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Plasma

Plasma is the liquid component of blood, comprising water, salts, and various proteins, lipids, and other molecules.
It plays a crucial role in the body's homeostasis, serving as a transport medium for nutrients, hormoes, and waste products.
Plasma research is vital for understanding the complex physiological processes underlying human health and disease.
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Most cited protocols related to «Plasma»

All samples were stored at − 80 °C until use. Serum levels of C-reactive protein (CRP) were determined by an immuno-turbidimetric technique using an Olympus AU 400 biochemical analyzer (Olympus Optical, Tokyo, Japan), and erythrocyte sedimentation rate (ESR) was measured according to the Fahreus and Westergren method. ANAs were detected using indirect immunofluorescence on HEP2 cells, and the autoantibodies of the ENA complex (anti-U1RNP, anti-Ro, anti-La, anti-DNA-topoisomerase I, anti-Jo-1, anti-P protein, anti-Sm, and anti-centromere) were assayed by immunoblot. Plasma levels of Hsp90 were assessed by a high-sensitivity ELISA kit (eBioscience, Vienna, Austria) according to the manufacturer's protocol. The assay recognizes human Hsp90 alpha. The calculated sensitivity is 0.03 ng/mL. The absorbance value was established at 450 nm by an ELISA reader (SUNRISE; Tecan, Grödig, Austria).
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Publication 2021
Autoantibodies Biological Assay Cells Centromere DNA Topoisomerases, Type I Ducks Enzyme-Linked Immunosorbent Assay Homo sapiens HSP90 Heat-Shock Proteins Hypersensitivity Indirect Immunofluorescence OCA2 protein, human Plasma Sedimentation Rates, Erythrocyte Serum Proteins Turbidimetry Vision
The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) conducted the study under a cooperative agreement with the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). CKD-EPI collaborators provided data from clinical research studies and clinical populations.3 (link) GFR measurements were based on urinary or plasma clearance of exogenous filtration markers. Data from studies of urinary clearance of iothalamate were used for development and internal validation, and data from studies of other filtration markers were used for external validation. We included 13 studies with 5352 participants, who were randomly divided into separate data sets for development (3522) and internal validation (1830) (see Table S1a in the Supplementary Appendix, available with the full text of this article at NEJM .org). We included 5 other studies with 1119 participants for external validation (Table S1b in the Supplementary Appendix). We excluded studies involving transplant recipients because our preliminary analyses showed large variations among these studies in the relationship between serum cystatin C levels and measured GFR. The institutional review boards of all participating institutions approved the study.
The NIDDK was substantially involved in the design of the study and in the collection, analysis, and interpretation of the data; the NIDDK was not required to approve the final manuscript before submission for publication. The first author had full access to all the data in the study, vouches for the integrity of the data and the accuracy of the data analysis for the CKD-EPI database, and wrote the first draft of the manuscript. For a list of collaborators who provided data, see the Supplementary Appendix.
Publication 2012
Diabetic Nephropathy Digestive System Ethics Committees, Research Filtration Iothalamate Plasma Population Group Post-gamma-Globulin Serum Transplant Recipients Urine
In all simulation scenarios, causal effect estimates were obtained using established MR methods (multiplicative random effects IVW,7 (link) multiplicative random effects MR-Egger regression7 (link) and weighted median, all implemented using inverse-variance weights calculated under NOME), as well as the simple and the weighted MBEs. Each version of the MBE was evaluated using weights calculated with and without making the NOME assumption, thus yielding four MBEs. Each of these four methods was evaluated for two values of the tuning parameter ϕ{1,0.5} , totalling eight versions of the MBE method. Parametric bootstrap was used to estimate the standard errors of the MBE using the median absolute deviation from the median (multiplied by 1.4826 for asymptotically normal consistency) of the bootstrap distribution of causal effect estimates. These were used to derive symmetrical confidence intervals.
In each scenario, coverage, power and average causal effect estimates, standard errors, F¯GX1F¯GX and IGX2 statistics (which quantify the magnitude of violation of the NOME assumption in IVW and MR-Egger regression estimates, respectively7 (link),13 (link)) were obtained across 10 000 simulated datasets. Power was defined as the proportion of times that 95% confidence intervals excluded zero, and coverage as the proportion of times that 95% confidence intervals included the true causal effect.
MR methods were also applied to estimate the causal effect of plasma lipid fractions and urate levels on CHD risk. The magnitude of regression dilution bias in IVW and MR-Egger regression was assessed by the F¯GX1F¯GX and IGX2 statistics, respectively. Cochran’s Q test was used to test for the presence of horizontal pleiotropy (under the assumption that this is the only source of heterogeneity between β^Rj s other than chance).20 (link) All simulations and analyses were performed using R 3.3.1 [www.r-project.org]. R code for implementing the MBE is provided in Supplementary Methods (available as Supplementary data at IJE online).
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Publication 2017
Genetic Heterogeneity Lipids Plasma Technique, Dilution Urate
We obtained summary statistics (association P-values and Z-scores for direction of effect or allelic effects and standard errors) for lead T2D SNPs in GWAS meta-analyses of metabolic traits in European descent populations. Summary statistics were aligned to the T2D risk allele from the combined meta-analysis. We obtained summary statistics for lead SNPs in all newly discovered and established loci for glycemic traits in non-diabetic individuals from the MAGIC Investigators5 (link),34 . For fasting glucose and fasting insulin, the meta-analysis comprised up to 133,010 individuals, genotyped with GWAS arrays and imputed on up to ~2.5 million SNPs, or genotyped with Metabochip. We also considered surrogate estimates of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) derived by homeostasis model assessment in up to 38,238 individuals (from GWAS meta-analysis only since these traits were not investigated in the enlarged MAGIC Metabochip study). We obtained summary statistics for lead SNPs in the newly discovered T2D loci (also including GRB14 and HMG20A) for BMI in up to 119,600 individuals from the GIANT Consortium15 (link). To eliminate potential bias in BMI allelic effect estimates at T2D susceptibility loci54 (link), we restricted our attention to meta-analysis of population-based studies not ascertained for disease status for ~2.8 million directly genotyped and/or imputed SNPs. We obtained summary statistics for the same SNPs for plasma lipid concentrations from the Global Lipids Genetics Consortium16 (link). This meta-analysis comprised ~2.6 million directly genotyped and/or imputed SNPs assessed for association to plasma concentrations of: total cholesterol (up to 100,184 individuals); LDL (up to 95,454 individuals); HDL (up to 99,900 individuals); and triglycerides (up to 96,598 individuals).
We also examined T2D association summary statistics at lead SNPs for 37 established T1D susceptibility loci. For each of these SNPs, we reported the allelic OR (aligned to the T2D risk-allele) and P-values in: (i) our Stage 1 T2D meta-analysis; and (ii) a GWAS meta-analysis of 7,514 T1D cases and 9,045 population controls from European descent populations from the Type 1 Diabetes Genetics Consortium35 (link).
Publication 2012
Alleles Attention Cholesterol Diabetes Mellitus, Insulin-Dependent Europeans Genome-Wide Association Study Gigantism Glucose GRB14 protein, human Homeostasis Insulin Insulin Resistance Lipids Physiology, Cell Plasma Single Nucleotide Polymorphism Susceptibility, Disease Triglycerides
Do and colleagues16 (link) performed a two-sample MR analysis to evaluate the causal effect of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and triglycerides on coronary heart disease (CHD) risk, using a total of 185 genetics variants. Summary association results were obtained from the Global Lipids Genetics Consortium17 (link) and the Coronary Artery Disease Genome-Wide Replication and Meta-Analysis Consortium,18 (link) and were downloaded from Do and colleagues’ supplementary material (standard errors were estimated based on the regression coefficients and P-values). Genetic variants were classified as instruments for each lipid fraction using a statistical criterion (P < 1 × 10−8), resulting in 73 instruments for LDL-C, 85 for HDL-C and 31 for triglycerides.
White and colleagues19 (link) performed a similar analysis, but with plasma urate levels rather than lipid fractions. 31 variants associated with urate levels (P < 5 × 10−7) were used as genetic instruments, and the required summary statistics were obtained from the GWAS catalogue [https://www.ebi.ac.uk/gwas/].
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Publication 2017
Cholesterol, beta-Lipoprotein Coronary Arteriosclerosis DNA Replication Genetic Diversity Genome Genome-Wide Association Study Heart Disease, Coronary High Density Lipoprotein Cholesterol Lipids Plasma Reproduction Triglycerides Urate

