Self-reported maternal characteristics (e.g. demographics, medical, and obstetric history), and dietary and psychosocial (i.e. depression, resilience, traumatic and threatening experiences, anxiety, and perceived stress) information, are collected at the earliest prenatal visit. Anthropometry, self-reported exposure (e.g. alcohol, tobacco, marijuana, methamphetamines), and fetal physiology [e.g. heart rate (HR), heart rate variability (HRV), movement, HR-movement coupling] are collected at each prenatal visit. Biometry and Doppler ultrasound velocimetry of uterine and fetal vessels are performed for embedded study participants. Postnatal newborn and/or 1 month visits include autonomic, cardiorespiratory, cortical activity, and auditory assessments, self-reported exposure, infant care practices, infant anthropometrics, facial dysmorphology photographs, and the Amiel-Tison Neurologic Assessment at term.21 (link) At the 1 year visit, the Mullen Scales of Early Learning22 is administered to assess cognitive ability and motor development (embedded study), and infant anthropometrics and facial dysmorphology photographs are collected. Maternal (pregnancy through delivery) and infant (newborn to 1 year) charts are abstracted to obtain information regarding growth, physical exam/fetal structure, laboratory testing, medications and interventions, clinical events, co-morbidities, and diagnoses. Serious adverse events, unanticipated problems, and concomitant services are collected at each participant visit, contact, or event, and are reported according to regulatory guidelines.
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Laboratory Procedure
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Velocimetry
Velocimetry
Velociimetry is the measurement and analysis of fluid velocities, often using non-invasive optical techniques such as particle image velocimetry (PIV) and laser Doppler velocimetry (LDV).
This field of study is crucial for understanding fluid dynamics, turbulence, and the behavior of complex systems across a wide range of scientific and engineering applications, including aerodynamics, hydrodynamics, and biofluid mechanics.
Researchers in velociimetry leverage advanced instrumentation and computational methods to quantify velocity fields with high spatial and temporal resolution, enabling detailed investigations of flow phenomena.
Effective velociimetry research requires the identification and application of robust, reproducible measurement protocols, which can be facilitated by AI-driven comparisons of published methods as offered by the PubCompare.ai tool.
This field of study is crucial for understanding fluid dynamics, turbulence, and the behavior of complex systems across a wide range of scientific and engineering applications, including aerodynamics, hydrodynamics, and biofluid mechanics.
Researchers in velociimetry leverage advanced instrumentation and computational methods to quantify velocity fields with high spatial and temporal resolution, enabling detailed investigations of flow phenomena.
Effective velociimetry research requires the identification and application of robust, reproducible measurement protocols, which can be facilitated by AI-driven comparisons of published methods as offered by the PubCompare.ai tool.
Most cited protocols related to «Velocimetry»
Anxiety
Auditory Perception
Blood Vessel
Cannabis sativa
Care, Prenatal
Cognition
Cortex, Cerebral
Diagnosis
Diet
Ethanol
Face
Fetal Structures
Infant
Infant, Newborn
Methamphetamine
Mothers
Movement
Nervous System, Autonomic
Neurologic Examination
Obstetric Delivery
Pharmaceutical Preparations
Physical Examination
physiology
Pregnancy
Rate, Heart
Tobacco Products
Ultrasounds, Doppler
Uterus
Velocimetry
Verbascum
The first quantitative measurements of blood pressure were performed in animals by Hales in 1733 [24 , 25 (link)]. Early reports of intra-arterial pressure measurement in the human are from 1912, when Bleichröder [26 ] cannulated his own radial artery. It is unlikely that he recorded his BP although it would have been possible at that time: Frank developed accurate and fast manometers that could measure pulsatile pressure in 1903 [27 ]. Invasive measurement of BP was confined to the physiology labs for quite some time [28 (link), 29 (link)]. However in the 1950s and 1960s, with the development of refined insertion techniques [30 (link)] and Teflon catheters it became standard clinical practice. High fidelity catheter-tip manometers, such as used to measure pressure gradients across a coronary stenosis, were introduced by Murgo and Millar in 1972 [31 ]. Table 1 gives an overview of BP methods.
