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Apple

Apple is a commonly cultivated fruit-bearing tree or shrub belonging to the rose family.
The edible fruit, also called an apple, is typically round, red or yellow, and crisp in texture.
Apples are a rich source of vitamins, minerals, and fiber, and are widely consumed fresh, cooked, or processed into various food products.
They are an important commercial crop with many cultivated varieties adapted to different climates and uses.
Apples have a long history of cultivation and cultural significance, and are an important component of many cuisines around the world.

Most cited protocols related to «Apple»

1
Heatmap Illustrator (HemI) Software
In this work, we presented a novel software package of HemI (Heatmap Illustrator, version 1.0), which used a red, green, and blue tricolor in a 256 color mode. Given a selected color scale, the total color space will be automatically processed into a numerical matrix (768 rows * 3 columns) by Java. Then the inputted gene or protein expression data can be linearly normalized as below: More frequently, researchers prefer to visualize the logarithmic relations between different conditions and molecular expression levels. Thus, the original data can also be normalized as below: While
In both equations, the Max cannot be equal to Min, and both OV and Min values must be greater than 0 in Eq. 2. The calculated NVs were then mapped to the color matrix, while the tricolor values of the nearest number of rows were visualized.
For further analysis of the data in heatmaps, several clustering approaches such as the hierarchical and k-means clustering algorithms, were also integrated. To calculate the distance, three types of linkage criteria (Table 3) and seven kinds of metrics (Table 4) were adopted for the two algorithms, respectively.
HemI 1.0 was written in Java 1.6 (J2SE 6.0) and packaged with Install4j 4.0.8. We developed six packages to support three major ×86/×64 operating systems (OSs), including Windows, Unix/Linux, and Mac. The stability and applicability of HemI was rigorously tested under Windows XP/7, Ubuntu, and Apple Mac OS X 10.5 (Leopard).
Deng W., Wang Y., Liu Z., Cheng H, & Xue Y. (2014). HemI: A Toolkit for Illustrating Heatmaps. PLoS ONE, 9(11), e111988.
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Publication 2014
Genes Leopard Proteins
2
Dietary Assessment Using FFQ and 24-hour Recalls
The FFQ, originally developed for the TLGS, was a Willett-format questionnaire modified based on Iranian food items25 and contains questions about average consumption and frequency for 168 food items during the past year.7 The food items were chosen according to the most frequently consumed items in the national food consumption survey in Iran.25 Because different recipes are used for food preparation, the FFQ was based on food items rather than dishes, eg, beans, different meats and oils, and rice. Subjects indicated their food consumption frequencies on a daily basis (eg, for bread), weekly basis (eg, for rice and meat), monthly basis (eg, for fish), yearly basis (eg, for organ meats), or a never/seldom basis according to portion sizes that were provided in the FFQ. For each food item on the FFQ, a portion size was specified using USDA serving sizes (eg, bread, 1 slice; apple, 1 medium; dairy, 1 cup) whenever possible; if this was not possible, household measures (eg, beans, 1 tablespoon; chicken meat, 1 leg, breast, or wing; rice, 1 large, medium, or small plate) were chosen. Table 1shows food items and portion sizes used in the FFQ. Trained dietary interviewers with at least 3 of experience in the Nationwide Food Consumption Survey project25 or TLGS26 (link) administered the FFQs and 24-hour DRs during face-to-face interviews. The interviewer read out the food items on the FFQ, and recorded their serving size and frequency. The interview session took about 45 minutes. The interviewer for FFQ1 and FFQ2 was the same for each participant. Daily intakes of each food item were determined based on the consumption frequency multiplied by the portion size or household measure for each food item.27 The weight of seasonal foods, like some fruits, was estimated according to the number of seasons when each food was available.
Dietary data were also collected monthly by means of twelve 24-hour DRs that lasted for 20 minutes on average. For all subjects, 2 formal weekend day (Thursday and Friday in Iran) and 10 weekdays were recalled. All recall interviews were performed at subjects’ homes to better estimate the commonly used household measures and to limit the number of missing subjects. Detailed information about food preparation methods and recipe ingredients were considered by interviewers. To prevent subjects from intentionally altering their regular diets, participants were informed of the recall meetings with dietitians during the evening before the interview. All recalls were checked by investigators, and ambiguities were resolved with the subjects. Mixed dishes in 24-hour DRs were converted into their ingredients according to the subjects’ report on the amount of the food item consumed, thus taking into account variations in meal preparation recipes. For instance, broth or soup ingredients—usually vegetables (carrot or green beans), noodles, barley, etc.—differed according to subjects’ meal preparation. Because the only available Iranian food composition table (FCT)28 analyzes a very limited number of raw food items and nutrients, we used the USDA FCT29 as the main FCT; the Iranian FCT was used as an alternative for traditional Iranian food items, like kashk, which are not included in the USDA FCT.
