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Apolipoproteins

Apolipoproteins are a class of proteins that play a crucial role in the transportation and metabolism of lipids within the body.
These proteins serve as structural components of lipoproteins, such as chylomicrons, very low-density lipoproteins (VLDLs), low-density lipoproteins (LDLs), and high-density lipoproteins (HDLs), enabling the efficient transport of cholesterol, triglycerides, and other lipids in the bloodstream.
Apolipoproteins also act as cofactors for enzymes involved in lipid metabolism, and they help regulate the uptake and utilization of lipids by various tissues.
Imbalances or dysfunctions in apolipoprotein levels or activities have been implicated in the development of cardiovascular diseases, metabolic disorders, and other health conditions.
Understanding the roles and mechanisms of apolipoproteins is crucial for the diagnosis, prevention, and treatment of these important health issues.

Most cited protocols related to «Apolipoproteins»

Comprehensive Biomarker Profiling in Disease Phenotypes

Cited 24 times

It was our objective to unravel and to validate laboratory biomarkers significantly associated with disease phenotypes and risks. An extensive panel of laboratory tests covering 83 analytes and biomarkers (clinical chemistry, hematology, immunology, endocrinology and metabolism) was performed on fresh biospecimen directly on the day of sample collection in a highly standardized manner (Table 4). It is a major goal to investigate and to identify novel genetic modifiers of phenotypes and disease risk. To this end, we aimed to genotype all participants using the Affymetrix AXIOM-CEU genome-wide SNP array, addressing a total of 587,352 variants. Genotyping was accompanied by genome-wide gene expression analyses for the whole blood, which was collected in Tempus Blood RNA Tubes (Life Technologies) and transferred to -80 °C prior to further processing. Isolated RNA was processed and hybridised to Illumina HT-12 v4 Expression BeadChips (Illumina, San Diego, CA, USA) and scanned on the Illumina HiScan. We investigated the association of metabolic biomarkers (quantitiative tandem mass spectrometry: amino acidy, fatty acid oxidation, steroids, sterols, eicosanoids, phospholipids, tri- and diacylglycerols, apolipoproteins and others) with major disease phenotypes, the genome and other biomarkers. For the whole blood analysis of 26 amino acids, free carnitine and 34 acylcarnitines, 40 μl of native EDTA whole blood were spotted on filter paper WS 903 (GE Healthcare, Germany). Dried blood spots were stored at -80 °C after 3 h of drying until batch analysis. Sample pretreatment and measurement are described in detail elsewhere [74 (link), 75 (link)]. Analyses were performed on an API 2000 and API 4000 tandem mass spectrometer (Applied Biosystems, Germany). Quantification was performed using ChemoView™ 1.4.2 software (Applied Biosystems, Germany).
In a subcohort of over 900 participants comprising all age groups, a detailed leukocyte subtype phenotyping with an extensive antibody panel was performed from EDTA whole blood samples using 10 colour flow cytometry (Navios flow cytometer, Beckman Coulter, Pasadena, CA, USA) [76 (link), 77 ].

Loeffler M., Engel C., Ahnert P., Alfermann D., Arelin K., Baber R., Beutner F., Binder H., Brähler E., Burkhardt R., Ceglarek U., Enzenbach C., Fuchs M., Glaesmer H., Girlich F., Hagendorff A., Häntzsch M., Hegerl U., Henger S., Hensch T., Hinz A., Holzendorf V., Husser D., Kersting A., Kiel A., Kirsten T., Kratzsch J., Krohn K., Luck T., Melzer S., Netto J., Nüchter M., Raschpichler M., Rauscher F.G., Riedel-Heller S.G., Sander C., Scholz M., Schönknecht P., Schroeter M.L., Simon J.C., Speer R., Stäker J., Stein R., Stöbel-Richter Y., Stumvoll M., Tarnok A., Teren A., Teupser D., Then F.S., Tönjes A., Treudler R., Villringer A., Weissgerber A., Wiedemann P., Zachariae S., Wirkner K, & Thiery J. (2015). The LIFE-Adult-Study: objectives and design of a population-based cohort study with 10,000 deeply phenotyped adults in Germany. BMC Public Health, 15, 691.

