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Cholesterol

Cholesterol is a lipid molecule essential for various physiological processes in the body.
It serves as a structural component of cell membranes, facilitates the production of steroid hormones, and plays a role in bile acid synthesis.
Cholesterol can be derived from dietary sources or produced by the liver.
Imbalances in cholesterol levels, such as high levels of low-density lipoprotein (LDL) cholesterol or low levels of high-density lipoprotein (HDL) cholesterol, are associated with an increased risk of cardiovascular disease.
Effective management of cholestrol levels through lifestyle modifications, medication, and other interventions is crucial for maintaining overall health and reducing the burden of heart-related conditions.

Most cited protocols related to «Cholesterol»

Genetic Variants Prioritization for Lipid Traits

We genotyped 196,710 genetic variants prioritized on the basis of prior GWAS for cardiovascular and metabolic phenotypes using the Illumina iSelect Metabochip⁸ genotyping array. To design the Metabochip, we used our previous GWAS of ~100,000 individuals^{4 (link)} to prioritize 5,023 SNPs for HDL cholesterol, 5,055 for LDL cholesterol, 5,056 for triglycerides, and 938 for total cholesterol. These independent SNPs represent most loci with P < .005 in our original GWAS for HDL cholesterol, LDL cholesterol and triglycerides and with P < .0005 for total cholesterol. An additional 28,923 SNPs were selected for fine-mapping of 65 previously identified lipid loci. The Metabochip also included 50,459 SNPs prioritized based on GWAS of non-lipid traits and 93,308 SNPs selected for fine-mapping of loci associated with non-lipid traits (5 of these loci were associated with blood lipids by the analyses described here).

Willer C.J., Schmidt E.M., Sengupta S., Peloso G.M., Gustafsson S., Kanoni S., Ganna A., Chen J., Buchkovich M.L., Mora S., Beckmann J.S., Bragg-Gresham J.L., Chang H.Y., Demirkan A., Den Hertog H.M., Do R., Donnelly L.A., Ehret G.B., Esko T., Feitosa M.F., Ferreira T., Fischer K., Fontanillas P., Fraser R.M., Freitag D.F., Gurdasani D., Heikkilä K., Hyppönen E., Isaacs A., Jackson A.U., Johansson Å., Johnson T., Kaakinen M., Kettunen J., Kleber M.E., Li X., Luan J., Lyytikäinen L.P., Magnusson P.K., Mangino M., Mihailov E., Montasser M.E., Müller-Nurasyid M., Nolte I.M., O’Connell J.R., Palmer C.D., Perola M., Petersen A.K., Sanna S., Saxena R., Service S.K., Shah S., Shungin D., Sidore C., Song C., Strawbridge R.J., Surakka I., Tanaka T., Teslovich T.M., Thorleifsson G., Van den Herik E.G., Voight B.F., Volcik K.A., Waite