Practical noninvasive (intermittent) BP measurement became possible when Riva-Rocci presented his air-inflatable arm cuff connected to a manometer in 1896 [32 , 33 (link)]. By deflating the cuff and feeling for the pulse, systolic BP could be determined. In 1905 Korotkoff [34 , 35 (link)] advanced the technique further with the auscultatory method making it possible to determine diastolic pressure as well. In 1903 Cushing recommended BP monitoring using the Riva-Rocci sphygmomanometer for patients under general anesthesia [36 (link)]. Nowadays, automated assessment of BP with oscillometric devices is commonly used. These devices determine BP by analyzing the oscillations measured in the cuff-pressure. The pressure in the cuff is first brought above systolic pressure and then deflated to below diastolic pressure. Oscillations are largest when cuff pressure equals mean arterial pressure. Proprietary algorithms determine systolic and diastolic values from the oscillations. Oscillometers may be inaccurate [37 ], and provided values that are frequently lower than direct BP measurements in critically ill patients, [38 (link), 39 (link)] whereas detection of large BP changes is unreliable [40 (link)]. Due to its intermittent nature hyper- and hypotensive periods may be missed [2 (link)].
“Semi-continuous noninvasive methods” based on radial arterial tonometry require an additional arm cuff to calibrate arterial pressure [41 (link)–43 (link)]. The use of these devices may become problematic under conditions with significant patient motion or surgical manipulation of the limbs [43 (link), 44 (link)]. However, tonometry devices have contributed greatly to the knowledge of the relation between the pressure wave shape and cardiovascular function [45 (link), 46 (link)].
Methods for measurement of blood pressure and cardiac output
System | Method | Company | CO | BP | ||
---|---|---|---|---|---|---|
Nexfin | Finger cuff technology/pulse contour analysis | BMEYE | + | ___ | + | ___ |
Finometer | Finger cuff technology/pulse contour analysis | FMS | + | ___ | + | ___ |
LIFEGARD® ICG | Thoracic electrical bioimpedance | CAS Medical Systems, Inc. | + | ___ | + | … |
BioZ Monitor | Impedance cardiography | CardioDynamics International Corporation | + | ___ | + | … |
Cheetah reliant | “Bioreactance” | Cheetah Medical | + | ___ | + | … |
Cardioscreen/Niccomo | Impedance cardiography and impedance plethysmography | Medis Medizinische Messtechnik GmbH | + | ___ | + | … |
AESCULON | Electrical “velocimetry” | Osypka Medical GmbH | + | ___ | + | … |
HIC-4000 | Impedance cardiography | Microtronics Corp Bio Imp Tech, Inc. | + | ___ | ||
NICaS | Regional impedance | NImedical | + | ___ | ||
IQ2 | 3-dimensional impedance | Noninvasive Medical Technologies | + | ___ | ||
ICON | Electrical “velocimetry” | Osypka Medical GmbH | + | ___ | ||
PHYSIO FLOW | Thoracic electrical bioimpedance | Manatec biomedical | + | ___ | ||
AcQtrac | Thoracic impedance | Väsamed | + | ___ | ||
esCCO | Pulse wave transit time | Nihon Kohden | + | ___ | ||
TEBCO | Thoracic electrical bioimpedance | HEMO SAPIENS INC. | + | ___ | ||
NCCOM 3 | Impedance cardiography | Bomed Medical Manufacturing Ltd | + | ___ | ||
RheoCardioMonitor | Impedance cardiography | Rheo-Graphic PTE | + | ___ | ||
HemoSonic™ 100 | transesophageal Doppler | Arrow Critical Care Products | + | ___ | ||
ECOM | Endotracheal bioimpedance | ConMed Corporation | + | ___ | ||
CardioQ-ODM™ | Oesophageal Doppler | Deltex | + | ___ | ||
TECO | Transesophageal Doppler | Medicina | + | ___ | ||
ODM II | Transesophageal Doppler | Abbott | + | ___ | ||
HDI/PulseWave™ CR-2000 | Pressure waveform analysis | Hypertension Diagnostics, Inc | + | _ _ | + | _ _ |
USCOM 1A | Transthoracic Doppler | Uscom | + | _ _ | ||
NICO | Rebreathing Fick | Philips Respironics | + | … | ||
Innocor | Rebreathing Fick | Innovision A/S | + | … | ||
