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Blood Volume

Blood Volume: The total amount of blood present in the circulatory system.
Blood volume can be affected by factors such as hydration, blood loss, and certain medical conditions.
Accurately measuring blood volume is crucial for optimizing research and treatments related to blood-based disorders, cardiovascular health, and fluid balance.
PubCompare.ai provides a powerful AI-driven platform to help researchers efficiently locate and compare the best protocols from published literature, preprints, and patents, ensuring their blood volume studies are reproducible and impactful.
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Most cited protocols related to «Blood Volume»

Dynamic Causal Modelling is a framework for fitting differential equation models of neuronal activity to brain imaging data using Bayesian inference. The DCM approach can be applied to functional Magnetic Resonance Imaging (fMRI), Electroencephalographic (EEG), Magnetoencephalographic (MEG), and Local Field Potential (LFP) data [22] (link). The empirical work in this paper uses DCM for fMRI. DCMs for fMRI comprise a bilinear model for the neurodynamics and an extended Balloon model [23] (link) for the hemodynamics. The neurodynamics are described by the following multivariate differential equation where indexes continuous time and the dot notation denotes a time derivative. The th entry in corresponds to neuronal activity in the th region, and is the th experimental input.
A DCM is characterised by a set of ‘exogenous connections’, , that specify which regions are connected and whether these connections are unidirectional or bidirectional. We also define a set of input connections, , that specify which inputs are connected to which regions, and a set of modulatory connections, , that specify which intrinsic connections can be changed by which inputs. The overall specification of input, intrinsic and modulatory connectivity comprise our assumptions about model structure. This in turn represents a scientific hypothesis about the structure of the large-scale neuronal network mediating the underlying cognitive function. A schematic of a DCM is shown in Figure 1.
In DCM, neuronal activity gives rise to fMRI activity by a dynamic process described by an extended Balloon model [24] for each region. This specifies how changes in neuronal activity give rise to changes in blood oxygenation that are measured with fMRI. It involves a set of hemodynamic state variables, state equations and hemodynamic parameters, . In brief, for the th region, neuronal activity causes an increase in vasodilatory signal that is subject to autoregulatory feedback. Inflow responds in proportion to this signal with concomitant changes in blood volume and deoxyhemoglobin content . Outflow is related to volume through Grubb's exponent
[20] (link). The oxygen extraction is a function of flow where is resting oxygen extraction fraction. The Blood Oxygenation Level Dependent (BOLD) signal is then taken to be a static nonlinear function of volume and deoxyhemoglobin that comprises a volume-weighted sum of extra- and intra-vascular signals [20] (link)
where is resting blood volume fraction. The hemodynamic parameters comprise and are specific to each brain region. Together these equations describe a nonlinear hemodynamic process that converts neuronal activity in the th region to the fMRI signal (which is additionally corrupted by additive Gaussian noise). Full details are given in [20] (link),[23] (link).
In DCM, model parameters are estimated using Bayesian methods. Usually, the parameters are of greatest interest as these describe how connections between brain regions are dependent on experimental manipulations. For a given DCM indexed by , a prior distribution, is specified using biophysical and dynamic constraints [20] (link). The likelihood, can be computed by numerically integrating the neurodynamic (equation 1) and hemodynamic processes (equation 2). The posterior density is then estimated using a nonlinear variational approach described in [23] (link),[25] (link). Other Bayesian estimation algorithms can, of course, be used to approximate the posterior density. Reassuringly, posterior confidence regions found using the nonlinear variational approach have been found to be very similar to those obtained using a computationally more expensive sample-based algorithm [26] (link).
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Publication 2010
BLOOD Blood Vessel Blood Volume Brain Cell Respiration Cognition deoxyhemoglobin Diencephalon Electroencephalography Hemodynamics Homeostasis Neurons Oxygen Vasodilation
Several BP measurement methods are now available. The main methods include catheterization, auscultation, oscillometry, volume clamping, and tonometry.
Catheterization is the gold standard method [6 (link)]. This method measures instantaneous BP by placing a strain gauge in fluid contact with blood at any arterial site (e.g., radial artery, aorta). However, the method is invasive.
Auscultation, oscillometry, and volume clamping are noninvasive methods. These methods employ an inflatable cuff.
Auscultation is the standard clinical method [7 (link)]. This method measures systolic and diastolic BP by occluding an artery with a cuff and detecting the Korotkoff sounds using a stethoscope and manometer during cuff deflation. The first sound indicates the initiation of turbulent flow and thus systolic BP, while the fifth sound is silent and indicates the renewal of laminar flow and thus diastolic BP.