L.L., Wong A., Wu Y., Zhang W., Absher D., Asiki G., Barroso I., Been L.F., Bolton J.L., Bonnycastle L.L., Brambilla P., Burnett M.S., Cesana G., Dimitriou M., Doney A.S., Döring A., Elliott P., Epstein S.E., Ingi Eyjolfsson G., Gigante B., Goodarzi M.O., Grallert H., Gravito M.L., Groves C.J., Hallmans G., Hartikainen A.L., Hayward C., Hernandez D., Hicks A.A., Holm H., Hung Y.J., Illig T., Jones M.R., Kaleebu P., Kastelein J.J., Khaw K.T., Kim E., Klopp N., Komulainen P., Kumari M., Langenberg C., Lehtimäki T., Lin S.Y., Lindström J., Loos R.J., Mach F., McArdle W.L., Meisinger C., Mitchell B.D., Müller G., Nagaraja R., Narisu N., Nieminen T.V., Nsubuga R.N., Olafsson I., Ong K.K., Palotie A., Papamarkou T., Pomilla C., Pouta A., Rader D.J., Reilly M.P., Ridker P.M., Rivadeneira F., Rudan I., Ruokonen A., Samani N., Scharnagl H., Seeley J., Silander K., Stančáková A., Stirrups K., Swift A.J., Tiret L., Uitterlinden A.G., van Pelt L.J., Vedantam S., Wainwright N., Wijmenga C., Wild S.H., Willemsen G., Wilsgaard T., Wilson J.F., Young E.H., Zhao J.H., Adair L.S., Arveiler D., Assimes T.L., Bandinelli S., Bennett F., Bochud M., Boehm B.O., Boomsma D.I., Borecki I.B., Bornstein S.R., Bovet P., Burnier M., Campbell H., Chakravarti A., Chambers J.C., Chen Y.D., Collins F.S., Cooper R.S., Danesh J., Dedoussis G., de Faire U., Feranil A.B., Ferrières J., Ferrucci L., Freimer N.B., Gieger C., Groop L.C., Gudnason V., Gyllensten U., Hamsten A., Harris T.B., Hingorani A., Hirschhorn J.N., Hofman A., Hovingh G.K., Hsiung C.A., Humphries S.E., Hunt S.C., Hveem K., Iribarren C., Järvelin M.R., Jula A., Kähönen M., Kaprio J., Kesäniemi A., Kivimaki M., Kooner J.S., Koudstaal P.J., Krauss R.M., Kuh D., Kuusisto J., Kyvik K.O., Laakso M., Lakka T.A., Lind L., Lindgren C.M., Martin N.G., März W., McCarthy M.I., McKenzie C.A., Meneton P., Metspalu A., Moilanen L., Morris A.D., Munroe P.B., Njølstad I., Pedersen N.L., Power C., Pramstaller P.P., Price J.F., Psaty B.M., Quertermous T., Rauramaa R., Saleheen D., Salomaa V., Sanghera D.K., Saramies J., Schwarz P.E., Sheu W.H., Shuldiner A.R., Siegbahn A., Spector T.D., Stefansson K., Strachan D.P., Tayo B.O., Tremoli E., Tuomilehto J., Uusitupa M., van Duijn C.M., Vollenweider P., Wallentin L., Wareham N.J., Whitfield J.B., Wolffenbuttel B.H., Ordovas J.M., Boerwinkle E., Palmer C.N., Thorsteinsdottir U., Chasman D.I., Rotter J.I., Franks P.W., Ripatti S., Cupples L.A., Sandhu M.S., Rich S.S., Boehnke M., Deloukas P., Kathiresan S., Mohlke K.L., Ingelsson E, & Abecasis G.R. (2013). Discovery and Refinement of Loci Associated with Lipid Levels. Nature genetics, 45(11), 1274-1283.

