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IGBP1 protein, human

IGBP1 protein is a key regulator in humans, playing a crucial role in various cellular processes.
This protein is involved in signal transduction pathways, cell growth, and differentiation.
Understanding the function and regulation of IGBP1 can provide valuable insights into human health and disease.
Leveraging the power of PubCompare.ai, researchers can optimize their investigations, uncover the best protocols from literature, pre-prints, and patents, and unlock the secrets of IGBP1 protein through reproducible and accuarate findings.

Most cited protocols related to «IGBP1 protein, human»

One microgram of total RNA was reverse transcribed using Ambion’s RETROscript kit with oligo(dT) primers for the 2-step qRT-PCR assays. Gene specific primers were used to amplify message by qPCR on a Cepheid Smart Cycler (Sunnyvale, CA) using the Qiagen SYBR Green master mix or on an ABI 7500 using the ABI SYBR Green master mix (Foster City, CA). Primer sets were designed against the complete nucleotide sequence, as deposited on GenBank, using Vector NTI 9.0.0 (InforMax, Frederick, MD). The optimum annealing temperature for each primer set was determined prior to the analysis of experimental samples. The specificity of each primer set and molecular weight of the amplicon were monitored by dissociation curve analysis and further verified by analysis using Agilent’s Bioanalyzer 2100. A sample volume of 25 μl was used for all assays, which contained a 1X final concentration of SYBR green PCR master mix, 400 nM gene specific primers, and 1 μl template. All samples and standards were run in triplicate, except for the azaspiracid time course which was run in duplicate. Assays were run using the following protocol: 95°C for 15 min or 10 min (Qiagen or ABI master mix, respectively), 94°for 15 sec, gene specific annealing temperature (55°-64°C) for 40 sec, 72°C for 1 min for 40 cycles, followed by a gradual increase in temperature from 60°C to 95°C during the dissociation stage. Table 3 (in the Supplemental information) details the genes validated by qPCR and assay conditions.
Following amplification, the instrument software was used to set the baseline and threshold for each reaction. A cycle threshold (Ct) was assigned at the beginning of the logarithmic phase of PCR amplification and the difference in the Ct values of the control and experimental samples were used to determine the relative expression of the gene in each sample. Prior to quantitative analysis, a standard curve was constructed using serial dilutions of RT product (species and tissue specific) and the efficiency of each primer set was determined using the equation [(10 (-1/-slope)-1)·100]. Efficiencies of 90-110% were required to include the qPCR assay in array validation. Relative expression levels between samples were then calculated as fold changes, where each PCR cycle represents a two-fold change. Therefore, the assay-specific efficiency was not used in the calculation of relative expression levels. For each experiment, a specific gene was chosen for normalization that did not exhibit any significant change in expression via microarray. All mouse experiments used tubulin, alpha 4 (NM_009447) for normalization while the human AZA study utilized an alpha tubulin-like gene (NM_145042). Statistical analysis was performed using a Wilcoxon/Kruskal-Wallis nonparametric test or a one-way ANOVA in JMP version 5.1.2 (SAS Institute Inc., Cary, NC).
Publication 2006
alpha-Tubulin azaspiracid Base Sequence Biological Assay Cloning Vectors Gene Expression Genes Homo sapiens IGBP1 protein, human Microarray Analysis Mus neuro-oncological ventral antigen 2, human Oligonucleotide Primers Oligonucleotides SYBR Green I Technique, Dilution Tissue Specificity Tubulin
Evaluation of significant hits revealed a small number of false positives due to recurrent artifacts that escaped our filters and our unmatched normal panel. To systematically identify recurrent artifacts leading to false positives in the screens for positive and negative selection, we used ShearwaterML (Gerstung et al., 2014 (link), Martincorena et al., 2015 (link)).