Oscillometry is the most popular non-invasive, automatic method [8 (link), 9 (link)]. This method measures mean, diastolic, and systolic BP by also using a cuff but with a pressure sensor inside it. The measured cuff pressure not only rises and falls with cuff inflation and deflation but also shows tiny oscillations indicating the pulsatile blood volume in the artery. The amplitude of these oscillations varies with the applied cuff pressure, as the arterial elasticity is nonlinear. The BP values are estimated from the varying oscillation amplitudes using the empirical fixed-ratios principle. When evaluated against auscultation using an Association for the Advancement of Medical Instrumentation (AAMI) protocol, some oscillometric devices achieve BP errors within the AAMI limits of 5 mmHg bias and 8 mmHg precision [10 ]. However, oscillometry is unreliable in subjects with certain conditions such as atrial fibrillation, stiff arteries, and pre-eclampsia [11 ].
Volume clamping is a non-invasive, automatic method used in research [12 (link), 13 ]. This method measures instantaneous (finger) BP by using a cuff and a photoplethysmography (PPG) sensor to measure the blood volume (see Section V.A). The blood volume at zero transmural pressure is estimated via oscillometry. The cuff pressure is then continually varied to maintain this blood volume throughout the cardiac cycle via a fast servo-control system. The applied cuff pressure may thus equal BP. Volume clamping devices also achieve BP errors within AAMI limits when evaluated against auscultation and near AAMI limits when evaluated against radial artery catheterization [14 (link)].
However, cuff use has several drawbacks. In particular, cuffs are cumbersome and time consuming to use, disruptive during ambulatory monitoring, especially while sleeping, and do not readily extend to low resources settings.
Tonometry is another non-invasive method used in research that, in theory, does not require an inflatable cuff [15 , 16 ]. This method measures instantaneous BP by pressing a manometer-tipped probe on an artery. The probe must flatten or applanate the artery so that its wall tension is perpendicular to the probe. However, manual and automatic applanation have proven difficult. As a result, in practice, the measured waveform has been routinely calibrated with cuff BP whenever a BP change is anticipated [17 (link)].
In sum, the existing BP measurement methods are invasive, manual, or require a cuff. So, none are suitable for ubiquitous (i.e., ultra-convenient, unobtrusive, and low cost) monitoring.
Publication 2015
Aorta Arteries Arteries, Radial Atrial Fibrillation Auscultation BLOOD Blood Pressure Blood Volume Cardiac Volume Catheterization Clinical Protocols Diastole Elasticity Fingers Gold Manometry Medical Devices Oscillometry Photoplethysmography Pre-Eclampsia Pressure Pressure, Diastolic Sound Stethoscopes Strains Systole Systolic Pressure Tonometry
Blood specimens were rocked for 5 minutes before a white blood cell (WBC) count was measured using the Hemocue white blood cell system (HemoCue, Sweden). Based upon the WBC count, a volume of blood was subjected to erythrocyte lysis (ammonium chloride solution). After centrifugation, nucleated cells were re-suspended in PBS and attached as a monolayer on custom made glass slides. The glass slides are the same size as standard microscopy slides but have a proprietary coating that allows maximal retention of live cells. Each slide can hold approximately 3 million nucleated cells, thus the number of cells plated per slide depended on the patients WBC count.
For HD-CTC detection in cancer patients for this study, 4 slides are used as a test. The remaining slides created for each patient are stored at −80°C for future experiments. Four slides were thawed from each patient, then cells were fixed with 2% paraformaldehyde, permeabilized with cold methanol, and non-specific binding sites were blocked with goat serum. Slides were subsequently incubated with monoclonal anti-pan cytokeratin antibody (Sigma) and CD45-Alexa 647 (Serotec) for 40 minutes at 37°C. After PBS washes, slides were incubated with Alexa Fluor 555 goat anti-mouse antibody (Invitrogen) for 20 minutes at 37°C. Cells were counterstained with DAPI for 10 minutes and mounted with an aqueous mounting media.
Publication 2012
Alexa Fluor 555 Antibodies, Anti-Idiotypic Binding Sites BLOOD Blood Volume Cells Centrifugation Chloride, Ammonium Common Cold Cytokeratin DAPI Erythrocytes Goat Leukocyte Count Leukocytes Malignant Neoplasms Methanol Microscopy Monoclonal Antibodies Mus paraform Patients Retention (Psychology) Serum
Each of the PV(t) signals, from a ROI or a pixel in the coarse grid, is presented in one of the following ways in this paper:PVraw(t)no processing other than spatial averaging over ROI or coarse grid cell;PVAC(t)the mean over time of PVraw(t) is subtracted (= PVraw(t) minus DC); orPVBP(t)band-pass filtered PVraw(t) signal.For the band-pass (BP) filter Butterworth coefficients (4th order) were used in a phase neutral (forward and reverse) digital filter (“filtfilt” in Matlab®). Cut-off frequencies for the BP filter will be listed in the results section.
Fast Fourier transforms (“FFT“ in Matlab®) on PV(t) signals were performed to determine the power and phase spectra for PV(t). Zero padding of PV(t) prior to the Fourier transform was used to allow for a finer discretization of the frequency. We will refer to an nth order zero padding if the original signal was expanded on both sides with a 0 signal of length n times the original time span.