Publication 2013

BLOOD Cardiovascular System Cholesterol Cholesterol, beta-Lipoprotein Genetic Diversity Genome-Wide Association Study High Density Lipoprotein Cholesterol Lipids Phenotype Single Nucleotide Polymorphism Triglycerides

Comprehensive Analysis of T2D Genetic Associations

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 Investigators^{5 (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 Consortium^{15 (link)}. To eliminate potential bias in BMI allelic effect estimates at T2D susceptibility loci^{54 (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 Consortium^{16 (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 Consortium^{35 (link)}.

Morris A.P., Voight B.F., Teslovich T.M., Ferreira T., Segrè A.V., Steinthorsdottir V., Strawbridge R.J., Khan H., Grallert H., Mahajan A., Prokopenko I., Kang H.M., Dina C., Esko T., Fraser R.M., Kanoni S., Kumar A., Lagou V., Langenberg C., Luan J., Lindgren C.M., Müller-Nurasyid M., Pechlivanis S., Rayner N.W., Scott L.J., Wiltshire S., Yengo L., Kinnunen L., Rossin E.J., Raychaudhuri S., Johnson A.D., Dimas A.S., Loos R.J., Vedantam S., Chen H., Florez J.C., Fox C., Liu C.T., Rybin D., Couper D.J., Kao W.H., Li M., Cornelis M.C., Kraft P., Sun Q., van Dam R.M., Stringham H.M., Chines P.S., Fischer K., Fontanillas P., Holmen O.L., Hunt S.E., Jackson A.U., Kong A., Lawrence R., Meyer J., Perry J.R., Platou C.G., Potter S., Rehnberg E., Robertson N., Sivapalaratnam S., Stančáková A., Stirrups K., Thorleifsson G., Tikkanen E., Wood A.R., Almgren P., Atalay M., Benediktsson R., Bonnycastle L.L., Burtt N., Carey J., Charpentier G., Crenshaw A.T., Doney A.S., Dorkhan M., Edkins S., Emilsson V., Eury E., Forsen T., Gertow K., Gigante B., Grant G.B., Groves C.J., Guiducci C., Herder C., Hreidarsson A.B., Hui J., James A., Jonsson A., Rathmann W., Klopp N., Kravic J., Krjutškov K., Langford C., Leander K., Lindholm E., Lobbens S., Männistö S., Mirza G., Mühleisen T.W., Musk B., Parkin M., Rallidis L., Saramies J., Sennblad B., Shah S., Sigurðsson G., Silveira A., Steinbach G., Thorand B., Trakalo J., Veglia F., Wennauer R., Winckler W., Zabaneh D., Campbell H., van Duijn C., Uitterlinden89- A.G., Hofman A., Sijbrands E., Abecasis G.R., Owen K.R., Zeggini E., Trip M.D., Forouhi N.G., Syvänen A.C., Eriksson J.G., Peltonen L., Nöthen M.M., Balkau B., Palmer C.N., Lyssenko V., Tuomi T., Isomaa B., Hunter D.J., Qi L., Shuldiner A.R., Roden M., Barroso I., Wilsgaard T., Beilby J., Hovingh K., Price J.F., Wilson J.F., Rauramaa R., Lakka T.A., Lind L., Dedoussis G., Njølstad I., Pedersen N.L., Khaw K.T., Wareham N.J., Keinanen-Kiukaanniemi S.M., Saaristo T.E., Korpi-Hyövälti E., Saltevo J., Laakso M., Kuusisto J., Metspalu A., Collins F.S., Mohlke K.L., Bergman R.N., Tuomilehto J., Boehm B.O., Gieger C., Hveem K., Cauchi S., Froguel P., Baldassarre D., Tremoli E., Humphries S.E., Saleheen D., Danesh J., Ingelsson E., Ripatti S., Salomaa V., Erbel R., Jöckel K.H., Moebus S., Peters A., Illig T., de Faire U., Hamsten A., Morris A.D., Donnelly P.J., Frayling T.M., Hattersley A.T., Boerwinkle E., Melander O., Kathiresan S., Nilsson P.M., Deloukas P., Thorsteinsdottir U., Groop L.C., Stefansson K., Hu F., Pankow J.S., Dupuis J., Meigs J.B., Altshuler D., Boehnke M, & McCarthy M.I. (2012). Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nature genetics, 44(9), 981-990.

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

Comprehensive Evaluation of Fatty Liver Disease

Besides a clinical and laboratory evaluation, each subject underwent a liver ultrasonography, an anthropometric assessment and a 7-day diary of food intake (7DD) [1 (link)]. HBsAg and anti-HCV antibodies were assessed and subjects with anti-HCV antibodies underwent an HCV-RNA assessment to confirm HCV infection [1 (link),14 (link)]. ALT, aspartate transaminase (AST), GGT, glucose, triglycerides and cholesterol were measured by standard laboratory methods after 8-hr fasting. Insulin was measured by radio-immuno-assay (ADVIA Insulin Ready Pack 100, Bayer Diagnostics, Milan, Italy), with intra- and inter-assay coefficients of variation < 5%. FL was diagnosed by the same operator at ultrasonography [6 (link)]. Weight, stature, circumferences (waist and hip) and skinfolds (triceps, biceps, subscapular and suprailiac) were measured by two trained dietitians who had been standardized before and during the study according to standard procedures [15 ]. Body mass index (BMI) was calculated as weight (kg)/stature (m)²and the sum of 4 skinfolds by summing triceps, biceps, subscapular and suprailiac skinfolds [16 (link),17 (link)]. The 7DD was administered to the subjects by two trained dietitians, who discussed it with the subject when she/he returned it one week later [18 (link)]. To avoid the confounding effect of seasonality on food intake, the 7DD diary was administered to a similar number of patients with and without SLD each month [19 ]. Mean daily ethanol intake was calculated as the mean value of ethanol intake as assessed by the 7DD [20 ]. The study protocol was approved and supervised by the Scientific Committee of the Fondo per lo Studio delle Malattie del Fegato (Trieste, Italy), and all subjects gave their written informed consent to participate.

Bedogni G., Bellentani S., Miglioli L., Masutti F., Passalacqua M., Castiglione A, & Tiribelli C. (2006). The Fatty Liver Index: a simple and accurate predictor of hepatic steatosis in the general population. BMC Gastroenterology, 6, 33.