The food items on the FFQ and DR were grouped according to their nutrient contents, based on other studies,30 (link) and modified according to our dietary patterns. Seventeen food groups were thus obtained, as follows: 1) whole grains, 2) refined grains, 3) potatoes, 4) dairy products, 5) vegetables, 6) fruits, 7) legumes, 8) meats, 9) nuts and seeds, 10) solid fat, 11) liquid oil, 12) tea and coffee, 13) salty snacks, 14) simple sugars, 15) honey and jams, 16) soft drinks, and 17) desserts and snacks (Table 1). The 168 food items on the FFQ were allocated to these 17 food groups, and the amounts in grams of each item were summed to obtain the daily intake of each food group.
Hosseini Esfahani F., Asghari G., Mirmiran P, & Azizi F. (2010). Reproducibility and Relative Validity of Food Group Intake in a Food Frequency Questionnaire Developed for the Tehran Lipid and Glucose Study. Journal of Epidemiology, 20(2), 150-158.
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Publication 2010
Barley Bread Breast Carrots Cereals Chickens Coffee Dairy Products Diet Dietitian Eating Fabaceae Face Fishes Food Fruit Honey Households Hyperostosis, Diffuse Idiopathic Skeletal Interviewers Meat Mental Recall Monosaccharides Nutrients Nuts Oryza sativa Plant Embryos Potato Raw Foods Snacks Sodium Chloride, Dietary Soft Drinks Vegetables Whole Grains
3
Systematic review of glycaemic index data
We conducted a literature search of MEDLINE from January 1981 through December 2007 using the terms “glyc(a)emic index” and “glyc(a)emic load.” We restricted the search to human studies published in English using standardized methodology. We performed a manual search of relevant citations and contacted experts in the field. Unpublished values from our laboratory and elsewhere were included. Values listed in previous tables (6 (link),7 ) were not automatically entered but reviewed first. Final data were divided into two lists. Values derived from groups of eight or more healthy subjects were included in the first list. Data derived from testing individuals with diabetes or impaired glucose metabolism, from studies using too few subjects (n ≤ 5), or showing wide variability (SEM > 15) were included in the second list. Some foods were tested in only six or seven normal subjects but otherwise appeared reliable and were included in the first list. Two columns of GI values were created because both glucose and white bread continue to be used as reference foods. The conversion factor 100/70 or 70/100 was used to convert from one scale to the other. In instances where other reference foods (e.g., rice) were used, this was accepted provided the conversion factor to the glucose scale had been established. To avoid confusion, the glucose scale is recommended for final reporting. GL values were calculated as the product of the amount of available carbohydrate in a specified serving size and the GI value (using glucose as the reference food), divided by 100. Carbohydrate content was obtained from the reference paper or food composition tables (8 ). The relationship between GI values determined in normal subjects versus diabetic subjects was tested by linear regression. Common foods (n = 20), including white bread, cornflakes, rice, oranges, corn, apple juice, sucrose, and milk were used for this analysis.
Atkinson F.S., Foster-Powell K, & Brand-Miller J.C. (2008). International Tables of Glycemic Index and Glycemic Load Values: 2008. Diabetes Care, 31(12), 2281-2283.
Publication 2008
Bread Carbohydrates Corns Diabetes Mellitus Food Glucose Healthy Volunteers Homo sapiens Metabolism Milk, Cow's Oryza sativa Sucrose
4
Mindboggle-101: Benchmark Brain MRI Dataset
We selected 101 T1-weighted brain MR images that are: (1) publicly accessible with a non-restrictive license, (2) from healthy participants, (3) of high quality to ensure good surface reconstruction, and (4) part of a multi-modal acquisition (T2*-weighted, diffusion-weighted scans, etc.). Five subjects were scanned specifically for this dataset (MMRR-3T7T-2, Twins-2, and Afterthought-1). Scanner acquisition and demographic information are included as Supplementary Material and are also available on the http://mindboggle.info/data website. Table 1 lists the data sets that comprise the Mindboggle-101 data set. These include the 20 test–retest subjects from the “Open Access Series of Imaging Studies” data (Marcus et al., 2007 (link)), the 21 test–retest subjects from the “Multi-Modal Reproducibility Resource” (Landman et al., 2011 (link)), with two additional subjects run under the same protocol in 3T and 7T scanners, 20 subjects from the “Nathan Kline Institute Test–Retest” set, 22 subjects from the “Nathan Kline Institute/Rockland Sample”, the 12 “Human Language Network” subjects (Morgan et al., 2009 (link)), the Colin Holmes 27 template (Holmes et al., 1998 (link)), two identical twins (including author AK), and one brain imaging colleague.
