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43-63

Q: What are some common applications of proteins and peptides with 43-63 amino acid residues?

Proteins and peptides in the 43-63 residue range are found in a variety of biological processes and can serve diverse functions. They may act as signaling molecules, regulatory factors, or structural components. Knowing the 43-63 amino acid length can provide insights into the size, structure, and potential roles of these biomolecules in areas like cell signaling, gene expression, and tissue development.

Q: How can the 43-63 amino acid range help researchers understand the properties of a protein or peptide?

The 43-63 residue range is an important characteristic that can offer clues about the size, structure, and potential function of a protein or peptide. Researchers can use this information to make inferences about the biomolecule's folding, stability, and interaction capabilities, which can in turn guide further studies and applications.

Q: What are some common challenges in working with proteins and peptides in the 43-63 amino acid range?

One key challenge is ensuring the reproducibility and optimization of experimental protocols involving 43-63 residue proteins and peptides. Variation in factors like expression, purification, and analysis can impact the reliability of research findings. Fortunatley, PubComapre.ai can help address this by allowing researchers to easily compare and identify the most effective protocols from the literature, pre-prints, and patents.

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43-63: A numeric range representing the number of amino acid residues in a peptide or protein.
This length is an important characteristic that can provide insights into the size, structure, and potential function of the biomolecule.
Proteins and peptides with 43 to 63 amino acids are commonly found in various biological processes and may have diverse roles, such as signaling, regulatory, or structural functions.
Knowing the 43-63 residue range can help researchers understand the properties and potential applications of these proteins and peptides in their studies.

Most cited protocols related to «43-63»

Adiposity Indices and Disease Risk Association

Cited 9 times

The primary objective of this study was to compare the association between adiposity indices and obesity-related diseases or mortality risk, as well we wanted to show the association patterns or shapes. The established 4 or 5 categories of BMI is useful for clinical application, however, those categorization was limited to display the association patterns. Thus, we used more detailed categorization of BMI and corresponding categorization of each indices. BMI was categorized into 10 groups: <18.5, 18.5–20, 20–21.5, 21.5–23, 23–25, 25–26.5, 26.5–28, 28–30, 30–32.5, ≥32.5 kg/m², where the fifth group (23–25) was the reference group. The 10 groups consisted of 3.99%, 8.21%, 13.50%, 17.71%, 24.53%, 13.87%, 8.65%, 5.81%, 2.67%, and 1.06%, respectively. WC, WHR, ABSI, and WWI were also categorized into 10 groups so that the distribution of the 10 groups was approximately the same as that of BMI for fair comparisons of prediction ability (Table 1).Table 1

Ten groups of adiposity indices with the 5^th group as the reference.

Group	1	2	3	4	5	6	7	8	9	10
BMI \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(\frac{{\rm{kg}}}{{{\rm{m}}}^{2}})$$\end{document}(kgm2)	<18.5	18.5~20	20~21.5	21.5~23	23.5~25	25~26.5	26.5~28	28~30	30~32.5	≥32.5
WC(cm)	<64	64~68.8	68.8~73.9	73.9~78.6	78.6~84.8	84.8~88.8	88.8~92.2	92.2~97.4	97.4~103	≥103
WHtR \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(\frac{{\rm{cm}}}{{\rm{m}}})$$\end{document}(cmm)	<0.4	0.4~0.43	0.43~0.45	0.45~0.48	0.48~0.51	0.51~0.54	0.54~0.56	0.56~0.60	0.60~0.63	≥0.63
ABSI \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(\frac{cm\,\ast \,k{g}^{5/6}}{{m}^{2/3}})$$\end{document}(cm⁎kg5/6m2/3)	<6.95	6.95~7.23	7.23~7.49	7.49~7.72	7.72~8.04	8.04~8.26	8.26~9.46	8.46~8.77	8.77~9.13	≥9.13
WWI \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(\frac{{\rm{c}}{\rm{m}}}{\surd kg})$$\end{document}(cm√kg)	<8.83	8.83~9.22	9.22~9.56	9.56~9.89	9.89~10.34	10.34~10.72	10.72~11.05	11.05~11.51	11.51~12.11	≥12.11

Park Y., Kim N.H., Kwon T.Y, & Kim S.G. (2018). A novel adiposity index as an integrated predictor of cardiometabolic disease morbidity and mortality. Scientific Reports, 8, 16753.