Most recents protocols related to «Plasma»

Example 66

The activity of SYN-PKU-2002 was assessed in vivo. To prepare the cells for the study, SYN-PKU901 and SYN-PKU-2002 overnight cultures were each used to inoculate 4 2 L flasks containing 500 mL of LB with DAP100 ug/mL. These cultures were grown for 1 hr and 45 min and then moved to the anaerobic chamber supplying 90% N2, 5% CO2, and 5% H2 for 4 hours. Cells were then spun down at 4600×G for 12 min and resuspended in 10 mL of formulation buffer (Glycerol: 15% (v/v), Sucrose: 10% (w/v) (100 g/L), MOPS: 10 mM (2.1 g/L), NaCl: 25 mM (1.46 g/L)). Several 40 ul aliquots were removed to be used for cell counting and activity determination. The viability as determined by cellometer count (in quadruplicate) 6.94e10 cfu/ml (+/−5.78e9).

Activity was determined using a plate based assay. Briefly, 1×108 cfu as determined by cellometer were added to 1 ml of prewarmed assay buffer (1× M9 minimal media containing 0.5% glucose, 50 mM MOPS, and 50 mM phenylalanine) in a microfuge tube, vortexed briefly, and immediately placed in a heat block or water bath at 37 degrees Celsius for static incubation (t=0). Supernatant samples from cells re-suspended in assay buffer were analyzed for the abundance of TCA over several time points using spectrophotometer at an absorbance of 290 nm. The accurate OD290 window for TCA detection occurs in a relatively narrow concentration range. For this reason, supernatant samples were diluted to ensure that the absorbance measurement fell into the linear range for detection. Measurements were compared to a TCA standard curve. Activity was determined to be 2.72 umol/hr/le9 cfu (+/−0.15 umol/hr/le9 cfu).