ShearwaterML is a variant calling algorithm that relies on building a base-specific error model by using a large collection of unmatched normal samples. Sequencing artifacts caused by Illumina sequencing errors, PCR errors, DNA damage in a library, misalignment of reads or other causes, are expected to appear at similar frequencies in sequencing libraries of tumor or healthy (normal) tissue. Thus, all mutations identified in genes detected as significant by dNdScv were re-evaluated by ShearwaterML, comparing the number of reads supporting the mutation in the mutant sample to the frequency of errors seen across a large panel of TCGA normal samples from the same cancer type using a beta-binomial likelihood model (Martincorena et al., 2015 (link)).
To build a reliable panel of normal samples for each TCGA dataset and avoid filtering out genuine driver mutations, we excluded from the panels any normal sample with suggestive evidence of a mutation (> = 3 supporting reads) in a list of 344 recurrently mutated sites in known cancer genes. This reduces the risk of including samples in the normal panel with significant tumor contamination or hematopoietic clonal expansions (Xie et al., 2014 (link)).
P-values resulting from ShearwaterML were adjusted for multiple testing using Benjamini and Hochberg’s false discovery rate, correcting for n = NS tests to avoid a discovery bias (where N is the number of sites tested and S is the number of samples in each cancer type). Mutations with q-value > 0.20 were removed and dNdScv was re-run on the cleaned dataset. 49 genes were found to be heavily affected by artifacts, with more than 50% of the mutations found in them being considered artifactual by ShearwaterML. These genes were conservatively excluded from any significant hits in the positive selection screen.
The 49 genes heavily-affected by artifacts are: AGAP10, AL445989.1, ANAPC1, ANKRD36C, AQP7, BMI1, C16orf3, CD209, CDC27, CDC7, CRIPAK, DTD2, EP400, FAM104B, FRG1, FRG1B, GNAQ, HLA-DRB5, HSPD1, IGBP1, KBTBD6, KRT14, KRT5, KRT6A, KRTAP1-5, KRTAP4-11, KRTAP4-3, KRTAP4-8, KRTAP4-9, KRTAP5-5, KRTAP9-9, MLLT3, MUC4, MUC8, NCOA6, PABPC1, PCDHB12, POTEC, POTEM, PPFIBP1, PRKRIR, PTH2, RGPD3, RGPD8, RP11-176H8.1, SLC35G6, TMEM219, TPT1 and UBBP4.
Publication 2017
Apc1 Subunit, Anaphase-Promoting Complex-Cyclosome BMI1 protein, human CD209 protein, human Clone Cells DNA Library EP400 protein, human Gene, Cancer Genes Hematopoietic System HLA-DRB5 Antigen IGBP1 protein, human KRT5 protein, human KRT14 protein, human Malignant Neoplasms MUC4 protein, human Mutation NCOA6 protein, human Neoplasms Retinitis Pigmentosa 11 Specimen Collection Tissues Vision
SCFAs including acetate, propionate, butyrate, isobutyrate, valerate, and isovalerate were analysed as described previously67 (link). To ensure the homogenicity of the intestine content sample, the freeze-dried samples were prepared using a Vacuum freeze-dryer (Hrist ALPHA 2-4/LSC, Germany) at −80 °C. Briefly, freeze-dried samples (0.5–0.6 g) were weighed into 10 ml centrifuge tubes and mixed with 8 ml ddH2O, homogenised, and centrifuged in sealed tube at 7,000 g and 4 °C for 10 min. A mixture of the supernatant fluid and 25% metaphosphoric acid solution (0.9 and 0.1 ml, respectively) was centrifuged at 20,000 g and 4 °C for 10 min after standing in a 2 ml sealed tube at 4 °C for over 2 h. The supernatant portion was then filtered through a 0.45-μm polysulfone filter and analysed using Agilent 6890 gas chromatography (Agilent Technologies, Inc, Palo Alto, CA, USA) with a flame ionisation detector and a 1.82 m × 0.2 mm I.D. glass column that was packed with 10% SP-1200/1% H3PO4 on the 80/100 Chromosorb W AW (HP, Inc., Boise, ID, USA). The concentration of NH3-N in the supernatant fluid was measured at 550 nm using a UV-2450 spectrophotometer (Shimadzu, Kyoto, Japan)68 . The bioamines including 1,7-heptyl diamine, cadaverine, phenylethylamine, putrescine, trytamine, tyramine, spermidine, and spermine, as well as the indoles and skatoles, were analysed as described previously69 .
Publication 2016
Acetate Butyrates Cadaverine Diamines Flame Ionization Freezing Gas Chromatography Homozygote IGBP1 protein, human Indoles Intestinal Contents metaphosphoric acid Phenethylamines polysulfone Propionate Putrescine Spermidine Spermine Tyramine Vacuum Valerates