We preprocessed and segmented T1-weighted MRI volumes and constructed cortical surfaces using FreeSurfer’s standard recon-all image processing pipeline4 (Dale et al., 1999 (link); Fischl et al., 1999 (link)). Since it has been demonstrated recently that FreeSurfer results can vary depending on software version, operating system, and hardware (Gronenschild et al., 2012 (link)), every group of subjects was processed by FreeSurfer with the same computer setup. All images were run on Apple OSX 10.6 machines, except for two (Twins-2, run on Ubuntu 11.04), and all were run using FreeSurfer version 5.1.0, except for the OASIS-TRT-20, which were run using 5.0.0 (manual labeling was completed prior to the availability of v5.1.0). Following an initial pass, JT inspected segmentation and surface reconstructions for errors (manual edits to the gray–white tissue segmentation were required for a single subject: HLN-12-2). FreeSurfer then automatically labeled the cortical surface using its DK cortical parcellation atlas ([lh,rh].curvature.buckner40.filled.desikan_killiany.2007 06 20gcs for left and right hemispheres). Vertices along the cortical surface are assigned a given label based on local surface curvature and average convexity, prior label probabilities, and neighboring vertex labels (S’egonne et al., 2004 (link); Desikan et al., 2006 (link)). The region definitions of the labeling protocol represented by the DK atlas are described in Desikan et al. (2006 (link)).
Klein A, & Tourville J. (2012). 101 Labeled Brain Images and a Consistent Human Cortical Labeling Protocol. Frontiers in Neuroscience, 6, 171.
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Publication 2012
Brain Cortex, Cerebral CREB3L1 protein, human Diffusion Gonadorelin Healthy Volunteers Reconstructive Surgical Procedures Tissues Twins Twins, Monozygotic
5
Evaluating Mental Health Mobile Apps
A systematic search of the Apple iTunes store was conducted on September 19, 2013, following the PRISMA guidelines for systematic literature reviews [18 (link)]. An exhaustive list of mental-health related mobile apps was created. The following search terms were employed, “Mindfulness” OR “Depression” OR “Wellbeing” OR “Well-being” OR “Mental Health” OR “Anger” OR “CBT” OR “Stress” OR “Distress” OR “Anxiety”.
App inclusion criteria were: (1) English language; (2) free of charge; (3) availability in the Australian iTunes store; and (4) from iTunes categories, “Health & Fitness”, “Lifestyle”, “Medical”, “Productivity”, “Music”, “Education”, and “Utilities”. The category inclusion criteria were based on careful scrutiny of the titles and types of apps present in those categories.
There were 60 apps that were randomly selected using a randomization website [19 ]. The first ten were used for training and piloting purposes. There were two expert raters: (1) a research officer with a Research Masters in Psychology and two years’ experience in mobile app development, and (2) a PhD candidate with a Masters degree in Applied Psychology and over nine years information technology experience, that trialled each of the first 10 apps for a minimum of 10 minutes and then independently rated their quality using the Mobile App Rating Scale (MARS). The raters convened to compare ratings and address ambiguities in the scale content until consensus was reached. The MARS was revised based on that experience, and the remaining 50 mental health and well being related apps were trialled and independently rated. A minimum sample size of 41 is required to establish whether the true interrater reliability lies within .15 of a sample observation of .80, with 87% assurance (based on 10,000 simulation runs) [20 (link)]. The sample size of 50, therefore, provides substantial confidence in the estimation of the interrater reliability in the current study. Data were analyzed with SPSS version 21 (SPSS Inc, Chicago, IL, USA). The internal consistency of the MARS quality subscales and total quality score was calculated using Cronbach alpha. This indicates the degree (correlations) to which items measuring the same general construct produce similar scores. Interrater reliability of the MARS subscales and total score was determined by the intraclass correlation coefficient (ICC) [21 (link)]. This statistic allows for the appropriate calculation of weighted values of rater agreement and accounts for proximity, rather than equality of ratings. A two-way mixed effects, average measures model with absolute agreement was utilized [22 (link)]. The concurrent validity of the MARS total score was examined in relation to the Apple iTunes App Store average star rating for each app (collected from the Apple iTunes App Store on September 19, 2013).
Stoyanov S.R., Hides L., Kavanagh D.J., Zelenko O., Tjondronegoro D, & Mani M. (2015). Mobile App Rating Scale: A New Tool for Assessing the Quality of Health Mobile Apps. JMIR mHealth and uHealth, 3(1), e27.
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Publication 2015
Anger Anxiety CTSB protein, human Mental Health Mindfulness prisma