Vigileo/FloTrac | Pulse contour analysis | Edwards Lifesciences | – | ___ | – | ___ |
LiDCOplus PulseCO | Transpulmonary lithium dilution/pulse contour analysis | LiDCO Ltd | – | ___ | – | ___ |
PiCCO2 | Transpulmonary thermodilution/pulse contour analysis | PULSION Medical Systems AG | – | ___ | – | ___ |
MOSTCARE PRAM | Pulse contour analysis | Vytech | – | ___ | – | ___ |
Vigilance | Pulmonary artery catheter thermodilution | Edwards Lifesciences | – | … | – | ___ |
DDG | Dye-densitogram analyzer | Nihon Kohden | – | … | ||
Truccom | Pulmonary artery catheter thermodilution | Omega Critical Care | – | … | ||
COstatus | Ultrasound dilution | Transonic Systems Inc. | – | + | … | |
CNAP Monitor 500 | Finger cuff technology | CNSystems Medizintechnik AG | + | ___ | ||
SphygmoCor® CPV System | Applanation tonometry | AtCor Medical | + | _ _ | ||
TL-200 T-LINE | Applanation tonometry | Tensys Medical, Inc. | + | _ _ |
+ noninvasive, – invasive, ___ continuous, _ _ semi-continuous, … intermittent
“Semi-continuous noninvasive methods” based on radial arterial tonometry require an additional arm cuff to calibrate arterial pressure [41 (link)–43 (link)]. The use of these devices may become problematic under conditions with significant patient motion or surgical manipulation of the limbs [43 (link), 44 (link)]. However, tonometry devices have contributed greatly to the knowledge of the relation between the pressure wave shape and cardiovascular function [45 (link), 46 (link)].
Animals
Arteries
Arteries, Radial
Auscultation
Cardiovascular Physiological Phenomena
Catheters
Cheetahs
Coronary Stenosis
Critical Care
Critical Illness
Determination, Blood Pressure
Diagnosis
Diastole
Electricity
Esophagus
General Anesthesia
Heart
Homo sapiens
Lithium
Manometry
Medical Devices
Operative Surgical Procedures
Oscillometry
Patients
physiology
Pressure
Pressure, Diastolic
Pulse Rate
Reliance resin cement
Sphygmomanometers
Systole
Systolic Pressure
Technique, Dilution
Teflon
Thermodilution
Tonometry
Velocimetry
Women who agreed to participate were given follow-up appointments at about 20, 28, and 36 weeks' gestation in the National Institute for Health Research Cambridge Clinical Research Facility (Cambridge, UK). All research scans after the dating scan were done with a Voluson i system (GE Healthcare, Fairfield CT, USA) by one of a team of six sonographers, all of whom received standard training. All ultrasound examinations followed the same protocols as those used in the clinical service.13 , 14 (link) At the 20 week research appointment, participants were given a novel questionnaire we created to obtain details about their medical history and demographic characteristics.11 (link) The 20 week scan had both routine (review of fetal anatomy and biometric measurements) and research (uterine and umbilical artery Doppler flow velocimetry) elements. Women were informed about routine elements (any concerns about the fetal anatomy and of the fetal measurements at the 20 week scan), but women and clinicians were masked to the research elements (results of the uterine and umbilical Dopplers). At the 28 and 36 week research appointments, umbilical and uterine artery Doppler flow velocimetry were repeated, and ultrasonographic measurement of fetal biparietal diameter, head circumference, abdominal circumference, and femur length were also done using standard techniques. An estimated fetal weight (EFW) percentile was calculated by use of the Hadlock equations and reference standard.15 (link), 16 (link) Uteroplacental Dopplers, biometry, and EFW results from the research ultrasound scans at 28 and 36 weeks were not reported to the participant or the clinician. However, both were informed about incidental findings, specifically previously undiagnosed placenta praevia, severe oligohydramnios (amniotic fluid index <5), a previously undiagnosed fetal abnormality, or non-cephalic presentation at the time of the 36 week scan.
