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Biological Factors

Biological Factors are components or characteristics of living organisms that influence their function and behavior.
This includes factors such as genetic makeup, physiology, biochemistry, and environmental influences.
These factors play a key role in the reproduction, growth, and overall health of living organisms.
Understanding biological factors is crucial for researchers studying topics like human and animal health, plant biology, and ecology.
By considering these factors, researchers can develop more accurate and reproducble experiments that yield reliable results.
Pubcompar.ai's AI-driven platform can help locate the best research protocols by comparing them side-by-side, enehancing your research and ensuring reproducible findings.

Most cited protocols related to «Biological Factors»

SARTools (Statistical Analysis of RNA-Seq data Tools) addresses these limitations by proposing a comprehensive, easy-to-use, DESeq2- and edgeR-based R pipeline that covers all the steps of a differential analysis, from the quality control of raw count data to the detection of differentially expressed genes. It applies to experimental designs involving one biological factor with two or more levels, such as time series or KO vs. WT experiments. When more than two levels are included in the design, all pairwise comparisons are performed. A blocking factor can be specified to take into account data pairing or the presence of a batch effect (e.g. day of preparation effect). However, SARTools does not handle complex experimental designs with interactions since it involves a careful definition of the design formula and of the contrasts to be tested according to the biological question under study. Indeed, it is neither desired nor safe to automate this part of the analysis process. Users who would have to analyse complex experimental designs are encouraged to use directly either DESeq2 or edgeR which both provide extensive help about this kind of experiments.
SARTools is composed of an R package and two R script templates that allow to run the analysis with either DESeq2 or edgeR. Both scripts rely on each package-specific functions as often as possible, and on SARTools functions to export figures and tables and to generate the HTML report. Each script starts with a section of about 15 parameters that refer to (i) paths to input files and the working directory where the analysis will be performed, (ii) project identification, (iii) experimental design, (iv) normalization and statistical test, (v) filtering process and (vi) plotting. Parameters (i) to (iii) have to be adapted to each analysis. The other parameters have default values and can be left unchanged but are accessible to advanced users if they wish to tune the analysis or the reporting more finely.
SARTools requires two types of input files: count data files containing raw counts and a target file that describes the experimental design [13 (link)]. Count data files are sample-specific and are composed of two columns (a unique feature identifier and a raw feature count) with no header. Note that the alignment and counting steps are out of the scope of SARTools and have to be carried out before using specific tools. HTSeq-count output files can be used as input for instance [14 (link)]. The target file contains one row per sample and at least three columns with headers: a unique sample identifier or label, the name of the associated raw counts file and the sample biological condition (see Table 1). If a blocking factor has to be accounted for (e.g. in case of batch effect or paired samples), it is reported in a fourth column. These input files are read by SARTools to build a matrix of integer values in which the intersection of the i-th row and the j-th column reflects how many reads have been mapped to feature i in sample j. This matrix is then used as input for DESeq2 or edgeR.
The source code of the package and instructions to quickly install it are available on GitHub (https://github.com/PF2-pasteur-fr/SARTools). Fig 1 describes the successive steps of the workflow and provides the names of the scripts and R functions corresponding to each step. Furthermore, the Galaxy wrappers to integrate SARTools into a Galaxy instance [15 (link)–17 (link)] are available on the Galaxy Tool Shed of the Institut Franҫais de Bioinformatique at http://toolshed.france-bioinformatique.fr/view/lgueguen/sartools_1_1_0. Galaxy is known to be very user-friendly for biologists and allows them to create worflows to deal with RNA-Seq data. Many tools were already available for the cleaning, mapping and counting steps and SARTools now offers the possibility to run the differential analysis step within the Galaxy environment.
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Publication 2016
Biological Factors Biopharmaceuticals blocking factor Contrast Media Genes RNA-Seq
To enhance interpretation of all presented graphical and numerical summaries we used three worked examples of NMAs. The first example compares 14 antimanic drugs for acute mania [14] (link). The network included 47 studies reporting on efficacy (measured as the number of responders out of total randomized) and 64 studies reporting on acceptability (measured as the number of dropouts out of total randomized). The second example is a network of 62 studies that evaluate the effectiveness of four different percutaneous coronary interventions for non-acute coronary artery disease [15] (link). The third example is a network of 27 studies forming a star-shaped network (i.e. all active treatments are compared only with placebo) that evaluated the effectiveness of six biologic agents for rheumatoid arthritis [16] (link). The outcome in this network was benefit from treatment defined as a 50% improvement in patient- and physician-reported criteria of the American College of Rheumatology (ACR50). The datasets and the STATA routines can be found online in www.mtm.uoi.gr and more detail is provided in the Appendix S1. To be able to carry out the analysis described below, version 3.01 (or later) of the command metan, version 2.6.1 (or later) of metareg and version 2.5.5 (or later) of mvmeta are required.
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Publication 2013
Antimanic Agents BAMBI protein, human Biological Factors Coronary Arteriosclerosis Mania Patients Percutaneous Coronary Intervention Physicians Placebos Rheumatoid Arthritis
Heterogeneity is an important feature in both pairwise and network meta-analysis. In pairwise meta-analysis, visual inspection of the forest plot, the I2 measure, the variance of the distribution of random effects and its 95% confidence intervals, or the Q-test are used to infer about the magnitude of heterogeneity and place the summary effect into context. In NMA, the between-studies variance often assumed to be common across comparisons, is typically used to present heterogeneity across the network. Although multivariate heterogeneity measures such as for multivariate meta-analysis have been developed [38] (link), they have not been applied yet to NMA. Note that in NMA we often assume a common heterogeneity variance across all pairwise comparisons. Some comparisons can be affected more than others by the magnitude of the common heterogeneity variance estimate regarding the amount of additional uncertainty anticipated in future studies. We suggest the presentation of NMA mean summary effects together with their predictive intervals to facilitate interpretation of the results in the light of the magnitude of heterogeneity.
Predictive intervals (PrI) provide an interval within which the estimate of a future study is expected to be [39] (link), [40] . They are computed as where is the percentile of the t-distribution with degrees of freedom (in NMA we suggest this is set to number of studies – number of comparisons with data – 1 [41] ) and is the meta-analysis summary effect.
A forest plot of the estimated summary effects along with their confidence intervals and their corresponding PrI for all comparisons summarizes in one plot the relative mean effects, predictions and the impact of heterogeneity on each comparison. Such a plot is presented in Figure 6 for the biologics in rheumatoid arthritis. The estimated common between-study variance was 0.26 and all six active treatments appear more effective than placebo. The plot indicates that for only one of these comparisons (infliximab vs. placebo) the PrI is wide enough compared with the CI to suggest that in a future study the active treatment can appear less effective than placebo, although the lower CI limit does not cross the line of no effect.
Such a plot can be produced using our STATA command intervalplot after running the mvmeta command as follows:
. intervalplot, mvmetaresultsFor dichotomous outcomes the option eform can be added to plot the estimates on the odds ratio or risk ratio scale (instead of their logarithms).
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Publication 2013
Biological Factors Forests Genetic Heterogeneity Infliximab Light Placebos Rheumatoid Arthritis