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Publication 2006

Aspartate Transaminase Biological Assay Body Height Cholesterol Dietitian Eating Ethanol Glucose Hepatitis B Surface Antigens Hepatitis C Hepatitis C Antibodies Index, Body Mass Insulin Liver Patients Radioimmunoassay Triglycerides Ultrasonography

Predictive Model for Fatty Liver

Continuous variables are given as medians and interquartile ranges (IQR) because of skewed distributions. Comparisons of continuous variables between subjects with and without FL were performed with the Mann-Whitney test and those of nominal variables with the Fisher's exact test. To identify candidate predictors of FL, we performed a stepwise logistic regression analysis on 1000 bootstrap samples of 496 subjects (probability to enter = 0.05 and probability to remove = 0.1) [21 (link)]. All variables besides gender were evaluated as continuous predictors. Linearity of logits was ascertained using the Box-Tidwell procedure [22 (link)]. To obtain a linear logit, we transformed age using the coefficient suggested by the Box-Tidwell procedure [(age/10) ^4.9255] and ALT, AST, GGT, insulin and triglycerides using natural logarithms (log_e). The logits of the other predictors (BMI, waist circumference, glucose, cholesterol, ethanol and the sum of 4 skinfolds) were linear.
Candidate predictors identified at bootstrap analysis were evaluated using three stepwise logistic models before obtaining a final prediction model (probability to enter = 0.01 and probability to remove = 0.02; these more stringent levels were used to protect against type I errors). The goodness of fit of the models was evaluated using the Hosmer-Lemeshow statistic and their accuracy was assessed by calculating the non-parametric area (AUC) under the receiver-operating curve (ROC) with 95% confidence intervals (95%CI) [23 ,24 ]. The standard errors of the regression coefficients of the final model were calculated using 1000 bootstrap samples of 496 subjects. The probabilities obtained from the final model were multiplied by 100 to obtain the fatty liver index (FLI). The sensitivity (SN), specificity (SP), positive likelihood ratio (LR+) and negative likelihood ratio (LR-) of 10-value intervals of FLI were calculated [23 ]. Statistical analysis was performed using STATA 9.2 (StataCorp, College Station, Texas, USA).

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Publication 2006

Cholesterol Ethanol Fatty Liver Gender Glucose Hypersensitivity Insulin Triglycerides Waist Circumference

Design and Development of Metabochip

The Metabochip was designed by representatives of the Body Fat Percentage [9] (link), CARDIoGRAM (coronary artery disease and myocardial infarction) [10] (link), DIAGRAM (type 2 diabetes) [11] (link), GIANT (anthropometric traits) [3] (link), [12] (link), [13] (link), Global Lipids Genetics (lipids) [4] (link), HaemGen (hematological measures) [14] (link), ICBP (blood pressure) [15] (link), MAGIC (glucose and insulin) [16] (link)–[18] (link), and QT-IGC (QT interval) [19] (link), [20] (link) GWAS meta-analysis consortia. The array is comprised of SNPs selected across two tiers of traits (Table 1). Tier 1 is comprised of eleven traits deemed to be of primary interest: type 2 diabetes (T2D), fasting glucose, coronary artery disease and myocardial infarction (CAD/MI), low density lipoprotein (LDL) cholesterol, high density lipoprotein (HDL) cholesterol, triglycerides, body mass index (BMI), systolic and diastolic blood pressure, QT interval, and waist-to-hip ratio adjusted for BMI (WHR). Tier 2 is comprised of twelve traits of secondary interest: fasting insulin, 2-hour glucose, glycated hemoglobin (HbA1c), T2D age of diagnosis, early onset T2D (diagnosis age<45 years), waist circumference adjusted for BMI, height, body fat percentage, total cholesterol, platelet count, mean platelet volume, and white blood cell count.
We included three design classes of SNPs on the Metabochip (Table 2):
In total, 217,695 SNPs were chosen for the array (Table 2). 20,970 SNPs (9.6%) failed during the assay manufacturing process, resulting in 196,725 SNPs available for genotyping. A summary file annotating each Metabochip SNP with ascertainment criteria, SNP assay, a list of unintended duplicate SNPs (Supplementary Table S4), and reference strand orientation for alleles is provided at http://www.sph.umich.edu/csg/kang/MetaboChip/.

