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INSRR protein, human

Q: What are the common applications and use cases for the INSRR protein, human?

The INSRR (insulin receptor-related receptor) protein in humans is involved in insulin-like signaling pathways and is thought to play a role in metabolic regulation and neurological processes. It is expressed in various tissues, including the brain, kidneys, and reproductive organs. Researchers studying the INSRR protein can utilize it in a variety of applications, such as investigating its role in insulin sensitivity, glucose homeostasis, and neurological functions. Additionally, the INSRR protein may be a potential target for therapies related to metabolic disorders, neurological conditions, and reproductive health.

Q: How can PubCompare.ai help optimize INSRR protein, human research?

PubCompare.ai is a powerful AI-driven platform that can greatly assist researchers studying the INSRR protein, human. The platform allows you to screen protocol literature more efficiently by leveraging AI to pinpoint critical insights. This can help you identify the most effective protocols related to the INSRR protein for your specific research goals. The platform's AI-driven analysis can highlight key differences in protocol effectiveness, enabling you to choose the best option for reproducibility and accuracy, thus enhancing your productivity and confidence in your findings.

Q: Are there any variations or different types of the INSRR protein, human?

While the INSRR protein in humans is a single tyrosine kinase receptor, there can be different isoforms or variants of the protein due to alternative splicing or post-translational modifications. These variations may result in differences in the protein's structure, localization, or functionality, which can impact its role in signaling pathways and physiological processes. Researchers should be aware of these potential variations when studying the INSRR protein and ensure they are using the appropriate isoform or variant for their specific research questions and experimental setups.

Q: What are some common usage challenges or considerations when working with the INSRR protein, human?

One of the key challenges in working with the INSRR protein, human is the fact that its precise physiological functions are not fully understood. Researchers must carefully design their experiments and interpret their findings to account for the complex and potentially overlapping roles of INSRR in insulin-like signaling, metabolic regulation, and neurological processes. Additionally, the expression and localization of INSRR in different tissues and cell types may require specialized techniques or considerations when studying its functions. Proper controls, validation of reagents, and a thorough understanding of the available literature are essential when conducting research on the INSRR proetien, human.

INSRR (insulin receptor-related receptor) is a tyrosine kinase receptor that is closely related to the insulin receptor.
It is expressed in various tissues, including the brain, kidneys, and reproductive organs.
The precise physiological functions of INSRR are not fully understood, but it is thought to be involved in insulin-like signaling pathways and may play a role in metabolic regulation and neurological processes.
Researchers studying the INSRR protein can utilize the PubCompare.ai platform to optimize their research by discovering the best protocols and products, locating relevant information from literature, pre-prints, and patents, and identifying the most reliable and effective approaches.
This can enhance productivity and confidence in their findings, while improving reproducibility and accuracy.

Most cited protocols related to «INSRR protein, human»

Identification of Off-Target Regions for C9orf72 Repeat Expansions

Cited 26 times

Paired IRRs originating from expanded STRs may align to other genomic locations, especially if the STR is short in the reference genome at the target location. We refer to the loci where IRRs may misalign as off-target regions. Identifying off-target regions enables us to reduce the search for IRRs to a few regions instead of the whole genome. In order to obtain off-target regions for the C9orf72 repeat, we searched through the 182 samples in cohort one that had an expanded repeat according to the original RP-PCR results to identify all the GGGGCC IRRs. The search was performed through the whole genome for read pairs with a low mapping quality (MAPQ = 0) and a weighted purity score of at least 0.9. The mapping positions of all identified IRRs were merged if they were closer than 500 bp, and the resulting 29 loci that were present in five or more samples were designated as off-target regions (Supplemental Fig. 4) and were used to find additional reads from the C9orf72 repeat expansion.

Dolzhenko E., van Vugt J.J., Shaw R.J., Bekritsky M.A., van Blitterswijk M., Narzisi G., Ajay S.S., Rajan V., Lajoie B.R., Johnson N.H., Kingsbury Z., Humphray S.J., Schellevis R.D., Brands W.J., Baker M., Rademakers R., Kooyman M., Tazelaar G.H., van Es M.A., McLaughlin R., Sproviero W., Shatunov A., Jones A., Al Khleifat A., Pittman A., Morgan S., Hardiman O., Al-Chalabi A., Shaw C., Smith B., Neo E.J., Morrison K., Shaw P.J., Reeves C., Winterkorn L., Wexler N.S., Housman D.E., Ng C.W., Li A.L., Taft R.J., van den Berg L.H., Bentley D.R., Veldink J.H, & Eberle M.A. (2017). Detection of long repeat expansions from PCR-free whole-genome sequence data. Genome Research, 27(11), 1895-1903.