Publication 2015

acylcarnitine Age Groups Amino Acids Apolipoproteins Biological Markers BLOOD Carnitine Diglycerides Edetic Acid Eicosanoids Exanthema Fatty Acids Flow Cytometry Gene Expression Profiling Genes, Modifier Genome Hematologic Tests Immunoglobulins Leukocytes Metabolism Phospholipids Radionuclide Imaging Specimen Collection Steroids Sterols System, Endocrine Tandem Mass Spectrometry

Harmonization of Cardiovascular Risk Factors

Cited 19 times

Details of study selection, data collection, and harmonization procedures of the Emerging Risk Factors Collaboration (ERFC) have been described previously.13 (link)
One hundred twelve prospective studies of cardiovascular risk factors, involving a total of 1.2 million participants, have shared individual records in the ERFC (eFigure 1, available at http://www.jama.com). These studies were approximately population-based (ie, did not select participants on the basis of having previous cardiovascular disease); recorded cause-specific mortality or vascular morbidity using accepted criteria; and had accrued more than 1 year of follow-up. eTable 1 lists details of the 68 studies—involving a total of 302 430 participants without any known history of CHD (ie, myocardial infarction [MI], angina, or stroke, which were defined in each study) at the initial (“baseline”) examination—that had complete information at baseline on total cholesterol, HDL-C, and triglyceride levels and several conventional risk factors (ie, age, sex, smoking status, history of diabetes mellitus, systolic blood pressure, body mass index). References for studies in eTable 1 are in eAppendix 1 and in a previously published reference list.13 (link) Twenty-two studies with 91 307 participants had information on the preceding variables plus apo B and apo AI, and 8 studies with 44 234 participants had directly measured LDL-C values. The AMORIS study provided data for the ERFC, but it could not be incorporated into the current analyses because AMORIS did not measure baseline levels of HDL-C, blood pressure, smoking, body mass index, or diabetes.14 (link)
All but 1 study used enzymatic methods to assay triglyceride, and all but 2 studies used precipitation methods to assay HDL-C (eTable 2). For assay of apolipoproteins, 16 (link) studies used immunoturbidimetry or nephelometry, 4 used immunoradiometric assays, 1 used immunoelectrophoresis, and 1 involved immunochemical methods. For assay of LDL-C, 4 studies used ultracentrifugation, 2 used direct homogeneous methods, 1 used chemical precipitation, and 1 used electrophoresis. In registering fatal outcomes, all contributing studies used coding from the International Classification of Diseases to at least 3 digits and ascertainment was based on death certificates. Fifty-two of 68 contributing studies also involved medical records, autopsy findings, and other supplementary sources to help classify deaths. Sixty-two studies used standard definitions of MI based on World Health Organization criteria. Fifty-six studies reported diagnosis of strokes on the basis of typical clinical features and characteristic changes on brain imaging, and all at tempted to provide attribution of stroke subtype.

Major Lipids, Apolipoproteins, and Risk of Vascular Disease. (2009). JAMA : the journal of the American Medical Association, 302(18), 1993-2000.

Publication 2009

Angina Pectoris APOB protein, human Apolipoprotein A-I Apolipoproteins Autopsy Biological Assay Blood Pressure Blood Vessel Brain Cardiovascular Diseases Cerebrovascular Accident Chemical Precipitation Cholesterol Diabetes Mellitus Diagnosis Electrophoresis Enzymes Fatal Outcome Fingers Immunoelectrophoresis Immunoradiometric Assays Immunoturbidimetry Index, Body Mass Myocardial Infarction Nephelometry Systolic Pressure Triglycerides Ultracentrifugation

NMR Lipoprotein Particle Analysis Protocol

Cited 14 times

EDTA blood samples were obtained at the time of enrollment into the WHS and stored in vapor phase liquid nitrogen (−170° C). Samples for lipoprotein particle analysis by proton NMR spectroscopy were thawed, aliquoted (200 ul), refrozen, and shipped on dry ice to LipoScience, Inc. (Raleigh, NC). Particle concentrations of lipoproteins of different sizes were calculated from the measured amplitudes of their spectroscopically distinct lipid methyl group NMR signals. Weighted-average lipoprotein particle sizes are derived from the sum of the diameter of each subclass multiplied by its relative mass percentage based on the amplitude of its methyl NMR signal.5 Particle diameters and coefficients of variation (CVs) are shown in Supplementary Table 1. The NMR lipoprotein variables that we examined are those that are provided when ordering an NMR lipoprotein profile for clinical use.
In a laboratory (N. Rifai, Children's Hospital, Boston, MA) certified by the National Heart, Lung, and Blood Institute/Centers for Disease Control and Prevention Lipid Standardization program, baseline samples were thawed and analyzed for standard lipids and apolipoproteins. Standard lipids were directly measured using reagents from Roche Diagnostics (Indianapolis, IN), with CVs <3%. Apolipoproteins B₁₀₀ and A-1 were measured using immunoturbidimetric assays (DiaSorin, Stillwater, Minn), with CVs of 5% and 3%, respectively.