Beginning 4 days prior to the study (i.e., Days −4-1), Pah ENU2/2 mice (˜11-15 weeks of age) were maintained on phenylalanine-free chow and water that was supplemented with 0.5 grams/L phenylalanine. On the day of the study, mice were randomized into treatment groups according to weight as follows: Group 1: SYN-PKU901 (n=9); Group 2: Group 2: SYN-PKU-2002 (n=9). Blood samples were collected by sub-mandibular skin puncture to determine baseline phenylalanine levels. Mice were then administered single dose of phenylalanine by subcutaneous injection at 0.1 mg per gram body weight, according to the average group weight. At 1, 2 and 3 h post Phe challenge, the bacteria (or water) were administered to mice by oral gavage (3×250 ul). Whole blood was collected via submandibular bleed at each time point. Urine collection in metabolic caging commenced immediately after the 1st bacterial dose and continued to be collected for the duration of the study (4 hours).

Blood samples were kept on ice until processing for plasma in a centrifuge (2000 g for 10 min at 4 C) within 20 min of collection. Plasma was then transferred into a 96-well plate for MS analysis. Urine was collected in 5 mL tubes and volumes were recorded before transferring samples to MS for analysis. Results are shown in FIG. 17A and FIG. 17B and show that SYN-PKU-2002 causes decreased changes in phenylalanine post-Phe injection and produces hippurate, in a similar manner as SYN-PKU-710.

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Patent 2024
Bacteria Bath Biological Assay BLOOD Buffers Cells Glucose Glycerin hippurate Mandible morpholinopropane sulfonic acid Mus Plasma Punctures Serum Skin Sodium Chloride Subcutaneous Injections Sucrose Tube Feeding Urine Urine Specimen Collection

Example 8

Characterization of Absorption, Distribution, Metabolism, and Excretion of Oral [14C]Vorasidenib with Concomitant Intravenous Microdose Administration of [13C315N3]Vorasidenib in Humans

Metabolite profiling and identification of vorasidenib (AG-881) was performed in plasma, urine, and fecal samples collected from five healthy subjects after a single 50-mg (100 μCi) oral dose of [14C]AG-881 and concomitant intravenous microdose of [13C3 15N3]AG-881.

Plasma samples collected at selected time points from 0 through 336 hour postdose were pooled across subjects to generate 0—to 72 and 96-336-hour area under the concentration-time curve (AUC)-representative samples. Urine and feces samples were pooled by subject to generate individual urine and fecal pools. Plasma, urine, and feces samples were extracted, as appropriate, the extracts were profiled using high performance liquid chromatography (HPLC), and metabolites were identified by liquid chromatography-mass spectrometry (LC-MS and/or LC-MS/MS) analysis and by comparison of retention time with reference standards, when available.

Due to low radioactivity in samples, plasma metabolite profiling was performed by using accelerator mass spectrometry (AMS). In plasma, AG-881 was accounted for 66.24 and 29.47% of the total radioactivity in the pooled AUC0-72 h and AUC96-336 h plasma, respectively. The most abundant radioactive peak (P7; M458) represented 0.10 and 43.92% of total radioactivity for pooled AUC0-72 and AUC96-336 h plasma, respectively. All other radioactive peaks accounted for less than 6% of the total plasma radioactivity and were not identified.

The majority of the radioactivity recovered in feces was associated with unchanged AG-881 (55.5% of the dose), while no AG-881 was detected in urine. In comparison, metabolites in excreta accounted for approximately 18% of dose in feces and for approximately 4% of dose in urine. M515, M460-1, M499, M516/M460-2, and M472/M476 were the most abundant metabolites in feces, and each accounted for approximately 2 to 5% of the radioactive dose, while M266 was the most abundant metabolite identified in urine and accounted for a mean of 2.54% of the dose. The remaining radioactive components in urine and feces each accounted for <1% of the dose.

Overall, the data presented indicate [14C]AG-881 underwent moderate metabolism after a single oral dose of 50-mg (100 μCi) and was eliminated in humans via a combination of metabolism and excretion of unchanged parent. AG-881 metabolism involved the oxidation and conjugation with glutathione (GSH) by displacement of the chlorine at the chloropyridine moiety. Subsequent biotransformation of GSH intermediates resulted in elimination of both glutamic acid and glycine to form the cysteinyl conjugates (M515 and M499). The cysteinyl conjugates were further converted by a series of biotransformation reactions such as oxidation, S-dealkylation, S-methylation, S-oxidation, S-acetylation and N-dealkylation resulting in the formation multiple metabolites.

A summary of the metabolites observed is included in Table 2

TABLE 2
Retention
ComponentTimeMatrix
designation(Minutes)[M + H]+Type of BiotransformationPlasmaUrineFeces
Unidentified 17.00UnknownX
M2667.67a267N-dealkylationX
Unidentified 2UnknownX
Unidentified 3UnknownX
Unidentified 4UnknownX
Unidentified 5UnknownX
M51519.79b516OxidationX
M460-120.76b461OxidationX
M49921.22b500Dechloro-glutathioneXX
conjugation + hydrolysis
M51621.89b517Oxidative-deaminationX
M460-221.98b461OxidationX
M47222.76b473S-dealkylation + S-X
acetylation + reduction
M47622.76b477OxidationX
Unidentified 6UnknownX
M47423.63b475OxidationX
Unidentified 7UnknownX
M43025.88b431AG-881-oxidationX
M42630.62b427S-dealkylation + methylationX
M45831.03c459AG-69460X*
AG-88139.41b415AG-881XX
M42847.40b429S-dealkylation + oxidationX
Table 3 contains a summary of protonated molecular ions and characteristic product ions for AG-881 and identified metabolites