Voight B.F., Kang H.M., Ding J., Palmer C.D., Sidore C., Chines P.S., Burtt N.P., Fuchsberger C., Li Y., Erdmann J., Frayling T.M., Heid I.M., Jackson A.U., Johnson T., Kilpeläinen T.O., Lindgren C.M., Morris A.P., Prokopenko I., Randall J.C., Saxena R., Soranzo N., Speliotes E.K., Teslovich T.M., Wheeler E., Maguire J., Parkin M., Potter S., Rayner N.W., Robertson N., Stirrups K., Winckler W., Sanna S., Mulas A., Nagaraja R., Cucca F., Barroso I., Deloukas P., Loos R.J., Kathiresan S., Munroe P.B., Newton-Cheh C., Pfeufer A., Samani N.J., Schunkert H., Hirschhorn J.N., Altshuler D., McCarthy M.I., Abecasis G.R, & Boehnke M. (2012). The Metabochip, a Custom Genotyping Array for Genetic Studies of Metabolic, Cardiovascular, and Anthropometric Traits. PLoS Genetics, 8(8), e1002793.

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Publication 2012

ADCAD1 Alleles Biological Assay Blood Pressure Body Fat Cholesterol Diabetes Mellitus, Non-Insulin-Dependent Diagnosis Genome-Wide Association Study Gigantism Glucose Hemoglobin, Glycosylated High Density Lipoprotein Cholesterol Index, Body Mass Insulin Leukocyte Count Lipids Low-Density Lipoproteins Platelet Counts, Blood Pressure, Diastolic Single Nucleotide Polymorphism Systole Triglycerides Volumes, Mean Platelet Waist-Hip Ratio Waist Circumference

Most recents protocols related to «Cholesterol»

Immunogenicity of Betacoronavirus mRNA Vaccines

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Example 20

The instant study is designed to test the immunogenicity in rabbits of candidate betacoronavirus (e.g., MERS-CoV, SARS-CoV, HCoV-OC43, HCoV-229E, HCoV-NL63, HCoV-NL, HCoV-NH or HCoV-HKU1 or a combination thereof) vaccines comprising a mRNA polynucleotide encoding the spike (S) protein, the S1 subunit (S1) of the spike protein, or the S2 subunit (S2) of the spike protein obtained from a betacoronavirus (e.g., MERS-CoV, SARS-CoV, HCoV-OC43, HCoV-229E, HCoV-NL63, HCoV-NL, HCoV-NH or HCoV-HKU1).

Rabbits are vaccinated on week 0 and 3 via intravenous (IV), intramuscular (IM), or intradermal (ID) routes. One group remains unvaccinated and one is administered inactivated betacoronavirus. Serum is collected from each rabbit on weeks 1, 3 (pre-dose) and 5. Individual bleeds are tested for anti-S, anti-S1 or anti-S2 activity via a virus neutralization assay from all three time points, and pooled samples from week 5 only are tested by Western blot using inactivated betacoronavirus (e.g., inactivated MERS-CoV, SARS-CoV, HCoV-OC43, HCoV-229E, HCoV-NL63, HCoV-NL, HCoV-NH or HCoV-HKU1).

In experiments where a lipid nanoparticle (LNP) formulation is used, the formulation may include a cationic lipid, non-cationic lipid, PEG lipid and structural lipid in the ratios 50:10:1.5:38.5. The cationic lipid is DLin-KC2-DMA (50 mol %) or DLin-MC3-DMA (50 mol %), the non-cationic lipid is DSPC (10 mol %), the PEG lipid is PEG-DOMG (1.5 mol %) and the structural lipid is cholesterol (38.5 mol %), for example.

US11872278B2. Combination HMPV/RSV RNA vaccines (2024-01-16). ModernaTX, Inc. [US]. Inventors: Giuseppe Ciaramella [US], Sunny Himansu [US].

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Patent 2024

Antigens Betacoronavirus Biological Assay Cations Cholesterol Coronavirus 229E, Human Coronavirus OC43, Human Hemorrhage Human coronavirus HKU1 Lipid Nanoparticles Lipids Middle East Respiratory Syndrome Coronavirus M protein, multiple myeloma NL63, Human Coronavirus Oryctolagus cuniculus Polynucleotides Protein Subunits Rabbits RNA, Messenger Serum Severe acute respiratory syndrome-related coronavirus spike protein, SARS-CoV-2 Vaccines Virus Physiological Phenomena