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Publication 2009
Body Image Cognition Eating Disorders Feeding Behaviors Food Human Body IGBP1 protein, human Laxatives Obsessions Parts, Body Psychometrics
In order to investigate cross-frequency interactions we have developed a tool calculating coherence between a low frequency signal and the time-course of the power at higher frequencies. Let the signal {Xt} be represented by the time series X1, X2, …, XN. First, the time-course of power S1(f2), S2(f2), …, SN(f2) was estimated for frequency f2 by applying a sliding tapered time-window followed by a Fourier transformation:
Here where is the sampling frequency. The function is a Hanning taper K data points-long equaling the length of the sliding time window. The length of the time-window decreased with frequency: where M denoted the numbers of cycles per time window. We chose to use M = 7 cycles (for the intracranial data we used M = 6 cycles). Next, the coherency Coh(f1,f2) was estimated between signal {Xt} and the estimate of the time-course of power{St(f2)} for a given frequency f1. The coherency was calculated with respect to the two time series divided into M segments being L = 2048 data points long (for the intracranial data we used L = 1024 due to the lower sampling frequency): where
The coherence was the absolute value of the coherency . The phase difference between the signal at f1 and the power at f2 is given by the angle of the coherency . In this case refer to a 2048 points Hanning window and * to the complex conjugate. This allowed us to characterize the phase-to-power cross-frequency interaction with respect to f1 and f2 sensor by sensor. To assess reliability of the estimated coherence, a statistical analysis was performed by randomly shifting the time course of signal {Xt} (at least 3001 points) with respect to the estimated power {St(f2)} and recalculating the coherence for the channel with the most pronounced effect (based on visual inspection). Repeating this 200 times yielded a distribution of coherence values. Further, a maximum coherence value between the alpha band and any of the frequencies between 2 and 100 Hz has been identified in every randomization. The proportion of the randomization coherence values above the coherence value to be tested corresponded to the p-value.
In order to assess phase relationship between alpha and gamma power, we bandpass-filtered the MEG data +/−4 Hz of the alpha peak frequency determined from the power spectra in each subject with acausal FFT filter. Next, 0.6 s of unfiltered epochs of data phase aligned to the alpha cycles were extracted. This was done by identifying the peaks of the alpha cycles in the bandpass-filtered data. Time-frequency representations (TFRs) of power were calculated for each segment using Fourier transforms calculated for short sliding time windows. Power estimates were averaged across trials. We applied a Hanning taper to an adaptive time window of 6 cycles for each frequency between 2–150 Hz (ΔT = 6/f). This analysis was done using the FieldTrip toolbox (http://www.ru.nl/neuroimaging/fieldtrip).
Publication 2008
Acclimatization EPOCH protocol Gamma Rays IGBP1 protein, human

Most recents protocols related to «IGBP1 protein, human»