Mora S., Otvos J.D., Rifai N., Rosenson R.S., Buring J.E, & Ridker P.M. (2009). Lipoprotein Particle Profiles by Nuclear Magnetic Resonance Compared with Standard Lipids and Apolipoproteins in Predicting Incident Cardiovascular Disease in Women. Circulation, 119(7), 931-939.

Publication 2009

Apolipoprotein B-100 Apolipoproteins BLOOD Diagnosis Dry Ice Edetic Acid Heart Immunoturbidimetric Assay Lipids Lipoproteins Lung Nitrogen Protons Spectroscopy, Nuclear Magnetic Resonance

Fasting Lipid Biomarkers and CVD Risk

Cited 10 times

Statistical analyses were performed using STATA version 8.2 (STATA Corporation, College Station, Texas). Statistical comparisons between the fasting and nonfasting groups were obtained from student's t-tests for continuous variables expressed as means, from Kruskal-Wallis tests for variables expressed as medians, and chi-square tests for categorical variables. The levels of the lipids and apolipoproteins were examined as a function of time since the last meal divided into 2-hour intervals.
Following guidelines from the Department of Health and Human Services,14 lipid biomarkers were divided into quintiles based on the distribution among women not taking hormone replacement. Because the distributions differed between fasting and nonfasting participants for some of the lipid measurements, quintile cut-points were defined separately in each of the fasting and nonfasting samples. To address whether the results may differ on the basis of the cut-points uses, analyses were also performed per 1-standard deviation (1-SD) increment in each lipid variable. The 1-SD increments were similar for fasting and nonfasting samples. In order to examine the predictive value of levels of lipids and apolipoproteins for CVD risk depending on the time since the last meal divided into 2-hour intervals, analyses were repeated per 1-SD increments within strata of time since last meal by 2-hour intervals. Cox proportional hazard regression models were used to calculate the hazard ratios (HRs) and 95% confidence intervals (CIs) according to these quintiles and per 1-SD increments.
To examine the extent to which each lipid or lipoprotein biomarker was associated with incident events, we considered each lipid variable in a separate model that adjusted for non-lipid risk factors (age, randomized treatment assignment, smoking status, menopausal status, postmenopausal hormone use, blood pressure, diabetes, and body mass index). Analyses for triglycerides were additionally adjusted for total and HDL cholesterol based on prior work from this cohort.8 (link) To address any potential confounding from time of day for the blood draw, we additionally adjusted the multivariable models for time of blood draw, which did not affect the findings. P value for linear trend was obtained using the median value for each quintile. All P-values were two-tailed. Finally, statistical tests for interaction between fasting status and each of the lipids or apolipoproteins in relation to incident CVD were obtained using likelihood ratio tests.

Mora S., Rifai N., Buring J.E, & Ridker P.M. (2008). Fasting Compared with Nonfasting Lipids and Apolipoproteins for Predicting Incident Cardiovascular Events. Circulation, 118(10), 993-1001.

Publication 2008

Apolipoproteins Biological Markers BLOOD Blood Pressure Diabetes Mellitus High Density Lipoprotein Cholesterol Hormones Index, Body Mass Lipid A Lipids Lipoproteins Menopause PER1 protein, human Therapy, Hormone Replacement Triglycerides Woman

Lipid and Biomarker Measurements in JUPITER

Cited 8 times

Standard lipids, apolipoproteins, hsCRP, and glucose measurements were performed in a central laboratory on fasting blood samples as previously described (Supplemental Methods).^{13 (link)} Consistent with previous JUPITER analyses, on-treatment concentrations were defined as values obtained after one year of randomized treatment.^{11 (link)-14 (link)} IM lipoproteins were measured at Quest Diagnostics Nichols Institute (San Juan Capistrano, CA) (Supplemental Methods and Supplemental Table 1).