TABLE 3
RetentionCharacteristic
MetaboliteTimeProposed MetaboliteProduct Ions
designation(Minutes)[M + H]+Identification(m/z)Matrix
M266 7.88a267[Figure (not displayed)]
188, 187Urine
M51519.79b516[Figure (not displayed)]
429, 260, 164, 153Feces
M460-120.76b461[Figure (not displayed)]
379, 260, 164Feces
M49921.22b500[Figure (not displayed)]
437, 413, 260, 164, 137Urine Feces
M51621.89b517[Figure (not displayed)]
427, 260, 164, 153Feces
M460-221.98b461[Figure (not displayed)]
369, 260, 164, 139, 121, 93Feces
M47222.76b473[Figure (not displayed)]
429, 260, 179, 164, 153Feces
M47622.76b477[Figure (not displayed)]
395, 260, 164, 139, 119Feces
M47423.63b475[Figure (not displayed)]
260, 164, 68Feces
M43025.88b431[Figure (not displayed)]
260, 164, 155, 68Feces
M42630.62b427[Figure (not displayed)]
260, 164, 151Feces
M45831.03b459[Figure (not displayed)]
380, 311, 260, 183, 164, 130Plasma Fecesd
AG-88139.41b415[Figure (not displayed)]
319, 277, 260, 240, 164, 139, 119, 68Plasma Fecesd
M42847.40b429[Figure (not displayed)]
260, 164, 153Feces
Notes
aRetention time from analysis of a urine sample
bRetention time from analysis of a feces sample
cRetention time from analysis of a plasma sample
dM458 was only detected in feces by mass spectrometry, not by radioprofiling.
The proposed (theoretical) biotransformation pathways leading to the observed metabolites are shown in FIG. 1.

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Patent 2024
Acetylation AG 30 Biotransformation Chlorine Dealkylation Deamination Elements, Radioactive Feces Glutamic Acid Glutathione Glycine Healthy Volunteers High-Performance Liquid Chromatographies Homo sapiens Hydrolysis Intravenous Infusion Ions Liquid Chromatography Mass Spectrometry Metabolism Methylation Parent Plasma Radioactivity Retention (Psychology) Tandem Mass Spectrometry Urinalysis Urine vorasidenib

Example 1

95 g of manganese (purity: 99.95%; purchased from Taewon Scientific Co., Ltd.) and 5 g of high-purity graphite (purity: 99.5%; purchased from Taewon Scientific Co., Ltd.) were placed in a water-cooled copper crucible of an argon plasma arc melting apparatus (manufactured by Labold AG, Germany, Model: vacuum arc melting furnace Model LK6/45), and melted at 2,000 K under an argon atmosphere. The melt was cooled to room temperature at a cooling rate of 104 K/min to obtain an alloy ingot. The alloy ingot was crushed to a particle size of 1 mm or less by hand grinding. Thereafter, the obtained powders were magnetically separated using a Nd-based magnet to remove impurities repeatedly, and the Mn4C magnetic powders were collected. The collected Mn4C magnetic powders were subjected to X-ray diffraction (XRD) analysis (measurement system: D/MAX-2500 V/PO, Rigaku; measurement condition: Cu—Kα ray) and energy-dispersive X-ray spectroscopy (EDS) using FE-SEM (Field Emission Scanning Electron Microscope, MIRA3 LM).

FIGS. 2(a) and 2 (b) show an X-ray diffraction pattern and an energy-dispersive X-ray spectroscopy graph of the Mn4C magnetic material produced according to Example 1 of the present disclosure, respectively.

As can be seen in FIG. 2(a), the Mn4C magnetic material showed diffraction peaks of (111), (200), (220), (311) and (222) crystal planes at 2θ values of 40°, 48°, 69°, 82° and 88°, respectively, in the XRD analysis. Thus, it can be seen that the XRD patterns of the Mn4C magnetic material produced according to Example 1 are well consistent with the patterns of the cubic perovskite Mn4C. In addition, the Mn4C magnetic material shows several very weak diffraction peaks that can correspond to Mn23C6 and Mn. That is, the diffraction peak intensity at 2θ values of 43° and 44°, which correspond to Mn and Mn23C6 impurities, is as very low as about 2.5% of the diffraction intensity of the peak corresponding to the (111) plane. Through this, it can be seen that the powders obtained in Example 1 have high-purity Mn4C phase. The lattice parameter of the Mn4C is estimated to be about 3.8682 Å.

FIG. 2(b) shows the results of analyzing the atomic ratio of Mn:C in the powder by EDS. The atomic ratio of Mn:C is 80.62:19.38, which is very close to 4:1 within the experimental uncertainties. Thus, it can be seen that the powder is also confirmed to be Mn4C.

The M-T curve of the field aligned Mn4C powder obtained in Example 1 was measured under an applied field of 4 T and at a temperature ranging from 50 K to 400 K. Meanwhile, the M-T curve of the randomly oriented Mn4C powder was measured under an applied field of 1 T. The Curie temperature of Mn4C was measured under 10 mT while decreasing temperature from 930 K at a rate of 20 K/min.

