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High Density Lipoproteins

High Density Lipoproteins (HDL) are a class of lipoproteins that transport cholestrol and other lipids in the bloodstream.
HDL plays a crucial role in the body's lipid metabolism and cardiovascular health.
This MeSH term provides a concise overveiw of HDL, its functions, and its importance in medical research.

Most cited protocols related to «High Density Lipoproteins»

1
Estimating Attributable Disease Burden
Cited 45 times
Four components were used for the calculations to estimate the attributable burden for a given risk–outcome pair: (1) the estimate of the burden metric being assessed for the cause (ie, number of deaths, years of life lost [YLLs], years lived with disability [YLDs], or DALYs); (2) the exposure levels for the risk factor; (3) the counterfactual level of risk factor exposure or theoretical minimum risk exposure level (TMREL); and (4) the relative risk of the outcome relative to the TMREL. For a given risk–outcome pair, we estimated attributable DALYs as total DALYs for the outcome multiplied by the population attributable fraction (PAF) for the risk–outcome pair for each age, sex, location, and year. The same logic applies to estimating attributable deaths, YLLs, and YLDs. The PAF is the proportion by which the outcome would be reduced in a given population and in a given year if the exposure to a risk factor in the past were reduced to the counterfactual level of the TMREL. The PAF for each individual risk–outcome pair is estimated independently and incorporates all burden for the outcome that is attributable to the risk, whether directly or indirectly. For example, the burden of ischaemic heart disease attributable to high body-mass index (BMI) includes the burden resulting from the direct effect of BMI on ischaemic heart disease risk, as well as the burden through the effects of BMI on ischaemic heart disease that are mediated through other risks (eg, high systolic blood pressure [SBP] and high low-density lipoprotein [LDL] cholesterol). When aggregating PAFs across multiple risks we used a mediation adjustment to compute the excess attenuated risk for each of 205 mediation-risk-cause sets (appendix 1 section 5).
Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. (2018). Lancet (London, England), 392(10159), 1923-1994.
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Publication 2018
Cholesterol Disabled Persons Heart High Density Lipoproteins Index, Body Mass Low-Density Lipoproteins Myocardial Ischemia Population at Risk Systolic Pressure
2
Comprehensive NMR-Based Metabolic Profiling
Cited 16 times
A high-throughput serum nuclear magnetic resonance (NMR) spectroscopy platform [28] was utilized to quantify 67 metabolic measures that represent a broad molecular signature of the systemic metabolite profile. The metabolite set covers multiple metabolic pathways, and includes lipoprotein lipids, fatty acids, amino acids, and glycolysis precursors (Table S1). Fourteen lipoprotein subclasses were analyzed as part of the metabolite profile, with the subclass sizes defined as follows: extremely large very-low-density lipoproteins (VLDLs) (particle diameter from 75 nm upwards), five VLDL subclasses (average particle diameters of 64.0 nm, 53.6 nm, 44.5 nm, 36.8 nm, and 31.3 nm), intermediate-density lipoproteins (28.6 nm), three low-density lipoprotein (LDL) subclasses (25.5 nm, 23.0 nm, and 18.7 nm), and four high-density lipoprotein (HDL) subclasses (14.3 nm, 12.1 nm, 10.9 nm, and 8.7 nm). The NMR-based metabolite profiling employed in this study has previously been used in various epidemiological studies [25] (link)–[31] (link), and details of the experimentation have been described [28] ,[32] (link),[33] (link). Furthermore, 15 additional measures, including various inflammatory markers, liver function surrogates, hormones, and blood pressure, were analyzed (Text S1). These additional metabolic measures, assayed in at least two of the cohorts, were selected to complement the comprehensive characterization of cardiometabolic effects of adiposity across multiple pathways and to enhance comparability with prior Mendelian randomization studies [7] (link),[14] (link)–[16] (link).
Würtz P., Wang Q., Kangas A.J., Richmond R.C., Skarp J., Tiainen M., Tynkkynen T., Soininen P., Havulinna A.S., Kaakinen M., Viikari J.S., Savolainen M.J., Kähönen M., Lehtimäki T., Männistö S., Blankenberg S., Zeller T., Laitinen J., Pouta A., Mäntyselkä P., Vanhala M., Elliott P., Pietiläinen K.H., Ripatti S., Salomaa V., Raitakari O.T., Järvelin M.R., Smith G.D, & Ala-Korpela M. (2014). Metabolic Signatures of Adiposity in Young Adults: Mendelian Randomization Analysis and Effects of Weight Change. PLoS Medicine, 11(12), e1001765.