Most recents protocols related to «Apple»

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
Anti-inflammatory Effects of Apple Stem Cell Extracts
Not available on PMC !

Example 6

In an inflammatory reaction, activated cells (such as macrophages) release a variety of pro-inflammatory cytokines (such as tumor necrosis factor alpha (TNF-α). The released cytokines can be assayed as a measure of inflammatory activity. To evaluate the anti-inflammatory role of apple stem cell extracts, mouse RAW 264.7 cell lines mouse macrophages were used as an adherent monolayer on petri dishes. These cells could be harvested easily without damage caused by enzymes or cell scrapers. The macrophages were stimulated in suspension with lipopolysaccharide (LPS) to initiate an inflammatory response. Cells were seeded into 12-well cell culture plates containing the apple stem cell extract treatment materials. After 16-18 hours, the medium conditioned by the macrophages was harvested and the cytokine profile in the medium determined with enzyme-linked immunosorbent assays (ELISA) by measuring TNF-α levels.

Method: Three concentration of ASC (6.25, 12.5 and 25 μg/mL in media) were tested for the anti-inflammatory effect. RAW 264.7 mouse macrophage cells were maintained in DMEM containing Glutamax supplemented with 10% FBS, penicillin (100 U/ml) and streptomycin (100 μg/ml). The macrophages treated with LPS (1:500 dilution of a 0.1 mg/ml solution of LPS in phosphate buffered saline (PBS)) to produce a pro-inflammatory response. The ASC treatment was performed with a final concentration of 1×105 macrophages in wells of a 12-well plate. The cytokine assay was performed using a TNF-α ELISA from R&D Systems of Minneapolis, Minnesota.

Results indicated (Table 6, FIG. 5) that LPS alone produced an inflammatory response more than 1000 times that of unstimulated cells as measured by TNF-α expression. Treatment with ASC on the induced macrophages showed a dose-dependent decrease of TNF-α expression. ASC concentrations of 6.25, 12.5, and 25 μg/mL reduced TNF-α activity in the induced cells by 72.1, 92.1 and 94.5%, respectively. This reduced TNF-α at doses of 12.5 and 25 μg/ml was statistically significant with p≤0.05 for 25 μg/ml and p≤0.02 in 12.5 μg/ml. The apple stem cell extracts thus exerted an anti-inflammatory effect on the activated macrophage cells.