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Publication 2017

Genome INSRR protein, human

Lung Cancer Trends by Histology and Smoking

Cited 13 times

SEER data were used to analyze trends among whites and blacks during 1977 to 2010 in the nine original registries of Atlanta, Connecticut, Detroit, Hawaii, Iowa, New Mexico, San Francisco-Oakland, Seattle-Puget Sound, and Utah [9 ], and among white non-Hispanics, Asian/PIs and Hispanic whites in the 13 registries: the SEER9 registries plus Los Angeles, San Jose-Monterey, rural Georgia, and Alaska Natives during 1992–2010 [10 ].
Site recode in SEER*stat software version 8.1.2[11 ] was used to select microscopically-confirmed lung and bronchus cancer cases. Histologic groupings were created using ICD-O-3 [12 ] morphology codes. Six main histologic type categories were formed: squamous cell carcinoma, small cell carcinoma, adenocarcinoma, large cell carcinoma, other specified carcinoma, and unspecified types. The morphology codes were: squamous cell carcinoma (8051-2, 8070-6, 8078, 8083-4, 8090, 8094, 8120, 8123); small cell carcinoma (8002, 8041-5); adenocarcinoma (8015, 8050, 8140-1, 8143-5, 8147, 8190, 8201, 8211, 8250-5, 8260, 8290, 8310, 8320, 8323, 8333, 8401, 8440, 8470-1, 8480-1, 8490, 8503, 8507, 8550, 8570-2, 8574, 8576); large cell carcinoma (8012-4, 8021, 8034, 8082); other specified carcinoma (8003-4, 8022, 8030-3, 8035, 8200, 8240-1, 8243-6, 8249, 8430, 8525, 8560, 8562, 8575); and unspecified malignant neoplasms (carcinoma not otherwise specified [NOS] 8010-1, 8020, 8230; non-small cell carcinoma 8046 ; malignant neoplasm NOS 8000-1). We omitted cases specified as a non-carcinoma (8580-9999) or that appeared to be a metastasis (8005, 8095, 8124, 8130, 8146, 8160, 8170, 8231, 8247, 8263, 8312, 8340-1, 8350, 8370, 8441, 8460, 8500, 8501, 8510, 8524, 8530, 8551). A code for non-small cell carcinoma (8046) was added to ICD-O-3 in 2001, which was also used for some cases diagnosed prior to 2001.
We calculated incidence counts, rates per 100,000 person-years, rate ratios (IRRs) and 95% CIs by histologic type, period, gender, racial/ethnic group, and 5-year age group. Rates were age-adjusted using the 2000 US standard for all ages combined and for 10-year age groups 25–34 to 75–84 and 85+ years for calendar years 1977–1981, 1982–1986, 1987–1991, 1992–1996, 1997–2000, 2001–2005, and 2006–2010, with each 5 years except for the 4-year interval 1997–2000 to allow assessment of the change to ICD-O-3 in 2001. Year of birth was estimated by subtracting the age group mid-year from the diagnosis period mid-year. All rates were plotted using the period mid-year and a semi-logarithmic scale such that a change of 1% per year was depicted by a slope of 10 degrees [13 (link)], achieved by having one y-axis log cycle the same length as 40 years on the x-axis.
The prevalence of ever smoking at age 35 by year of birth through 1950–54 was available from 1885–89 for whites and from 1900–04 for blacks in the Smoking and Tobacco Control Monograph 8 [14 ]. More recent ever smoking data for the age group 35–44 were based on National Health Interview Data for each 5^th year 1990–2010 [15 ]. The prevalence of current cigarette smoking aged 18 and older by race/ethnicity and sex were from several National Health Interview Surveys [16 ]. Rates among whites and blacks were from surveys conducted every 5 years 1965 – 2005 and 2008, 2009, and 2010, with rates for the three years averaged and plotted at their mid-point, 2009.5. Asian and Hispanic rates were for 3-year period averages, 1990–1992, 1999–2001, and 2008–2010. Rates were not readily available for ever smoking among Asians or Hispanics or current smoking among Asians. Temporal trends in ever and current smoking prevalence were plotted as were the incidence rates, so that the slopes of the curves are comparable [13 (link)].

Lewis D.R., Check D.P., Caporaso N.E., Travis W.D, & Devesa S.S. (2014). U.S. Lung Cancer Trends by Histologic Type. Cancer, 120(18), 2883-2892.