The Patient Health Questionnaire-4(PHQ-4; 4 items) [29 (link)] and Primary Care Post-Traumatic Stress Disorder Screen for DSM-5 (PC-PTSD-5; 5 items) [30 (link)] were adopted to evaluate the convergent validity of the Korean fear scale. Convergent validity indicates to how closely a test is related to other tests that measure similar constructs. The PHQ-4 scale consists of two sub-scales, namely anxiety(2 items) and depression(2 items) [29 (link)]. The total score of each sub-scale in PHQ-4 ranges from 0 to 6 and ≥3 total scores of each sub-scale refer to having anxiety and depressive symptoms, respectively. The Cronbach’s alpha of PHQ-4 was 0.82(the sub-scales’ coefficients = 0.75[anxiety] and 0.78[depression]) [29 (link)]. The PC-PTSD-5 scale was developed to screen the posttraumatic stress disorder (PTSD) [30 (link)]. The total score ranges from 0 to 5. A higher score of PC-PTSD-5 indicates a higher risk of PTSD, and the cut-off score of PTSD high risk is 3. The test-retest reliability of PC-PTSD-5(r) was 0.83 [30 (link)]. Both PHQ-4 and PC-PTSD-5 scales were previously validated in Korean [31 (link), 32 (link)]. The Pearson’s correlation coefficient (i)between the total score of the K-FS-8 and total score of the PHQ-4 and (ii)between the total score of the K-FS-8 and total score of the PHQ-4 and PC-PTSD were computed for the convergent validity of the K-FS-8.
For internal consistency, the corrected item-total correlation was adopted to assess the internal construct validity of the scale. A correlation coefficient ≥0.4 indicated adequate internal construct validity [33 (link)]. To assess the known-group validity, independent t-tests were used to compare the mean scores of the K-FS-8 between males and females. To compare the mean scores across the different age groups and marital status, an one-way ANOVA test was adopted. In addition, Cohen’s d effect sizes–trivial (<0.2), small (≥0.2 and <0.5), moderate (≥0.5 and <0.8) and large (≥0.8) were computed [34 (link)]. Last but not least, the fear levels of the participants were investigated using descriptive statistics.
Publication 2023
Age Groups Anxiety Depressive Symptoms Fear Females IGBP1 protein, human Koreans Males neuro-oncological ventral antigen 2, human Post-Traumatic Stress Disorder Primary Health Care
LiCl and pilocarpine were purchased from Sigma-Aldrich (St. Louis, MO, USA), sulforaphane from APExBIO (USA), and dihydroethidium (hydroethidine) from Thermo Fisher Scientific. Mouse monoclonal anti-nitrotyrosine, clone 1A6 (05–233) from Scintila, rabbit polyclonal anti-4-hydroxynonenal from Alpha diagnostic, HNE 11-S. Other reagents and chemicals were mostly from Sigma-Aldrich (St. Louis, MO, USA).
Publication 2023
3-nitrotyrosine Clone Cells Diagnosis dihydroethidium hydroethidine IGBP1 protein, human Mus Pilocarpine Rabbits sulforafan

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Publication 2023
Flour Freezing IGBP1 protein, human Powder Proteins Vacuum Whole Grains
Moisture, crude protein, and crude lipid in the whole fish body were analyzed by AOAC (1995). Moisture was measured by drying samples at 110°C to a constant weight. Crude protein was measured based on the Kjeldahl method, and crude lipid was measured by the Soxhlet extraction method (FOSS, Soxtec 2050).
The freeze dryer (ALPHA 1–4 LD freeze dryer, Kleist, Germany) was used to dry the raw materials and experimental diets to a constant weight at –50°C. The automatic amino acid analyzer (L-8900, Hitachi) was used to determine the amino acid profiles of freeze-dried samples after acid hydrolysis (6 N HCl at 110°C for 24 h).
Publication 2023
Acids Amino Acids Diet Fishes FOS protein, human Freezing Hydrolysis IGBP1 protein, human Lipid Droplet Lipids Proteins
Plant samples were collected for laboratory analyses in individual experimental plots. Five consecutive stalks (which were cut with a sharp knife above the soil) were taken completely randomly in five places. The samples were put in a plastic bag, labelled immediately upon arrival from the field. The tillers were incubated in the laboratory storage at 5 °C and 90% RH. After 3 days the samples were cut and well mixed. A representative from each sample was frozen and stored at least at −20 °C in a freezer (Whirpool AFG 6402 E-B, Benton Harbor, MI, USA). For laboratory analyses the frozen material was freeze-dried (Alpha model 1–4 LSC, Martin Christ, Osterode, Germany). The materials were lyophilized to permanent weight with a moisture content <2%. Dried wheats were also milled to obtain a fine powder (particle size 0.3–0.5 mm mesh) using an Ultra-Centrifuge Retsch mill ZM 100 (Retsch, Germany). The ground samples were stored in the dark in air-tight bags in desiccators.
Publication 2023
Freezing IGBP1 protein, human Plants Powder Stalking Triticum aestivum

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Sourced in Germany
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