The present review focuses on 11 key methodological issues related to GT3X/+ data collection and processing criteria: (1) device placement, (2) sampling frequency, (3) filter, (4) epoch length, (5) non-wear-time definition, (6) what constitutes a valid day and a valid week, (7) registration period protocol, (8) SED and PA intensity classification, (9) PAEE algorithms, (10) sleep algorithms, and (11) step counting. Available information was classified into two different types of studies: (1) any cross-sectional, longitudinal, or intervention study which used the GT3X/+ device and met the inclusion criteria indicated in Sect. 2.3 (objective 1); and (2) studies focused on validation, calibration, or comparison of functions related to data collection or processing criteria (objective 2). Therefore, the practical considerations provided for each age group are based on the results from the validation/calibration studies (see Table 1 ). Furthermore, we provide a summary of all data extracted from the validation/calibration papers included in this review by age group in the Electronic Supplementary Material Appendix S1 . Inclusion/exclusion criteria and analytical methods were specified in advance and registered in the PROSPERO (http://www.crd.york.ac.uk/PROSPERO/ ) international database of systematic reviews (CRD42016039991) [32 (link)]. The study is conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [33 (link)].
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Chemicals & Drugs
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Amino Acid
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GZMB protein, human
GZMB protein, human
GZMB, or Granzyme B, is a serine protease enzyme involved in the induction of apoptosis in target cells.
It is primarily expressed by cytotoxic T lymphocytes and natural killer cells, and plays a crucial role in the immune system's response to viral infections and tumor cells.
GZMB acts by cleaving specific substrates within the target cell, triggering a cascade of events that leads to programmed cell death.
Understanding the mechanisms and functions of GZMB is an important area of research, with potential applications in immuinotherapy, cancer treatment, and the study of immune-related disorders.
It is primarily expressed by cytotoxic T lymphocytes and natural killer cells, and plays a crucial role in the immune system's response to viral infections and tumor cells.
GZMB acts by cleaving specific substrates within the target cell, triggering a cascade of events that leads to programmed cell death.
Understanding the mechanisms and functions of GZMB is an important area of research, with potential applications in immuinotherapy, cancer treatment, and the study of immune-related disorders.
Most cited protocols related to «GZMB protein, human»
Age Groups
EPOCH protocol
GZMB protein, human
Medical Devices
Sleep
The analysis of validity drew upon results from a Multi-Instrument Comparison (MIC) study. An online survey was administered in Australia, Canada, Germany, Norway, UK and the US by a global panel company, CINT Australia Pty Ltd [22 ]. For reasons discussed in Sect. 5 , the present paper only used results from Australia and the US. Respondents were asked to complete, inter alia, the ten instruments described above—AQoL-8D, the five other MAU and three SWB instruments, and the SF-36.
The personal and medical details recorded by the panel company were used to recruit individuals from seven major disease groups and from the ‘healthy public’, i.e. those who did not report any chronic disease and who obtained a score of at least 70 on a 100-point VAS measuring overall health. Respondents with one of the seven chronic diseases were asked to complete a relevant disease-specific questionnaire. The seven disease groups were arthritis, asthma, cancer, depression, diabetes, hearing loss and coronary heart disease (CHD).
Eight ‘edit criteria’ were employed to determine whether each individual’s answers were unreliable and should be removed from the sample. The criteria were based upon a comparison of duplicated or similar questions. Additionally, results were deleted when an individual’s (recorded) completion time was <20 min, which was judged to be the minimum time in which the 230 questions could be answered. The ‘healthy’ public were recruited to achieve a sample with demographic and educational characteristics that were broadly representative of the total population. Edit procedures, the questionnaire and its administration are described by Richardson et al. [23 ]. The survey was approved by the Monash University Human Research Ethics Committee (MUHREC), approval CF11/1758: 2011000974.
In the second, smaller survey to determine test–retest reliability, 285 (different) Australian respondents were invited to complete a baseline survey and to complete two follow-up surveys spaced a fortnight apart. At each of the three stages, the AQoL instruments were administered. Quotas were imposed to ensure that the initial sample was representative of the age, gender and educational profile of the Australian population (MUHREC approval CF11/3192: 2011001748).