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Publication 2014
Amino Acids Blood Pressure Fatty Acids Glycolysis High Density Lipoproteins Hormones Inflammation Lipids Lipoproteins Lipoproteins, IDL Liver Low-Density Lipoproteins Magnetic Resonance Imaging Obesity Serum Spectroscopy, Nuclear Magnetic Resonance Very Low Density Lipoprotein
3
Tehran Cardiometabolic Genetic Study
Cited 13 times
The TCGS is a prospective family-based GWAS cohort that has been followed since 1999 within the Tehran Lipid and Glucose Study (TLGS), which includes over 15,000 initially healthy subjects >3 years old, who have already been followed for more than 20 years. Participants have been followed for the development of common disorders such as myocardial infarction, stroke, diabetes mellitus, hypertension, obesity, familial hypercholesterolemia, hyperlipidemia, habitation (eg, smoking and physical activity), and biochemical factors (ie, high cholesterol, low high-density lipoproteins, high triglycerides).
The concept of designing a genomic bank from TLGS samples was first presented to the Endocrine Research Center (ERC) and the Iranian molecular medicine network, and was funded by FA and MSD (grant number 147, 2004; grant number 265, 2008). In 2008, a project determining pedigrees according to genetic relationships was funded by ERC (grant number 321), with MSD and AAM as principal investigators. Funding of the main study began in June 2012 with an agreement between the Research Institute for Endocrine Sciences (RIES) and the deCODE genetic company (Reykjavik, Iceland), with FA and MSD as primary investigators. The final protocol for the genetic study was written by FA, MSD, MSF, and DK, and was submitted to the Ministry of Health and Medical Education in August 2012. The protocol was approved by the National Committee for Ethics in Biomedical Research in December 2012.
In this paper, we describe the TCGS (and its parent TLGS) from the perspectives of cohort assembly, follow-up, endpoint validation, baseline plasma phenotyping, DNA extraction, genotyping, participant confidentiality, power, and sample size, and discuss the TCGS in the context of other ongoing GWASs being performed in related areas. The study is organized into 5 phases: (1) cohort assembly and prospective follow-up, (2) genomic sample extraction, (3) phenotype and outcome gathering, (4) chip typing and genotype analysis, and (5) drawing family trees.
Daneshpour M.S., Fallah M.S., Sedaghati-Khayat B., Guity K., Khalili D., Hedayati M., Ebrahimi A., Hajsheikholeslami F., Mirmiran P., Ramezani Tehrani F., Momenan A.A., Ghanbarian A., Amouzegar A., Amiri P, & Azizi F. (2017). Rationale and Design of a Genetic Study on Cardiometabolic Risk Factors: Protocol for the Tehran Cardiometabolic Genetic Study (TCGS). JMIR Research Protocols, 6(2), e28.
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Publication 2017
Cerebrovascular Accident Developmental Disabilities Diabetes Mellitus DNA Chips Education, Medical Genome Genome-Wide Association Study Genotype Glucose Healthy Volunteers High Blood Pressures High Density Lipoproteins Hypercholesterolemia Hyperlipidemia Hyperlipoproteinemia Type IIa Hypertriglyceridemia Lipid A Lipids Low-Density Lipoproteins Myocardial Infarction Obesity Parent Phenotype Plasma Reproduction System, Endocrine
4
Multivariate GWAS Analysis in Diverse Mouse and Human Cohorts
Cited 12 times
We analyzed two data sets: the Hybrid Mouse Diversity Panel (HMDP)31 (link) and the Northern Finland Birth Cohort 1966 (NFBC1966) Study32 (link).
The HMDP data includes 100 inbred strains with four phenotypes (high-density lipoprotein, HDL; total cholesterol, TC; triglycerides, TG; unesterified cholesterol, UC) and four million high quality fully imputed SNPs (SNPs are downloaded from http://mouse.cs.ucla.edu/mousehapmap/full.html). We excluded mice with missing phenotypes for any of these four phenotypes. We excluded non-polymorphic SNPs, and SNPs with a minor allele frequency less than 5%. For SNPs that have identical genotypes, we tried to retain only one of them (by using “--indep-pairwise 100 5 0.999999” option in PLINK33 (link)). This left us with 98 strains, 656 individuals and 108,562 SNPs. We quantile transformed each phenotype to a standard normal distribution to guard against model mis-specification. We used the product of centered genotype matrix as an estimate of relatedness16 (link),17 (link),34 ,35 (link). Note that the sample size used here is smaller than the original study31 (link), and the phenotypes are quantile-transformed instead of log transformed for robustness.