FIGS. 3(a) to 3(c) show the M-T curves of the Mn4C magnetic material, produced according to Example 1 of the present disclosure, under magnetic fields of 4 T, 1 T, and 10 mT, respectively.

FIG. 3 shows magnetization-temperature (M-T) curves indicating the results of measuring the temperature-dependent magnetization intensity of the Mn4C magnetic material, produced in Example 1, using the vibrating sample magnetometer (VSM) mode of Physical Property Measurement System (PPMS®) (Quantum Design Inc.).

According to the Néel theory, the ferrimagnets that contain nonequivalent substructures of magnetic ions may have a number of unusual forms of M-T curves below the Curie temperature, depending on the distribution of magnetic ions between the substructures and on the relative value of the molecular field coefficients. The anomalous M-T curves of Mn4C, as shown in FIG. 3(a), can be explained to some extent by the Néel's P-type ferrimagnetism, which appears when the sublattice with smaller moment is thermally disturbed more easily. For Mn4C with two sublattices of MnI and MnII, as shown in FIG. 1, the MnI sublattice might have smaller moment.

FIG. 3(a) shows the temperature dependence of magnetization of the Mn4C magnetic material produced in Example 1. The magnetization of Mn4C measured at 4.2K is 6.22 Am2/kg (4 T), corresponding to 0.258μB per unit cell. The magnetization of the Mn4C magnetic material varies little at temperatures below 50 K, and is quite different from that of most magnetic materials, which undergo a magnetization deterioration with increasing temperature due to thermal agitation. Furthermore, the magnetization of the Mn4C magnetic material increases linearly with increasing temperature at temperatures above 50 K. The linear fitting of the magnetization of Mn4C at 4 T within the temperature range of 100 K to 400 K can be written as M=0.0072T+5.6788, where M and T are expressed in Am2/kg and K, respectively. Thus, the temperature coefficient of magnetization of Mn4C is estimated to be about ˜2.99*10−4μB/K per unit cell. The mechanisms of the anomalous thermomagnetic behaviors of Mn4C may be related to the magnetization competition of the two ferromagnetic sublattices (MnI and MnII) as shown in FIG. 1.

FIG. 3(b) shows the M-T curves of the Mn4C powders at temperatures within the range of 300 K to 930 K under 1 T. The linear magnetization increment stops at 590 K, above which the magnetization of Mn4C starts to decrease slowly first and then sharply at a temperature of about 860 K. The slow magnetization decrement at temperatures above 590 K is ascribed to the decomposition of Mn4C, which is proved by further heat-treatment of Mn4C as described below.

According to one embodiment of the present disclosure, the saturation magnetization of Mn4C increases linearly with increasing temperature within the range of 50 K to 590 K and remains stable at temperatures below 50 K. The increases in anomalous magnetization of Mn4C with increasing temperature can be considered in terms of the Néel's P-type ferrimagnetism. At temperatures above 590 K, the Mn4C decomposes into Mn23C6 and Mn, which are partially oxidized into the manganosite when exposed to air. The remanent magnetization of Mn4C varies little with temperature. The Curie temperature of Mn4C is about 870 K. The positive temperature coefficient (about 0.0072 Am2/kgK) of magnetization in Mn4C is potentially important in controlling the thermodynamics of magnetization in magnetic materials.

The Curie temperature Te of Mn4C is measured to be about 870 K, as shown in FIG. 3(c). Therefore, the sharp magnetization decrement of Mn4C at temperatures above 860 K is ascribed to both the decomposition of Mn4C and the temperature near the Tc of Mn4C.

FIG. 4 is a graph showing the magnetic hysteresis loops of the Mn4C magnetic material, produced according to Example 1 of the present disclosure, at 4.2 K, 200 K and 400 K. The magnetic hysteresis loops were measured by using the PPMS system (Quantum Design) under a magnetic field of 7 T while the temperature was changed from 4 K to 400 K.

As shown in FIG. 4, the positive temperature coefficient of magnetization was further proved by the magnetic hysteresis loops of Mn4C as shown in FIG. 4. The Mn4C shows a much higher magnetization at 400 K than that at 4.2 K. Moreover, the remanent magnetization of Mn4C varies little with temperature and is Δ3.5 Am2/kg within the temperature range of 4.2 K to 400 K. The constant remanent magnetization of Mn4C within a wide temperature range indicates the high stability of magnetization against thermal agitation. The coercivities of Mn4C at 4.2 K, 200 K, and 400 K were 75 mT, 43 mT, and 33 mT, respectively.

The magnetic properties of Mn4C measured are different from the previous theoretical results. A corner MnI moment of 3.85μB antiparallel to three face-centered MnII moments of 1.23μB in Mn4C was expected at 77 K. The net moment per unit cell was estimated to be 0.16μB. In the above experiment, the net moment in pure Mn4C at 77 K is 0.26μB/unit cell, which is much larger than that expected by Takei et al. It was reported that the total magnetic moment of Mn4C was calculated to be about 1μB, which is almost four times larger than the 0.258μB per unit cell measured at 4.2 K, as shown in FIG. 4.

FIG. 5 is an enlarged view of the temperature-dependent XRD patterns of the Mn4C magnetic material produced according to Example 1 of the present disclosure.