The personal and medical details recorded by the panel company were used to recruit individuals from seven major disease groups and from the ‘healthy public’, i.e. those who did not report any chronic disease and who obtained a score of at least 70 on a 100-point VAS measuring overall health. Respondents with one of the seven chronic diseases were asked to complete a relevant disease-specific questionnaire. The seven disease groups were arthritis, asthma, cancer, depression, diabetes, hearing loss and coronary heart disease (CHD).
Eight ‘edit criteria’ were employed to determine whether each individual’s answers were unreliable and should be removed from the sample. The criteria were based upon a comparison of duplicated or similar questions. Additionally, results were deleted when an individual’s (recorded) completion time was <20 min, which was judged to be the minimum time in which the 230 questions could be answered. The ‘healthy’ public were recruited to achieve a sample with demographic and educational characteristics that were broadly representative of the total population. Edit procedures, the questionnaire and its administration are described by Richardson et al. [23 ]. The survey was approved by the Monash University Human Research Ethics Committee (MUHREC), approval CF11/1758: 2011000974.
In the second, smaller survey to determine test–retest reliability, 285 (different) Australian respondents were invited to complete a baseline survey and to complete two follow-up surveys spaced a fortnight apart. At each of the three stages, the AQoL instruments were administered. Quotas were imposed to ensure that the initial sample was representative of the age, gender and educational profile of the Australian population (MUHREC approval CF11/3192: 2011001748).
Arthritis
Asthma
Diabetes Mellitus
Disease, Chronic
Ethics Committees, Research
Gender
GZMB protein, human
Hearing Impairment
Heart Disease, Coronary
Homo sapiens
Malignant Neoplasms
Biological Assay
Biological Markers
CD274 protein, human
Cells
Diploid Cell
Disease Progression
Gene Expression
Genes
Genome
GZMA protein, human
GZMB protein, human
Immunohistochemistry
Malignant Neoplasms
Mutation
Neoplasms
Patients
Pharmaceutical Preparations
PRF1 protein, human
Safety
Short Tandem Repeat
TBX21 protein, human
Tissues
Tumor Burden
GZMB protein, human
Proteins
Unbiased and reliable imputation of the missing data requires the correct specification of the imputation model, namely which variables to include, how to deal with their distributions and how to capture their correlations. The imputation model should include all variables that are associated with both the missing data and CEA outcomes (costs and health outcomes), and all covariates that are in the analysis model [20 (link)]. Although all variables collected in the RCT could, in principle, be included, this can be unwise in practice because a large number of variables can make the model difficult to estimate. Therefore, some selection to identify the most predictive variables may be required. The imputation should be implemented separately by randomised treatment allocation [25 ]. This explicitly recognises in the imputation model that imputations are different between treatment groups, hence that the posterior distribution of the missing data given the observed may be different between treatment groups. Imputing the treatment groups together but including all possible interactions would only recognise differential means by treatment group and not a differential covariance structure.
Costs and QALYs can be imputed at more or less disaggregated level, from counts of each type of resource use or domains of the HR-QOL instrument to costs or QALYs over the period of follow-up. A balance needs to be struck between maintaining the data structure (hence imputing at more disaggregated level) and achieving a stable imputation model (which becomes more difficult as more variables with missing data are added [26 (link)]). The choice of approach should be informed by the structure of the data, the pattern of missing data and by testing a variety of approaches. We tentatively suggest the following:3 . Further research on the assessment of these alternative approaches is warranted.
Irrespective of the level of aggregation, data on costs and QALYs are unlikely to be normally distributed. This can be an issue because most readily available software packages that implement MICE tend to rely on normality for the imputation of continuous variables. One option is to transform the data towards normality, e.g. with log transformation. After imputation, the variables are back transformed to the original scale before applying the analysis model. This back transformation does not require correcting for non-normal errors (also referred to as smearing [28 (link)]) because the imputed value is drawn from the posterior predictive distribution. Another option is to use predictive mean matching. In predictive mean matching, the missing observation is imputed with an observed value from another individual whose predicted value is close to the predicted value of the individual with the missing observation [29 ]. This ensures that only plausible values of the missing variable are imputed (e.g. costs are always positive and HR-QOL is always ≤1). Two-part models may be used for variables with a large proportion of zeros (e.g. costs), with or without transforming the non-zero values or in combination with predictive mean matching [30 (link), 31 ].