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Publication 2012
Biological Factors Cells Cross-Linking and Immunoprecipitation Followed by Deep Sequencing Freezing Gene Expression HEK293 Cells Heterogeneous-Nuclear Ribonucleoprotein Group F Heterogeneous-Nuclear Ribonucleoproteins Heterogeneous-Nuclear Ribonucleoprotein U Heterogeneous Nuclear Ribonucleoprotein A1 Heterogeneous Nuclear Ribonucleoprotein A2-B1 Homo sapiens lipofectamine 2000 Microarray Analysis Novus RNA, Small Interfering RNA-Seq Transfection trizol
3 µg of Anti-CD8a-APC or anti-CD8a-PE (clone: 53-6.7 from eBioscience) or purified rabbit anti-mouse collagen IV (Novus Biologicals) were injected intravenously (i.v.). Three minutes later, the animals were sacrificed, lavaged to remove cells in the airway, bled, and perfused with 10 ml of cold PBS. The spleen, LNs, lung, liver, and small intestine were harvested within 12min, and lymphocytes were isolated as described (19 (link)). Immunofluorescence staining was performed as described (6 (link)).
Publication 2012
Animals Biological Factors Cells Clone Cells Collagen Type IV Common Cold Immunofluorescence Intestines, Small Liver Lung Lymphocyte Mus Novus Rabbits Spleen

Most recents protocols related to «Biological Factors»

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Example 10

This example provides in vitro IC50 data for the blocking of the interaction between recombinant human PD-1 (PD-1-Fc Chimera; Sino Biologics) and human PD-L1 expressed CHO cells by anti-PD-L1 antibody G12. Here, CHO cells expressing PD-L1 were pre-incubated with G12 prior to the addition of rhPD-1-Fc chimeric protein. After incubation and washing, PD-1 binding to cell surface expressed PD-L1 was detected using an Alexa-Fluor 647 tagged anti-PD-1 antibody by flow cytometry (Intellicyt HTFC; FL-4H). This example shows that anti-PD-L1 monoclonal antibody G12 was able to inhibit efficiently the binding of PD-1 to PD-L1 expressed on the surface of CHO cells.

Results: As shown in FIG. 8 and Table 4, the IC50 for blocking of the PD-1/PD-L1 cellular interaction by G12 is 1.76E-09 M. Data was collected on the Intellicyt HTFC flow cytometer, processed using FlowJo software, and analyzed and plotted in Graph Pad Prizm using non-linear regression fit. Data points are shown as the median fluorescence detected in the FL-4H channel+/−Std Error.