Plethysmographic signals are sometimes presented inverted to render the intensity proportional to blood pressure or volume. In this communication, all signals will be presented directly: a higher signal corresponds to a higher reflectance and smaller blood volume. Although we will indicate how pixel values (the basic measurement unit) relate to reflectance, all signals will be presented in pixel values.
Publication 2008
Blood Pressure Blood Volume Grid Cells Strains
The parameters of the three-element Windkessel outflow models were calculated as described below. Given a target diastolic (Pd) and systolic (Ps) pressure, and flow rate at the inlet (Qin(t)), the initial estimate for the net peripheral resistance (RT) was calculated as [50 (link)]
RT=Pm-PoutQ¯in,Pm=Pd+13(Ps-Pd), where in is the mean flow rate and Pm is the mean blood pressure, assumed uniform throughout the arterial network. We then calculated the resistance R1 + R2 at the outlet of each terminal vessel that yields the desired flow distribution and satisfies
1RT=j=2M1R1j+R2j, where M is the number of terminal branches and j = 1 corresponds to the aortic root. For each individual outlet, the proximal resistance (R1) is assumed to be equal to the characteristic impedance of the upstream 1-D domain; i.e.
R1=ρfcdAd, where cd and Ad are, respectively, the wave speed and area at diastolic pressure (Pd). This choice of R1 minimizes the magnitude of the waves reflected at the outlet of the 1-D domain [38 ].
The total compliance (CT) was calculated from either (i) the time constant τ = 1.79 s of the exponential fall-off of pressure during diastole given in [51 ] or (ii) using an approximation to
CT=dVdP , where V(t) is the total blood volume contained in the systemic arteries. According to [50 (link)],
CT=τRT, which can be calculated once RT is determined using Eq. (13). Alternatively,
CT=dVdP can be approximated by [50 (link)]
CT=Qmax-QminPs-PdΔt, where Qmax and Qmin are the maximum and minimum flow rates at the inlet and Δt is the difference between the time at Qmax and the time at Qmin. We use both Eqs. ( 16) and (17) depending on the available input data.
According to [52 (link)] we have
CT=Cc+Cp,Cc=i=1NC0Di,Cp=j=2MR2jCjR2j+R1j, where Cc is the total arterial conduit compliance, Cp is the total arterial peripheral compliance, N is the total number of vessels in the 1-D domain, M < N is the number of terminal branches (j = 1 denotes the inlet and is not included in the sum), R1, R2, and C are parameters of the three-element Windkessel model (Fig. 1) and C0D is the compliance of each vessel, which is calculated as
C0D=AdLρf(cd)2, where L is the length of the vessel. We calculated Cp = CTCc and distributed it following the methodology described by Alastruey et al. [52 (link)] More specifically, we have
Cj=CpRTR2j+R1j, where j is the terminal compliance of each branch distributed in proportion to flow as described by Stergiopulos et al. [2 (link)]. We then introduced a correction factor to arrive at the final value of Cj:
Cj=CjR2j+R1jR2j=CpRTR2j.
This expression follows from a linear analysis of the 1-D equations in a given arterial network in which each terminal branch is coupled to a three-element Windkessel model [52 (link)].
For all of the simulations, the Windkessel compliances and resistances (Cj, j = 2, …, M), (
R1j and
R2j , j = 2, …, M) were iteratively calculated to achieve physiologically realistic pressure ranges. To reach a desired pulse pressure (Ppulse = PsPd) and diastolic pressure (Pd) at a particular vessel, we calculated
RT0 and
CT0 given by Eqs. (13) and (16) or (17) using the iterative formulae
RTn+1=RTn+ΔPmnQ¯in,ΔPmn=Pd-Pdn,
CTn+1=CTn-Qmax-Qmin(Ppulsen)2ΔtΔPpulsen,ΔPpulsen=Ppulse-Ppulsen, where the superscript n is the iteration number of the windkessel parameter estimation process performed using the 1-D formulation, and
Pdn and
Ppulsen are the diastolic and pulse pressure, respectively, at a specific target location in the 1-D model, typically the inlet, at each iteration. Equations (22) and (23) follow from a first-order Taylor expansion of Eqs. (13) and (17) around the current mean and pulse pressures
Pmn and
Ppulsen , respectively, with
ΔPmn approximated using the change in diastolic pressure. The total compliance was adjusted by altering the total peripheral compliance Cp, since the total conduit compliance Cc is a function of the vessel geometry and wall stiffness. This process was iterated using the 1-D model until
Pdn and
Ppulsen were smaller than 1% of the target Pd and Ppulse, respectively. Fig. 2 shows the evolution of the systolic, mean and diastolic pressure, net peripheral resistance and total compliance calculated using the 1-D formulation to match the target systolic and diastolic pressures for the baseline aorta model. The final values of the Windkessel compliances and resistances were used in the 3-D counterparts of the 1-D models.
Other methods have been proposed in the literature to estimate the parameters of the outflow boundary conditions. A root-finding method is described by Spilker and Taylor [53 (link)] in the context of 3-D models with compliant arterial walls. Devault et al. proposed a Kalman-filter based methodology in a 1-D model of the circle of Willis [54 (link)].
Publication 2013
A-A-1 antibiotic Aorta Aortic Root Arteries Biological Evolution Blood Vessel Blood Volume Circle of Willis Diastole PDN-1 Plant Roots Pressure Pressure, Diastolic Pulse Pressure Systole Total Peripheral Resistance Vascular Resistance