Publication 2014

Adenocarcinoma Age Groups Alaskan Natives Asian Persons Birth Bronchogenic Carcinoma Carcinoma Carcinoma, Large Cell Carcinoma, Small Cell Diagnosis Epistropheus Ethnicity Hispanics INSRR protein, human Lung Malignant Neoplasms Negroes Neoplasm Metastasis Racial Groups Sound Squamous Cell Carcinoma White Person

Analyze Repeat Expansions with EHdn

Cited 12 times

EHdn supports case-control and outlier analyses of the underlying dataset. The case-control analysis is based on a one-sided Wilcoxon rank-sum test. It is appropriate for situations where a significant subset of cases is expected to contain expansions of the same repeat.
The outlier analysis is appropriate for heterogeneous cohorts where enrichment for any specific expansion is not expected. The outlier analysis bootstraps the sampling distribution of the 95% quantile and then calculates the z-scores for cases that exceed the mean of this distribution. The z-scores are used for ranking the repeat regions. Similar outlier-detection frameworks were also developed for exSTRa [23 (link)] and STRetch [25 (link)].
Both the case-control and the outlier analyses can be applied either to the counts of anchored IRRs or to the counts of paired IRRs. We refer to these as locus or motif methods, respectively. The high-ranking regions flagged by the analysis of anchored IRRs correspond to approximate locations of putative repeat expansions. The high-ranking motifs flagged by the analysis of paired IRRs correspond to the overall enrichment for repeats with that motif.

Dolzhenko E., Bennett M.F., Richmond P.A., Trost B., Chen S., van Vugt J.J., Nguyen C., Narzisi G., Gainullin V.G., Gross A.M., Lajoie B.R., Taft R.J., Wasserman W.W., Scherer S.W., Veldink J.H., Bentley D.R., Yuen R.K., Bahlo M, & Eberle M.A. (2020). ExpansionHunter Denovo: a computational method for locating known and novel repeat expansions in short-read sequencing data. Genome Biology, 21, 102.

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Publication 2020

Genetic Heterogeneity INSRR protein, human

Genotyping Repeat Expansions from Sequencing Data

Cited 9 times

Genotype probabilities for repeats of size up to the read length are calculated using a similar model as the one used for SNPs (Li et al. 2009 (link)). Namely, P(G|R) = P(R|G) · P(G)/P(R) where the genotype G is a tuple of repeat sizes with the number of entries equal to the ploidy of the chromosome containing the repeat. The probability P(R|G) is expressed in terms of the probabilities P(r_i|H_i) for individual reads r_i and repeat alleles H_i as described (Li et al. 2009 (link)).
If r_i is a spanning read containing m repeat units, P(r_i|H_i = n) = π · f(m| p, n, s), where π is defined as above (“Repeat size estimation from IRRs”). The frequency function f is defined by f(m|p, n, s) ∼ p(1 − p)^d, where m, n, s are non-negative integers bounded by the maximum number of repeat units in a read which we denote by u, p ∈ (0, 1) corresponds to the proportion of molecules with repeat of the expected size, and d = |n − m| if |n − m| < s and d = s otherwise. Note that f is defined similarly to the geometric frequency function with parameter d representing the deviation from n, the expected repeat size (which can be at most s). If r_i is a flanking or in-repeat read containing m repeat units,

P (r_{i} | H_{i} = n) = π \cdot \sum_{i = m}^{u} f (i | p, n, s)

. In all our analyses, the parameters p and s were set to 0.97 and 5. The values were chosen to maximize Mendelian consistency of genotype calls in Platinum Genome pedigree samples (Eberle et al. 2017 (link)) on an unrelated set of repeats.
We use read-length-sized repeats as a stand-in for repeats longer than the read length. If only one allele is expanded, we estimate the full size of the repeat as described above. If both alleles are expanded, the size intervals are estimated similarly by assuming that between 0 and 50% of in-repeat reads come from the short allele and between 50% and 100% of in-repeat reads come from the long allele.

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Publication 2017

Alleles Chromosomes Genome Genotype INSRR protein, human Platinum Single Nucleotide Polymorphism

Modeling COVID-19 and Weather Dynamics

Cited 8 times

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Publication 2020

COVID 19 Forests INSRR protein, human Meteorological Factors Microtubule-Associated Proteins

Most recents protocols related to «INSRR protein, human»