Mora S., Caulfield M.P., Wohlgemuth J., Chen Z., Superko H.R., Rowland C.M., Glynn R.J., Ridker P.M, & Krauss R.M. (2015). Atherogenic Lipoprotein Subfractions Determined by Ion Mobility and First Cardiovascular Events After Random Allocation to High-Intensity Statin or Placebo: The JUPITER Trial. Circulation, 132(23), 2220-2229.

Publication 2015

Apolipoproteins BLOOD C Reactive Protein Diagnosis Glucose Lipids Lipoproteins

Most recents protocols related to «Apolipoproteins»

Serum Biomarkers in Diabetic Nephropathy

We have collected serum specimens from DN patients and healthy. Detection of biomarkers in serum with commercial Elisa kits, ELISA method, in DN patients and healthy. Follow the experimental steps in the Elisa kit instructions to detect the expression level of the marker in the serum (Apolipoprotein CI ELISA kit, Abcam, ab108808, USA).

Yu K., Li S., Wang C., Zhang Y., Li L., Fan X., Fang L., Li H., Yang H., Sun J, & Yang X. (2023). APOC1 as a novel diagnostic biomarker for DN based on machine learning algorithms and experiment. Frontiers in Endocrinology, 14, 1102634.

Publication 2023

Apolipoproteins Biological Markers Enzyme-Linked Immunosorbent Assay Patients Serum

Biomarkers of Physical and Cognitive Function

Blood samples were collected via a venipuncture during an in-person baseline assessment. The biomarkers chosen for this project included: total cholesterol (mg/dL), hemoglobin (g/dL), serum albumin (g/dL), and serum vitamin D (25-hydroxyvitamin D; ng/mL) deficient (< 20 ng/ml), and at least one Apolipoprotein ε4 allele (APOE-ε4). These biomarkers were selected based upon previous studies (Atkinson et al., 2005 (link), 2007 (link)) and are known to influence physical function and cognitive function. All biomarkers were collected at month 0 with the exception of hemoglobin (values were from the 36-month visit because it was not collected at month 0, and serum vitamin D is from the 24-month visit).

Handing E.P., Hayden K.M., Leng X.I, & Kritchevsky S.B. (2023). Predictors of cognitive and physical decline: Results from the Health Aging and Body Composition Study. Frontiers in Aging Neuroscience, 15, 1122421.

Publication 2023

Alleles ApoE protein, human Apolipoproteins Biological Markers BLOOD Calcifediol Cholesterol Cognition Ergocalciferol Hemoglobin Phlebotomy Physical Examination Serum Serum Albumin

Cognitive Screening in Subjective Cognitive Decline

Individuals who were diagnosed as SCD were eligible for the study. Subjects who visited the hospital due to persistent cognitive worsening and were diagnosed with SCD after dementia work-ups are consecutively recruited. The dementia work up included detailed neuropsychological test battery, MRI, and routine blood sampling for syphilis, thyroid function, vitamin B deficiencies, and apolipoprotein epsilon genotyping. The inclusion criteria were as follows (Table 2): Age ≥ 60 years of age; Existence of persistent self-reported cognitive complaints; Normal performance (above −1.0 standard deviation [SD] of norms) on all subtests of neuropsychological test battery named Seoul Neuropsychological Screening Battery (SNSB);^{[4 ]} Clinical dementia rating (CDR) score of 0;^{[5 ]} Agreement to participate in the study and could visit the hospital for annual evaluations. The exclusion criteria were the following: Mild cognitive impairment (MCI) or dementia; Brain lesions known to cause cognitive impairment (tumor, stroke, or subdural hematoma); Any neurological disorders such as Parkinson’s disease, Huntington’s disease, epilepsy, or normal pressure hydrocephalus; Major psychiatric disorders such as uncontrolled depression, schizophrenia, alcoholism, or drug dependency; Abnormal blood laboratory findings such as abnormal thyroid function, low vitamin B12 or low folate, or positive syphilis serology, and; Hearing loss that is impossible to perform phone-based cognitive tests.

Hong Y.J., Lee S.B., Kim S.H., Lee M.A., Park J.W, & Yang D.W. (2023). Development of a home-based cognitive test for cognitive monitoring in subjective cognitive decline with high risk of Alzheimer’s disease. Medicine, 102(9), e33096.