Most recents protocols related to «Blood Volume»

Example 1

The sequence coding for the light chain variable region of the antibody was inserted into vector pFUSE2ss-CLIg-hK (Invivogen, Catalog Number: pfuse2ss-hclk) using EcoRI and BsiWI restriction sites to construct a light chain expression vector. The sequence coding for the heavy chain variable region of the antibody was inserted into vector pFUSEss-CHIg-hG2 (Invivogen, Catalog Number: pfusess-hchg2) or vector pFUSEss-CHIg-hG4 (Invivogen, Catalog Number: pfusess-hchg4) using EcoRI and NheI restriction sites to construct a heavy chain expression vector.

The culture and transfection of Expi293 cells were performed in accordance with the handbook of Expi293™ Expression System Kit from Invitrogen (Catalog Number: A14635). The density of the cells was adjusted to 2×106 cells/ml for transfection, and 0.6 μg of the light chain expression vector as described above and 0.4 μg of the heavy chain expression vector as described above were added to each ml of cell culture, and the supernatant of the culture was collected four days later.

The culture supernatant was subjected to non-reduced SDS-PAGE gel electrophoresis in accordance with the protocol described in Appendix 8, the Third edition of the “Molecular Cloning: A Laboratory Manual”.

Pictures were taken with a gel scanning imaging system from BEIJING JUNYI Electrophoresis Co., LTD and in-gel quantification was performed using Gel-PRO ANALYZER software to determine the expression levels of the antibodies after transient transfection. Results were expressed relative to the expression level of control antibody 1 (control antibody 1 was constructed according to U.S. Pat. No. 7,186,809, which comprises a light chain variable region as set forth in SEQ ID NO: 10 of U.S. Pat. No. 7,186,809 and a heavy chain variable region as set forth in SEQ ID NO: 12 of U.S. Pat. No. 7,186,809, the same below) (control antibody 2 was constructed according to U.S. Pat. No. 7,638,606, which comprises a light chain variable region as set forth in SEQ ID NO: 6 of U.S. Pat. No. 7,638,606 and a variable region as set forth in SEQ ID NO: 42 of U.S. Pat. No. 7,638,606, the same below). See Tables 2a-2c below for the results.

TABLE 2a
Expression levels of the antibodies of the present
invention after transient transfection (antibodies whose
expression levels are significantly higher than that of control antibody 1):
Number ofExpression level vsNumber of Expression level vs
the antibodycontrol antibody 1the antibodycontrol antibody 1
L1021H10002.08L1000H10281.27
L1020H10001.58L1000H10151.19
L1000H10271.56L1000H10321.18
L1000H10241.51L1000H10261.15
L1000H10251.48L1021H10291.12
L1001H10001.48L1000H10301.1
L1021H10161.43L1024H10311.08
L1000H10141.35L1000H10161.05

