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Ginkgo biloba

Ginkgo biloba is a deciduous tree native to China, widely cultivated for its unique fan-shaped leaves and potential therapeutic applications.
This ancient plant has been the subject of extensive research, examining its effects on cognitive function, circulation, and antioxidant properties.
PubCompare.ai offers an AI-driven platform to streamline Ginkgo biloba research, helping scientists locate the best protocols and products from literature, pre-prints, and patents.
With enhanced reproducibility and accuracy, researchers can optimize their studies and make more informed decisions, advancing the understanding and utilization of this remarkable botanical.
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Most cited protocols related to «Ginkgo biloba»

1
Inferring Single-Cell Copy Number from Sequencing Data
Cited 11 times
Since we are analyzing single-cell data, we expect every genomic locus to have an integer copy number (CN) value. Furthermore, the quantized nature of single-cell data means that the same number of reads per bin should separate every sequential CN state, e.g., ~50 reads for CN 1, ~100 reads for CN 2, ~150 reads for CN 3, etc. While biological and technical noise prevent read counts from segregating perfectly into distinct CN states, read counts should still be centered around integer CN states.
The most direct approach for determining the CN state of each cell is available for users that have a priori knowledge of the ploidy of each sample. For example, cells that are DAPI-stained prior to cell sorting can be gated based on their fluorescence activity, and ploidy can be determined by comparing its fluorescence activity to that of a reference cell with a known CN state. With these data, Ginkgo determines the copy number state of each sample by scaling the segmented bin counts such that the mean bin count is equal to the ploidy of the sample. Finally bin counts are rounded to integer copy number values. Advances in fluorescence activated cell sorting (FACS) will make this copy number prediction even more accurate in time, although cells that are incorrectly sorted and placed into wells with more than one cell will show much higher fluorescence activity and will have an incorrectly inferred copy number state.
Since FACS data is not always available for analysis and has potential for error, Ginkgo provides an alternative to determine the copy number of each sample. As discussed earlier, before determining the CN state of a cell, the cell is binned, normalized, and segmented. This copy number profile has a mean of one and is referred to as the raw copy number profile (RCNP). If the true genome-wide copy number of a sample were equal to X, the scaled copy number profile (SCNP) would then be the product of RCNP and X, and the final integer copy number profile (FCNP) would be the rounded value of the SCNP so all segments contain an integer value.
With these relationships, Ginkgo infers the genome-wide copy number X using numerical optimization. For a given cell, Ginkgo first determines the SCNP and FCNP for all possible values of X in the set [1.50, 1.55, 1.60, …, 5.90, 5.95, 6.00]. Ginkgo then computes the sum of square (SoS) error between the SCNP and the RCNP for each value of X and selects the value of X with the smallest SoS error. Once the multiplier is identified and applied, the scaled bins are rounded to generate the final integer copy number profile for each sample. Intuitively, this is equivalent to finding the copy number multiplier that causes the normalized segmented bin counts to best align with integer copy number values.
Garvin T., Aboukhalil R., Kendall J., Baslan T., Atwal G.S., Hicks J., Wigler M, & Schatz M.C. (2015). Interactive analysis and assessment of single-cell copy-number variations. Nature methods, 12(11), 1058-1060.
Publication 2015
Biopharmaceuticals Cells DAPI Fluorescence Genome Ginkgo biloba
2
Validating Single-Cell Sequencing Techniques
Cited 10 times
We validate Ginkgo by reproducing major findings of several single cell sequencing studies that employ three different WGA techniques: MALBAC, DOP-PCR/WGA4, and MDA. Take together, we analyze the data characteristics of nine datasets across five tissue types (Table 1). The Ginkgo parameters for these datasets are described in the main text, and additional parameters are noted below.
Reads were mapped to hg19 using bowtie and only uniquely mapped reads (mapping quality score >= 25) were kept. Mapped read counts ranged from 1,538,234 (Ni et al.) to 30,638,853 (Lu et al.) with a mean of 15,827,886. To perform an unbiased comparison, all samples were randomly downsampled to 1,538,234 reads to match the lowest available coverage.