The NFBC1966 data contains 5402 individuals with multiple metabolic traits measured and 364,590 SNPs typed. We selected four phenotypes (high-density lipoprotein, HDL; low-density lipoprotein, LDL; triglycerides, TG; C-reactive protein, CRP) among them, following previous studies3 (link). We selected individuals and SNPs following previous studies11 (link),32 (link) with the software PLINK33 (link). Specifically, we excluded individuals with missing phenotypes for any of these four phenotypes or having discrepancies between reported sex and sex determined from the X chromosome. We excluded SNPs with a minor allele frequency less than 1%, having missing values in more than 1% of the individuals, or with a Hardy-Weinberg equilibrium p value below 0.0001. This left us with 5,255 individuals and 319,111 SNPs. For each phenotype, we quantile transformed the phenotypic values to a standard normal distribution, regressed out sex, oral contraceptives and pregnancy status effects32 (link), and quantile transformed the residuals to a standard normal distribution again. We replaced the missing genotypes for a given SNP with its mean genotype value. We used the product of centered and scaled genotype matrix as an estimate of relatedness11 (link),17 (link),34 ,35 (link).
In both data sets, we quantile transformed each single phenotype to a standard normal distribution to guard against model misspecification. Although this strategy does not guarantee that the transformed phenotypes follow a multivariate normal distribution jointly, it often works well in practice when the number of phenotypes is small (see, e.g. 22 ). For both data sets, we used a standard mvLMM with an intercept term (without any other covariates), and test each SNP in turn. Because the software MTMM relies on the commercial software ASREML to estimate the variance components in the null model, we modified the MTMM source code so that it can read in the estimated variance components from GEMMA.
Zhou X, & Stephens M. (2014). Efficient Algorithms for Multivariate Linear Mixed Models in Genome-wide Association Studies. Nature methods, 11(4), 407-409.
Publication 2014
Birth Cohort Cholesterol Contraceptives, Oral C Reactive Protein Genotype High Density Lipoproteins Hybrids Low-Density Lipoproteins Mice, Laboratory Neutrophil Phenotype Pregnancy Strains Triglycerides X Chromosome
5
Rare Variant Analysis of HDL Levels
Cited 11 times
We applied SAIGE-GENE to the high-density lipoprotein (HDL) levels in 69,500 Norwegian samples from a population-based HUNT study15 (link),16 (link). About 70,000 HUNT participants were genotyped using Illumina HumanCoreExome v1.0 and 1.1 and imputed using Minimac338 with a merged reference panel of Haplotype Reference Consortium (HRC)39 and whole genome sequencing data (WGS) for 2,201 HUNT samples. Variants with imputation r2 < 0.8 were excluded from further analysis. Participation in the HUNT Study is based on informed consent, and the study has been approved by the Data Inspectorate and the Regional Ethics Committee for Medical Research in Norway. Total 13,416 genes with at least two rare (MAF ≤ 1%) missense and/or stop-gain variants with imputation r2 ≥ 0.8 were tested. Variants were annotated using Seattle Seq Annotations (http://snp.gs.washington.edu/SeattleSeqAnnotation138/). We used 249,749 pruned genotyped markers to estimate relatedness coefficients in the full GRM for Step 1 and used the relative coefficient cutoff ≥ 0.125 for the sparse GRM.
We have also analyzed 53 quantitative traits and 10 binary traits using SAIGE-GENE in the UK Biobank for 408,910 participants with White British ancestry2 (link). UK Biobank protocols were approved by the National Research Ethics Service Committee and participants signed written informed consent. Markers that were imputed by the HRC39 panel with imputation info score ≥ 0.8 were used in the analysis. Total 15,342 genes with at least two rare (MAF ≤ 1%) missense and stop-gain variants that were directly genotyped or successfully imputed from HRC (imputation score ≥ 0.8) were tested. We used 340,447 pruned markers, which were pruned from the directly genotyped markers using the following parameters, were used to construct GRM: window size of 500 base pairs (bp), step-size of 50 bp, and pairwise r2 < 0.2. We used the relative coefficient cutoff ≥ 0.125 for the sparse GRM.
Zhou W., Zhao Z., Nielsen J.B., Fritsche L.G., LeFaive J., Taliun S.A., Bi W., Gabrielsen M.E., Daly M.J., Neale B.M., Hveem K., Abecasis G.R., Willer C.J, & Lee S. (2020). Scalable generalized linear mixed model for region-based association tests in large biobanks and cohorts. Nature genetics, 52(6), 634-639.
Publication 2020
Ethics Committees, Research Genes Haplotypes High Density Lipoproteins