The thermomagnetic behaviors of Mn4C are related to the variation in the lattice parameters of Mn4C with temperature. It is known that the distance of near-neighbor manganese atoms plays an important role in the antiferro- or ferro-magnetic configurations of Mn atoms. Ferromagnetic coupling of Mn atoms is possible only when the Mn—Mn distance is large enough. FIG. 5 shows the diffraction peaks of the (111) and (200) planes of Mn4C at temperatures from 16 K to 300 K. With increasing temperature, both (111) and (200) peaks of Mn4C shifted to a lower degree at temperatures between 50 K and 300 K, indicating an enlarged distance of Mn—Mn atoms in Mn4C. No peak shift is obviously observed for Mn4C at temperatures below 50 K. The distance of nearest-neighbor manganese atoms plays an important role in the antiferro- or ferro-magnetic configurations of Mn atoms and thus has a large effect on the magnetic properties of the compounds.

Thus, it can be seen that the abnormal increase in magnetization of Mn4C with increasing temperature occurs due to the variation in the lattice parameters of Mn4C with temperature.

The powder produced in Example 1 was annealed in vacuum for 1 hour at each of 700 K and 923 K, and then subjected to X-ray spectroscopy, and the results thereof are shown in FIG. 6.

The magnetization reduction of Mn4C at temperatures above 590 K is ascribed to the decomposition of Mn4C, which is proved by the XRD patterns of the powders after annealing Mn4C at elevated temperatures. FIG. 6 shows the structural evolution of Mn4C at elevated temperatures. When Mn4C is annealed at 700 K, a small fraction of Mn4C decomposes into a small amount of Mn23C6 and Mn. The presence of manganosite is ascribed to the spontaneous oxidation of the Mn precipitated from Mn4C when exposed to air after annealing. The fraction of Mn23C6 was enhanced significantly for Mn4C annealed at 923 K, as shown in FIG. 6.

These results prove that the metastable Mn4C decomposes into stable Mn23C6 at temperatures above 590 K. The presence of Mn4C in the powder annealed at 923 K indicates a limited decomposition rate of Mn4C, from which the Tc of Mn4C can be measured. Both Mn23C6 and Mn are weak paramagnets at ambient temperature and elevated temperatures. Therefore, the magnetic transition of the Mn4C magnetic material at 870 K is ascribed to the Curie point of the ferrimagnetic Mn4C.

The Mn4C shows a constant magnetization of 0.258μB per unit cell below 50 K and a linear increment of magnetization with increasing temperature within the range of 50 K to 590 K, above which Mn23C6 precipitates from Mn4C. The anomalous M-T curves of Mn4C can be considered in terms of the Néel's P-type ferrimagnetism.

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Patent 2024
Alloys Argon Atmosphere Biological Evolution Cells Copper Cuboid Bone Debility Energy Dispersive X Ray Spectroscopy Face Fever fluoromethyl 2,2-difluoro-1-(trifluoromethyl)vinyl ether Graphite Ions Magnetic Fields Manganese perovskite Physical Processes Plasma Powder Radiography Scanning Electron Microscopy Spectrum Analysis Vacuum Vision X-Ray Diffraction

Example 2

Twenty-eight (28) healthy, adult male and female (non-childbearing potential) subjects were enrolled in the study in total; 14 subjects in each study part (Parts 1 and 2). A minimum of 8 female subjects were enrolled in the study (i.e., a minimum of 4 female subjects per study part). Each subject participated in either Part 1 or Part 2, but not both.

Part 1

On Day 1 of Treatment Period 1, a single oral dose of 20 mg mitapivat sulfate was administered. Serial blood samples for plasma assay of mitapivat concentrations and its CYP3A4 metabolite, referred to herein as the “Metabolite” (structure below),

[Figure (not displayed)]
were collected from predose to 120 hours following administration of mitapivat sulfate. In Treatment Period 2, an oral dose of 200 mg itraconazole was administered once daily (QD) for 9 consecutive days (Day 1 through Day 9 of Treatment Period 2) with a single oral dose of 20 mg mitapivat sulfate coadministered on Day 5. Serial blood samples for plasma assay of mitapivat and the Metabolite concentrations were collected from predose to 120 hours following coadministration of mitapivat sulfate and itraconazole on Day 5.

In Treatment Period 1, mitapivat sulfate was administered orally with approximately 240 mL of water. In Treatment Period 2, on Days 1 to 4, itraconazole was administered alone immediately followed by approximately 220 mL of water, and on Day 5, itraconazole was administered first (no water) and was immediately followed by mitapivat sulfate administration with approximately 220 mL of water. Study drugs (mitapivat sulfate and itraconazole) were administered following an overnight fast of at least 10 hours on Day 1 of Treatment Period 1 (mitapivat sulfate only) and Day 5 of Treatment Period 2 (mitapivat sulfate and itraconazole), and subjects remained fasted for 4 hours after dosing. On all other dosing days, itraconazole was administered following a predose fast of at least 4 hours and subjects remained fasted for at least 2 hours after dosing.

Part 2

On Day 1 of Treatment Period 1, a single oral dose of 50 mg mitapivat sulfate was administered. Serial blood samples for plasma assay of mitapivat and the Metabolite concentrations were collected from predose to 120 hours following administration of mitapivat sulfate. In Treatment Period 2, an oral dose of 600 mg rifampin was administered QD for 12 consecutive days (Day 1 through Day 12 of Treatment Period 2) with a single oral dose of 50 mg mitapivat sulfate coadministered on Day 8. Serial blood samples for plasma assay of mitapivat sulfate and the Metabolite concentrations were collected from predose to 120 hours following coadministration of mitapivat and rifampin on Day 8.

