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EGFR protein, human

EGFR (Epidermal Growth Factor Receptor) is a cell-surface receptor tyrosine kinase that plays a crucial role in cellular processes such as proliferation, differentiation, and survival.
This protein is expressed in a variety of tissues and is often overexpressed or mutated in many types of cancer, making it an important target for cancer therapies.
EGFR protein studies are crucial for understanding its structure, function, and signaling pathways, as well as developing new therapeutic strategies.
Researchers can leverage PubCompare.ai, an AI-driven research platform, to optimize their EGFR protein experiments by exploring protocols from literature, pre-prints, and patents, and utilizing AI-powered comparisons to identify the best methodologies and products.
This can enhance research reproducibility and efficiency, making PubCompare.ai a one-stop solution for EGFR protein studies.

Most cited protocols related to «EGFR protein, human»

1
Chronic Kidney Disease Prevalence Estimation
Cited 150 times
We estimated CKD prevalence using data from 4563 participants from the 1999–2000 and 2001–2002 cycles of the National Health and Nutrition Examination Survey (NHANES) who were 20 years of age or older and had data available on serum creatinine or cystatin C levels (Tables S4 and S5).29 We used recommended NHANES methods for weighting and variance estimation.30 (link)–32 (link) CKD was defined as an eGFR of less than 60 ml per minute per 1.73 m2 with the use of the equations described above or persistent albuminuria (urinary albumin-to-creatinine ratio [with albumin measured in milligrams and creatinine measured in grams] of >30) and the guideline-recommended GFR stages.8 ,29 We assessed the number of participants with CKD or an eGFR lower than key thresholds for clinical decision making (60, 45, and 30 ml per minute per 1.73 m2) as compared with the number of participants according to the current eGFRcr(ASR) equation. Prevalence estimates were applied to the 2019 U.S. Census estimate of 246.6 million adults 20 years of age or older. Additional details are provided in the Methods section in the Supplementary Appendix.
Inker L.A., Eneanya N.D., Coresh J., Tighiouart H., Wang D., Sang Y., Crews D.C., Doria A., Estrella M.M., Froissart M., Grams M.E., Greene T., Grubb A., Gudnason V., Gutiérrez O.M., Kalil R., Karger A.B., Mauer M., Navis G., Nelson R.G., Poggio E.D., Rodby R., Rossing P., Rule A.D., Selvin E., Seegmiller J.C., Shlipak M.G., Torres V.E., Yang W., Ballew S.H., Couture S.J., Powe N.R, & Levey A.S. (2021). New Creatinine- and Cystatin C–Based Equations to Estimate GFR without Race. The New England journal of medicine, 385(19), 1737-1749.
Publication 2021
Adult Albumins Creatinine EGFR protein, human Post-gamma-Globulin Serum Urine
2
Intensive Blood Pressure Reduction Outcomes
Cited 102 times
Definitions of study outcomes are outlined in the Supplementary Appendix. A committee whose members were unaware of the study-group assignments adjudicated the clinical outcomes specified in the protocol. The primary hypothesis was that treatment to reach a systolic blood-pressure target of less than 120 mm Hg, as compared with a target of less than 140 mm Hg, would result in a lower rate of the composite outcome of myocardial infarction, acute coronary syndrome not resulting in myocardial infarction, stroke, acute decompensated heart failure, or death from cardiovascular causes. Secondary outcomes included the individual components of the primary composite outcome, death from any cause, and the composite of the primary outcome or death from any cause.
We also assessed renal outcomes, using a different definition for patients with chronic kidney disease (eGFR <60 ml per minute per 1.73 m2) at baseline and those without it. The renal outcome in participants with chronic kidney disease at baseline was a composite of a decrease in the eGFR of 50% or more (confirmed by a subsequent laboratory test) or the development of ESRD requiring long-term dialysis or kidney transplantation. In participants without chronic kidney disease at baseline, the renal outcome was defined by a decrease in the eGFR of 30% or more to a value of less than 60 ml per minute per 1.73 m2. Incident albuminuria, defined for all study participants by a doubling of the ratio of urinary albumin (in milligrams) to creatinine (in grams) from less than 10 at baseline to greater than 10 during follow-up, was also a prespecified renal outcome.