Most recents protocols related to «High Density Lipoproteins»

1
Investigating β2AR-βarr Binding Interactions
Not available on PMC !

Example 1

This example demonstrates that the binding interaction of βarr with the β2-adrenergic receptor (β2AR).

The binding of βarr to GPCRs is mainly initiated through an interaction with the phosphorylated receptor C terminus, and conformational changes induced in βarr by this interaction promote coupling to the receptor TM core, as shown in FIG. 1. Co-immunoprecipitation experiments confirmed that heterotrimeric Gs protein, but not βarr1, can interact with purified non-phosphorylated β2-adrenergic receptor (β2AR), as shown in FIG. 2A.

To verify that this apparent lack of interaction with βarr is not simply due to poor complex stability, two assays capable of detecting complex formation in situ were performed. First, competition radioligand binding was used to measure the allosteric effects of transducers on ligand binding to the receptor. As described by the ternary complex model, first for G proteins and later for βarrs, ligand-induced changes in receptor conformation enhance the binding and affinity of transducers, which reciprocally increase ligand affinity by stabilizing an active receptor state (De Lean A, et al. (1980) J Biol Chem 255(15):7108-7117., Gurevich V V, et al. (1997) J Biol Chem 272(46):28849-28852). When wild-type (WT) β2AR was reconstituted in high-density lipoprotein (HDL) particles to mimic a cellular membrane environment (Denisov I G & Sligar S G (2016) Nat Struct Mol Biol 23(6):481-486), G protein enhanced the affinity of the full agonist isoproterenol for non-phosphorylated HDL-β2AR by nearly 1000-fold, as expected, but βarr1 had no effect even at micromolar concentrations, as shown in FIG. 2B.

Second, to directly monitor β2AR conformational changes associated with activation, the C265 at the cytoplasmic end of TM6 was labeled with monobromobimane, an environmentally sensitive fluorophore. Receptor activation leads to an outward movement of TM6 that places the bimane label in a more solvent-exposed position, causing a decrease in fluorescence and a shift in λmax (Yao X J, et al. (2009) Proc Natl Acad Sci USA 106(23):9501-9506). Indeed, isoproterenol reduced β2AR-bimane fluorescence compared to control (DMSO), and addition of Gs but not βarr1 further attenuated fluorescence, as shown in FIG. 2C.

The results of this example demonstrate that non-phosphorylated β2AR fails to form a productive interaction with βarr.