Optimizing Dendrimer Nanoparticles for siRNA Delivery

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Example 8

To evaluate which lipid composition within the dendrimer nanoparticles lead to improved siRNA delivery, the identity and concentration of different phospholipids and PEG-lipids were varied. Three different cell lines (HeLa-Luc, A549-Luc, and MDA-MB231-Luc) were used. The cells were present at 10K cells per well and a 24 hour incubation. The readout was determined 24 hours post transfection. In the nanoparticles, DSPC and DOPE were used as phospholipids and PEG-DSPE, PEG-DMG, and PEG-DHD were used as PEG-lipids. The compositions contain a lipid or dendrimer:cholesterol:phospholipid:PEG-lipid mole ratio of 50:38:10:2. The mole ratio of lipid/dendrimer to siRNA was 100:1 with 100 ng dose being used. The RiboGreen, Cell-titer Fluor, and OneGlo assays were used to determine the effectiveness of these compositions. Results show the relative luciferase activity in HeLa-Luc cells (FIG. 17A), A549-Luc (FIG. 17B), and MDA-MB231-Luc (FIG. 17C). The six formulations used in the studies include: dendrimer (lipid)+cholesterol+DSPC+PEG-DSPE (formulation 1), dendrimer (lipid)+cholesterol+DOPE+PEG-DSPE (formulation 2), dendrimer (lipid)+cholesterol+DSPC+PEG-DMG (formulation 3), dendrimer (lipid)+cholesterol+DOPE+PEG-DMG (formulation 4), dendrimer (lipid)+cholesterol+DSPC+PEG-DSPE (formulation 5), and dendrimer (lipid)+cholesterol+DOPE+PEG-DHD (formulation 6).

Further experiments were run to determine which phospholipids showed the increased delivery of siRNA molecules. A HeLa-Luc cell line was used with 10K cells per well, 24 hour incubation, and readout 24 hours post transfections. The compositions contained either DOPE or DOPC as the phospholipid with PEG-DHD as the PEG-lipid. The ratio of lipid (or dendrimer):cholesterol:phospholipid:PEG-lipid was 50:38:10:2 in a mole ratio with the mole ratio of dendrimer (or lipid) to siRNA of 200:1. These compositions was tested at a 50 ng dose using the Cell-titer Fluor and OneGlo assays. These results are shown in FIGS. 18A & 18B.

US11858884B2. Compositions and methods for modulating gene or gene product in cells (2024-01-02). The Board of Regents of The University of Texas System [US]. Inventors: Daniel J. Siegwart [US], Kejin Zhou [US].

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Patent 2024

1,2-oleoylphosphatidylcholine Biological Assay Cell Lines Cells Cholesterol Dendrimers Figs HeLa Cells Lipid Nanoparticles Lipids Luciferases Nevus Obstetric Delivery Phospholipids polyethylene glycol-distearoylphosphatidylethanolamine RNA, Small Interfering Transfection

Evaluation of mRNA-based hMPV Vaccines

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Example 13

The instant study is designed to test the efficacy in cotton rats of candidate hMPV vaccines against a lethal challenge using an hMPV vaccine comprising mRNA encoding Fusion (F) glycoprotein, major surface glycoprotein G, or a combination of both antigens obtained from hMPV. Cotton rats are challenged with a lethal dose of the hMPV.

Animals are immunized intravenously (IV), intramuscularly (IM), or intradermally (ID) at week 0 and week 3 with candidate hMPV vaccines with and without adjuvant. Candidate vaccines are chemically modified or unmodified. The animals are then challenged with a lethal dose of hMPV on week 7 via IV, IM or ID. Endpoint is day 13 post infection, death or euthanasia. Animals displaying severe illness as determined by >30% weight loss, extreme lethargy or paralysis are euthanized. Body temperature and weight are assessed and recorded daily.

US11872278B2. Combination HMPV/RSV RNA vaccines (2024-01-16). ModernaTX, Inc. [US]. Inventors: Giuseppe Ciaramella [US], Sunny Himansu [US].

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Patent 2024

Animals Antigens Body Temperature Cations Cholesterol Euthanasia Glycoproteins Human Metapneumovirus Infection Lethargy Lipid Nanoparticles Lipids Membrane Glycoproteins Pharmaceutical Adjuvants Rats, Cotton RNA, Messenger Rodent Vaccines

Cholesterol Metabolism in STAT1 Mice

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Example 9

CH25H was originally known to regulate cholesterol metabolism. However, when we compared the body weight, lipid deposition in liver and key enzymes involved in lipid metabolism, there was no significant change between WT and STAT1−/− mice (FIGS. 19A-19F). As shown in FIG. 19A, there was no significant difference of the body weight between APP/PS1 and APP/PS1/STAT1−/− mice. As shown in FIG. 19B, there was no significant difference in lipid deposition in liver cells between APP/PS1 and APP/PS1/STAT1−/− mice. Further, as shown in FIGS. 19C-19F, there were no significant difference of the expression of key enzymes including LPL, ABCA1, APOE, HMGCR between APP/PS1 and APP/PS1/STAT1−/− mice.