Prespecified subgroups of interest for all outcomes were defined according to status with respect to cardiovascular disease at baseline (yes vs. no), status with respect to chronic kidney disease at baseline (yes vs. no), sex, race (black vs. non-black), age (<75 vs. ≥75 years), and baseline systolic blood pressure in three levels (≤132 mm Hg, >132 to <145 mm Hg, and ≥145 mm Hg). We also planned a comparison of the effects of systolic blood-pressure targets on incident dementia, changes in cognitive function, and cerebral small-vessel ischemic disease; these results are not presented here.
A Randomized Trial of Intensive versus Standard Blood-Pressure Control. (2015). The New England journal of medicine, 373(22), 2103-2116.
Publication 2015
Acute Coronary Syndrome Albumins Cardiovascular Diseases Cardiovascular System Cerebral Small Vessel Diseases Cerebrovascular Accident Chronic Kidney Diseases Cognition Congestive Heart Failure Creatinine Dementia Dialysis EGFR protein, human Kidney Kidney Failure, Chronic Kidney Transplantation Myocardial Infarction Patients Systolic Pressure Urine
3
Chronic Kidney Disease Consortium Analysis
Cited 94 times
Details of the Chronic Kidney Disease Prognosis Consortium (CKD-PC) were described previously.3 (link)-6 (link) To be included in the consortium, a study had to have at least 1,000 participants (not applied to studies only enrolling CKD patients [CKD cohorts]6 (link)), information at baseline on eGFR and urine albumin levels, and a minimum of 50 events for any of the outcomes of interest. As recommended,1 (link),2 (link) we preferentially selected urine albumin-to-creatinine ratio (ACR) as the measure of albuminuria. However, we also accepted urine albumin excretion and urine protein-to-creatinine ratio (PCR) as well as a qualitative measurement using dipstick.1 (link) This analysis consists of data from 45 cohorts (25 general population cohorts, seven high-risk cohorts with participants selected for cardiovascular or kidney disease risk factors, and 13 CKD cohorts). Data transfer (from collaborating cohorts to the CKD-PC Data Coordinating Center) and analyses for the present study were conducted between March 2011 and March 2012. This study is based on secondary data analysis of pre-existing, de-identified/de-linked dataset, and was approved by the Institutional Review Board at the Johns Hopkins Bloomberg School of Public Health. Information about ethical review of individual studies is available in the publications of the constituent cohorts.21 (link),24 (link),27 (link)-69 (link)
Matsushita K., Mahmoodi B.K., Woodward M., Emberson J.R., Jafar T.H., Jee S.H., Polkinghorne K.R., Shankar A., Smith D.H., Tonelli M., Warnock D.G., Wen C.P., Coresh J., Gansevoort R.T., Hemmelgarn B.R, & Levey A.S. (2012). Comparison of risk prediction using the CKD-EPI equation and the MDRD Study equation for estimated glomerular filtration rate. JAMA : the journal of the American Medical Association, 307(18), 10.1001/jama.2012.3954.
Publication 2012
Albumins Cardiovascular System Creatinine EGFR protein, human Ethical Review Ethics Committees, Research Kidney Diseases Patients Prognosis Proteins Urine
4
Investigating the Impact of eGFR-Associated Genetic Variants on Kidney and Cardiovascular Diseases
Cited 64 times
To test the combined effect of eGFR-associated SNPs on outcomes related
to clinically diagnosed CKD, a GRS-based association analysis was conducted on
the basis of summary GWAS results, as described previously73 (link),74 (link). Genetic association results with diseases were obtained
for 452,264 UK Biobank participants available in the GeneAtlas75 (link) database for glomerular
diseases (ICD-10 codes N00-N08; 2,289 cases); acute renal failure (N17; 4,913
cases); chronic renal failure (N18; 4,905 cases); urolithiasis (N20-N23; 7,053
cases); hypertensive diseases (I10-I15; 84,910 cases); and ischemic heart
diseases (I20-I25; 33,387 cases). Asthma (J45; 28,628 cases) was included as a
negative control. The log(estimated OR) value provided by the GeneAtlas PheWAS
interface was used as the effect size, and its standard error was calculated