Gestational age was defined on the basis of ultrasonographic estimation at the time of the first scan, as recommended.3 Distributions of all measurements in the research scans were similar to previously reported reference cohorts (appendix ). Summary statistics for reproducibility and reliability of research scans (assessed by two sonographers, scanning the same woman twice at the same appointment, each masked to the results of the other's scan) are tabulated for 45 women at 20 weeks' gestation and 44 women at 36 weeks' gestation (appendix ). Coefficients of variation were less than 5% for fetal biometry and EFW, and between 5% and 10% for uteroplacental Doppler at both timepoints.
Women were selected for additional, clinically indicated scans in the third trimester of pregnancy as per routine clinical care, using local and national guidelines (eg, the NICE Guidelines on low risk women,3 women with diabetes,17 and women with hypertensive disorders18 ). Women were also screened with serial measurement of the symphyseal-fundal height. All women carried their maternity notes, which included a chart of the normal range of measurements for fetuses in relation to gestational age. Referral for an ultrasound scan was made by the midwife or doctor providing clinical care. Results of all clinically indicated scans were reported and paper copies were filed in both the participant's hand-held notes and hospital case records.
Screening status in relation to EFW was classified on the basis of the last scan before birth (which could be the 28 week scan or the 36 week scan for universal ultrasonography, depending on the gestational age at delivery). Screen positive was defined as an EFW less than the 10th percentile, using an externally derived reference range15 (link), 16 (link) (for both selective and universal ultrasonography). Screen negative was defined as an EFW of the 10th percentile or more (both selective and universal ultrasonography), or if no clinically indicated scan had been done at gestational age of 26 weeks or later (only selective ultrasonography).
Gestational age was defined on the basis of ultrasonographic estimation at the time of the first scan, as recommended.3 Distributions of all measurements in the research scans were similar to previously reported reference cohorts (
Women were selected for additional, clinically indicated scans in the third trimester of pregnancy as per routine clinical care, using local and national guidelines (eg, the NICE Guidelines on low risk women,3 women with diabetes,17 and women with hypertensive disorders18 ). Women were also screened with serial measurement of the symphyseal-fundal height. All women carried their maternity notes, which included a chart of the normal range of measurements for fetuses in relation to gestational age. Referral for an ultrasound scan was made by the midwife or doctor providing clinical care. Results of all clinically indicated scans were reported and paper copies were filed in both the participant's hand-held notes and hospital case records.
Screening status in relation to EFW was classified on the basis of the last scan before birth (which could be the 28 week scan or the 36 week scan for universal ultrasonography, depending on the gestational age at delivery). Screen positive was defined as an EFW less than the 10th percentile, using an externally derived reference range15 (link), 16 (link) (for both selective and universal ultrasonography). Screen negative was defined as an EFW of the 10th percentile or more (both selective and universal ultrasonography), or if no clinically indicated scan had been done at gestational age of 26 weeks or later (only selective ultrasonography).
Abdomen
ARID1A protein, human
Care, Prenatal
Childbirth
Congenital Abnormality
Diabetes Mellitus
Femur
Fetal Weight
Fetus
Gestational Age
Head
Midwife
Obstetric Delivery
Oligohydramnios
Physical Examination
Physicians
Placenta Previa
Pregnancy
Radionuclide Imaging
Ultrasonography
Umbilical Arteries
Umbilicus
Uterine Arteries
Uterus
Velocimetry
Woman
For traction force measurements, cells seeded on gels were placed on an inverted microscope (Nikon Eclipse Ti). Single cells were tracked for 12 h, while we acquired phase contrast images of the cells and fluorescence images of the embedded nanobeads using a 40x objective. Then, cells were trypsinized and an image of bead position in the relaxed state of the gel was acquired. By comparing bead positions with and without cells, a map of gel deformations caused by cells was first obtained using custom particle imaging velocimetry software15 . Then, after assuming that gel displacements were caused by forces exerted by cells in the cell-gel contact area, the corresponding map of cell forces was calculated using a previously described Fourier transform algorithm16 (link), 29 (link). The average forces per unit area exerted by each cell were then calculated. Force measurements for each cell were taken each hour during the measurement, and the average value for all time measurements was used. Total strain energy exerted by the cells was calculated by performing a scalar product between gel displacement and force for each pixel in the force map, and then adding the result for the entire traction map. Phase contrast images were also used to calculate average cell spreading areas as a function of substrate stiffness.