TABLE 2b
Expression levels of the antibodies of the present
invention after transient transfection (antibodies whose
expression levels are slightly lower than that of control antibody 1):
Number of Expression level vsNumber of Expression level vs
the antibodycontrol antibody 1the antibodycontrol antibody 1
L1000H10310.99L1017H10000.85
L1021H10310.99L1020H10160.84
L1020H10290.96L1000H10090.81
control anti-0.93L1000H10070.8
body 2
L1012H10000.89L1000H10230.8
L1019H10000.87L1020H10270.78
L1020H10310.87L1024H10070.77
L1021H10200.87L1000H10130.75
L1000H10290.86L1020H10070.74
L1008H10000.86L1021H10070.74
L1000H10010.85L1000H10210.71

TABLE 2c
Expression levels of the antibodies of the present
invention after transient transfection (antibodies whose
expression levels are significantly lower than that of control antibody 1):
Number ofExpression level vsNumber of Expression level vs
the antibodycontrol antibody 1the antibodycontrol antibody 1
L1000H10200.69L1024H10000.52
L1010H10000.69L1000H10080.51
L1000H10220.67L1000H10370.5
L1000H10120.64L1007H10000.49
L1022H10000.64L1016H10000.49
L1011H10000.63L1000H10170.47
L1000H10110.62L1000H10350.46
L1000H10330.62L1012H10270.46
L1020H10200.61L1018H10000.44
L1000H10360.6L1023H10000.43
L1021H10270.6L1012H10160.42
L1012H10070.59L1013H10000.41
L1009H10000.57L1000H10340.4
L1012H10200.57L1000H10180.35
L1012H10310.56L1000H10190.34
L1000H10380.54L1015H10000.27
L1012H10290.54L1014H10000.17
L1000H10100.53

Example 4

6-8 week-old SPF Balb/c mice were selected and injected subcutaneously with antibodies (the antibodies of the present invention or control antibody 2) in a dose of 5 mg/kg (weight of the mouse). Blood samples were collected at the time points before administration (0 h) and at 2, 8, 24, 48, 72, 120, 168, 216, 264, 336 h after administration. For blood sampling, the animals were anesthetized by inhaling isoflurane, blood samples were taken from the orbital venous plexus, and the sampling volume for each animal was about 0.1 ml; 336 h after administration, the animals were anesthetized by inhaling isoflurane and then euthanized after taking blood in the inferior vena cava.

No anticoagulant was added to the blood samples, and serum was isolated from each sample by centrifugation at 1500 g for 10 min at room temperature within 2 h after blood sampling. The collected supernatants were immediately transferred to new labeled centrifuge tubes and then stored at −70° C. for temporary storage. The concentrations of the antibodies in the mice were determined by ELISA:

1. Preparation of Reagents

sIL-4Rα (PEPRO TECH, Catalog Number: 200-04R) solution: sIL-4Rα was taken and 1 ml ddH2O was added therein, mixed up and down, and then a solution of 100 μg/ml was obtained. The solution was stored in a refrigerator at −20° C. after being subpacked.

Sample to be tested: 1 μl of serum collected at different time points was added to 999 μl of PBS containing 1% BSA to prepare a serum sample to be tested of 1:1000 dilution.

Standard sample: The antibody to be tested was diluted to 0.1 μg/ml with PBS containing 1% BSA and 0.1% normal animal serum (Beyotime, Catalog Number: ST023). Afterwards, 200, 400, 600, 800, 900, 950, 990 and 1000 μl of PBS containing 1% BSA and 0.1% normal animal serum were respectively added to 800, 600, 400, 200, 100, 50, 10 and 0 μl of 0.1 μg/ml antibodies to be tested, and thus standard samples of the antibodies of the present invention were prepared with a final concentration of 80, 60, 40, 20, 10, 5, 1, or 0 ng/ml respectively.

2. Detection by ELISA

250 μl of 100 μg/ml sIL-4Rα solution was added to 9.75 ml of PBS, mixed up and down, and then an antigen coating buffer of 2.5 μg/ml was obtained. The prepared antigen coating buffer was added to a 96-well ELISA plate (Corning) with a volume of 100 μl per well. The 96-well ELISA plate was incubated overnight in a refrigerator at 4° C. after being wrapped with preservative film (or covered). On the next day, the 96-well ELISA plate was taken out and the solution therein was discarded, and PBS containing 2% BSA was added thereto with a volume of 300 μl per well. The 96-well ELISA plate was incubated for 2 hours in a refrigerator at 4° C. after being wrapped with preservative film (or covered). Then the 96-well ELISA plate was taken out and the solution therein was discarded, and the plate was washed 3 times with PBST. The diluted standard antibodies and the sera to be detected were sequentially added to the corresponding wells, and three duplicate wells were made for each sample with a volume of 100 μl per well. The ELISA plate was wrapped with preservative film (or covered) and incubated for 1 h at room temperature. Subsequently, the solution in the 96-well ELISA plate was discarded and then the plate was washed with PBST for 3 times. Later, TMB solution (Solarbio, Catalog Number: PR1200) was added to the 96-well ELISA plate row by row with a volume of 100 μl per well. The 96-well ELISA plate was placed at room temperature for 5 minutes, and 2 M H2SO4 solution was added in immediately to terminate the reaction. The 96-well ELISA plate was then placed in flexstation 3 (Molecular Devices), the values of OD450 were read, the data were collected and the results were calculated with Winnonlin software. The pharmacokinetic results were shown in FIG. 1 and Table 6 below.

