume measurements?

Lung volume measurements have a wide range of important applications in clinical and research settings, including: 1. Diagnosis and monitoring of respiratory diseases: Tracking changes in lung volumes can help diagnose and manage conditions like COPD, asthma, and pulmonary fibrosis. 2. Evaluating exercise capacity: Measuring lung volumes before and after exercise can provide insights into cardiovascular and respiratory fitness. 3. Perioperative assessment: Preoperative lung volume measurements can help predict postoperative respiratory complications. 4. Personalized treatment planning: Detailed lung volume data can inform the optimization of ventilator settings, oxygen therapy, and other interventions. 5. Research on lung physiology: Understanding normal and abnormal lung volumes is crucial for advancing our knowledge of respiratory system function.

What are the key benefits of using PubCompare.ai for lung volume measurement research?

The key benefits of using PubCompare.ai for lung volume measurement research include: 1. Increased effieciency: The platform allows you to quickly screen a large volume of relevant protocols, saving time and effort. 2. Enhanced accuracy: PubCompare.ai's AI-driven analysis helps identify the most effective and reproducible lung volume measurement techniques. 3. Improved research quality: Access to the best available protocols can lead to more accurate and meaningful results in your studies. 4. Streamlined workflow: The platform integrates seamlessly into your research process, minimizing disruptions and optimizing productivity.

How does PubCompare.ai's AI-powered analysis work for lung volume measurements?

PubCompare.ai's AI-powered analysis of lung volume measurement protocols works by: 1. Rapidly screening a large volume of published literature, pre-prints, and patents to identify the most relevant protocols. 2. Extracting key details about each protocol, such as the measurement technique, sample characteristics, and reported accuracy/reproducibility. 3. Applying advanced machine learning algorithms to compare the relative effectiveness of the different protocols. 4. Highlighting the critical insights, such as the most efficient and reliable lung volume measurement methods for specific research goals or patient populations. This AI-driven approach enables researchers to make more informed decisions about the optimal lung volume measurement protocols to use, streamlining the research process and leading to more accurate and meaningful results.

More about "Lung Volume Measurements"

Pulmonary Function Testing, Respiratory Physiology, Lung Volume Assessment, Lung Capacity Measurement, Spirometry, Plethysmography, Gas Dilution, Imaging Techniques, Forced Vital Capacity (FVC), Total Lung Capacity (TLC), Residual Volume (RV), Inspiratory Capacity (IC), Expiratory Reserve Volume (ERV), Vital Capacity (VC), Tidal Volume (TV), Minute Ventilation (VE), Diffusing Capacity (DLCO), Airway Resistance (Raw), Airway Conductance (Gaw), Respiratory Mechanics, Ventilatory Parameters, Respiratory Disorders, Lung Disease Diagnosis, Pulmonary Function Evaluation, Respiratory Function Tests, Lung Volume Determination, Respiratory Measurements, Ventilation Assessment, Respiratory System Analysis, Lung Volume Quantification, Pulmonary Volume Metrics, Respiratory Airflow Dynamics, Respiratory Physiology Optimization, Respiratory Diagnostics, Respiratory Monitoring, Respiratory Health Evaluation.