In order to compute the GC biases across all nine datasets we calculate the lowess fit of the log base 2 normalized read counts with respect to the bin GC content for each sample. A sample with no GC bias would have a flat normalized read count of zero across all bins and all GC values. After the lowess fit, we monitor the bias of each cell by calculating the proportion of bins that show a two fold change from the expected coverage in either direction (by +/− 1, log base 2).
Garvin T., Aboukhalil R., Kendall J., Baslan T., Atwal G.S., Hicks J., Wigler M, & Schatz M.C. (2015). Interactive analysis and assessment of single-cell copy-number variations. Nature methods, 12(11), 1058-1060.
Publication 2015
Ginkgo biloba Histocompatibility Testing
3
Validating Single-Cell Sequencing Techniques
Cited 10 times
We validate Ginkgo by reproducing major findings of several single cell sequencing studies that employ three different WGA techniques: MALBAC, DOP-PCR/WGA4, and MDA. Take together, we analyze the data characteristics of nine datasets across five tissue types (Table 1). The Ginkgo parameters for these datasets are described in the main text, and additional parameters are noted below.
Reads were mapped to hg19 using bowtie and only uniquely mapped reads (mapping quality score >= 25) were kept. Mapped read counts ranged from 1,538,234 (Ni et al.) to 30,638,853 (Lu et al.) with a mean of 15,827,886. To perform an unbiased comparison, all samples were randomly downsampled to 1,538,234 reads to match the lowest available coverage.
In order to compute the GC biases across all nine datasets we calculate the lowess fit of the log base 2 normalized read counts with respect to the bin GC content for each sample. A sample with no GC bias would have a flat normalized read count of zero across all bins and all GC values. After the lowess fit, we monitor the bias of each cell by calculating the proportion of bins that show a two fold change from the expected coverage in either direction (by +/− 1, log base 2).
Garvin T., Aboukhalil R., Kendall J., Baslan T., Atwal G.S., Hicks J., Wigler M, & Schatz M.C. (2015). Interactive analysis and assessment of single-cell copy-number variations. Nature methods, 12(11), 1058-1060.
Publication 2015
Ginkgo biloba Histocompatibility Testing
4
Robust Assessment of Microbial Community Diversity
Cited 10 times
All statistical tests performed in this study were considered significant at P<0.05 unless indicated otherwise; however, we provide the precise P-values wherever possible. Multivariate analysis of microbial diversity was performed according to Anderson and Willis (2003) and included (1) a robust unconstrained ordination to determine the major variance components, (2) a compatible constrained analysis with reference to the hypothesis, (3) a rigorous statistical test of the hypothesis and (4) a characterization of the taxa responsible for the multivariate patterns. For this purpose, we used (1) principal coordinate analysis (PCO; Gower, 1966 ); (2) canonical analysis of principal coordinates (CAP; Anderson and Willis, 2003 ); (3) permutational analysis of variance (PERMANOVA; Anderson, 2001 ), permutational analysis of multivariate dispersions (PERMDISP; Anderson, 2006 (link)) and analysis of similarity (ANOSIM; Clarke, 1993 ); and (4) correlation-based indicator species analysis (De Cáceres and Legendre, 2009 (link)). Each method comes with its own advantages and limitations such that the combined use of these methods provides a robust assessment of the hypothesis. Differences in β-diversity were measured using Bray–Curtis similarities calculated based on normalized and square root transformed OTU abundances (Hartmann et al., 2012 (link)). PCO, CAP, PERMANOVA, PERMDISP and ANOSIM were performed using the homonymous routines in PRIMER6+ (Clarke and Gorley, 2006 ). Significance levels calculated in CAP, PERMANOVA, PERMDISP and ANOSIM were determined with 105 permutations. Adjustments for multiple testing were performed using the Benjamini–Hochberg correction (Benjamini and Hochberg, 1995 ) in the R package MULTTEST (Pollard et al., 2013 ). Correlations between resemblance matrices were determined using a non-parametric Mantel-type test implemented as the RELATE routine in PRIMER6+.Estimates of α-diversity were based on evenly rarefied OTU abundance matrices and included observed richness Sobs and Smith–Wilson evenness Evar (Smith and Wilson, 1996 ) as calculated in MOTHUR. Sampling effort was estimated using Good's coverage (Good, 1953 ). Rarefaction curves of the observed richness were calculated in MOTHUR using 1000-fold resampling without replacement. Biplot correlations between PCO scores and α-diversity metrics were calculated using the ‘corr.axes' function in MOTHUR. In order to maximize comparability with analysis of β-diversity, management effects on α-diversity were examined using univariate PERMANOVA, ANOSIM and PERMDISP based on Euclidean distances.