Validation is the final step in the development of the imputation model. There is little guidance on how to assess whether the imputation procedure is producing valid results. One option is to assess whether the distributions of observed and imputed values are similar [32 (link), 33 ]. Another option is to compare the results with an alternative method that assumes the same missing data mechanism.
Costs and QALYs can be imputed at more or less disaggregated level, from counts of each type of resource use or domains of the HR-QOL instrument to costs or QALYs over the period of follow-up. A balance needs to be struck between maintaining the data structure (hence imputing at more disaggregated level) and achieving a stable imputation model (which becomes more difficult as more variables with missing data are added [26 (link)]). The choice of approach should be informed by the structure of the data, the pattern of missing data and by testing a variety of approaches. We tentatively suggest the following:
For QALYs, imputing the individual domains may be advantageous if the distribution of HR-QOL scores (typically with a spike at 1 and/or bimodal) is difficult to replicate with an imputation model at the score level or if the individual domains are missing rather than the whole questionnaire. In practice, either approach may be equally valid as suggested by a recent simulation study comparing imputing EQ-5D at individual domains or index score level [27 ].
For costs, imputing at the total cost level is likely to be appropriate when the different types of resource use that make up the cost have the same pattern of missing data. Since it is generally recommended to report the resource use components [8 (link)–10 (link)], a pragmatic approach is to impute at both aggregate and disaggregate levels as alternative sensitivity analyses, but having more confidence in the former.
Imputing at the resource use level is probably better when the different types of resource use have different patterns of missing data. If this makes the imputation model difficult to estimate, the key drivers of costs can be imputed at a resource level (e.g. length of stay in hospital, inpatient admissions) and the other items as one cost variable.
Irrespective of the level of aggregation, data on costs and QALYs are unlikely to be normally distributed. This can be an issue because most readily available software packages that implement MICE tend to rely on normality for the imputation of continuous variables. One option is to transform the data towards normality, e.g. with log transformation. After imputation, the variables are back transformed to the original scale before applying the analysis model. This back transformation does not require correcting for non-normal errors (also referred to as smearing [28 (link)]) because the imputed value is drawn from the posterior predictive distribution. Another option is to use predictive mean matching. In predictive mean matching, the missing observation is imputed with an observed value from another individual whose predicted value is close to the predicted value of the individual with the missing observation [29 ]. This ensures that only plausible values of the missing variable are imputed (e.g. costs are always positive and HR-QOL is always ≤1). Two-part models may be used for variables with a large proportion of zeros (e.g. costs), with or without transforming the non-zero values or in combination with predictive mean matching [30 (link), 31 ].
Validation is the final step in the development of the imputation model. There is little guidance on how to assess whether the imputation procedure is producing valid results. One option is to assess whether the distributions of observed and imputed values are similar [32 (link), 33 ]. Another option is to compare the results with an alternative method that assumes the same missing data mechanism.