US11866482B2. System and method for homogenous GPCR phosphorylation and identification of beta-2 adrenergic receptor positive allosteric modulators (2024-01-09). Duke University [US]. Inventors: Robert J. Lefkowitz [US], Seungkirl Ahn [US], Biswaranjan Pani [US], Alem W. Kahsai [US], Laura Wingler [US], Dean Staus [US].
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Patent 2024
Adrenergic Agents beta-2 Adrenergic Receptors Biological Assay Co-Immunoprecipitation Cytoplasm Fluorescence GTP-Binding Proteins high density lipoprotein receptors High Density Lipoproteins Homozygote Isoproterenol Ligands monobromobimane Movement Phosphorylation Plasma Membrane Proteins Solvents Sulfoxide, Dimethyl Transducers
2
Metabolic and Thyroid Dysfunction Evaluation
Blood samples were collected in the morning after an overnight fast before participants received any medical treatment. Serum levels of free triiodothyronine (FT3), free thyroxine (FT4), thyroid stimulating hormone (TSH), antithyroglobulin (TgAb), thyroid peroxidase antibody (TPOAb), TC, TG, high-density lipoprotein (HDL-C), low-density lipoprotein (LDL-C), and glucose were assessed. Lipid markers (TC, TG, HDL-C, LDL-C) and glucose were measured on a Cobas E610 (Roche, Basel, Switzerland). Thyroid hormones were assayed on a Roche C6000 Electrochemiluminescence Immunoassay Analyzer (Roche Diagnostics, Indianapolis, IN, USA). Measurements were conducted in the laboratory of the First Hospital, Shanxi Medical University. The nurses measured the patients’ weight, height, and blood pressure. We calculated body mass index (BMI) according to the following formula: BMI = Weight (kg)/Height (m) 2.
According to previous studies in the Chinese population (38 (link), 39 (link)), metabolic disturbances and thyroid dysfunction were defined as follows: (1) overweight or obesity: BMI≥24; (2) hyperglycemia: glucose≥6.1mmol/L; (3) hypertension: SBP≥140 mmHg and/or DBP≥90mmHg; (4) hypertriglyceridemia: TG≥2.3 mmol/L; (5) low HDL: HDL-C ≤ 1.0 mmol/L; (6) hypercholesterolemia: TC≥6.2 mmol/L or LDL-C≥4.1 mmol/L; (7)abnormal TgAb: TgAb≥115 IU/L; (8) abnormal TPOAb: TPOAb ≥34 IU/L; (9) subclinical hypothyroidism (SCH): TSH >4.2 mIU/L with normal fT4 concentration (10–23 pmol/L); (10) hyperthyroidism: TSH<0.27 mIU/L and FT4 >23 pmol/L, and (11) hypothyroidism: TSH >4.2 mIU/L with low FT4 concentration (<10 pmol/L).
Peng P., Wang Q., Lang X., Liu T, & Zhang X.Y. (2023). Clinical symptoms, thyroid dysfunction, and metabolic disturbances in first-episode drug-naïve major depressive disorder patients with suicide attempts: A network perspective. Frontiers in Endocrinology, 14, 1136806.
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Publication 2023
anti-thyroglobulin antibody BLOOD Blood Pressure Chinese Diagnosis Glucose High Blood Pressures high density lipoprotein-1 High Density Lipoproteins Hypercholesterolemia Hyperglycemia Hyperthyroidism Hypertriglyceridemia Hypothyroidism Immunoassay Index, Body Mass LDL-1 Liothyronine Lipids Low-Density Lipoproteins Nurses Obesity Patients Serum Thyroid Gland Thyroid Hormones thyroid microsomal antibodies Thyrotropin Thyroxine
3
Blood Biomarker Profiling in Mice
Mice at the fed state or fasted for 12 h were sacrificed, and whole blood was collected from retrobulbar venous plexus and centrifuged for 10 min at 12,000 × g to obtain plasma. Plasma glucose, total cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL), very low-density lipoprotein (VLDL), and total triglycerides were measured using commercial kits (Nanjing Jiancheng Bioengineering Institute, Nanjing, China). ELISA kits (Sinoukbio, Beijing, China) were used to measure plasma insulin, C-peptide, non-esterified fatty acid (NEFA), leptin, IL (interleukin) 4, IL6, IL10, resistin, interferon γ (IFNγ) and monocyte chemotactic protein-1 (MCP1) following the manufacturer’s instruction.