US11873321B2. Compositions and methods for treating alzheimer's disease (2024-01-16). Generos Biopharma Ltd. [CN]. Inventors: Xin-Yuan Fu [US], Yi Zhou [SG].

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Patent 2024

ABCA1 protein, human ApoE protein, human Body Weight Cholesterol Enzymes Figs Hepatocyte HMGCR protein, human Lipid Metabolism Lipids Liver Metabolism Mice, Laboratory STAT1 protein, human

Elafibranor Improves Bile Acid Levels

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Example 2

The effect of elafibranor was further tested in relation to parameters more directly related to cholestatic diseases than ALP and γGT levels. Thus, it was explored whether treated subjects show a decrease in plasma total bile acids. The measurement of serum 7α-hydroxy-4-cholesten-3-one (7α-HCO, or 7αC4, or C4) is a method for monitoring the enzymatic activity of hepatic cholesterol 7α-hydroxylase, the rate-limiting and major regulatory enzyme in the synthesis of bile acids. Thus a decrease in C4 level reflects a decrease in total bile acids in the patient.

In NASH patients with high ALP level at baseline, elafibranor was orally administered at a dose of either 80 mg or 120 mg per day over 52 weeks.

A total of 62 NASH patients with high ALP levels were randomized: 23 in the placebo group, 16 in the elafibranor 80 mg group and 23 in the elafibranor 120 mg group.

Bile acids precursor levels were improved in the patients having received both elafibranor doses, in a dose-dependent manner.

US11857523B2. Methods of treatment of cholestatic diseases (2024-01-02). Genfit [FR]. Inventors: Remy Hanf [FR].

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Patent 2024

Anabolism Bile Acids Cholestasis Cholesterol elafibranor enzyme activity Enzymes Mixed Function Oxygenases Nonalcoholic Steatohepatitis Patients Placebos Plasma Serum

Top products related to «Cholesterol»

Cholesterol by Merck Group

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Cholesterol is a lab equipment product that measures the concentration of cholesterol in a given sample. It provides quantitative analysis of total cholesterol, HDL cholesterol, and LDL cholesterol levels.

Amplex red cholesterol assay kit by Thermo Fisher Scientific

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The Amplex Red Cholesterol Assay Kit is a fluorometric assay used to measure total cholesterol levels in biological samples. The kit utilizes the Amplex Red reagent, which produces a fluorescent product upon reaction with hydrogen peroxide generated from the cholesterol oxidase-catalyzed oxidation of cholesterol.

Fetal bovine serum (fbs) by Thermo Fisher Scientific

Sourced in United States, China, United Kingdom, Germany, Australia, Japan, Canada, Italy, France, Switzerland, New Zealand, Brazil, Belgium, India, Spain, Israel, Austria, Poland, Ireland, Sweden, Macao, Netherlands, Denmark, Cameroon, Singapore, Portugal, Argentina, Holy See (Vatican City State), Morocco, Uruguay, Mexico, Thailand, Sao Tome and Principe, Hungary, Panama, Hong Kong, Norway, United Arab Emirates, Czechia, Russian Federation, Chile, Moldova, Republic of, Gabon, Palestine, State of, Saudi Arabia, Senegal

Fetal Bovine Serum (FBS) is a cell culture supplement derived from the blood of bovine fetuses. FBS provides a source of proteins, growth factors, and other components that support the growth and maintenance of various cell types in in vitro cell culture applications.

Chloroform by Merck Group

Sourced in United States, Germany, United Kingdom, India, Italy, Spain, France, Canada, Switzerland, China, Australia, Brazil, Poland, Ireland, Sao Tome and Principe, Chile, Japan, Belgium, Portugal, Netherlands, Macao, Singapore, Sweden, Czechia, Cameroon, Austria, Pakistan, Indonesia, Israel, Malaysia, Norway, Mexico, Hungary, New Zealand, Argentina

Chloroform is a colorless, volatile liquid with a characteristic sweet odor. It is a commonly used solvent in a variety of laboratory applications, including extraction, purification, and sample preparation processes. Chloroform has a high density and is immiscible with water, making it a useful solvent for a range of organic compounds.