GZMB protein, human
Hypersensitivity
Inpatient
Mus
Most recents protocols related to «GZMB protein, human»
Example 7
Impact of IL-2 signalling on Teff responses is characterised in a T cell activation assay, in which intracellular granzyme B (GrB) upregulation and proliferation are examined. Previously frozen primary human Pan T cells (Stemcell Technologies) are labelled with eFluor450 cell proliferation dye (Invitrogen) according to manufacturer's recommendation, and added to 96-U-bottom well plates at 1×105 cells/well in RPMI 1640 (Life Technologies) containing 10% FBS (Sigma), 2 mM L-Glutamine (Life Technologies) and 10,000 U/ml Pen-Strep (Sigma). The cells are then treated with 10 μg/ml anti-CD25 antibodies or control antibodies followed by Human T-Activator CD3/CD28 (20:1 cell to bead ratio; Gibco) and incubated for 72 hrs in a 37° C., 5% CO2 humidified incubator. To assess T cell activation, cells are stained with the eBioscience Fixable Viability Dye efluor780 (Invitrogen), followed by fluorochrome labelled antibodies for surface T cell markers (CD3-PerCP-Cy5.5 clone UCHT1 Biolegend, CD4-BV510 clone SK3 BD Bioscience, CD8-Alexa Fluor 700 clone RPA-T8 Invitrogen, CD45RA-PE-Cy7 clone HI100 Invitrogen, CD25-BUV737 clone 2A3 BD Bioscience) and then fixed and permeabilized with the eBioscience™ Foxp3/Transcription Factor Staining Buffer Set (Invitrogen) before staining for intracellular GrB and intranuclear FoxP3 (Granzyme B-PE clone GB11 BD Bioscience, FoxP3-APC clone 236A/E7). Samples are acquired on the Fortessa LSR X20 Flow Cytometer (BD Bioscience) and analysed using the BD FACSDIVA software. Doublets are excluded using FCS-H versus FCS-A, and lymphocytes defined using SSC-A versus FCS-A parameters. CD4+ and CD8+ T cell subsets gated from the live CD3+ lymphocytes are assessed using a GrB-PE-A versus proliferation eFluor450-A plot. Results are presented as percentage of proliferating GrB positive cells from the whole CD4+ T cell population. Graphs and statistical analysis is performed using GraphPad Prism v7. (results not shown)
Anti-Antibodies
Antibodies
Biological Assay
Buffers
CD4 Positive T Lymphocytes
Cell Proliferation
Cells
Clone Cells
CY5.5 cyanine dye
Eragrostis
Fluorescent Dyes
Freezing
Glutamine
GZMB protein, human
Homo sapiens
IL2RA protein, human
Lymphocyte
prisma
Protoplasm
Stem Cells
Streptococcal Infections
T-Lymphocyte
T-Lymphocyte Subsets
Transcriptional Activation
Transcription Factor
Example 7
Five groups including tucaresol, tucaresol plus PD-1 or PD-L1 antibody, tucaresol plus CTLA-4 antibody, CTLA-4 antibody plus PD-1 or PD-L1 antibody, and tucaresol plus plinabulin are tested to determine their effect in an animal xenograft model.
The combined treatment with tucaresol and the checkpoint inhibitor(s) is tested in comparison with the treatment with tucaresol alone, the treatment with checkpoint inhibitor alone, or combination of checkpoint inhibitors. The tests are performed using seven to ten-week old athymic (nu/nu) mice that were injected subcutaneously with human tumor cell lines (of either solid or liquid tumor origin, for example of breast, lung, colon, brain, liver, leukemia, myeloma, lymphoma, sarcoma, pancreatic or renal origin). Six to ten testing groups are prepared, and each group includes 10 mice.
Each treatment starts at tumor size between 40-150 mm3 and continues until Day 24-56, when the animals are necropsied. To determine the efficacy of each treatment, the following data are collected: mortality; the body weight of the mice assessed twice weekly both prior to treatments; the rate of tumor growth as determined by the tumor size measurement (twice every week); the tumor growth index; overall survival rate; the tumor weight at necropsy; and the time required to increase tumor size 10 fold.
Animal Model
Animals
Autopsy
Body Weight
Brain
Breast
CD274 protein, human
Cell Cycle Checkpoints
Cell Line, Tumor
Colon
Combined Modality Therapy
CTLA4 protein, human
Genes, Neoplasm
GZMB protein, human
Heterografts
Homo sapiens
Immunoglobulins
inhibitors
Kidney
Leukemia
Liver
Lung
Lymphoma
Mice, Nude
Multiple Myeloma
Mus
Neoplasms
Pancreas
plinabulin
Sarcoma
Thymic aplasia
tucaresol
Example 3
Five groups including tucaresol, tucaresol plus PD-1 or PD-L1 antibody, tucaresol plus CTLA-4 antibody, CTLA-4 antibody plus PD-1 or PD-L1 antibody, and tucaresol plus plinabulin are tested to determine the effects on in vitro cytokine production by CD4 and CD8 T cells (e.g, IFN-gamma and IL-2 cells).