In Part 2, study drugs were administered with approximately 240 mL of water on all dosing days including the coadministration of mitapivat sulfate and rifampin on Day 8 of Treatment Period 2. Mitapivat sulfate and rifampin was administered following an overnight fast of at least 10 hours on Day 1 of Treatment Period 1 (mitapivat sulfate only) and Day 8 of Treatment Period 2 (both mitapivat sulfate and rifampin) and subjects remained fasted for 4 hours after dosing. On all other dosing days, rifampin was administered following a predose fast of at least 4 hours and subjects remained fasted for at least 2 hours after dosing. There was a washout period of 7 days between the mitapivat sulfate dose in Treatment Period 1 and the first itraconazole (Part 1) or rifampin (Part 2) dose in Treatment Period 2. All study drugs were consumed within 5 minutes for both Part 1 and Part 2.

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Patent 2024
Adult Biological Assay Cytochrome P-450 CYP3A4 Cytochrome P-450 CYP3A4 Inducers Cytochrome P-450 CYP3A4 Inhibitors Drug Interactions Females Itraconazole Males mitapivat mitapivat sulfate Plasma Rifampin

Example 24

For groups 1-12, see study design in FIG. 32, the 21mer Atrogin-1 guide strand was designed. The sequence (5′ to 3′) of the guide/antisense strand was UCGUAGUUAAAUCUUCUGGUU (SEQ ID NO: 14237). The guide and fully complementary RNA passenger strands were assembled on solid phase using standard phospharamidite chemistry and purified over HPLC. Base, sugar and phosphate modifications that are well described in the field of RNAi were used to optimize the potency of the duplex and reduce immunogenicity. Purified single strands were duplexed to get the double stranded siRNA described in figure A. The passenger strand contained two conjugation handles, a C6-NH2 at the 5′ end and a C6-SH at the 3′ end. Both conjugation handles were connected to siRNA passenger strand via phosphodiester-inverted abasic-phosphodiester linkers. Because the free thiol was not being used for conjugation, it was end capped with N-ethylmaleimide.

For groups 13-18 see study design in FIG. 32, a 21mer negative control siRNA sequence (scramble) (published by Burke et al. (2014) Pharm. Res., 31(12):3445-60) with 19 bases of complementarity and 3′ dinucleotide overhangs was used. The sequence (5′ to 3′) of the guide/antisense strand was UAUCGACGUGUCCAGCUAGUU (SEQ ID NO: 14228). The same base, sugar and phosphate modifications that were used for the active MSTN siRNA duplex were used in the negative control siRNA. All siRNA single strands were fully assembled on solid phase using standard phospharamidite chemistry and purified over HPLC. Purified single strands were duplexed to get the double stranded siRNA. The passenger strand contained two conjugation handles, a C6-NH2 at the 5′ end and a C6-SH at the 3′ end. Both conjugation handles were connected to siRNA passenger strand via phosphodiester-inverted abasic-phosphodiester linker. Because the free thiol was not being used for conjugation, it was end capped with N-ethylmaleimide.

Antibody siRNA Conjugate Synthesis Using Bis-Maleimide (BisMal) Linker

Step 1: Antibody Reduction with TCEP

Antibody was buffer exchanged with 25 mM borate buffer (pH 8) with 1 mM DTPA and made up to 10 mg/ml concentration. To this solution, 4 equivalents of TCEP in the same borate buffer were added and incubated for 2 hours at 37° C. The resultant reaction mixture was combined with a solution of BisMal-siRNA (1.25 equivalents) in pH 6.0 10 mM acetate buffer at RT and kept at 4° C. overnight. Analysis of the reaction mixture by analytical SAX column chromatography showed antibody siRNA conjugate along with unreacted antibody and siRNA. The reaction mixture was treated with 10 EQ of N-ethylmaleimide (in DMSO at 10 mg/mL) to cap any remaining free cysteine residues.

Step 2: Purification

The crude reaction mixture was purified by AKTA Pure FPLC using anion exchange chromatography (SAX) method-1. Fractions containing DAR1 and DAR2 antibody-siRNA conjugates were isolated, concentrated and buffer exchanged with pH 7.4 PBS.

Anion Exchange Chromatography Method (SAX)-1.

Column: Tosoh Bioscience, TSKGel SuperQ-5PW, 21.5 mm ID×15 cm, 13 um

Solvent A: 20 mM TRIS buffer, pH 8.0; Solvent B: 20 mM TRIS, 1.5 M NaCl, pH 8.0; Flow Rate: 6.0 ml/min

Gradient:

a.% A% BColumn Volume
b.10001
c.81190.5
d.505013
e .40600.5
f.01000.5
g.10002

Anion Exchange Chromatography (SAX) Method-2

Column: Thermo Scientific, ProPac™ SAX-10, Bio LC™, 4×250 mm

Solvent A: 80% 10 mM TRIS pH 8, 20% ethanol; Solvent B: 80% 10 mM TRIS pH 8, 20% ethanol, 1.5 M NaCl; Flow Rate: 0.75 ml/min

Gradient:

a.Time% A% B
b.0.09010
c.3.009010
d.11.004060
e.14.004060
f.15.002080
g.16.009010
h.20.009010

Step-3: Analysis of the Purified Conjugate

The purity of the conjugate was assessed by analytical HPLC using anion exchange chromatography method-2 (Table 22).