Overall management effects on soil chemistry were examined using PCO combined with multi- and univariate PERMANOVA and ANOSIM of Euclidean distances based on z-transformed data. The relationship between β-diversity and soil chemistry was analysed using nonparametric multivariate regression between the soil chemical parameters and the OTU-based resemblance matrices implemented as distance-based linear modelling (McArdle and Anderson, 2001 ) in PRIMER6+ and run with 105 permutations. Models were built using a step-wise selection procedure and the adjusted R2 selection criterion.
The association strength (that is, the point biserial correlation coefficient R) of each OTU with a particular farming system or farming system combination was determined using correlation-based indicator species analysis (De Cáceres and Legendre, 2009 (link)) with all possible combinations (De Cáceres et al., 2010 ) and correction for unequal sample sizes where necessary (Tichy et al., 2006 ). Based on the rationale that an OTU can occupy a certain niche provided by multiple farming systems, considering all possible combinations is important to detect these associations (De Cáceres et al., 2010 ). The analyis was peformed in GINKGO (Bouxin, 2005 ) with 105 permutations. P-value adjustments for multiple comparisons were performed using the false discovery rate correction according to Storey (2002) . Q-values were determined using QVALITY (Käll et al., 2009 (link)) and associations were considered significant at q<0.05. Singletons and doubletons, that is, OTUs that were represented by only one or two sequences across the whole data set, hold little indicator potential and were not included in the analysis.
Various network appproaches were used to analyse the data sets. Directed networks visualizing the OTU distribution across the taxonomic tree were generated using the prefuse layout algorithm in CYTOSCAPE 3.0 (Shannon et al., 2003 (link)). Bipartite networks were generated using the systems as source nodes and the OTUs as target nodes, with edges (that is, lines connecting nodes) corresponding to positive associations of particular OTUs with specific systems or system combinations. Bipartite networks were generated using the edge-weighted spring-embedded layout algorithm in CYTOSCAPE with edges weighted according to the association strength. OTU co-correlations between all pairs of significantly (q<0.05) associated OTUs were calculated using Spearman's rank correlation coefficient. P-values were adjusted using QVALITY and co-correlations were considered significant at q<0.01. Based on this information, co-correlation networks were construced using the edge-weighted spring-embedded layout algorithm in CYTOSCAPE with edges weighted according to the correlation coefficient.
Hartmann M., Frey B., Mayer J., Mäder P, & Widmer F. (2014). Distinct soil microbial diversity under long-term organic and conventional farming. The ISME Journal, 9(5), 1177-1194.
Publication 2014
Dyspnea Epistropheus Ginkgo biloba Muscle Rigidity Plant Roots Trees
5
Ginkgo Biloba Cognitive Trial
Cited 10 times
A 2 × 2 factorial design involving two separate randomisations, resulting in participants being randomised to one of four arms, was employed. Both factors consisted of two levels: medication group (Ginkgo and placebo); and level of follow-up (minimal or intensive). This produced four groups: the Ginkgo group with intensive follow-up, the Ginkgo group with minimal follow-up, the placebo group with intensive follow-up and the placebo group with minimal follow-up. The randomisation codes were generated usingthe computer algorithm RCODE v.4.8 (Schwabe, 2002).
The Ginkgo vs. placebo component of the trial was double-blind, but it was not possible to blind the Hawthorne component of the trial as both researchers and participants needed to know when the next assessment would be.
McCarney R., Warner J., Iliffe S., van Haselen R., Griffin M, & Fisher P. (2007). The Hawthorne Effect: a randomised, controlled trial. BMC Medical Research Methodology, 7, 30.
Full text: Click here
Publication 2007
Arm, Upper Ginkgo biloba Pharmaceutical Preparations Placebos Visually Impaired Persons