The release of pro-inflammatory cytokines (IL-1β, IL-6, IL 12p40) is quantified by ELISA. The assays are performed as described by Martin et al., Cancer Immuno Immunothe (2014) 63(9):925-38. (2014) and Müller et al, Cancer Immunol Res (2014) 2(8), 741-55. Compounds are prepared as a 10 mM stock solution in DMSO and subsequently diluted to the final concentration in cell culture medium for use in the cell line studies and are examined using serial dilution over a concentration range of 1 nM to 10 μM.
Biological Assay
CD8-Positive T-Lymphocytes
CD274 protein, human
Cell Culture Techniques
Cell Lines
Cells
Culture Media
Cytokine
Cytotoxic T-Lymphocyte Antigen 4
Enzyme-Linked Immunosorbent Assay
Gamma Rays
GZMB protein, human
Immunoglobulins
Inflammation
Interleukin-1 beta
Interleukin-12 Subunit p40
Malignant Neoplasms
plinabulin
Sulfoxide, Dimethyl
Technique, Dilution
tucaresol
Example 2
Five groups including tucaresol, tucaresol plus PD-1 or PD-L1 antibody, tucaresol plus CTLA-4 antibody, CTLA-4 antibody plus PD-1 or PD-L1 antibody, and tucaresol plus plinabulin are tested to determine the potentiation of T cell proliferative response.
Markers for cell maturation (CD40, CD80, CD86, MHC II) are measured by FACS analysis in the SP37A3 immature mouse dendritic cell (DC) cell line after 20 hours of incubation with the test compounds. The assays are performed as described by Martin et al., Cancer Immuno Immunothe (2014) 63(9):925-38. (2014) and Müller et al, Cancer Immunol Res (2014) 2(8), 741-55. Compounds are prepared as a 10 mM stock solution in DMSO and subsequently diluted to the final concentration in cell culture medium for use in the cell line studies and were examined using serial dilution over a concentration range of 1 nM to 10 μM.
Biological Assay
CD274 protein, human
Cell Culture Techniques
Cell Lines
Cells
Culture Media
Cytotoxic T-Lymphocyte Antigen 4
Dendrites
GZMB protein, human
Immunoglobulins
Malignant Neoplasms
Mus
plinabulin
Sulfoxide, Dimethyl
T-Lymphocyte
Technique, Dilution
tucaresol
In Sects. 2.2 through 2.6 we will define a method that, given lcNGS data for two or more individuals and two alternative pedigrees relating them, computes a likelihood ratio. Combining this ratio with a prior odds yields a posterior odds which may be used to choose between the pedigrees. To assess such a method, the best alternative is of course to apply it and competing methods to large numbers of cases where the true pedigree is known in each case. Such comparisons are limited by the availability of data.
An alternative, used in this paper, is to compare competing methods on simulated data. The assessment is then divided into two tasks: Showing that the simulation model yields data that is realistic in relevant ways, and comparing methods on the simulated data. The simulation model used in this paper uses population models and inheritance models presented in Sects.2.3 and 2.4 , where the inheritance model includes linkage (i.e., effects of crossovers inside the considered pedigree). When two loci are strongly linked, their alleles will often be inherited together, as haplotypes, through the pedigree, thus strongly influencing the information about the pedigree contained in the data. This motivates why data simulation should contain linkage.
The population model includes important standard features such as kinship, however, it does not include linkage disequilibrium (LD, i.e., effects of crossovers outside of the considered pedigree). This means that the effect of LD on competing methods is not assessed. Current methods for handling LD include grouping markers together [9 (link)] or using an multiorder Markov chain [19 ]. Both ideas may be possible to combine with our approach. We have chosen to defer treatment of LD to a later paper.
Section2.2 presents the observational model we use to simulate lcNGS data from simulated genotypes. This is a simplified model simulating only counts of reads at each locus. Section 3.1 contains a small study and an argument why we believe this observational model captures features of lcNGS data essential for relationship inference, in particular when one or more of the samples are based on small amounts of DNA.