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Publication 2018

43-63 Obesity

Sleep Health Questionnaire Survey in Japan

Cited 5 times

The study protocol was approved by the Ethics Committee of the Japanese Foundation for Sleep and Health Sciences, Tokyo, Japan. The study was conducted as part of a web-based, cross-sectional questionnaire survey on sleep health in the Japanese general population in February 2015. In this survey, participants were recruited by Rakuten Research Inc., an online marketing research company employing approximately 2.3 million Japanese individuals. An e-mail containing a link to the online questionnaire was randomly sent to individuals throughout Japan who were stratified by district, gender, and age. Participants’ age ranged from 20 to 69 years. A total of 10,000 completed responses to the questionnaire were received. Part of this pooled data has been published elsewhere [18 (link)]. Among the respondents, 4505 (3160 males, 1345 females; 43.57 ± 11.63 years) general workers who had completed the questionnaire and who had a sleep debt index (SDI) score (discrepancy between self-reported ideal and real sleep time) and SJL score of zero or more, both of which were calculated by the following method (Figure 1), were selected and analyzed. In general, the percentage with SDI <0 and SJL <0 were very low [19 (link)]. It was suggested that participants with a negative value constitute a discrete population with atypical characteristic features [20 (link)]. Based on the findings, the present study decided the exclusion criteria.

Okajima I., Komada Y., Ito W, & Inoue Y. (2021). Sleep Debt and Social Jetlag Associated with Sleepiness, Mood, and Work Performance among Workers in Japan. International Journal of Environmental Research and Public Health, 18(6), 2908.

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Publication 2021

43-63 Ethics Committees Females Gender Japanese Males Sleep Workers

Exploring Street Connectivity and Active Transportation

Cited 5 times

Additional detail concerning the survey and variables analyzed here are presented in Huang et al. 2009 [34 (link)]. This study is based on a subset of data from the 2001 California Health Interview Survey (CHIS). This large (N = 55,428 households) random digit dial telephone survey in California is administered in seven languages (English, Spanish, Mandarin, Cantonese, Vietnamese, Korean and Khmer) and had a response rate, based on the American Association for Public Opinion Research equation RR4 [35 ], of 43.3% with a cooperation rate of 63.7% (weighted to account for the sample design) and 77.1% (unweighted).
We studied residents of San Diego and Los Angeles counties where over 70% of survey respondents supplied the name of the nearest intersection to their residence (In LA County, 8728/12196 = 71.5%, and in SD County 1952/2672 = 73%). These addresses were geocoded to represent the location of each respondent for purposes of this analysis. After exclusion of respondents with missing or invalid data, 8506 respondents from LA and 1883 respondents from SD were used in the analysis. These two counties were the only ones with nearest intersection data available in CHIS 2001.
The paper has two sections. In the first, we characterize street connectivity based on GIS-derived measures from buffers around the nearest intersections to respondents homes. In the second section we used a combination of CHIS variables, Census data, and the street connectivity data in a model-based analysis to explore the relative contributions of street connectivity and other variables to active transportation (AT).

Berrigan D., Pickle L.W, & Dill J. (2010). Associations between street connectivity and active transportation. International Journal of Health Geographics, 9, 20.