Publication 2023

Alcoholic Intoxication, Chronic Apolipoproteins BLOOD Brain Cerebrovascular Accident Cobalamins Cognition Cognitive Impairments, Mild Cognitive Testing Dementia Disorders, Cognitive Drug Dependence Epilepsy Folate Hearing Impairment Hematoma, Subdural Huntington Disease Hydrocephalus, Normal Pressure Major Depressive Disorder Neoplasms Nervous System Disorder Neuropsychological Tests Parkinson Disease Schizophrenia Syphilis Syphilis Serodiagnosis Thyroid Diseases Thyroid Gland Vitamin B Deficiency

Genetic Variants in APOA5 and Lipid Profiles

Plasma samples were obtained from individuals enrolled in the PMBB. Patients participating in the PMBB are consented for the storage of biological specimens, genetic sequencing, and access to all available electronic health record data. The study was approved by the Institutional Review Board of the University of Pennsylvania. APOA5 variant carriers and controls were identified from a subset of individuals in the PMBB who had exome sequencing (N=41,759). Non-carriers were matched by age, sex, ancestry, BMI, and history of diabetes mellitus diagnosis and screened to exclude individuals carrying any nonsynonymous coding variants in APOA5 and APOC3. N=73 apoA-V G162C (rs2075291) variant carriers were matched 1:1 to non-carriers (N=73). N=13 apoA-V Q252X (rs149808404) variant carriers were matched 1:3 to non-carriers (N=39). Fasting status of PMBB donors is unknown at time of blood draw.
Carrier and non-carrier plasma samples were assayed for TG, total cholesterol, low-density lipoprotein cholesterol (LDL-C), non-high-density lipoprotein (non-HDL-C), high-density lipoprotein (HDL-C), free cholesterol, phospholipids, apoA-I, apoB, apoC-II, and apoC-III using an Axcel clinical autoanalyzer (Alfa Wassermann Diagnostic Technologies). The association of plasma lipid and apolipoprotein concentrations or ratios with carrier status was determined using a multivariate linear regression analysis adjusted for age, sex, ancestry, BMI and history of diabetes mellitus diagnosis as covariables. Statistical analysis was performed using R (version 4.2.1)⁴⁴.
N=5 representative carrier and non-carrier plasma samples were pooled for separation by fast protein liquid chromatography (FPLC) on a Superose 6 gel-filtration column (GE Healthcare Life Sciences) into 500μl fractions. TG and cholesterol content in 100μl of each fraction was assayed using Infinity Liquid Stable triglyceride or cholesterol reagent (Thermo Fisher Scientific) in 96-well microplates then read with Synergy Multi-Mode Microplate Reader (BioTek).

Stankov S., Vitali C., Park J., Nguyen D., Mayne L., Englander S.W., Levin M.G., Vujkovic M., Hand N.J., Phillips M.C, & Rader D.J. (2023). Comparison of the structure-function properties of wild-type human apoA-V and a C-terminal truncation associated with elevated plasma triglycerides. medRxiv.

Publication Preprint 2023

APOA1 protein, human APOB protein, human ApoC-III Apolipoprotein C II Apolipoproteins Apolipoproteins A Biopharmaceuticals Birth BLOOD Cholesterol Cholesterol, beta-Lipoprotein Diabetes Mellitus Diagnosis Donors Ethics Committees, Research Gel Chromatography High Density Lipoproteins Lipids Patients Phospholipids Plasma Proteins Triglycerides