Cells
Fluorescence
Microscopy
Microscopy, Phase-Contrast
Strains
Traction
Velocimetry
Heart
Heart Valves
Hemodynamics
physiology
Pressure
Sinus, Aortic
Velocimetry
Most recents protocols related to «Velocimetry»
The flow around healthy Cassiopea sp. was imaged using the Particle Image Velocimetry (PIV) methods from3 (link), such that both the incurrent and excurrent flow of the feeding current could be observed. The imaging setup consisted of a 45 × 45 × 45 cm aquarium, filled with artificial seawater. The water was seeded using 10 μm reflective hollow glass spheres for particle image velocimetry (PIV). An Edgertronic high-speed camera filming at 50 frames per second provided a field of view ca. 30 × 30 cm. Two 2-W continuous wave DPSS lasers (wavelength = 520 nm), spread through cylindrical lenses to produce narrow light sheets, were staggered one above the other to illuminate a single coronal plane across the entire field of view. One jellyfish at a time (n = 9) was placed on the bottom in the center of the aquarium, such that the laser sheet crossed the center of the animal. After allowing it to settle for about 10 min, 30 s of video were recorded at 50 frames per second. Analysis using the LaVision software package produced a PIV time-average over the 30 s. PIV was processed with interrogation windows between 48 and 64 pixels, at 50% overlap.
Animals
Continuous Wave Lasers
Lens, Crystalline
Light
Reading Frames
Velocimetry
Time-lapse light microscopic images were taken using established protocols [26 ]. In short, glioblastoma cells were plated on PA gels overnight before imaging, Mayo cells were plated on laminin coated PA gels with media containing 2% serum to promote adhesion, and UCSD cells were plated on Matrigel coated PA gels without serum. Time-lapse phase-contrast images were taken for 10 h to track cell migration using a Nikon Eclipse TE200 microscope with a Plan Fluor 10 × /0.30NA objective. Time-lapse phase-contrast images were taken for 3 min to measure F-actin flow using a Nikon Eclipse TE200 microscope with a Plan Fluor ELWD 40 × /0.60NA objective. Phase-contrast and epifluorescence images were taken before and after glioblastoma cells detached by treating with 0.05% trypsin to determine the cell traction using a Nikon Eclipse TE200 microscope with a Plan Fluor ELWD 40 × /0.60NA objective with a PhotoFluor II light source (89 North) and a mCherry/EGFP filter set.
Random motility coefficients (RMCs) of cell migration and cell area and aspect ratio were determined based on established protocols [26 ]. In short, cell position and region in 10-h time-lapse images were tracked using a custom MATLAB code. The RMC of an individual cell was derived based on the mean squared displacement in the 2D diffusion equation described in Bangasser et al., 2017 [26 ]. The cell area and aspect ratio were calculated based on the recorded cell region. RMCs were measured for Mayo MES (Mayo 16(70), 46(90), 59(90)), PN (Mayo 64(100), 80(150), 85 (100)), CL(Mayo 6(50), 38(50), 76(50), 91 (50), 195 (30)) cells and UCSD MES (380), PN (180) cells on PA gels of 0.7, 4.6, 9.3, 19.5 kPa.
Actin retrograde flow of an individual cell was calculated based on established protocols [26 ]. Briefly, kymographs were made along the axis of moving protrusion features for 3-min time lapse images and actin flow was measured by analyzing the kymograph using a custom MATLAB code. Actin flow was measured for Mayo MES (Mayo 16(80), 46(50), 59(100)), PN (Mayo 64(130), 80(120), 85 (170)), CL(Mayo 6(80), 38(60), 76(60), 91(60), 195 (90)) cells and UCSD MES (250), PN (100) cells on PA gels of 0.7, 4.6, 9.3, 19.5 kPa.