Epidemiology of Bloodstream Infections

Crude incidence rates (IR) were determined by dividing the number of BSI episodes with the population of Skåne. To increase comparability and to adjust for changes in population structure over time, age-standardised rates were estimated using the direct standardisation method and the 2013 European standard population [15 ]. Due to the focus on the ageing population, age-specific rates were described in 0–49, 50–64, 65–79 and ≥ 80 year strata. Crude rates were used for sex stratified IRs. Differences in IRs were estimated using incidence rate ratios (IRR). All IRs are presented as cases per 100,000 person-years, and IR and IRRs are presented with 95% confidence intervals.
To model change in overall IR over time, we fitted segmented regressions with the number of BSI episodes as the dependent variable, age and year as independent variables and the natural log of population as offset [16 (link)]. The results are expressed as annual percent change (APC) for each segment as well as the average annual percent change (AAPC) for the entire period [17 (link)]. For hypothesis testing of differences in the proportion of resistant isolates across sexes, the chi-square test was used. To test trends in the proportion of resistant isolates, Cochrane-Armitage trend tests were used, using Bonferroni correction for multiple testing. A p value of <0.05 was considered significant.

Ljungquist O., Blomstergren A., Merkel A., Sunnerhagen T., Holm K, & Torisson G. (2023). Incidence, aetiology and temporal trend of bloodstream infections in southern Sweden from 2006 to 2019: a population-based study. Eurosurveillance, 28(10), 2200519.

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Publication 2023

Europeans Gender INSRR protein, human

Immune Landscape and Prognosis Analysis

We carried out single-sample gene set enrichment analysis (ssGSEA) to analyze the expression of corresponding markers of 28 immune cell types (29 (link)), thereby obtaining the abundance of these cell types in patient tumor tissues. Then, the cells related to prognosis were screened by univariate Cox (uni-Cox) regression (P< 0.05).
The prognosis-related immune cells were termed A cells, and the A cells were paired with all 28 immune cells (termed B cells) in turn to form a set of A|B pairs. If the A-cell abundance exceeded the B-cell abundance for a given cell pair, the value of that pair was recorded as 1; otherwise, it was recorded as 0. This method enables the relative cell abundance to be considered without dependence on the absolute number; this avoids the variation caused by the use of different methods for gene measurement and annotation and differential cell abundance analysis. A matrix containing values of 0 or 1 was constructed, from which cell pairs with 0 or 1 accounting for more than 80% of the total were removed. In the human body, the content of some immune cells is much higher than that of other immune cells, such as neutrophils. The remaining cell pairs were screened by uni-Cox regression analysis (P< 0.05) to obtain those correlated with prognosis. We applied the least absolute shrinkage and selection operator (LASSO) Cox regression analysis (glmnet package) to avoid overfitting and obtain the remaining cell pairs. Then, each cell pair was assigned the optimal coefficient by multivariate Cox (multi-Cox), and the IRRS was generated as follows:
The receiver operating characteristic (ROC) curves, the Kaplan–Meier survival curves, the GEO datasets, and the cohort from Liu et al. were used to verify the effectiveness of the IRRS in predicting prognosis and immunotherapy effect.

Li M., Long X., Bu W., Zhang G., Deng G., Liu Y., Su J, & Huang K. (2023). Immune-related risk score: An immune-cell-pair-based prognostic model for cutaneous melanoma. Frontiers in Immunology, 14, 1112181.

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Publication 2023

B-Lymphocytes Cells Genes Human Body Immunotherapy INSRR protein, human Neoplasms Neutrophil Patients Prognosis Tissues

Predictive Nomogram for Outcomes

A nomogram was constructed to predict specific outcomes based on the IRRS and clinical variables using the rms package (38 ). ROC curves, calibration curves, and decision curve analysis (DCA) curves were drawn to verify the reliability of the nomogram. In addition, the nomogram was compared with the traditional TNM staging system by calculating the integrated discrimination improvement (IDI). Finally, the Dynnom package (cran.r-project.org/web/packages/rms) was used to generate an online version of the nomogram model with an interactive interface for clinical applications.

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Publication 2023

Discrimination, Psychology INSRR protein, human

Predicting Melanoma Outcomes

The integrated research design is presented in Figure 1. Transcription profiles and clinical data of cutaneous melanoma patients were obtained from The Cancer Genome Atlas (TCGA; https://portal.gdc.cancer.gov/; TCGA-SKCM cohort). After removing cases with duplication, lack of expression profiles, or lack of survival data, the data of 458 patients were included in the training group for the construction of the IRRS score. The GSE65904, GSE54467, GSE91061, and GSE115821 datasets from the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) and a cohort from Liu et al. were used as testing sets for validation (16 (link)–20 (link)). Missing values in the clinical or pathological data of patients were filled using the missForest package (21 (link)–28 (link)).