TABLE 4
G12
Inhibition of PD-1/PD-L1CHO-PD-L1/1.76E−09
Interaction IC50 (M)rhPD-1-Fc

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Patent 2024
Alexa Fluor 647 Antibodies, Anti-Idiotypic Antigens Binding Proteins Biological Factors CD274 protein, human Cell Communication Cells Chimera CHO Cells Flow Cytometry Fluorescence Homo sapiens Immunoglobulins isononanoyl oxybenzene sulfonate Monoclonal Antibodies Proteins Psychological Inhibition
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Example 7

The MTT Cell Proliferation assay determines cell survival following apple stem cell extract treatment. The purpose was to evaluate the potential anti-tumor activity of apple stem cell extracts as well as to evaluate the dose-dependent cell cytotoxicity.

Principle: Treated cells are exposed to 3-(4,5-dimethythiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT). MTT enters living cells and passes into the mitochondria where it is reduced by mitochondrial succinate dehydrogenase to an insoluble, colored (dark purple) formazan product. The cells are then solubilized with DMSO and the released, solubilized formazan is measured spectrophotometrically. The MTT assay measures cell viability based on the generation of reducing equivalents. Reduction of MTT only occurs in metabolically active cells, so the level of activity is a measure of the viability of the cells. The percentage cell viability is calculated against untreated cells.

Method: A549 and NCI-H520 lung cancer cell lines and L132 lung epithelial cell line were used to determine the plant stem cell treatment tumor-specific cytotoxicity. The cell lines were maintained in Minimal Essential Media supplemented with 10% FBS, penicillin (100 U/ml) and streptomycin (100 μg/ml) in a 5% CO2 at 37 Celsius. Cells were seeded at 5×103 cells/well in 96-well plates and incubated for 48 hours. Triplicates of eight concentrations of the apple stem cell extract were added to the media and cells were incubated for 24 hours. This was followed by removal of media and subsequent washing with the phosphate saline solution. Cell proliferation was measured using the MTT Cell Proliferation Kit I (Boehringer Mannheim, Indianapolis, IN) New medium containing 50 μl of MTT solution (5 mg/ml) was added to each well and cultures were incubated a further 4 hours. Following this incubation, DMSO was added and the cell viability was determined by the absorbance at 570 nm by a microplate reader.

In order to determine the effectiveness of apple stem cell extracts as an anti-tumor biological agent, an MTT assay was carried out and IC50 values were calculated. IC50 is the half maximal inhibitory function concentration of a drug or compound required to inhibit a biological process. The measured process is cell death.

Results: ASC-Treated Human Lung Adenocarcinoma Cell Line A549.

TABLE 7
Results of cytotoxicity of apple stem cell extract on lung cancer cell
line A549 as measured by MTT assay (performed in triplicate).
Values of replicates are % of cell death.
Concentration*replicatereplicatereplicateMean of% Live
(μg/ml)123replicatesSDSEMCells
25093.1890.8690.3491.461.510.878.54
10086.8885.1885.6985.920.870.5014.08
5080.5879.4981.0480.370.800.4619.63
2574.2873.8176.3974.831.380.7925.17
12.567.9868.1371.7569.282.131.2330.72
6.2561.6762.4567.1063.742.931.6936.26
3.12555.3756.7762.4558.203.752.1641.80
1.56249.0751.0857.8052.654.572.6447.35
0.78142.7745.4053.1547.115.403.1252.89

Results: ASC-Treated Human Squamous Carcinoma Cell Line NCI-H520.

TABLE 8
Results of cytotoxicity of apple stem cell extract on lung cancer
cell line NCI-H520 measured by MTT assay (performed in triplicate).
Values of replicates are % of cell death.
Concen-%
tration*replicatereplicatereplicateMean ofLive
(μg/ml)123replicatesSDSEMcell
25088.2889.2987.7388.430.790.4611.57
10078.1379.1978.1378.480.610.3521.52
5067.9869.0968.5468.540.560.3231.46
2557.8358.9958.9458.590.660.3841.41
12.547.6848.8949.3448.640.860.5051.36
6.2537.5338.7939.7538.691.110.6461.31
3.12527.3728.6930.1528.741.390.8071.26
1.56217.2218.5920.5618.791.680.9781.21
0.781 7.07 8.4810.96 8.841.971.1491.16

Results: ASC-treated Lung Epithelial Cell Line L132.

TABLE 9
Results of cytotoxicity of apple stem cell extract on
lung epithelial cell line L132 as measured by MTT assay
(performed in triplicate). Values of replicates are % of cell death.
Concen-rep-rep-rep-Mean%
tration*licatelicatelicateofLive
(μg/ml)123replicatesSDSEMcell
25039.5142.5244.0342.022.301.3357.98
10032.9334.4433.6933.690.750.4466.31
5030.6028.9430.5230.020.940.5469.98
2527.9627.8127.1327.630.440.2572.37
12.525.6225.5525.4025.520.120.0774.48
6.2523.1320.8718.6120.872.261.3179.13
3.12513.3411.0811.8312.081.150.6687.92
1.562 6.56 7.31 9.57 7.811.570.9192.19
0.781 8.06 4.30 3.54 5.302.421.4094.70

Summary Results: Cytotoxicity of Apple Stem Cell Extracts.