Our likelihood method for pedigree inference uses exactly the same likelihood as the one used in data simulation. In any simulation study, when simulation is done using a particular probability distribution, it will be optimal to use the same distribution for likelihood computations. What our study illustrates is the size of the performance reduction when using a likelihood method that ignores linkage or the uncertainty in genotypes that is inherent in lcNGS data. Finally, we compare our approach with NgsRelate [18 (link)] which uses a maximum likelihood procedure to find the most likely Jacquard coefficients. NgsRelate does not account for genetic linkage between the included genetic markers.
An alternative, used in this paper, is to compare competing methods on simulated data. The assessment is then divided into two tasks: Showing that the simulation model yields data that is realistic in relevant ways, and comparing methods on the simulated data. The simulation model used in this paper uses population models and inheritance models presented in Sects.
The population model includes important standard features such as kinship, however, it does not include linkage disequilibrium (LD, i.e., effects of crossovers outside of the considered pedigree). This means that the effect of LD on competing methods is not assessed. Current methods for handling LD include grouping markers together [9 (link)] or using an multiorder Markov chain [19 ]. Both ideas may be possible to combine with our approach. We have chosen to defer treatment of LD to a later paper.
Section
Our likelihood method for pedigree inference uses exactly the same likelihood as the one used in data simulation. In any simulation study, when simulation is done using a particular probability distribution, it will be optimal to use the same distribution for likelihood computations. What our study illustrates is the size of the performance reduction when using a likelihood method that ignores linkage or the uncertainty in genotypes that is inherent in lcNGS data. Finally, we compare our approach with NgsRelate [18 (link)] which uses a maximum likelihood procedure to find the most likely Jacquard coefficients. NgsRelate does not account for genetic linkage between the included genetic markers.
Alleles
Genetic Markers
Genotype
GZMB protein, human
Haplotypes
Linkage, Genetic
Pattern, Inheritance
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More about "GZMB protein, human"
Granzyme B (GZMB) is a serine protease enzyme that plays a crucial role in the immune system's response to viral infections and tumor cells.
Primarily expressed by cytotoxic T lymphocytes and natural killer cells, GZMB is involved in the induction of apoptosis, or programmed cell death, in target cells.
This enzyme acts by cleaving specific substrates within the target cell, triggering a cascade of events that ultimately leads to the cell's demise.
Understanding the mechanisms and functions of GZMB is an important area of research, with potential applications in immunotherapy, cancer treatment, and the study of immune-related disorders.
Researchers often utilize tools and techniques like the Cytofix/Cytoperm kit, Ionomycin, GolgiPlug, LSRFortessa, GolgiStop, Cytofix/Cytoperm, FACSCanto II, LSRII flow cytometer, and Brefeldin A to study GZMB and its role in the immune system.
Additionally, the RNeasy Mini Kit can be used to extract and purify RNA from cells for further analysis.
By delving deeper into the intricacies of GZMB, scientists can uncover new insights that may lead to the development of more effective treatments and therapies for a variety of diseases and conditions.
With the help of AI-driven platforms like PubCompare.ai, researchers can streamline their work, enhance reproducibility, and identify the best protocols and products to support their GZMB-related investigations.
Primarily expressed by cytotoxic T lymphocytes and natural killer cells, GZMB is involved in the induction of apoptosis, or programmed cell death, in target cells.
This enzyme acts by cleaving specific substrates within the target cell, triggering a cascade of events that ultimately leads to the cell's demise.
Understanding the mechanisms and functions of GZMB is an important area of research, with potential applications in immunotherapy, cancer treatment, and the study of immune-related disorders.
Researchers often utilize tools and techniques like the Cytofix/Cytoperm kit, Ionomycin, GolgiPlug, LSRFortessa, GolgiStop, Cytofix/Cytoperm, FACSCanto II, LSRII flow cytometer, and Brefeldin A to study GZMB and its role in the immune system.
Additionally, the RNeasy Mini Kit can be used to extract and purify RNA from cells for further analysis.
By delving deeper into the intricacies of GZMB, scientists can uncover new insights that may lead to the development of more effective treatments and therapies for a variety of diseases and conditions.
With the help of AI-driven platforms like PubCompare.ai, researchers can streamline their work, enhance reproducibility, and identify the best protocols and products to support their GZMB-related investigations.