Most recents protocols related to «Ginkgo biloba»

1
Cytotoxicity of Plant Stem Cell Extracts
Not available on PMC !

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

US11872258B2. Oral cavity treatments (2024-01-16). None [US]. Inventors: Maryam Rahimi [US].
Full text: Click here
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
2
Biomass-Derived Activated Carbon Production
For the preparation of biomass-derived AC, detailed procedures involving pre-treatment, hydrothermal carbonization, and alkali-assisted activation were described in our previous work.15 The collected ginkgo leaves were completely dried in an electric oven at 90 °C and then shattered. To obtain fine powder, sieving operation was done with a 100 mesh sieve. In a typical run, 4.00 g of the fine powder was dispersed in 60 mL deionized water by vigorous stirring and then transferred to a 100 mL Teflon-lined autoclave, followed by heating at 180 °C for 12 h. After cooling to room temperature, the hydrothermal product was filtered, washed, and completely dried. Next, 2.00 g of the hydrothermal product was uniformly mixed with 1.00 g of KOH and then heated at 700 °C for 1 h under argon atmosphere. After natural cooling, the activated product was successively washed with diluted HCl and deionized water until the pH value was constant to be 7.0. The biomass-derived AC was finally achieved after conventional filtration and drying.
Wei L., Lu Y., Lu R, & Cui Y. (2023). Waste-biomass-derived activated carbon supported Co–Cu–P nanocatalysts for hydrolytic dehydrogenation of ammonia borane. RSC Advances, 13(11), 7614-7620.
Publication 2023
Alkalies Argon Atmosphere Electricity Filtration Ginkgo biloba Powder Teflon
3
Quantitative RT-qPCR Analysis of GbFLSa Gene
One microgram of total RNA was reverse-transcribed using PrimeScript RT Master Mix (Takara Biomedical Technology Co., Ltd, Beijing, China), and the diluted cDNA (threefold) was used as the template. Then, quantitative real-time fluorescent polymerase chain reaction (RT–qPCR) analysis was carried out using FastStart Universal SYBR Green Master with ROX for RT–PCR Kit (Roche, Indianapolis, IN, USA) to investigate the transcription levels of the GbFLSa gene. Finally, all primer sequences and reference genes used for RT–qPCR are listed in Supplemental Table S1. Gene expression analysis was performed for ginkgo using the 2–ΔΔCT method (Schmittgen and Livak, 2008 (link)) and for Populus via the 2–ΔCT method. The glyceraldehyde-3-phosphate and elongation factor 1-α (EF1α) genes were used as reference genes in ginkgo and Populus, respectively.
Guo J., Wu Y., Wang T., Xin Y., Wang G., Zhou Q, & Xu L.A. (2023). GbFLSa overexpression negatively regulates proanthocyanin biosynthesis. Frontiers in Plant Science, 14, 1093656.
Full text: Click here
Publication 2023
Biomedical Technology DNA, Complementary Elongation Factor 1alpha Gene Expression Profiling Genes Ginkgo biloba Glyceraldehyde 3-Phosphate Oligonucleotide Primers Populus Real-Time Polymerase Chain Reaction Reverse Transcriptase Polymerase Chain Reaction SYBR Green I Transcription, Genetic
4
Ginkgo Flavonoid Synthase Gene Cloning and Characterization
Based on an mRNA fragment annotated in the RNA-seq library of ginkgo (Wu et al., 2018 (link)), nested primers were designed to amplify full-length cDNA using the SMARTer RACE 5’/3’ Kit (Clontech, Japan) (Supplemental Table S1). Open reading frames (ORFs) were predicted by the NCBI ORFfinder, and the ORF of the GbFLSa gene was cloned to further determine its function. Moreover, ExPASy online software, DNAMAN 6.0 software, MEGA 7.0 software, SOPMA software and Gene Structure Display Server online software were used to analyze the FLS protein sequence (Wu et al., 2020a (link); Wu et al. 2020b (link)).
Guo J., Wu Y., Wang T., Xin Y., Wang G., Zhou Q, & Xu L.A. (2023). GbFLSa overexpression negatively regulates proanthocyanin biosynthesis. Frontiers in Plant Science, 14, 1093656.
Full text: Click here
Publication 2023
Amino Acid Sequence cDNA Library DNA, Complementary Genes Ginkgo biloba Oligonucleotide Primers Open Reading Frames RNA, Messenger RNA-Seq
5
Ginkgo Tissue Collection and Poplar Validation
Different tissues, including roots, stems, leaves, kernels, buds, and petioles, were collected from 25-year-old ginkgo trees at Nanjing Forestry University, Jiangsu, China. One-year-old seedlings of ginkgo leaves were obtained from April to October for different expression stages. For functional verification experiments, including transient expression and genetic transformation, we used Populus davidiana Dode × Populus bolleana Lauche cv. (“Shanxin Yang”) as a control (CK) poplar.
Guo J., Wu Y., Wang T., Xin Y., Wang G., Zhou Q, & Xu L.A. (2023). GbFLSa overexpression negatively regulates proanthocyanin biosynthesis. Frontiers in Plant Science, 14, 1093656.
Full text: Click here
Publication 2023
Ginkgo biloba Plant Roots Populus Seedlings Stem, Plant Tissues Transformation, Genetic Transients Trees

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More about "Ginkgo biloba"

Ginkgo biloba, a venerable deciduous tree native to China, has long been prized for its unique fan-shaped foliage and potential therapeutic applications.
This ancient botanical has been the subject of extensive scientific inquiry, with researchers exploring its effects on cognitive function, circulation, and antioxidant properties.
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Optimizing Ginkgo biloba studies is crucial, as this remarkable plant has shown promise in various areas.
DMSO (dimethyl sulfoxide) and Quercetin, two compounds found in Ginkgo biloba, have demonstrated antioxidant and anti-inflammatory properties that may contribute to its therapeutic potential.
Kaempferol, another bioactive constituent, has been linked to neuroprotective effects.
The extraction of Ginkgo biloba often involves the use of Methanol, a common solvent, and DMEM (Dulbecco's Modified Eagle Medium) is a common cell culture medium used in research.
To ensure the accuracy and reproducibility of Ginkgo biloba studies, researchers may employ techniques like Trifluoroacetic acid (TFA) analysis and the Qubit 2.0 Fluorometer for precise quantification.
Bilobalide, a unique Ginkgo biloba compound, has been the focus of numerous investigations due to its potential benefits for cognitive function and circulation.
Formic acid is another substance that may be utilized in the analysis of Ginkgo biloba.
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