Cholesterol by Avanti Polar Lipids

Sourced in United States

Cholesterol is a lipid compound found in animal cells. It is a core component of cell membranes and is essential for various physiological processes. Avanti Polar Lipids offers high-purity cholesterol for use in research and laboratory applications.

Methanol by Merck Group

Sourced in Germany, United States, Italy, India, United Kingdom, China, France, Poland, Spain, Switzerland, Australia, Canada, Sao Tome and Principe, Brazil, Ireland, Japan, Belgium, Portugal, Singapore, Macao, Malaysia, Czechia, Mexico, Indonesia, Chile, Denmark, Sweden, Bulgaria, Netherlands, Finland, Hungary, Austria, Israel, Norway, Egypt, Argentina, Greece, Kenya, Thailand, Pakistan

Methanol is a clear, colorless, and flammable liquid that is widely used in various industrial and laboratory applications. It serves as a solvent, fuel, and chemical intermediate. Methanol has a simple chemical formula of CH3OH and a boiling point of 64.7°C. It is a versatile compound that is widely used in the production of other chemicals, as well as in the fuel industry.

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.

Dimethyl sulfoxide (dmso) by Merck Group

Sourced in United States, Germany, United Kingdom, China, Italy, Sao Tome and Principe, France, Macao, India, Canada, Switzerland, Japan, Australia, Spain, Poland, Belgium, Brazil, Czechia, Portugal, Austria, Denmark, Israel, Sweden, Ireland, Hungary, Mexico, Netherlands, Singapore, Indonesia, Slovakia, Cameroon, Norway, Thailand, Chile, Finland, Malaysia, Latvia, New Zealand, Hong Kong, Pakistan, Uruguay, Bangladesh

DMSO is a versatile organic solvent commonly used in laboratory settings. It has a high boiling point, low viscosity, and the ability to dissolve a wide range of polar and non-polar compounds. DMSO's core function is as a solvent, allowing for the effective dissolution and handling of various chemical substances during research and experimentation.

Streptozotocin (stz) by Merck Group

Sourced in United States, Germany, China, Sao Tome and Principe, United Kingdom, India, Japan, Macao, Canada, France, Italy, Switzerland, Egypt, Poland, Hungary, Denmark, Indonesia, Singapore, Sweden, Belgium, Malaysia, Israel, Spain, Czechia

STZ is a laboratory equipment product manufactured by Merck Group. It is designed for use in scientific research and experiments. The core function of STZ is to serve as a tool for carrying out specific tasks or procedures in a laboratory setting. No further details or interpretation of its intended use are provided.

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.

More about "Cholesterol"

Cholesterol is a critical lipid molecule essential for numerous physiological processes in the human body.
It serves as a structural component of cell membranes, facilitates the production of vital steroid hormones, and plays a pivotal role in bile acid synthesis.
This versatile compound can be derived from dietary sources or produced by the liver through complex metabolic pathways.
Imbalances in cholesterol levels, such as elevated low-density lipoprotein (LDL) cholesterol or reduced high-density lipoprotein (HDL) cholesterol, are strongly associated with an increased risk of cardiovascular disease, a leading cause of morbidity and mortality worldwide.
Effective management of cholestrol levels through lifestyle modifications, targeted medication, and other evidence-based interventions is crucial for maintaining overall health and reducing the burden of heart-related conditions.
Researchers and scientists studying cholesterol homeostasis and its impact on human health can leverage advanced analytical tools like the Amplex Red Cholesterol Assay Kit and the Cobas 8000 analyzer to accurately quantify and analyze cholesterol levels in biological samples.
These advanced techniques, combined with the use of cell culture models and animal studies, can provide invaluable insights into the complex mechanisms underlying cholesterol regulation and its implications for various disease states.
Effective cholesterol research often involves the use of cell culture media, such as Fetal Bovine Serum (FBS), as well as solvents like chloroform and methanol for lipid extraction and purification.
Additionally, the use of DMSO and streptozotocin (STZ) may be relevant in certain experimental setups, such as those involving the induction of diabetes, a condition often accompanied by dysregulated cholesterol metabolism.
By leveraging the power of AI-driven tools like PubCompare.ai, researchers can efficiently locate the most relevant and effective protocols from the scientific literature, preprints, and patent databases, ultimately optimizing their cholesterol research and driving advancements in this critical area of biomedical science.