TABLE 10
IC50 values of the apple stem cell extracts on the on the target
cell lines as determined by MTT assay.
Target Cell
LineIC50
A54912.58
NCI-H52010.21
L132127.46

Apple stem cell extracts killed lung cancer cells lines A549 and NCI-H520 at relatively low doses: IC50s were 12.58 and 10.21 μg/ml respectively as compared to 127.46 μg/ml for the lung epithelial cell line L132. Near complete anti-tumor activity was seen at a dose of 250 μg/ml in both the lung cancer cell lines. This same dose spared more than one half of the L132 cells. See Tables 7-10. The data revealed that apple stem cell extract is cytotoxic to lung cancer cells while sparing lung epithelial cells. FIG. 6 shows a graphical representation of cytotoxicity activity of apple stem cell extracts on lung tumor cell lines A549, NCIH520 and on L132 lung epithelial cell line (marked “Normal”). The γ-axis is the mean % of cells killed by the indicated treatment compared to unexposed cells. The difference in cytotoxicity levels was statistically significant at p≤05.

Example 9

The experiment of Example 7 was repeated substituting other plant materials for ASC. Plant stem cell materials included Dandelion Root Extract (DRE), Aloe Vera Juice (AVJ), Apple Fiber Powder (AFP), Ginkgo Leaf Extract (GLE), Lingonberry Stem Cells (LSC), Orchid Stem Cells (OSC) as described in Examples 1 and 2. The concentrations of plant materials used were nominally 250, 100, 50, 25, 6.25, 3.125, 1.562, and 0.781 μg/mL. These materials were tested only for cells the human lung epithelial cell line L132 (as a proxy for normal epithelial cells) and for cells of the human lung adenocarcinoma cell line A549 (as a proxy for lung cancer cells).

A549 cells lung cancer cell line cytotoxicity results for each of the treatment materials.

DRE-Treated Lung Cancer Cell Line A549 Cells.

TABLE 11
Triplicate results of cell death of DRE-treated
A549 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates.
Concentration%
(μg/mL)-DRE-Live
treated A549% of cell deathMeanSDSEMcell
25080.4376.4074.8477.232.891.6722.77
10067.6075.2663.7768.885.853.3831.12
5065.3262.9459.9462.732.701.5637.27
2556.8357.9748.1454.315.383.1145.69
6.2555.5949.6949.1751.483.572.0648.52
3.12551.7648.4545.3448.523.211.8551.48
1.56243.6944.0036.0241.244.522.6158.76
0.78137.4726.1919.5727.749.055.2372.26

AVJ-Treated Lung Cancer Cell line A549 Cells.

TABLE 12
Triplicate results of cell death of AVJ-treated
A549 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates.
Concentration%
(μg/mL)-AVJ-treatedLive
A549% of cell deathMeanSDSEMcell
25076.8178.1675.8876.951.140.6623.05
10076.4075.2673.7175.121.350.7824.88
5065.3266.1559.9463.803.371.9536.20
2550.1048.4556.6351.734.322.5048.27
6.2547.5246.3846.1746.690.720.4253.31
3.12539.8638.6143.7940.752.701.5659.25
1.56232.4019.7730.5427.576.823.9472.43
0.78120.5015.6332.1922.778.514.9277.23

AFP-Treated Lung Cancer Cell line A549 Cells.

TABLE 13
Triplicate results of cell death of AFP-treated
A549 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates.
Concentration%
(μg/mL)-AFP-treatedLive
A549% of cell deathMeanSDSEMcell
25086.1387.9986.6586.920.960.5613.08
10079.5081.0682.0980.881.300.7519.12
5073.6072.4671.3372.461.140.6627.54
2568.0167.7066.9867.560.530.3132.44
6.2560.8762.1160.7761.250.750.4338.75
3.12549.4851.7650.7250.661.140.6649.34
1.56240.0641.7247.0042.933.622.0957.07
0.78139.2337.7836.8537.961.200.6962.04

GLE-treated Lung Cancer Cell line A549 Cells.

TABLE 14
Triplicate results of cell death of GLE-treated
A549 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates.
Concentration%
(μg/mL)-GLE-treatedLive
A549% of cell deathMeanSDSEMcell
25088.4291.4990.4490.121.560.909.88
10084.3983.7783.1683.770.610.3516.23
5079.4781.5876.7579.272.421.4020.73
2573.6072.5471.4072.511.100.6327.49
6.2562.8963.6859.9162.161.991.1537.84
3.12550.1854.4751.8452.162.171.2547.84
1.56246.9344.3043.3344.851.861.0755.15
0.78139.5639.3940.9639.970.870.5060.03

LSC-treated lung cancer cell lines A549 cells.