from the corresponding effect size and P value. When OR = 1,
the standard error was imputed by the median value of the remaining associations
of the trait. Of the147 eGFR index SNPs from the trans-ancestry GWAS
meta-analysis that were replicated and showed likely relevance to kidney
function, 144 were available in the UK Biobank dataset, and 259 of all 264
replicated trans-ancestry GWAS meta-analysis SNPs were available. The effect of
the GRS association (β) corresponds to the OR for the
disease depending on the relative change in eGFR, for example, OR =
1.10β for a 10% change in eGFR.
Alternatively, exp(β) can be interpreted as the OR for
the disease per unit change in log(eGFR).
Wuttke M., Li Y., Li M., Sieber K.B., Feitosa M.F., Gorski M., Tin A., Wang L., Chu A.Y., Hoppmann A., Kirsten H., Giri A., Chai J.F., Sveinbjornsson G., Tayo B.O., Nutile T., Fuchsberger C., Marten J., Cocca M., Ghasemi S., Xu Y., Horn K., Noce D., van der Most P.J., Sedaghat S., Yu Z., Akiyama M., Afaq S., Ahluwalia T.S., Almgren P., Amin N., Ärnlöv J., Bakker S.J., Bansal N., Baptista D., Bergmann S., Biggs M.L., Biino G., Boehnke M., Boerwinkle E., Boissel M., Bottinger E.P., Boutin T.S., Brenner H., Brumat M., Burkhardt R., Butterworth A.S., Campana E., Campbell A., Campbell H., Canouil M., Carroll R.J., Catamo E., Chambers J.C., Chee M.L., Chee M.L., Chen X., Cheng C.Y., Cheng Y., Christensen K., Cifkova R., Ciullo M., Pina Concas M., Cook J.P., Coresh J., Corre T., Sala C.F., Cusi D., Danesh J., Daw E.W., de Borst M.H., De Grandi A., de Mutsert R., de Vries A.P., Degenhardt F., Delgado G., Demirkan A., Di Angelantonio E., Dittrich K., Divers J., Dorajoo R., Eckardt K.U., Ehret G., Elliott P., Endlich K., Evans M.K., Felix J.F., Foo V.H., Franco O.H., Franke A., Freedman B.I., Freitag-Wolf S., Friedlander Y., Froguel P., Gansevoort R.T., Gao H., Gasparini P., Gaziano J.M., Giedraitis V., Gieger C., Girotto G., Giulianini F., Gögele M., Gordon S.D., Gudbjartsson D.F., Gudnason V., Haller T., Hamet P., Harris T.B., Hartman C.A., Hayward C., Hellwege J.N., Heng C.K., Hickst A.A., Hofer E., Huang W., Hutri-Kähönen N., Hwang S.J., ikram M.A., indridason O.S., Ingelsson E., ising M., Jaddoe V.W., Jakobsdottir J., Jonas J.B., Joshi P.K., Shilpa Josyula N., Jung B., Kähönen M., Kamatani Y., Kammerer C.M., Kanai M., Kastarinen M., Kerr S.M., Khor C.C., Kiess W., Kleber M.E., Koenig W., Kooner J.S., Körner A., Kovacs P., Kraja A.T., Krajcoviechova A., Kramer H., Krämer B.K., Kronenberg F., Kubo M., Kühnel B., Kuokkanen M., Kuusisto J., La Bianca M., Laakso M., Lange L.A., Langefeld C.D., Jen-Mai Lee J., Lehne B., Lehtimäki T., Lieb W., Cohort Study L., Lim S.C., Lind L., Lindgren C.M., Liu J., Liu J., Loeffler M., Loos R.J., Lucae S., Ann Lukas M., Lyytikäinen L.P., Mägi R., Magnusson P.K., Mahajan A., Martin N.G., Martins J., März W., Mascalzoni D., Matsuda K., Christa M.e.i.s.i.n.g.e.r., Meitinger T., Melander O., Metspalu A., Mikaelsdottir E.K., Milaneschi Y., Miliku K., Mishra P.P., Veteran Program V.A., Mohlke K.L., Mononen N., Montgomery G.W., Mook-Kanamori D.O., Mychaleckyj J.C., Nadkarni G.N., Nalls M.A., Nauck M., Nikus K., Ning B., Nolte I.M., Noordam R., O’Connell J., O’Donoghue M.L., Olafsson I., Oldehinkel A.J., Orho-Melander M., Ouwehand W.H., Padmanabhan S., Palmer N.D., Palsson R., Penninx B.W., Perls T., Perola M., Pirastu M., Pirastu N., Pistis G., Podgornaia A.I., Polasek O., Ponte B., Porteous D.J., Poulain T., Pramstaller P.P., Preuss M.H., Prins B.P., Province M.A., Rabelink T.J., Raffield L.M., Raitakari O.T., Reilly D.F., Rettig R., Rheinberger M., Rice K.M., Ridker P.M., Rivadeneira F., Rizzi F., Roberts D.J., Robino A., Rossing P., Rudan I., Rueedi R., Ruggiero D., Ryan K.A., Saba Y., Sabanayagam C., Salomaa V., Salvi E., Saum K.U., Schmidt H., Schmidt R., Schöttker B., Schulz C.A., Schupf N., Shaffer C.M., Shi Y., Smith A.V., Smith B.H., Soranzo N., Spracklen C.N., Strauch K., Stringham H.M., Stumvoll M., Svensson P.O., Szymczak S., Tai E.S., Tajuddin S.M., Tan N.Y., Taylor K.D., Teren A., Tham Y.C., Thiery J., Thio C.H., Thomsen H., Thorleifsson G., Toniolo D., Tönjes A., Tremblay J., Tzoulaki I., Uitterlinden A.G., Vaccargiu S., van Dam R.M., van der Harst P., van Duijn C.M., Velez Edward D.R., Verweij N., Vogelezang S., Völker Ü., Vollenweider