TABLE 6
Results of TNF-α release assay showing anti-inflammatory effects of apple
stem cell extracts on mouse RAW 264.7 macrophage cell line cells.
Values shown are averages of three sets of experiments.
ASC extracts dramatically reduced inflammatory responses in the target
cells, as exemplified by reduced TNF-α release
(greater inhibition of inflammation).
Apple Stem percent
Cell Extract TNF-α inhibition
Conc. (μg/ml)(pg/ml)vs. LPS
25481.8994.5
12.5687.992.1
6.252432.8972.1
LPS8712.630
unstimulated6.45

US11872258B2. Oral cavity treatments (2024-01-16). None [US]. Inventors: Maryam Rahimi [US].
Full text: Click here
Patent 2024
Anti-Inflammatory Agents Biological Assay Cell Culture Techniques Cell Extracts Cells Cytokine Enzyme-Linked Immunosorbent Assay Enzymes Hyperostosis, Diffuse Idiopathic Skeletal Inflammation Lipopolysaccharides Macrophage Mus Penicillins Phosphates Psychological Inhibition RAW 264.7 Cells Saline Solution Stem, Plant Stem Cells Streptomycin Technique, Dilution Tumor Necrosis Factor-alpha
3
Gastric Phenylpyruvate Levels in Pigs
Not available on PMC !

Example 71

Levels of gastric phenylpyruvate in two pigs (which had a duodenal canula) at various times prior and post administration of SYN-PKU-2002.

Two days prior to administration of of SYN-PKU-2002, two pigs were put on liquid diet with protein shake/apple juice for 2 days. On day 0, pigs were anesthetized and intubated, and ˜250 ml (˜50 g) Peptone, 3×10e12 bacteria (SYN-PKU-2001 in 30 ml)+24 ml 1M bicarbonate flush (2 g) were instilled at T=0.

Next, 1 ml gastric samples were taked at T=0, 15 min, 30 min, 45 min, 60 min, 75 min, 90 min, 105 min, 120 min. Samples were immediately spun down, the supernatant frozen and the tube with the pellet put at 4 C. Additionally, 1 ml blood samples were taken at T=0, 30 min, 60 min, 90 min, and 120 min, collected in heparinized tubes, spun, plasma collected and frozen. When possible, urine was collected and frozen. Results are shown in FIG. 20 and indicate that LAAD is active in the stomach.