Zhang X., Wang L., Wang Y., He L., Xu D., Yan E., Guo J., Ma C., Zhang P, & Yin J. (2023). Lack of adipocyte IP3R1 reduces diet-induced obesity and greatly improves whole-body glucose homeostasis. Cell Death Discovery, 9, 87.
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Publication 2023
BLOOD C-Peptide Cholesterol Enzyme-Linked Immunosorbent Assay Fatty Acids, Esterified Glucose High Density Lipoproteins IL10 protein, human Insulin Interferon Type II Interleukin-4 Leptin Low-Density Lipoproteins Monocyte Chemoattractant Protein-1 Mus Plasma RETN protein, human Triglycerides Veins Very Low Density Lipoprotein
4
Plasma Sample Preparation for Metabolic Profiling
At the end of the experiment, all mice were anaesthetized with sodium pentobarbital (50 mg/kg) intraperitoneally. The eyes were removed to collect the blood samples in 5 mL Vacutainer tubes containing the chelating agent ethylene diamine tetraacetic acid (EDTA). The samples were centrifuged at 4 ℃ for 15 min, and plasma samples were collected and stored at − 80 °C. Serum random blood glucose and lipid profiles such as Total cholesterol (TC), total triglycerides (TG), High-density lipoproteins (HDL) and Low-density lipoproteins (LDL) were measured with an Abbott Architect c16000 instrument (The First Affiliated Hospital of Shantou University Medical College).
Chen L., Wang X.J., Chen J.X., Yang J.C., Ling L.i.n., Cai X.B, & Chen Y.S. (2023). Caffeine ameliorates the metabolic syndrome in diet-induced obese mice through regulating the gut microbiota and serum metabolism. Diabetology & Metabolic Syndrome, 15, 37.
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Publication 2023
BLOOD Blood Glucose Chelating Agents Cholesterol Edetic Acid Eye High Density Lipoproteins Lipids Low-Density Lipoproteins Mice, House Pentobarbital Sodium Plasma Serum Triglycerides
5
Metabolic Markers in Dialysis Patients
Blood samples were collected after overnight fasting while continuing PD treatment, and relevant indicators were determined using standardized equipment and procedures, including serum albumin, AST, ALT, triglyceride, total cholesterol, low-density lipoprotein, high-density lipoprotein, phosphorus, calcium, sodium, potassium, parathyroid hormone, urea nitrogen, creatinine, uric acid, glycosylated hemoglobin, fasting blood glucose, high-sensitivity C-reactive protein (hsCRP), and hemoglobin. To rule out cognitive decline secondary to nutritional or endocrine changes, thyroid hormone levels in the blood were measured by standard laboratory methods. Dialysis adequacy was defined based on total Kt/V and creatinine clearance (Ccr).
Zhu X., Jing R., Li X., Zhang W., Tang Y, & Liu T. (2023). Left ventricular hypertrophy, carotid atherosclerosis, and cognitive impairment in peritoneal dialysis patients. BMC Cardiovascular Disorders, 23, 127.
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Publication 2023
BLOOD Blood Glucose Calcium, Dietary Cholesterol C Reactive Protein Creatinine Dialysis Disorders, Cognitive Hemoglobin Hemoglobin, Glycosylated High Density Lipoproteins Low-Density Lipoproteins Nitrogen Parathyroid Hormone Phosphorus Potassium Serum Albumin Sodium System, Endocrine Thyroid Hormones Triglycerides Urea Uric Acid