P., Waeber G., Waldenberger M., Wallentin L., Wang Y.X., Wang C., Waterworth D.M., Bin Wei W., White H., Whitfield J.B., Wild S.H., Wilson J.F., Wojczynski M.K., Wong C., Wong T.Y., Xu L., Yang Q., Yasuda M., Yerges-Armstrong L.M., Zhang W., Zonderman A.B., Rotter J.I., Bochud M., Psaty B.M., Vitart V., Wilson J.G., Dehghan A., Parsa A., Chasman D.I., Ho K., Morris A.P., Devuyst O., Akilesh S., Pendergrass S.A., Sim X., Böger C.A., Okada Y., Edwards T.L., Snieder H., Stefansson K., Hung A.M., Heid I.M., Markus S.c.h.o.l.z., Teumer A., Köttgen A, & Pattaro C. (2019). A catalog of genetic loci associated with kidney function from analyses of a million individuals. Nature genetics, 51(6), 957-972.
Publication 2019
Asthma Childbirth Chronic Kidney Diseases EGFR protein, human Genome-Wide Association Study High Blood Pressures Kidney Failure, Acute Single Nucleotide Polymorphism Urolithiasis
5
Single-Cell Tumor and Normal Cell Classification
Cited 48 times
Classification of tumor and normal cells was performed in two steps. We assumed that the major genetic distance among the cell populations is the difference between diploid and aneuploid genomes and therefore forced the single cells into two major clusters using hierarchical clustering with Ward linkage and Euclidean distance. To determine the identities of each clusters, we integrated the clustering results with the predefinition of the ‘confident normal cells’ that are defined by a very stringent criteria (seeOnline Methods section on Estimating Copy Number Baseline Values in Diploid Cells).The cluster that has significantly higher enrichment of predefined normal cells is defined as the normal diploid cell cluster. In cases where there is no significant difference in the enrichment test, we switch to the ‘GMM definition’ approach to determine if the consensus profiles of each cluster pass the ‘normal cell criteria’, where at least 95% of the regions fall into the neutral distribution. In some challenging samples that have aneuploidy too close to 2N, we use an alternative slower approach by predicting the cells one-by-one using the ‘GMM definition’ approach and ‘normal cell criteria’.
To evaluate the accuracy of this copy number-based classification of tumor and normal cells, we applied an empirical approach to decide tumor and normal cells based on clustering and expression of cancer-specific marker genes. We first clustered all single cells within a tumor using ‘SNN’ method in R package ‘Seurat’42 . Next we obtained the expression levels of a panel of four epithelial markers (EPCAM, KRT19, KRT18, and KRT8). We calculated the average expression values of this epithelial markers panel as a consolidated epithelial score in each cell. Single cell gene expression clusters with high epithelial scores (kernel density center is above 0) were labeled as putative tumor cell clusters. In tumors that have both normal epithelial and tumor epithelial cell clusters, we further applied evaluated cancer type specific markers, including KRT19 for PDAC tumor epithelial cells, KRT8 for ATC, EPCAM for TNBC and IBC, and EGFR for GBM cancer cells. Furthermore, expression clusters that expressed immune cells markers (CD45, CD3, CD4, CD8) or fibroblast markers (ACTA2, FN1) were classified as normal cells. Single cells that had consistent aneuploid prediction results in both CopyKAT and by gene expression clusters with high epithelial score were considered to be tumor cells. The prediction accuracy of CopyKAT using aneuploid copy number profiles alone was then calculated as the number of cells with the correct prediction divided by the total number of single cells in the analysis.
Gao R., Bai S., Henderson Y.C., Lin Y., Schalck A., Yan Y., Kumar T., Hu M., Sei E., Davis A., Wang F., Shaitelman S.F., Wang J.R., Chen K., Moulder S., Lai S.Y, & Navin N.E. (2021). Delineating copy number and clonal substructure in human tumors from single-cell transcriptomes. Nature biotechnology, 39(5), 599-608.
Publication 2021
ACTA2 protein, human Aneuploidy Anophthalmia with pulmonary hypoplasia Cells Diploid Cell Diploidy EGFR protein, human Epithelial Cells Fibroblasts Gene Expression Genome KRT8 protein, human KRT18 protein, human KRT19 protein, human Malignant Neoplasms Neoplasms Neoplasms, Epithelial Self Confidence TACSTD1 protein, human