US11879123B2. Bacteria for the treatment of disorders (2024-01-23). Synlogic Operating Company, Inc. [US]. Inventors: Dean Falb [US], Adam B. Fisher [US], Vincent M. Isabella [US], Jonathan W. Kotula [US], David Lubkowicz [US], Paul F. Miller [US], Yves Millet [US], Sarah Elizabeth Rowe [US].
Full text: Click here
Patent 2024
3-phenylpyruvate Bacteria Bicarbonates BLOOD Cannula Diet Duodenum Flushing Freezing Peptones Pigs Plasma Proteins Stomach Tremor Urine
4
Lignocellulosic Substrate Valorization via Solid-State Fermentation
Apple, banana, and grape fruits no longer suitable for consumption
were ground in a blender (Waring commercial, McConnellsburg) to obtain
particles of 1–2 mm of diameter. The whole digestate was thawed
and manually mixed with shredded fruits in a ratio of 70:30 w/w. The
substrate composition is reported in Table 1. Wood sawdust, a local carpentry byproduct,
was added to the whole digestate to reduce the free water and adjust
the moisture content to 73%. SSF was carried out in micropropagation
boxes equipped with a 0.45 μm filter (Microbox, Micropoli, Italy),
each containing 70 g of substrate. Filled boxes were subjected to
two consecutive cycles of sterilization (121 °C for 15 min each).
For the analyses, seven time points were considered: T0, T1, T2, T3,
T4, T5, and T6 corresponding to 0, 3, 6, 13, 20, 27, and 34 days after
inoculation. T. reesei RUT-C30 and T. atroviride Ta13 were routinely grown on slants
of PDA at 26 °C. Then, 100 μL of conidia suspension (106 in sterile distilled water) was inoculated in each box; four
replicate boxes were produced for each time point and for each strain.
In addition, four replicate boxes were not inoculated and served as
a control (SSF-NI). All of the boxes were incubated at 26 °C
and 60% relative humidity (RH) under illumination of 12 h light/12
h dark cycles, using daylight tubes 24 W/m2, in a climatic
chamber (model 720, Binder) for 34 days. For each time point, and
for each strain, one replicate was used for fungal biomass quantification
and stored at −80 °C until use. The remaining three replicates
were treated as described below to obtain the crude extract for enzymatic
and metabolomic analyses.
Bulgari D., Alias C., Peron G., Ribaudo G., Gianoncelli A., Savino S., Boureghda H., Bouznad Z., Monti E, & Gobbi E. (2023). Solid-State Fermentation of Trichoderma spp.: A New Way to Valorize the Agricultural Digestate and Produce Value-Added Bioproducts. Journal of Agricultural and Food Chemistry, 71(9), 3994-4004.
Full text: Click here
Publication 2023
Banana Complex Extracts Conidia DNA Replication Fruit G-substrate Grapes Humidity Light Sterility, Reproductive Sterilization Strains
5
Breast Cancer App Evaluation Across Platforms
This study included English and Korean BC-related apps for women who are at risk for BC across the life stages in the relevant app categories (health and fitness, medical, social, and lifestyle) that had been updated within the previous 3 years (July 2019-July 2022) and were available free of charge. We excluded apps that did not function correctly (eg, unreadable text or a blank screen), those that merely provided lists of conditions, and those intended for medical students that used self-made flashcards. In addition, we excluded apps that were developed with specific target users in mind (eg, those for health care professionals or children), to prompt a donation, or for trial recruitment. The eligible apps for Android and iOS were installed and alternately tested by each reviewer on a Samsung Galaxy S21 (Android version 11.0; Google LLC) and an iPhone 11 (iOS version 15.5; Apple Inc), respectively.
Yang S., Bui C.N, & Park K. (2023). Mobile Health Apps for Breast Cancer: Content Analysis and Quality Assessment. JMIR mHealth and uHealth, 11, e43522.
Full text: Click here
Publication 2023
Child CTSB protein, human Health Care Professionals Koreans Students, Medical Woman

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More about "Apple"

Apples are a versatile and widely cultivated fruit belonging to the rose family.
These crisp, often red or yellow fruits are a rich source of vitamins, minerals, and fiber, making them a popular choice for fresh consumption, cooking, and processing into various food products.
Apples have a long history of cultivation and cultural significance, and are an important component of many cuisines around the world.
In the realm of scientific research, apples have been the subject of extensive study.
Researchers have utilized a variety of tools and techniques to explore the properties and applications of apples and their components.
For example, MATLAB, a powerful software suite, has been employed to analyze and visualize data related to apples.
Apple pectin, a naturally occurring compound in the fruit, has also been the focus of research, with its potential uses in food, medicine, and other industries.
Another important tool in apple research is the TRIzol reagent, a widely used RNA extraction method.
This reagent has been instrumental in studying the genetic and molecular aspects of apples.
Similarly, Prism 6 and Prism 8, two popular data analysis software packages, have been utilized to graphically represent and interpret findings related to apples.
The PrimeScript RT reagent kit and the RNAprep Pure Plant Kit are also commonly used in apple research, facilitating the extraction and analysis of genetic material.
Gallic acid, a phenolic compound found in apples, has been investigated for its potential health benefits and antioxidant properties.
In the realm of data acquisition and analysis, the PowerLab system has been employed to study the physical and chemical properties of apples.
Additionally, pectin from apples has been the subject of extensive research, exploring its potential applications in food, pharmaceuticals, and other industries.
Overall, the wealth of research and tools available for the study of apples highlights the versatility and importance of this ubiquitous fruit.
Whether you're a scientist, a food enthusiast, or simply someone interested in the world around you, the insights gained from the study of apples can be truly fascinating and enlightening.