TABLE 6
Pharmacokinetic results of the antibodies of the present invention in mouse
Area
TimeUnder the
HalftoPeakdrug-timeVolume ofClearance
lifepeakconcentrationCurvedistributionrate
Numberhhμg/mlh*μg/mlml/kgml/h/kg
L1020H1031Mean269.347233.797679.28138.920.38
value
Standard105.730.000.42163.9122.480.09
deviation
L1012H1031Mean167.274845.59852.391.30.38
value
Standard8.520.001.86448.345.580.00
deviation
ControlMean56.67367.881132.68288.923.79
antibody 2value
Standard25.8416.970.2594.4249.451.12
deviation

Example 5

A series of pharmacokinetic experiments were carried out in Macaca fascicularises to further screen antibodies.

3-5 year-old Macaca fascicularises each weighting 2-5 Kg were selected and injected subcutaneously with antibodies (the antibodies of the present invention or control antibody 2) in a dose of 5 mg/kg (weight of the Macaca fascicularis). The antibody or control antibody 2 to be administered was accurately extracted with a disposable aseptic injector, and multi-point injections were made subcutaneously on the inner side of the thigh of the animal, and the injection volume per point was not more than 2 ml. Whole blood samples were collected from the subcutaneous vein of the hind limb of the animal at the time points before administration (0 h) and at 0.5, 2, 4, 8, 24, 48, 72, 120, 168, 240, 336 h, 432 h, 504 h, 600 h, 672 h after administration. The blood volume collected from each animal was about 0.1 ml each time.

No anticoagulant was added to the blood samples, and serum was isolated from each sample by centrifugation at 1500 g for 10 min at room temperature within 2 h after blood sampling. The collected supernatants were immediately transferred to new labeled centrifuge tubes and then stored at −70° C. for temporary storage. The concentrations of the antibodies in the Macaca fascicularises were determined according the method as described in Example 4. The pharmacokinetic results are shown in FIG. 2 and Table 7 below.

TABLE 7
Pharmacokinetic results of the antibodies of the present invention in macaca fascicularis
Area
TimeUnder the
HalftoPeakdrug-timeVolume ofClearance
lifepeakconcentrationCurvedistributionrate
Numberhhμg/mlh*μg/mlml/kgml/h/kg
L1020H1031Mean254.9548.0089.6522189.9175.940.22
value
Standard44.5733.9444.298557.1522.950.10
deviation
L1012H1031Mean185.75486516185.7373.410.28
value
Standard42.5433.944.52506.980.810.06
deviation
ControlMean37.031637.822773.2193.971.78
antibody 2value
Standard18.0311.316.75155.8442.470.07
deviation

Example 10

In vivo pharmacokinetics of the antibodies of the invention are further detected and compared in this Example, in order to investigate the possible effects of specific amino acids at specific positions on the pharmacokinetics of the antibodies in animals. The specific experimental method was the same as that described in Example 4, and the results are shown in Table 9 below.

TABLE 9
Detection results of in vivo pharmacokinetics of the antibodies of the present invention
Area
TimeUnder the
HalftoPeakdrug-timeVolume ofClearance
lifepeakconcentrationCurvedistributionrate
hhug/mlh*ug/mlml/kgml/h/kg
L1020H1031Mean185.494038.948188.8114.280.43
value
Standard18.5213.862.33510.476.50.05
deviation
L1012H1001Mean161.2648.0012.362491.19332.791.47
value
Standard54.300.002.26165.1676.910.20
deviation
L1001H1031Mean171.4156.0042.749273.7399.170.40
value
Standard6.1213.867.381868.6618.690.07
deviation
L1020H1001Mean89.0064.0020.113481.40164.141.30
value
Standard16.7013.862.14268.3922.860.20
deviation

From the specific sequence, the amino acid at position 103 in the sequence of the heavy chain H1031 (SEQ ID NO. 91) of the antibody (in CDR3) is Asp (103Asp), and the amino acid at position 104 is Tyr (104Tyr). Compared with antibodies that have no 103Asp and 104Tyr in heavy chain, the present antibodies which have 103Asp and 104Tyr have a 2- to 4-fold higher area under the drug-time curve and an about 70% reduced clearance rate.