Most recents protocols related to «EGFR protein, human»

1
Retargeted Oncolytic Virus Induces Tumor Killing
Not available on PMC !

Example 17

The gD glycoprotein of HSV was modified to mediate viral entry via the EGFR receptor and engineered to encode for FAP-CD3. To test the ability of a retargeted ONCR-virus that encodes FAP-CD3 to induce tumor killing, 5×106 A431 cells are implanted subcutaneously into the right flank Nude mice. When the flank tumors reach ˜100 mm3, mice are injected intravenously with a single dose or ONCR-EGFR-(FAP-CD3) at 1×106 PFU; 3×106 PFU; 1×107 PFU, or vehicle (PBS). Tumor measurements are recorded every 2 to 3 days. Tumor growth inhibition is calculated against vehicle control treated cohort.

US11865081B2. Oncolytic viral delivery of therapeutic polypeptides (2024-01-09). Virogin Biotech Canada Ltd. [CA]. Inventors: Mitchell H. Finer [US], Luke Evnin [US].
Patent 2024
CD3 Antigens Cells EGFR protein, human Glycoproteins Mice, Nude Mus Neoplasms Obstetric Delivery Psychological Inhibition Virus Virus Entry Receptors
2
Selective Kinase Inhibition Profiling
Not available on PMC !

Example 5

According to the above-mentioned method for measuring kinase inhibitory activity, the inhibitory activity of the active compounds on other multiple kinases was measured to characterize the kinase selectivity of active inhibitors.