TABLE 15
Triplicate results of cell death of LSC-treated
A549 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates.
Concentration
(μg/mL)% Live
LSC treated A549% of cell deathMeanSDSEMcell
25077.5478.8578.2078.200.650.3821.80
10077.1476.0476.5976.590.550.3223.41
5066.4268.5266.8267.251.120.6532.75
2559.8067.2264.1663.733.732.1536.27
6.2550.5348.8248.0749.141.260.7350.86
3.12541.1443.6042.7242.491.240.7257.51
1.56239.4739.7440.6139.940.600.3460.06
0.78138.5531.8336.7935.723.482.0164.28

OSC-treated Lung Cancer Cell line A549 Cells.

TABLE 16
Triplicate results of cell death of OSC-treated
A549 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates.
Concentration
(μg/mL)% Live
OSC-treated A549% of cell deathMeanSDSEMcell
25070.8465.5771.4969.303.251.8730.70
10048.8150.9157.2852.334.412.5547.67
5046.5949.6053.3349.843.381.9550.16
2538.7740.8136.5838.722.111.2261.28
6.2535.7440.7941.0539.193.001.7360.81
3.12534.5533.6837.0235.081.731.0064.92
1.56233.8633.4427.6331.643.482.0168.36
0.78121.3220.0034.8225.388.214.7474.62

L132 cells (“normal” lung epithelial cell line) cytotoxicity results for each of the treatment materials.

DRE-Treated Lung Epithelial Cell Line L132 cells.

TABLE 17
Triplicate results of cell death of DRE-treated
L132 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates.
Concentration% of %
(μg/mL)cellLive
DRE-treated L132deathMeanSDSEMcell
25086.6686.6186.6686.640.030.0213.36
10076.2977.3976.8476.840.550.3223.16
5065.9268.1767.0167.031.130.6532.97
2555.5458.9557.1957.231.700.9842.77
6.2545.1749.7347.3747.422.281.3252.58
3.12534.8040.5037.5437.612.851.6562.39
1.56224.4231.2827.7227.813.431.9872.19
0.78114.0522.0617.8918.004.012.3182.00

AVJ-Treated Lung Epithelial Cell Line L132 cells.

TABLE 18
Triplicate results of cell death of AVJ-treated
L132 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates
AFP-treated lung epithelial cell line L132 cells.
Concentration % of %
(μg/mL)cellLive
AVJ-treated L132deathMeanSDSEMcell
25057.0355.9353.6255.531.741.0044.47
10050.9949.7847.0449.272.031.1750.73
5044.9543.6340.4543.012.311.3456.99
2538.9137.4933.8636.752.601.5063.25
6.2532.8831.3427.2830.502.891.6769.50
3.12526.8425.1920.6924.243.181.8475.76
1.56220.8019.0514.1117.983.472.0082.02
0.78114.7612.90 7.5211.733.762.1788.27

AFP-Treated Lung Epithelial Cell Line L132 cells.

TABLE 19
Triplicate results of cell death of AFP-treated
L132 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates
AFP-treated lung epithelial cell line L132 cells.
Concentration
(μg/mL)% Live
AFP-treated L132% of cell deathMeanSDSEMcell
25056.1555.4357.1956.260.880.5143.74
10049.9548.2447.6448.611.200.6951.39
5043.7441.0538.0940.962.831.6359.04
2537.5433.8628.5433.324.532.6166.68
6.2531.3426.6718.9925.676.243.6074.33
3.12525.1419.489.4418.027.954.5981.98
1.56218.9412.2910.8714.034.312.4985.97
0.78112.73 5.10 6.81 8.214.002.3191.79

GLE-Treated Lung Epithelial Cell Line L132 cells.

TABLE 20
Triplicate results of cell death of GLE-treated
L132 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates
AFP-treated lung epithelial cell line L132 cells.
Concentration
(μg/mL)% Live
GLE-treated L132% of cell deathMeanSDSEMcell
25084.4283.2083.0883.570.740.4316.43
10080.0579.2978.5979.310.730.4220.69
5072.7571.5974.1072.811.260.7227.19
2580.0581.8679.9980.631.060.6119.37
6.2568.2670.1368.2668.881.080.6231.12
3.12560.6263.0760.6261.441.410.8238.56
1.56248.0748.7748.8348.560.420.2451.44
0.78146.2745.5746.6746.170.560.3253.83

LSC-Treated Lung Epithelial Cell Line L132 cells.

TABLE 21
Triplicate results of cell death of LSC-treated
L132 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates
AFP-treated lung epithelial cell line L132 cells.
Concentration
(μg/mL)% Live
LSC-treated L132% of cell deathMeanSDSEMcell
25086.4185.8285.7686.000.350.2014.00
10081.2181.2779.9980.820.720.4219.18
5075.9674.7473.5174.741.230.7125.26
2574.7472.7571.4772.991.650.9527.01
6.2570.1368.3268.2668.901.060.6131.10
3.12554.0358.0553.4455.172.511.4544.83
1.56253.9751.9851.9852.641.150.6647.36
0.78146.7945.6244.9245.78 0.940.54 54.22

OSC-Treated Lung Epithelial Cell Line L132 cells.