The expression levels of the antibodies of the present invention are also detected and compared, in order to investigate the possible effects of specific amino acids at specific positions on the expression of the antibodies. Culture and transfection of Expi293 cells were conducted according to Example 1, and the collected culture supernatant was then passed through a 0.22 μm filter and then purified by GE MabSelect Sure (Catalog Number: 11003494) Protein A affinity chromatography column in the purification system GE AKTA purifier 10. The purified antibody was collected and concentrated using Amicon ultrafiltration concentrating tube (Catalog Number: UFC903096) and then quantified. The quantitative results are shown in Table 10 below.

TABLE 10
Detection results of the expression
levels of the antibodies of the present invention
Expression level
Antibody(×10−2 mg/ml culture medium)
L1020H10318.39
L1001H10311.79
L1020H10014.04
L1012H10015.00
L1023H10014.63
L1001H10011.75

From the specific sequence, the amino acid at position 31 in the sequence of the light chain L1012 (SEQ ID NO. 44), L1020 (SEQ ID NO. 55) or L1023 (SEQ ID NO. 51) of the antibody (in CDR1) is Ser (31Ser). Compared with antibodies that have no 31Ser in light chain, the present antibodies which have 31Ser have a 2- to 5-fold higher expression level.

The above description for the embodiments of the present invention is not intended to limit the present invention, and those skilled in the art can make various changes and variations according to the present invention, which are within the protection scope of the claims of the present invention without departing from the spirit of the same.

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Patent 2024
Amino Acids Animals Antibodies Anticoagulants Antigens Asepsis BLOOD Blood Volume Buffers Cell Culture Techniques Cells Centrifugation Chromatography Chromatography, Affinity Cloning Vectors Culture Media Deoxyribonuclease EcoRI Drug Kinetics Electrophoresis Enzyme-Linked Immunosorbent Assay Hindlimb Human Body Immunoglobulin Heavy Chains Immunoglobulin Light Chains Immunoglobulins Interleukin-1 Isoflurane Light Macaca Macaca fascicularis Medical Devices Metabolic Clearance Rate Mice, Inbred BALB C Mus Open Reading Frames Pharmaceutical Preparations Pharmaceutical Preservatives SDS-PAGE Serum Staphylococcal Protein A Technique, Dilution Thigh Transfection Transients Ultrafiltration Veins Vena Cavas, Inferior

Example 9

To evaluate in vivo drug exposure and bioavailability, a single dose pharmacokinetic study in mice is performed. Romosozumab PARG (SEQ ID NO: 8) C-terminal variant is injected either intravenously (via tail vein) or subcutaneously at a dose of 1 mg/kg. Using nine animals per group, staggered sampling permits collection of data at a large number of time points without exceeding the maximum volume of blood that can be drawn from an individual animal. At each time point, 0.05 ml of blood is drawn. Animals 1 to 3 are sampled at 0.083, 24, 96 and 192 hours post-dose. Animals 4-6 are sampled at 1, 48, 168 and 240 hours. Animals 7-9 are sampled at 6, 72 and 192 hours. Serum is collected from the whole blood sample and test article concentration is determined by a binding immunoassay such as an ELISA (Enzyme-Linked ImmunoSorbant Assay). Changes in test article concentration over time can be used to calculate pharmacokinetic parameters via two compartment analysis. Parameters of interest include, but not limited to, area under the plasma concentration-time curve (AUC), half-life (t1/2) and clearance (CL) for each dose group. Bioavailability can be determined as the ratio of AUC for the subcutaneous dose to the AUC for the intravenous dose.

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Patent 2024
Animals BLOOD Blood Volume Enzyme-Linked Immunosorbent Assay Immunoassay Mice, House Pharmaceutical Preparations Plasma romosozumab Serum Tail Veins
Not available on PMC !

Example 20

FIG. 33A shows an exemplary case for spotting blood onto porous material that will be used for mass spectral analysis. The cartridge can have a vial with a volume at the center and vials for overflows. A plug, such as a soluble membrane containing a set amount of internal standard chemical, is used to block the bottom of the vial for volume control. A drop of blood is placed in the vial (FIG. 33B). The volume of the blood in the vial is controlled by flowing the extra blood into the overflow vials (FIG. 33B). The blood in the vial is subsequently dissolved in the membrane at the bottom, mixing the internal standard chemical into the blood (FIG. 33B). Upon dissolution of the plug, blood flows to the paper substrate, and eventually forms a dried blood spot having a controlled amount of sample and internal standard (FIG. 33B).