TABLE 4
Selective inhibitory activity of the active compound
6i on 16 kinases at a concentration of 100 nM
Compounds % inhibition @ 100 nM
6i (% inhibition @ 100 nM)
%% inhibitionAverage
Kinaseinhibition 12% inhibitionSD
EGFR40.943.642.31.9
EGFR73.475.774.51.6
L858R/T790M/
C797S
EGFR T790M57.056.856.90.1
FLT178.780.779.71.5
JAK191.789.390.51.7
FGFR38.113.710.94.0
KDR71.571.371.40.2
EGFR/T790M/76.677.977.20.9
L858R
EGFR L858R61.762.662.10.6
FGFR165.066.665.81.1
BTK49.151.350.21.6
FGFR283.8582.1183.01.2
PDGFR α98.91103.00101.02.9
PDGFR β100.98102.53101.81.1
SRC90.9893.4792.21.8

The results showed that the active compound 6i could effectively inhibit the PDGFRα and PDGFRβ kinases at the concentration of 100 nM, wherein the inhibition rate could be over 100%, but had relatively weak inhibitory activity on other kinases, indicating good kinase selectivity.

US11858914B2. Pyrimidine small-molecule compound and application thereof (2024-01-02). WENZHOU MEDICAL UNIVERSITY [CN]. Inventors: Xiaokun Li [CN], Zhiguo Liu [CN], Li Lin [CN], Xiaohui Zheng [CN], Jianchang Qian [CN].
Patent 2024
Cardiac Arrest Debility EGFR protein, human FGFR1 protein, human FGFR2 protein, human FGFR3 protein, human FLT1 protein, human Genetic Selection inhibitors Janus Kinase 1 Phosphotransferases Platelet-Derived Growth Factor alpha Receptor Platelet-Derived Growth Factor beta Receptor Psychological Inhibition SD 62
3
MEK Inhibitor Sensitivity Across Cancer Lines
Not available on PMC !

Example 20

240 cell lines representative of multiple cancer indications with known alterations in the MAPK pathway, including KRAS, NRAS, HRAS, NF1, EGFR, BRAF and CRAF mutations, were seeded overnight in 386-well plates, then treated with a 9-point dose response of exemplary MEK inhibitors (starting dose of 100 nM and 3-fold dilution) for 5 days. Cell viability was determined using a Cell Titer Glo (CTG) assay. Percent inhibition was calculated for all compounds utilizing staurosporine (1000 nM) treatment as a measure of maximal inhibition. IC50 and area under the curve (AUC) values were determined by fitting a variable slope, four parameters curve to the compound concentration to percent inhibition relationship.

Compared to RAS/RAF wild-type cell lines, increased sensitivity to MEK inhibitors, such as I-2, was observed in cell lines with KRAS, NRAS, BRAF Class I and III mutations, as well as CRAF-alterations (both CRAF mutations and fusions). Cell lines with mutations in PIK3CA, PTEN, NF1, EGFR and HRAS showed similar sensitivity to MEK inhibition to RAS/RAF wild-type cell lines.

US11878958B2. MEK inhibitors and uses thereof (2024-01-23). Ikena Oncology, Inc. [US]. Inventors: Alfredo C. Castro [US], Michael J. Burke [US], Thomas A. Wynn [US], Sabine K. Ruppel [US], Sergio L. Santillana Soto [US], Eric Haines [US], Lan Xu [US], Oksana Zavidij [US].
Patent 2024
Biological Assay BRAF protein, human Cell Lines Cells Cell Survival EGFR protein, human HRAS protein, human Hypersensitivity inhibitors K-ras Genes Malignant Neoplasms Mutation NRAS protein, human PIK3CA protein, human Psychological Inhibition PTEN protein, human Raf1 protein, human Staurosporine Technique, Dilution
4
Dose Response of mAbXcite-Cetuximab in Nude Mice
Not available on PMC !

Example 23

In this Example, the dose response of mAbXcite-cetuximab was studied. The present study utilized mAbXcite-cetuximab in which the conjugation utilized reductive amination (direct) chemistry, a 5-mer oligomer, and a load of about 3 oligomers per antibody.

In this Example, 7 week old female nude mice were implanted SC with 2.5×106 HCT-116 cells (HCT-116 is a human KRAS mutant colorectal cancer cell line). When tumor volume wan in the range of 51-80 mm3 mice were randomly divided into groups (9 mice/group) and treated with IVIG (as a source for anti-β-1,6-glucan antibodies) followed 2 hours later by vehicle control, cetuximab or mAbXcite EGFR. The dosing was determined by the half-life and was twice weekly. Various doses were assessed: 5, 10, 15 mg/kg for each antibody. Tumor volume was assessed by caliper measurement twice weekly. Body weight was monitored twice weekly. Mice were euthanized after tumors reached a volume of 2000 mm3 or if their body weight dropped by more than 15%.