TABLE 22
Triplicate results of cell death of OSC-treated
L132 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates
AFP-treated lung epithelial cell line L132 cells.
Concentration %
(μg/mL)Live
OSC-treated L132% of cell deathMeanSDSEMcell
25061.8462.3760.4461.551.000.5738.45
10054.1453.4452.1053.231.040.6046.77
5042.9442.3040.3241.851.370.7958.15
2535.9434.4833.3134.581.320.7665.42
6.2533.9632.6732.0332.890.980.5767.11
3.12527.4826.2026.7226.800.650.3773.20
1.562 9.80 7.29 7.35 8.151.430.8391.85
0.781 7.29 8.98 8.05 8.110.850.4991.89

Calculated values.

TABLE 23
Calculated IC50 doses (ug/mL) and therapeutic ratios
(IC50 for L132 cells/IC50 for A549 cells) for each
treatment material. Values greater than one indicate
that a material would be more selective in killing cancer
cells than normal cells. ASC results imported from
Example 8. These studies indicate that at least
some of the materials may be effective anti-cancer agents.
ASC has outstanding selectivity compared to other materials.
ASCDREAVJAFPGLELSCOSC
A549 12.589.82211.4811.9811.1 13.733.9 
IC50
L132 127.4656.88 62.6682.6577.6369.26715.38
IC50
Ther.10.15.8 5.56.97.0 0.70.5
Ratio

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Patent 2024
14-3-3 Proteins 43-63 61-26 A549 Cells Action Potentials Adenocarcinoma of Lung Aloe Aloe vera Antineoplastic Agents Biological Assay Biological Factors Biological Processes Bromides Cardiac Arrest Cell Death Cell Extracts Cell Lines Cell Proliferation Cells Cell Survival Cytotoxin diphenyl DNA Replication Epistropheus Epithelial Cells Fibrosis Formazans Genetic Selection Ginkgo biloba Ginkgo biloba extract Homo sapiens Lingonberry Lung Lung Cancer Lung Neoplasms Malignant Neoplasms Mitochondria Mitochondrial Inheritance Neoplasms Neoplastic Stem Cells Oral Cavity PEG SD-01 Penicillins Pharmaceutical Preparations Phosphates Plant Cells Plant Leaves Plant Roots Plants Powder Psychological Inhibition Saline Solution SD 31 SD 62 SEM-76 Squamous Cell Carcinoma Stem, Plant Stem Cells Streptomycin Succinate Dehydrogenase Sulfoxide, Dimethyl Taraxacum Tetrazolium Salts

Example 11

MPV.10.34.d IRC Effectiveness in Human Assays

While the in vitro functional test results of the above experiments were promising, the next desired step in the analysis was to perform similar experiments in human-based assays. To this end, the response of mock human cellular immune system components to tumor cells exposed to MPV.10.34.d IRC was examined in vitro. Human CMV (HCMV) was selected for this study since human CMV is highly prevalent (infecting 50-90% of the human population) and mostly asymptomatic in healthy individuals. (See, Longmate et al., Immunogenetics, 52(3-4):165-73, 2001; Pardieck et al., F1000Res, 7, 2018; and van den Berg et al., Med. Microbiol. Immunol., 208(3-4):365-373, 2019). Importantly, HCMV establishes a life-long persistent infection that requires long-lived cellular immunity to prevent disease. Hence, it is rational to hypothesize that a complex adaptive cell-mediated anti-viral immunity developed over many years to strongly control a viral infection in an aging person can be repurposed and harnessed to treat cancer.

In these experiments, CD8+ T cell responses to CMV peptides were tested in three different human tumor cell lines, including HCT116, OVCAR3, and MCF7. All three of these human tumor cell lines are HLA-A*0201 positive.

In vitro cytotoxicity assays. HTC112, human colon cancer cells, MCF7, human breast cancer cells, and OVCAR3, human ovarian cancer cells (all from ATCC, Manassas, VA, US) were seeded overnight at 0.01 to 0.2×106 per well per 100 μL per 96 well plate. The next day (about 20 to 22 hrs later), each cell line was incubated for one hour at 37° C. under the following conditions: (1) CMV peptide at a final concentration of 1 μg/mL (positive control), (2) MPV.10.34.d at a final concentration of 2.5 μg/mL (negative control), (3) CMV-conjugated MPV.10.34.d IRC at a final concentration of 2.5 μg/mL, (4) CMV-conjugated HPV16 IRC at a final concentration of 2.5 μg/mL, and (5) no antigen (negative control). After 1 hour, the cells were washed vigorously with 200 μL of media for three times to remove non-specific binding. Human patient donor CMV T cells (ASTARTE Biologics, Seattle, WA, US) were added at the E:T (effector cell:target cell) ratio of 10:1 and incubated in a tissue culture incubator for 24 hrs at 37 C, 5% CO2. The total final volume of each sample after co-culture was 200 μL. Cell viability was measured after co-culturing. Cell viability was measured with CELLTITER-GLO® (Promega, Madison, WI, US). This assay provides a luciferase-expressing chemical probe that detects and binds to ATP, a marker of cell viability. The amount of ATP generated from tumor cells was quantified according to manufacturer protocols. In these assays, reduced luciferase activity indicates cell death and suggests greater immune redirection and greater cytotoxicity.