Cell traction strain energy was determined using the established protocols based on the Fourier Transform Traction Cytometry [26 ]. Briefly, epifluorescence images before and after cells were detached were registered, aligned, and transformed using a custom MATLAB code. A gel displacement field was calculated by applying particle image velocimetry to the fluorospheres within the aligned epifluorescence images. The traction stress field was calculated using the Fourier Transform Traction Cytometry, and the total traction strain energy was calculated based on the dot products of the traction vectors and the displacement vectors. Strain energy was measured for Mayo MES (Mayo 16(40), 46(30), 59(40)), PN (Mayo 64(50), 80(30), 85 (50)), CL(Mayo 6(30), 38(30), 76(30), 91 (20), 195 (40)) cells and UCSD MES (100), PN (70) cells on PA gels of 0.7, 4.6, 9.3, 19.5 kPa.
Random motility coefficients (RMCs) of cell migration and cell area and aspect ratio were determined based on established protocols [26 ]. In short, cell position and region in 10-h time-lapse images were tracked using a custom MATLAB code. The RMC of an individual cell was derived based on the mean squared displacement in the 2D diffusion equation described in Bangasser et al., 2017 [26 ]. The cell area and aspect ratio were calculated based on the recorded cell region. RMCs were measured for Mayo MES (Mayo 16(70), 46(90), 59(90)), PN (Mayo 64(100), 80(150), 85 (100)), CL(Mayo 6(50), 38(50), 76(50), 91 (50), 195 (30)) cells and UCSD MES (380), PN (180) cells on PA gels of 0.7, 4.6, 9.3, 19.5 kPa.
Actin retrograde flow of an individual cell was calculated based on established protocols [26 ]. Briefly, kymographs were made along the axis of moving protrusion features for 3-min time lapse images and actin flow was measured by analyzing the kymograph using a custom MATLAB code. Actin flow was measured for Mayo MES (Mayo 16(80), 46(50), 59(100)), PN (Mayo 64(130), 80(120), 85 (170)), CL(Mayo 6(80), 38(60), 76(60), 91(60), 195 (90)) cells and UCSD MES (250), PN (100) cells on PA gels of 0.7, 4.6, 9.3, 19.5 kPa.
Cell traction strain energy was determined using the established protocols based on the Fourier Transform Traction Cytometry [26 ]. Briefly, epifluorescence images before and after cells were detached were registered, aligned, and transformed using a custom MATLAB code. A gel displacement field was calculated by applying particle image velocimetry to the fluorospheres within the aligned epifluorescence images. The traction stress field was calculated using the Fourier Transform Traction Cytometry, and the total traction strain energy was calculated based on the dot products of the traction vectors and the displacement vectors. Strain energy was measured for Mayo MES (Mayo 16(40), 46(30), 59(40)), PN (Mayo 64(50), 80(30), 85 (50)), CL(Mayo 6(30), 38(30), 76(30), 91 (20), 195 (40)) cells and UCSD MES (100), PN (70) cells on PA gels of 0.7, 4.6, 9.3, 19.5 kPa.
Actins
Cells
Cloning Vectors
Cultured Cells
Diffusion
Epistropheus
F-Actin
Gels
Glioblastoma
Kymography
Laminin
Light
Light Microscopy
matrigel
Microscopy
Microscopy, Phase-Contrast
Migration, Cell
Motility, Cell
Serum
Strains
Temporal Lobe
Traction
Trypsin
Velocimetry
Raw laser speckle images can be analyzed with temporal autocorrelation to find the decorrelation time and the corresponding velocity. According to established mathematical model [40] , the laser speckle intensity autocorrelation function and the intensity decorrelation time can be related directly as where is the correction factor associated with the illumination geometry and depends on the light scattering regime and motion types of the light scatterers. As the scattered light from the transparent sample was dominated by single scattering, we chose in this study. The movement velocity can be converted from the decorrelation time using the formula where is the laser source wavelength, and is the numerical aperture of the detection (imaging) objective. Autocorrelation was performed on the image intensity pixel by pixel, with a moving time window [41] that typically covered 20 frames.