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Patent 2024
BLOOD Blood Volume Cardiac Arrest Hematologic Tests Tissue, Membrane
Blood samples will be collected before surgery as part of regular medical care. No blood draw will be done specifically for this study. An additional volume of blood will be collected in two 5 ml serum separator tubes and seven 5 ml EDTA tubes and transported to the laboratories to be processed.
Tumor samples will be collected during surgery as part of regular medical care and sent to the pathology laboratory. The surplus tissue (including tumor and adjacent normal tissues) not required for diagnosis will be transported to the laboratory in vials containing medium supplemented in antibiotics and ROCK inhibitor Y-27632.
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Publication 2023
Antibiotics, Antitubercular BLOOD Blood Volume Diagnosis Edetic Acid Neoplasms Operative Surgical Procedures Serum Tissues Y 27632
SD female rats participated in the in vivo pharmacokinetic evaluation of DOX-BSA/MnO2 NPs. Therefore, 12 rats were fasted with ad libitum water access and split into two groups (each n = 6) randomly, including 1) DOX solution (5 mg/kg); 2) DOX-BSA/MnO2 NPs (5 mg/kg equivalent to DOX). Rats were given the DOX solution or the DOX-BSA/MnO2 NPs by tail injection. Then, about 500 μL blood was gathered in a 1.5 mL heparinized centrifuge tube at designated time intervals (2, 5, 10, 15, 20, 30, 40, 60, 90, 120, 180, and 300 min). An identical amount of normal saline heated to body temperature was administered intraperitoneally to recover blood volume. Samples were centrifuged at 13,000 rpm for 5 min immediately to recover plasma, which was kept at −80°C for additional processing.

1) Determination of DOX by HPLC

High-pressure liquid chromatography (HPLC) (LC-20A, Shimadzu, Tokyo, Japan) using a fluorescence detector measured DOX concentration in plasma, organs, or tumors. Plasma samples were extracted by precipitation of proteins (acetonitrile: dichloromethane = 1:4) and using daunorubicin (DNR) as an internal standard. The excitation and emission wavelengths used to monitor DOX were 238 and 554 nm, respectively. The mobile phase comprised acetonitrile with 0.1% trifluoroacetic acid (25:75, v/v); online mixing and pumping were performed using a quaternary pump at a 1.0 mL/min flow rate. DOX was separated by a Phenomenex C18 column (250 × 4.6 mm, 5 μm) at 30°C with a 10 μL injection volume. DOX and DNR were eluted in around 3 and 7 min, respectively. The developed HPLC method was verified in the specificity, linearity, precision, accuracy, recovery, limit of detection (LOD), as well as limit of quantitation (LOQ).
A two-compartment model with Phoenix WinNonlin 10.0 program (Pharsight, Mountain View, CA, United States) calculated the pharmacokinetic metrics. The following parameters were estimated: maximum plasma concentration (Cmax), area under the concentration-time curve from baseline to terminal time analyzed (AUC), mean residence time (MRT), clearance rate (Cl), volume of distribution V) and elimination half-life (t1/2).
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Publication 2023
A-A-1 antibiotic acetonitrile BLOOD Blood Volume Body Temperature Daunorubicin Females Fluorescence Liquid Chromatography Metabolic Clearance Rate Methylene Chloride Neoplasms Normal Saline Plasma Pressure Proteins Rattus Tail Trifluoroacetic Acid

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More about "Blood Volume"

Blood volume refers to the total amount of blood circulating in the cardiovascular system.
This crucial metric can be influenced by various factors, including hydration levels, blood loss, and certain medical conditions.
Accurately measuring and monitoring blood volume is essential for optimizing research and treatments related to blood-based disorders, cardiovascular health, and fluid balance.
Researchers can leverage powerful AI-driven platforms like PubCompare.ai to efficiently locate and compare the best protocols from published literature, preprints, and patents, ensuring their blood volume studies are reproducible and impactful.
This includes techniques such as using Histopaque-1077 for density gradient centrifugation, BD Vacutainer blood collection systems, and Ficoll-Paque PLUS for isolating peripheral blood mononuclear cells.
Additionally, tools like the CODA system can be used for non-invasive blood volume measurement, while Drabkin's reagent and TRIzol reagent can be employed for hemoglobin and RNA analysis, respectively.
Culturing cells in media supplemented with PBS and FBS can also provide valuable insights into blood volume-related processes.
By leveraging these advanced methods and technologies, researchers can streamline their blood volume research, optimizing their workflows and ensuring the accuracy and reproducibility of their findings.
This, in turn, can lead to more effective treatments and improved patient outcomes in a wide range of blood-related disorders and cardiovascular conditions.