Quantification of BAT and Plasma Proteins

Cited 1 time

BAT protein was extracted with RIPA lysis buffer system (Santa Cruz Biotechnology, Dallas, TX, USA). Plasma and extracted BAT protein mixed with 4x SDS sample buffer were boiled for 10 min according to our published protocol [31 (link)]. For the determination of UCP1 and beta-tubulin proteins, extracted BAT proteins (200 µg) were incubated with 2 µL of UCP1 (Abcam^®, Waltham, MA, USA) or beta-tubulin antibody (Invitrogen^®, Waltham, MA, USA) using a Dynabeads Protein A immunoprecipitation kit (Invitrogen, Vilnius, Lithuania) overnight at 4 °C. The eluted sample (30µL) was loaded onto a 4–20% Tris-HCl gradient gel (Bio-Rad Laboratories, Hercules, CA, USA). For the measurement of APOA4 and APOA1 proteins, plasma (2 µL) was loaded onto a 4–20% Tris-HCl gradient gel. The SDS gels were run at 60 voltages for 1 h and at 100 voltages until the protein standards were well separated. Proteins were then transferred to a polyvinylidene difluoride membrane (Bio-Rad Laboratories, CA, USA) for 2 h at a constant current of 350 mA. After the membrane was incubated in a 5% blotting-grade blocker solution (Bio-Rad Laboratories, CA, USA), the membranes were then incubated with one of either rabbit or mouse polyclonal antibodies in 5% bovine serum albumin (BSA): beta-tubulin (1:1000 dilution, #2128L, Cell Signaling Technology, Inc., Denvers, MA, USA), UCP1 (1:1000 dilution, #50-173-4107, Proteintech, Rosemont, IL, USA), TH (1: 1000 dilution, #2792S, Cell Signaling Technology, Inc.), ATGL (1:1000 dilution, #2439S, Cell Signaling Technology, Inc.), HSL (1:1000 dilution, #4107S, Cell Signaling Technology, Inc.), polyclonal APOA4 (1:5000 dilution, #PA5-14554, Invitrogen, Waltham, MA, USA) and APOA1 antibodies (1:5000 dilution, #PA5-29557, Invitrogen). After incubation with the primary antibody overnight at 4 °C, the immunoblots were washed and then incubated with horseradish peroxidase-conjugated goat anti-rabbit antibody (1:5000 dilution, Dako Cytomation, Santa Clara, CA, USA) for 1 h at 25 °C. Detection was achieved using the enhanced chemiluminescence system (Immobilon western chemiluminescent HRP substrate, EMD Millipore Corporation, Billerica, MA, USA). A C-Digit blot scanner (Li-Cor Biosciences, Lincoln, NE, USA) was used for development and visualization of the proteins, and infrared imaging was used for protein quantification. For BAT immunoblots, UCP1 protein concentrations were normalized to beta-tubulin. For plasma apolipoproteins, the average of band intensity of apolipoproteins in WT mice was considered as 100%.

LaRussa Z., Kuo H.C., West K., Shen Z., Wisniewski K., Tso P., Coschigano K.T, & Lo C.C. (2023). Increased BAT Thermogenesis in Male Mouse Apolipoprotein A4 Transgenic Mice. International Journal of Molecular Sciences, 24(4), 4231.

Publication 2023

Antibodies Antibodies, Anti-Idiotypic APOA1 protein, human APOA4 protein, human Apolipoproteins beta-Tubulin Bos taurus Brown Adipose Tissue Uncoupling Protein Buffers Chemiluminescence Fingers Gels Goat Immobilon Immunoblotting Immunoglobulins Immunoprecipitation Mus Plasma polyvinylidene fluoride Proteins Rabbits Radioimmunoprecipitation Assay Serum Albumin Staphylococcal Protein A Technique, Dilution Tissue, Membrane Tromethamine UCP1 protein, human

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More about "Apolipoproteins"

Apolipoproteins are a group of proteins essential for the transportation and metabolism of lipids (fats) in the body.
These proteins serve as structural components of lipoproteins, such as chylomicrons, very low-density lipoproteins (VLDLs), low-density lipoproteins (LDLs), and high-density lipoproteins (HDLs), enabling the efficient transport of cholesterol, triglycerides, and other lipids in the bloodstream.
Apolipoproteins also act as cofactors for enzymes involved in lipid metabolism, and they help regulate the uptake and utilization of lipids by various tissues.
Imbalances or dysfunctions in apolipoprotein levels or activities have been linked to the development of cardiovascular diseases, metabolic disorders, and other health conditions.
Understanding the roles and mechanisms of apolipoproteins is crucial for the diagnosis, prevention, and treatment of these important health issues.
To streamline your apolipoprotein research, consider utilizing cutting-edge tools like the Bio-Plex 200 system, Omniexpress chip, and ChemiDoc Imaging System.
These technologies can enhance your data collection and analysis, while the BCA reagent and Cobas Mira Plus Autoanalyzer can assist with protein quantification and sample processing.
Additionally, the use of serum separator tubes can help ensure accurate measurements of apolipoprotein levels in your samples.
By incorporating these insights and resources, you can optimize your apolipoprotein research protocols, improve reproducibility, and unlock the power of data-driven decision-making.
Remember to use SAS version 9.4 for your statistical analyses to ensure robust and reliable results.
With a deep understanding of apolipoproteins and the right tools at your disposal, you can advance your studies and contribute to the understanding and management of these important health conditions.