Particle Image Velocimetry (PIV) is a velocity measurement technique, which calculates a displacement vector for each interrogation window in the FOV with the aid of autocorrelation or cross-correlation techniques. PIVlab is a user-friendly, graphical user interface (GUI) based digital particle image velocimetry software embedded in Matlab [42] . In this study, we applied PIV analysis on raw speckle images to identify the local moving direction of scatterers. More specifically, we used PIVlab to generate vector velocity maps.
Particle Image Velocimetry (PIV) is a velocity measurement technique, which calculates a displacement vector for each interrogation window in the FOV with the aid of autocorrelation or cross-correlation techniques. PIVlab is a user-friendly, graphical user interface (GUI) based digital particle image velocimetry software embedded in Matlab [42] . In this study, we applied PIV analysis on raw speckle images to identify the local moving direction of scatterers. More specifically, we used PIVlab to generate vector velocity maps.
Cloning Vectors
Light
Microtubule-Associated Proteins
Movement
Reading Frames
Velocimetry
The subjects enrolled in this study were recruited from the endocrinology department of Wuhan Union Hospital from January 2019 to December 2019. This study has been approved by the Institutional Research Ethics Committee of Union Hospital, Huazhong University of Science and Technology. A prior written informed consent was obtained from each participant in this study. The subjects were dividedinto three groups: normal control (NC group, n=43), diabetes without cardiovascular diseases (DM group, n=60), and diabetes with cardiovascular diseases (CVD group, n=51). The inclusion criteria of subjects in the NC group were: no history of diabetes or cardiovascular diseases, normal glucose tolerance in OGTT, normal coronary angiography, no history of carotid artery disease or peripheral arterial disease. Diabetes were diagnosed according to the World Health Organization (WHO, 1999) (18 (link)). Data on vital signs, anthropometric factors, medical history and behaviors as well as physical activity were collected. Additional data, including weight, height, BMI, waist circumference, hip circumference, glycemia and glycated hemoglobin were obtained for each individual. Diabetes patients in CVD group were evidenced as follows: ischemic heart disease defined by clinical history, and/or ischemic electrocardiographic alterations; peripheral vascular disease including atherosclerosis obliterans and cerebrovascular disease based on history, physical examinations and Doppler velocimetry.
Atherosclerosis
Cardiovascular Diseases
Carotid Artery Diseases
Cerebrovascular Disorders
Coronary Angiography
Coronary Arteriosclerosis
Diabetes Mellitus
Electrocardiography
Glucose
Hemoglobin, Glycosylated
Immune Tolerance
Institutional Ethics Committees
Oral Glucose Tolerance Test
Patients
Peripheral Arterial Diseases
Peripheral Vascular Diseases
Physical Examination
Signs, Vital
System, Endocrine
Velocimetry
Waist Circumference
Image sequences taken by the time-lapse method are used for migration analysis. To determine the velocity and direction of migration in each cell, we need to determine the center coordinate of the cell. The way to determine the center coordinate in the previous study is to calculate the best-fit ellipsis or center of gravity from the cell contour [30 (link),31 (link),32 (link)]. However, there are two difficulties in yielding the cell contour automatically using a DIC image. One is setting the contour in the unclear part of the cell, and the other is setting the border of the connected cell. To overcome these obstacles, the gravity center of the nucleus fluorescent area was used as the center coordinate of the cell in this study.
The custom algorithm was developed by written python for cell tracing. First, the fluorescence image of the nucleus was binarized, and the noise was removed by filtering the area of fluorescence. To link the cell in each image, a particle tracking velocimetry (PTV) method, one of the velocity vector analysis methods, was applied. In the PTV method, when the flame rate of the camera is high enough compared with a particle velocity in the fluid, a velocity vector of a luminescent particle can be obtained by only linking the closest luminescent particle. In this study, the position of the center of gravity of the fluorescent region of the nucleus was treated in the same way as the luminescent particles in the PTV method, and the vector data of the cells were analyzed and used to explain cell migration (Figure 8 a). The flame rate of the cell migration in this study was four images/hour, which was high enough compared with the scale of cell migration.
The vector data of cells were sorted in four directions as shown inFigure 8 b, and then, the numbers of cells in each direction and the total number of cells were counted. The migration ratio of cells was obtained by dividing the cell count in one direction by the total cell count in all directions. The average velocity in each direction was also calculated from vector data of cells. Migration direction and the average velocity of cells were regarded to be the characteristics of cell migration.