The results are provided in FIG. 25. CMV-conjugated MPV.10.34.d IRC (“VERI-101” in FIGS. 25A, 25B, and 25C) was equally effective as CMV-conjugated HPV16 IRC (“CMV AIR-VLP” in FIGS. 25A, 25B, and 25C) in redirecting human healthy donor CMV pp65-specific CD8+ T-cells (Astarte Biologics, Inc., Bothell, WA, US) to kill immortalized HLA.A2 positive human colon cancer cells (HCT116), human ovarian cancer cells (OVCAR3), and human breast cancer cells (MCF7). The control samples (“No Ag” or “VERI-000” in FIGS. 25A, 25B, and 25C) showed no background tumor killing. Together, these data demonstrate that MPV.10.34.d IRC redirects mouse and human immune responses against tumor cells in vitro.

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Patent 2024
Acclimatization Antigens Antiviral Agents Biological Assay Biological Factors Cancer of Colon CD8-Positive T-Lymphocytes Cell Death Cell Line, Tumor Cell Lines Cells Cell Survival Cellular Immune Response Cellular Immunity Cytotoxin Figs HLA-A2 Antigen Homo sapiens Human papillomavirus 16 In Vitro Testing Luciferases Malignant Neoplasms Mammary Carcinoma, Human MCF-7 Cells Mus Neoplasms Ovarian Cancer Patients Peptides Persistent Infection Promega Response, Immune Response Elements System, Immune T-Lymphocyte Tissue Donors Tissues UL83 protein, Human herpesvirus 5 Virus Virus Diseases
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Example 2

CD4+/CD45RA+ T-cells are transduced with a lentivirus having nucleic acid sequences encoding a FOXP3 polypeptide having mutations as described herein, a receptor polypeptide, and a therapeutic gene product (FIG. 2). Here, a CD4+/CD45RA+ T-cell is transformed with a nucleic acid sequence encoding a FOXP3 polypeptide, a nucleic acid sequence encoding a CXCR3 chemokine receptor polypeptide, and is also transformed with a nucleic acid sequence encoding a scFv antigen-binding fragment that is capable of binding to an IL-6R antigen expressed on a cell associated with an autoimmune disease. The binding of the scFV to an epitope of IL-6R blocks the binding of IL-6R to IL-6. An antibody used in this example includes Tocilizumab, which is a humanized anti-IL-6R antibody. The variable light and heavy chain domains of Tocilizumab (See, U.S. Pat. No. 5,795,965) are provided to the cells using nucleic acid sequence encoding a scFv linked to a secretion signal and operably linked by a constitutive promoter such as EF-1α. Mutations are introduced into the amino acid sequence of Tocilizumab that render the heavy and light chains more favorable binding properties to the IL-6R (See, U.S. Pat. No. 8,562,991). Tregs are not known to naturally produce IL-6 blocking mediators (e.g., antibody or antigen-binding fragments to IL-6R). Therefore, expression of such blockers transformed into a CD4+/CD45RA+ T-cell along with an a nucleic acid sequence encoding a FOXP3 polypeptide will render the T-cells more effective in inflammatory environments than T-cells not transformed with the nucleic acid sequences described herein. The binding of the scFv to IL-6R+ is confirmed by flow cytometry. Secretion of the scFv is verified by ELISA, and the biological activity is confirmed by inhibition of IL-6 signaling in a reporter cell assay (e.g., IL-6 Luciferase stable reporter cell line from Novus Biologicals).