TABLE 22
SAX retention% purity
Conjugatetime (min)(by peak area)
TfR1-Atrogin-1 DAR19.299
TfR1-Scramble DAR18.993

In Vivo Study Design

The conjugates were assessed for their ability to mediate mRNA downregulation of Atrogin-1 in muscle (gastroc) in the presence and absence of muscle atrophy, in an in vivo experiment (C57BL6 mice). Mice were dosed via intravenous (iv) injection with PBS vehicle control and the indicated ASCs and doses, see FIG. 32. Seven days post conjugate delivery, for groups 3, 6, 9, 12, and 15, muscle atrophy was induced by the daily administration via intraperitoneal injection (10 mg/kg) of dexamethasone for 3 days. For the control groups 2, 5, 8, 11, and 14 (no induction of muscle atrophy) PBS was administered by the daily intraperitoneal injection. Groups 1, 4, 7, 10, and 13 were harvested at day 7 to establish the baseline measurements of mRNA expression and muscle weighted, prior to induction of muscle atrophy. At three days post-atrophy induction (or 10 days post conjugate delivery), gastrocnemius (gastroc) muscle tissues were harvested, weighed and snap-frozen in liquid nitrogen. mRNA knockdown in target tissue was determined using a comparative qPCR assay as described in the methods section. Total RNA was extracted from the tissue, reverse transcribed and mRNA levels were quantified using TaqMan qPCR, using the appropriately designed primers and probes. PPIB (housekeeping gene) was used as an internal RNA loading control, results were calculated by the comparative Ct method, where the difference between the target gene Ct value and the PPIB Ct value (ΔCt) is calculated and then further normalized relative to the PBS control group by taking a second difference (ΔΔCt).

Quantitation of tissue siRNA concentrations was determined using a stem-loop qPCR assay as described in the methods section. The antisense strand of the siRNA was reverse transcribed using a TaqMan MicroRNA reverse transcription kit using a sequence-specific stem-loop RT primer. The cDNA from the RT step was then utilized for real-time PCR and Ct values were transformed into plasma or tissue concentrations using the linear equations derived from the standard curves.

Results

The data are summarized in FIG. 33-FIG. 35. The Atrogin-1 siRNA guide strands were able to mediate downregulation of the target gene in gastroc muscle when conjugated to an anti-TfR mAb targeting the transferrin receptor, see FIG. 33. Increasing the dose from 3 to 9 mg/kg reduced atrophy-induced Atrogin-1 mRNA levels 2-3 fold. The maximal KD achievable with this siRNA was 80% and a tissue concentration of 40 nM was needed to achieve maximal KD in atrophic muscles. This highlights the conjugate delivery approach is able to change disease induce mRNA expression levels of Atrogin-1 (see FIG. 34), by increasing the increasing the dose. FIG. 35 highlights that mRNA down regulation is mediated by RISC loading of the Atrogin-1 guide strands and is concentration dependent.

Conclusions

In this example, it was demonstrated that a TfR1-Atrogin-1 conjugates, after in vivo delivery, mediated specific down regulation of the target gene in gastroc muscle in a dose dependent manner. After induction of atrophy the conjugate was able to mediate disease induce mRNA expression levels of Atrogin-1 at the higher doses. Higher RISC loading of the Atrogin-1 guide strand correlated with increased mRNA downregulation.

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Patent 2024
Acetate Anions Antibody Formation Antigens Atrophy Biological Assay Borates Buffers Carbohydrates Chromatography Complementary RNA Complement System Proteins Cysteine Dexamethasone Dinucleoside Phosphates DNA, Complementary Down-Regulation Ethanol Ethylmaleimide Freezing Genes Genes, Housekeeping High-Performance Liquid Chromatographies Immunoglobulins Injections, Intraperitoneal maleimide MicroRNAs Mus Muscle, Gastrocnemius Muscle Tissue Muscular Atrophy Nitrogen Obstetric Delivery Oligonucleotide Primers Pentetic Acid Phosphates Plasma PPIB protein, human Prospective Payment Assessment Commission Real-Time Polymerase Chain Reaction Retention (Psychology) Reverse Transcription RNA, Messenger RNA, Small Interfering RNA-Induced Silencing Complex RNA Interference Sodium Chloride Solvents Stem, Plant STS protein, human Sulfhydryl Compounds Sulfoxide, Dimethyl TFRC protein, human Tissues Transferrin tris(2-carboxyethyl)phosphine Tromethamine

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Plasma is the liquid component of blood, a crucial part of the body's homeostasis.
It serves as a transport medium for vital elements like nutrients, hormones, and waste products.
Plasma research is essential for understanding the complex physiological processes underlying human health and disease.
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Plasma research often involves techniques like the MiRNeasy Serum/Plasma Kit, QIAamp Circulating Nucleic Acid Kit, and BD Vacutainer for sample collection and processing.
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By leveraging the insights and tools available, researchers can streamline their plasma studies, optimize their workflows, and gain valuable insights into the complex physiological processes that underlie human health and disease.