To ensure the accuracy of the PTV method, vector data of cells were also obtained by manually tracing the coordinate of the nucleus gravity center and were used as a comparison target. This manual analysis method is called a manual method in this study.
The custom algorithm was developed by written python for cell tracing. First, the fluorescence image of the nucleus was binarized, and the noise was removed by filtering the area of fluorescence. To link the cell in each image, a particle tracking velocimetry (PTV) method, one of the velocity vector analysis methods, was applied. In the PTV method, when the flame rate of the camera is high enough compared with a particle velocity in the fluid, a velocity vector of a luminescent particle can be obtained by only linking the closest luminescent particle. In this study, the position of the center of gravity of the fluorescent region of the nucleus was treated in the same way as the luminescent particles in the PTV method, and the vector data of the cells were analyzed and used to explain cell migration (
The vector data of cells were sorted in four directions as shown in
To ensure the accuracy of the PTV method, vector data of cells were also obtained by manually tracing the coordinate of the nucleus gravity center and were used as a comparison target. This manual analysis method is called a manual method in this study.
Cell Nucleus
Cells
Cerebellar Nuclei
Cloning Vectors
Fluorescence
Gravity
Luminescence
Luminescent Measurements
Migration, Cell
Python
Velocimetry
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TransFluoSpheres are fluorescent microspheres designed for use in a variety of life science applications. They are available in multiple colors and sizes to suit different experimental needs.
Sourced in Italy
MyLab 25 is a portable ultrasound system designed for a variety of medical applications. It features a compact and lightweight design, high-quality imaging, and user-friendly controls. The system is capable of providing real-time diagnostic information to healthcare professionals.
Sourced in United States
The F8810 is a high-performance laboratory water bath. It is designed to maintain a consistent temperature for various applications, such as sample incubation, reagent preparation, and temperature control of experiments.
Sourced in Japan, United States
The TS100 is a microscope designed for laboratory use. It features a binocular eyepiece, plan achromatic objectives, and a built-in illuminator. The TS100 allows for observation and analysis of samples under magnification.
More about "Velocimetry"
Velocimetry is the measurement and analysis of fluid velocities, often utilizing non-invasive optical techniques such as particle image velocimetry (PIV) and laser Doppler velocimetry (LDV).
This field of study, also known as flow visualization or fluid dynamics analysis, is crucial for understanding the behavior of complex systems across a wide range of scientific and engineering applications, including aerodynamics, hydrodynamics, and biofluid mechanics.
Researchers in this field leverage advanced instrumentation, such as MATLAB, DaVis 7.2, Zetasizer Nano ZS, Vevo 770, Fastcam SA3, Eclipse Ti, and TransFluoSpheres, as well as computational methods, to quantify velocity fields with high spatial and temporal resolution, enabling detailed investigations of flow phenomena.
Effective velocimetry research requires the identification and application of robust, reproducible measurement protocols, which can be facilitated by AI-driven comparisons of published methods, as offered by the PubCompare.ai tool.
This powerful tool can help researchers improve their research outcomes by easily locating the best protocols from literature, pre-prints, and patents, while leveraging AI to identify the most reproducible and accurate methods.
This field of study, also known as flow visualization or fluid dynamics analysis, is crucial for understanding the behavior of complex systems across a wide range of scientific and engineering applications, including aerodynamics, hydrodynamics, and biofluid mechanics.
Researchers in this field leverage advanced instrumentation, such as MATLAB, DaVis 7.2, Zetasizer Nano ZS, Vevo 770, Fastcam SA3, Eclipse Ti, and TransFluoSpheres, as well as computational methods, to quantify velocity fields with high spatial and temporal resolution, enabling detailed investigations of flow phenomena.
Effective velocimetry research requires the identification and application of robust, reproducible measurement protocols, which can be facilitated by AI-driven comparisons of published methods, as offered by the PubCompare.ai tool.
This powerful tool can help researchers improve their research outcomes by easily locating the best protocols from literature, pre-prints, and patents, while leveraging AI to identify the most reproducible and accurate methods.