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Publication 2023

Familial Atypical Mole-Malignant Melanoma Syndrome Gene Expression Genome INSRR protein, human Malignant Neoplasms Patients Transcription, Genetic

Differential Gene Expression Analysis

The differentially expressed genes between the high and low IRRS groups were analyzed using the DESeq2 package, with threshold |log2 fold change (FC)| ≥2 and Benjamini–Hochberg-adjusted P-value<0.05 (30 (link)). Functional enrichment analysis and clustering of the identified biological processes were conducted using the clusterProfiler R package (31 (link)).
The main regulator (MR) is a gene located at the hub of a regulatory network that controls a large number of target genes (termed as its regulon). We used the MR4Cancer tool (http://cis.hku.hk/MR4Cancer) to identify potential MRs that could explain the DEGs between the high and low IRRS groups (32 (link)). An MR network diagram was drawn using Cytoscape.

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Publication 2023

Biological Processes Genes Genes, vif INSRR protein, human Regulon

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What are the common applications and use cases for the INSRR protein, human?

The INSRR (insulin receptor-related receptor) protein in humans is involved in insulin-like signaling pathways and is thought to play a role in metabolic regulation and neurological processes. It is expressed in various tissues, including the brain, kidneys, and reproductive organs. Researchers studying the INSRR protein can utilize it in a variety of applications, such as investigating its role in insulin sensitivity, glucose homeostasis, and neurological functions. Additionally, the INSRR protein may be a potential target for therapies related to metabolic disorders, neurological conditions, and reproductive health.

How can PubCompare.ai help optimize INSRR protein, human research?

PubCompare.ai is a powerful AI-driven platform that can greatly assist researchers studying the INSRR protein, human. The platform allows you to screen protocol literature more efficiently by leveraging AI to pinpoint critical insights. This can help you identify the most effective protocols related to the INSRR protein for your specific research goals. The platform's AI-driven analysis can highlight key differences in protocol effectiveness, enabling you to choose the best option for reproducibility and accuracy, thus enhancing your productivity and confidence in your findings.

Are there any variations or different types of the INSRR protein, human?

While the INSRR protein in humans is a single tyrosine kinase receptor, there can be different isoforms or variants of the protein due to alternative splicing or post-translational modifications. These variations may result in differences in the protein's structure, localization, or functionality, which can impact its role in signaling pathways and physiological processes. Researchers should be aware of these potential variations when studying the INSRR protein and ensure they are using the appropriate isoform or variant for their specific research questions and experimental setups.

What are some common usage challenges or considerations when working with the INSRR protein, human?

One of the key challenges in working with the INSRR protein, human is the fact that its precise physiological functions are not fully understood. Researchers must carefully design their experiments and interpret their findings to account for the complex and potentially overlapping roles of INSRR in insulin-like signaling, metabolic regulation, and neurological processes. Additionally, the expression and localization of INSRR in different tissues and cell types may require specialized techniques or considerations when studying its functions. Proper controls, validation of reagents, and a thorough understanding of the available literature are essential when conducting research on the INSRR proetien, human.

More about "INSRR protein, human"

The insulin receptor-related receptor (INSRR) is a tyrosine kinase receptor that is closely associated with the insulin receptor (IR).
It is expressed in various tissues, including the brain, kidneys, and reproductive organs.
The precise physiological functions of INSRR are not fully understood, but it is believed to be involved in insulin-like signaling pathways and may play a role in metabolic regulation and neurological processes.
Researchers studying the INSRR protein can utilize the PubCompare.ai platform to optimize their research.
This AI-driven platform can help them discover the best protocols and products, locate relevant information from literature, pre-prints, and patents, and identify the most reliable and effective approaches.
This can enhance productivity, improve confidence in their findings, and enhance reproducibility and accuracy.
INSRR is closely related to the insulin receptor (IR), and both are part of the insulin receptor superfamily.
This family of receptors is involved in insulin-like signaling, which is crucial for regulating metabolism, growth, and development.
INSRR is structurally similar to IR, and they share common signaling pathways, but INSRR may have distinct physiological functions.
Researchers analyzing the INSRR protein can employ various statistical software packages, such as SAS v9.4, SAS Enterprise Guide, SPSS Statistics for Windows, Version 24.0, Stata 14, Stata version 14, Stata Statistical Software, SAS version 9.4, STATA version 12, and R version 3.6.1, to explore the data and gain insights into the role of INSRR in metabolic regulation and neurological processes.
These tools can help researchers identify patterns, perform advanced analyses, and draw meaningful conclusions from their INSRR-related research.
By utilizing the PubCompare.ai platform and employing statistical software, researchers can optimize their INSRR protein research, enhance productivity, and improve the reliability and accuracy of their findings.
This can ultimately contribute to a better understanding of the physiological functions of INSRR and its potential implications in various health and disease contexts.