Analysis of data collected form this experiment was performed. Tumor volume was calculated based on the following formula: TV (mm3)={length (mm)×width (mm)2}/2. Results are shown in FIGS. 58 to 61.

US11865135B2. ß-1,6-glucan therapeutic antibody conjugates (2024-01-09). Innate Biotherapeutics, LLC [US]. Inventors: Gabriel Oscar Reznik [US], John James Kane [US], James Michael Siedlecki [US], Zuzana Dostalova [US], Isabelle Sansal-Castellano [US], Ifat Rubin-Bejerano [US], Hua Miao [US], Eric Steven Furfine [US].
Patent 2024
Amination Antibodies Body Weight Cell Lines Cetuximab Colorectal Carcinoma EGFR protein, human Females Figs Glucans HCT116 Cells Homo sapiens Immunoglobulins Intravenous Immunoglobulins K-ras Genes Mice, Nude Mus Neoplasms
5
Synergistic Cytotoxicity of Gefitinib and DZ1-Simvastatin Amide
Not available on PMC !

Example 4

A Crystal Violet Staining of a human lung carcinoma cell line (H-1975, a cell line with EGFR mutations), is performed, exposing the cells to various concentrations of Gefitinib alone, in comparison to combined exposure with DZ1-Simvastatin amide. As shown in FIG. 4, Gefitinib is used in concentrations of 64, 32, 16, 8, 4, 2, 1, 0.5 and 0 uM (columns 1-6, row A), the same concentrations of Gefitinib are combined with DZ-Simvastatin Amide in a concentration of 5 uM (row B) and 6 uM (row C). The results show a dramatic increase and synergism of the cytotoxic effects of the TKI Gefitinib by combination with DZ1-Simvastatin amide in a concentration-dependent manner, see e.g. effect of 0.5 uM Gefitinib combined with 6 uM DZ1-SIM Amide.

US11878000B2. Compounds and methods to sensitize cancer cells to tyrosine kinase inhibitors (2024-01-23). Cedars-Sinai Medical Center [US], Da Zen Theranostics, Inc. [US]. Inventors: Liyuan Yin [US], Yi Zhang [US], Stefan Mrdenovic [HR], Gina Chia Yi Chu [US], Ruoxiang Wang [US], Qinghua Zhou [CN], Jian Zhang [US], Leland W. K. Chung [US].
Patent 2024
Amides Cell Lines EGFR protein, human Gefitinib Homo sapiens Lung Cancer Malignant Neoplasms Mutation Simvastatin Violet, Gentian

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Anti egfr by Cell Signaling Technology
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9
Trizol reagent by Thermo Fisher Scientific
Sourced in United States, China, Japan, Germany, United Kingdom, Canada, France, Italy, Australia, Spain, Switzerland, Netherlands, Belgium, Lithuania, Denmark, Singapore, New Zealand, India, Brazil, Argentina, Sweden, Norway, Austria, Poland, Finland, Israel, Hong Kong, Cameroon, Sao Tome and Principe, Macao, Taiwan, Province of China, Thailand
TRIzol reagent is a monophasic solution of phenol, guanidine isothiocyanate, and other proprietary components designed for the isolation of total RNA, DNA, and proteins from a variety of biological samples. The reagent maintains the integrity of the RNA while disrupting cells and dissolving cell components.
10
Gefitinib by Selleck Chemicals
Sourced in United States, Germany, China, France
Gefitinib is a tyrosine kinase inhibitor used in laboratory research. It functions by inhibiting the epidermal growth factor receptor (EGFR) tyrosine kinase.

More about "EGFR protein, human"

EGFR, Epidermal Growth Factor Receptor, receptor tyrosine kinase, cell proliferation, cell differentiation, cell survival, cancer, therapeutic target, PubCompare.ai, FBS, P-EGFR, Lipofectamine 2000, Anti-EGFR, P-AKT, β-actin, SAS version 9.4, PVDF membranes, TRIzol reagent, Gefitinib