Top products related to «High Density Lipoproteins»

1
Cobas 8000 by Roche
Sourced in Switzerland, Germany, United States, France, Japan, Italy, China, United Kingdom, Sweden
The Cobas 8000 is a modular, automated in-vitro diagnostic system designed for high-throughput clinical chemistry and immunochemistry testing. It is used to perform a wide range of laboratory tests, including those for chemistry, immunoassay, and electrolyte analysis. The Cobas 8000 is capable of processing a large volume of samples efficiently and accurately.
2
Elisa kit by R&D Systems
Sourced in United States, United Kingdom, Germany, China, France, Japan, Canada, Netherlands, Mongolia, Switzerland, Australia
ELISA kits are laboratory tools used to detect and quantify specific proteins or other molecules in a sample. The kits utilize enzyme-linked immunosorbent assay (ELISA) technology to identify the target analyte. ELISA kits provide a standardized and reliable method for sample analysis.
3
Au5800 by Beckman Coulter
Sourced in United States, Japan, Germany, United Kingdom, China, Italy, Canada
The AU5800 is a chemistry analyzer designed for high-throughput clinical laboratory testing. It features advanced optics and automation to provide reliable and efficient sample processing. The core function of the AU5800 is to perform a variety of clinical chemistry tests, including immunoassays, on patient samples.
4
Cobas 6000 by Roche
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The Cobas 6000 is an automated clinical chemistry and immunoassay analyzer system designed for high-volume laboratory testing. It combines the features of two separate instruments, the Cobas c 501 module for clinical chemistry and the Cobas e 601 module for immunoassays, into a single integrated platform. The Cobas 6000 system is capable of performing a wide range of diagnostic tests, including biochemical, immunological, and specialty assays.
5
Au480 by Beckman Coulter
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The AU480 is an automated chemistry analyzer from Beckman Coulter designed for routine clinical chemistry testing. It utilizes spectrophotometric technology to perform a variety of assays on biological samples.
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Cobas integra 400 plus by Roche
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The Cobas Integra 400 Plus is a clinical chemistry analyzer designed for in-vitro diagnostics. It is capable of performing a variety of tests on biological samples to aid in the diagnosis and monitoring of medical conditions. The core function of the Cobas Integra 400 Plus is to analyze samples and provide quantitative results for clinical parameters.
7
Dimension rxl by Diamond Diagnostics
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The Dimension RXL is a clinical chemistry analyzer designed for routine and specialized diagnostic testing. It provides automated processing and analysis of a wide range of clinical chemistry assays. The Dimension RXL is capable of performing various analytical tests, including but not limited to, measurements of enzymes, electrolytes, and metabolites. This system is intended to assist healthcare professionals in the diagnosis and monitoring of various medical conditions.
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The Cobas Integra 400 is a versatile, automated clinical chemistry analyzer designed for use in medical laboratories. It is capable of performing a wide range of clinical chemistry tests, including assays for enzymes, substrates, and electrolytes. The Cobas Integra 400 is equipped with advanced technology to ensure accurate and reliable results, and it is designed to streamline laboratory workflow and improve efficiency.
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Ab65390 is a protein that functions as an antibody. It is a laboratory product used for research purposes.

More about "High Density Lipoproteins"

High-Density Lipoproteins (HDLs) are a class of lipid-transporting particles found in the bloodstream that play a crucial role in cardiovascular health.
Also known as 'good cholesterol,' HDLs are responsible for removing excess cholesterol from the body and transporting it to the liver for elimination.
This process, called reverse cholesterol transport, helps maintain a healthy balance of cholesterol levels and reduces the risk of atherosclerosis and other heart-related diseases.
HDLs are composed of various lipids, including cholesterol, triglycerides, and phospholipids, as well as specialized proteins called apolipoproteins.
The measurement and analysis of HDL levels are important in clinical practice and medical research.
Several laboratory instruments and techniques are commonly used to assess HDL levels, including the Cobas 8000 analyzer, ELISA kits, the AU5800 analyzer, the Cobas 6000 analyzer, the AU480 analyzer, the Cobas Integra 400 Plus, the Dimension RXL, the AU680 analyzer, and the Cobas Integra 400.
These instruments and methods provide accurate and reliable results, allowing healthcare professionals and researchers to monitor HDL levels and make informed decisions about patient care and HDL-related studies.
Understanding the role of HDLs in lipid metabolism and cardiovascular health is crucial for developing new treatments, improving prevention strategies, and advancing medical knowledge.
By utilizing the insights gained from the MeSH term description and the metadescription, researchers and clinicians can optimize their HDL research and drive breakthroughs in this important area of study.