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Patent 2024
Amino Acid Sequence Antibodies, Anti-Idiotypic Antigens Autoimmune Diseases Base Sequence Biological Assay Biological Factors Biopharmaceuticals CD4 Positive T Lymphocytes Cell Lines Cells Chemokine Chemokine Receptor CXCR3 protein, human CXCR3 Receptors Enzyme-Linked Immunosorbent Assay Epitopes Flow Cytometry IL6R protein, human Immunoglobulins Immunoglobulins, Fab Inflammation Lentivirus Light Luciferases Mutation Novus Polypeptides Proteins Psychological Inhibition secretion T-Lymphocyte Therapeutics tocilizumab
The muscles were cut on a cryostat at − 23 °C (7 μm), air-dried, and stored at − 20 °C. Slides were air-dried, rehydrated, and fixed in 4% paraformaldehyde (PFA) for 20 min at the time of staining. For CD63/DAPI/laminin staining, sections were incubated with mouse anti-CD63 IgG1 antibody (1:100 dilution, ab108950, Abcam, Cambridge, UK) and rabbit anti-laminin IgG antibody (1:100 dilution, L9393, Sigma-Aldrich, St. Louis, MO) overnight at 4 °C. Slides were washed in PBS, then incubated with Alexa Fluor 488 goat anti-mouse IgG1 (1:250 dilution, A11001, Invitrogen, Waltham, MA) and Alexa Fluor 594 goat anti-rabbit IgG (1:250 dilution, A11012, Invitrogen) secondary antibodies for 1 h at room temperature. Slides were washed in PBS and mounted with VectaShield fluorescent mounting media with DAPI (H-1200-10, Vector Laboratories, Newark, CA). For CD9/DAPI/dystrophin staining, sections were incubated with rabbit anti-CD9 IgG (1:100 dilution, SA35-08, Invitrogen) and mouse anti-dystrophin IgG2b (1:250 dilution, 08168, Sigma-Aldrich) overnight, followed by incubation with Alexa Fluor 594 goat anti-rabbit IgG (1:250 dilution, A11012, Invitrogen) and Alexa Fluor 647 goat anti-mouse IgG2b (1:250 dilution, A32728, Invitrogen) for 1 h at room temperature. For CD81/DAPI/dystrophin staining, sections were incubated with rabbit anti-CD81(1:100 dilution, SN206-01, Novus Biologicals, Centennial, CO) and mouse anti-dystrophin IgG2b (1:250 dilution, 08168, Sigma-Aldrich) overnight, followed by incubation with Alexa Fluor 594 goat anti-rabbit IgG (1:250 dilution, A11012, Invitrogen) and Alexa Fluor 647 goat anti-mouse IgG2b (1:250 dilution, A32728, Invitrogen) for 1 h at room temperature. For Pax7/CD9/DAPI/WGA staining, sections were subjected to epitope retrieval using sodium citrate (10 mM, pH 6.5) at 92 °C, followed by blocking of endogenous peroxidase activity with 3% hydrogen peroxide in PBS. Sections were incubated overnight in mouse anti-Pax7 IgG1 (1:100 dilution, Developmental Studies Hybridoma Bank, Iowa City, IA) and rabbit anti-CD9 IgG (1:100 dilution, SA35-08, Invitrogen), followed by incubation in goat anti-mouse biotin-conjugated secondary antibody (dilution 1:1,000, 115-065-205; Jackson ImmunoResearch, West Grove, PA) and Alexa Fluor 647 goat anti-rabbit IgG (1:250 dilution, A32733, Invitrogen) for 1 h at room temperature. Next, sections were incubated with streptavidin-HRP (1:500 dilution, S-911, Invitrogen) and Texas Red-conjugated Wheat Germ Agglutinin (WGA) (1:50 dilution, W21405, Invitrogen) at room temperature for 1 h, before incubation in Tyramide Signal Amplification (TSA) Alexa Fluor 488 (1:500 dilution, B40953, Invitrogen). Sections were mounted with VectaShield fluorescent mounting media with DAPI (H-1200-10, Vector Laboratories).
Images were captured with a Zeiss upright microscope (AxioImager M1, Oberkochen, Germany). To quantify the percentage of nuclei (DAPI+) expressing CD63, MyoVision software was used for automated analysis of nuclear density in cross-sections [39 (link)], and nuclei-expressing CD63 (identified as DAPI+/CD63+ events) were counted manually in a blinded manner by the same assessor for all sections using the Zen Blue software.
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Publication 2023
Alexa594 alexa fluor 488 Alexa Fluor 647 anti-IgG Antibodies Antibodies, Anti-Idiotypic Biological Factors Biotin Cardiac Arrest Cell Nucleus Cloning Vectors DAPI DMD protein, human Epitopes Goat Hybridomas IgG1 IgG2B Immunoglobulins Laminin Microscopy Mus Muscle Tissue Novus paraform PAX7 protein, human Peroxidase Peroxides Rabbits Sodium Citrate Streptavidin Technique, Dilution Tritium Wheat Germ Agglutinins

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More about "Biological Factors"

Biological factors are the intrinsic and extrinsic components that influence the functions and behaviors of living organisms.
These encompass a wide range of elements, including genetic makeup, physiology, biochemistry, and environmental influences.
These factors play a crucial role in the reproduction, growth, and overall health of living organisms.
Understanding biological factors is paramount for researchers studying diverse fields such as human and animal health, plant biology, and ecology.
By considering these factors, researchers can develop more accurate and reproducible experiments that yield reliable results.
Factors such as FBS (Fetal Bovine Serum), Penicillin/Streptomycin, DMEM (Dulbecco's Modified Eagle Medium), Penicillin, Streptomycin, L-Glutamine, β-Actin, and GlutaMAX are commonly utilized in biological research to maintain and manipulate cell cultures and study cellular processes.
Pubcompar.ai's AI-driven platform can help locate the best research protocols by comparing them side-by-side, enhancing your research and ensuring reproducible findings.
This powerful tool can help researchers optimize their experimental designs and ensure their experiments produce reliable, reproducible results.