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Publication 2010

43-63 allobarbital Buffers Fingers Hispanic or Latino Households Intersectional Framework Koreans Vietnamese

Avian Steroid Concentrations Across Compartments

Cited 4 times

In Study 1A, we examined corticosterone and T levels in plasma, whole blood and RBC (n = 12 subjects for corticosterone, 6 separate subjects for T). Blood samples (∼150 µl) were collected from the brachial vein using three heparinized microhematocrit tubes. Samples were obtained within 1.52 to 5.57 min (mean ± SEM = 2.79±0.28 min) of initial disturbance. The blood in one tube was used to measure steroids in whole blood, and blood in the other two tubes was pooled and centrifuged to separate plasma and RBC. Blood was centrifuged for 10 min at 13,500 g, after which plasma was removed. Some plasma was likely still present in the RBC pellet [17] (link). Whole blood, plasma, and RBC (∼50 µl each) were stored at −20°C. All subjects were sampled between 0800 and 0900 h to control for possible diel variation in steroid levels.
In Study 1B, we examined baseline and stressed levels of corticosterone in plasma, whole blood, and RBC. Blood samples were collected from the brachial vein. Baseline samples (n = 6) were all obtained within 3 min (2.20±0.25 min); samples collected in this time have baseline corticosterone concentrations [18] . Stressed samples were obtained from separate individuals (n = 5) after 60 min (63.43±0.59 min) of restraint in an opaque cloth bag. The same subjects could not be bled at both baseline and after 60 min, because of the quantity of blood required and the small size of zebra finches. In zebra finches, plasma CBG levels decrease after 60 min of restraint [15] (link), which might affect the compartmentalization of corticosterone between plasma and RBC. All subjects were sampled between 0800 and 0930 h.
For Studies 1A and 1B, steroid concentrations are given in ng/g, as RBC samples were weighed to determine the amount of tissue. For plasma and whole blood samples, ng/g measurements are nearly equivalent to ng/ml ([2] and unpub. results). For each subject, the amounts of plasma, whole blood, and RBC extracted and assayed were the same, except for two stressed subjects in Study 1B. For these two subjects, smaller (but equal) amounts of plasma and whole blood were used, since the steroid values were greater than the maximum point on the standard curve in an initial attempt.

Taves M.D., Schmidt K.L., Ruhr I.M., Kapusta K., Prior N.H, & Soma K.K. (2010). Steroid Concentrations in Plasma, Whole Blood and Brain: Effects of Saline Perfusion to Remove Blood Contamination from Brain. PLoS ONE, 5(12), e15727.

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Publication 2010

43-63 BLOOD Corticosterone Finches Plasma Steroids Tissues Veins Zebras

Accuracy Assessment of Alphafold2 Structure Predictions

Cited 3 times

AF2 (V2.0.1) is used for structure predictions with the required databases downloaded from the AF2 GitHub repository^{3 (link)}. Table 1 summarizes the protein models used in the present work. The AF2 structure models of these proteins are shown in Fig. S1 of the Supplementary Information (SI). All protein sequences can be found in the Appendix of the SI.Table 1

Proteins models used in the present work.

Protein	AA¹	MSA²	pLDDT³	IUPRED2³	RMSF (Å)³	PCC⁶	Slope⁶	Int.⁶
a. LanM	133	1832	83.9 ± 19.1	0.39 ± 0.16	5.8 ± 3.3	− 0.84	− 4.9	113
b. DeHa4	300	1890	96.3 ± 6.9	0.25 ± 0.14	0.9 ± 0.9	− 0.94	− 7.2	103
c. PAS-A Domain	108	1138	81.4 ± 16.3	0.20 ± 0.09	1.0 ± 0.7	− 0.65	− 15.5	97
d. AFP Type III	66	1080	96.4 ± 5.7	0.20 ± 0.07	0.7 ± 0.7	− 0.97	− 8.4	103
e. GNE	722	5273	93.2 ± 11.4	0.21 ± 0.12	3.0 ± 1.1	− 0.75	− 9.6	105
f. PAS-Kinase	1323	8644	52.9 ± 27.5	0.43 ± 0.25	5.0 ± 3.9	− 0.63	− 4.0	77
g. inaZ	1200	2050	88.6 ± 16.5	0.41 ± 0.07	3.8 ± 3.2	− 0.65	− 3.3	101
h. Heterodimer⁴: PAS-A, kinase	108 287	1138 1908	89.5 ± 13.0	0.14 ± 0.10	1.3 ± 0.7	− 0.65	− 11.7	110
i. Homodimer⁵: MtMerR	146 146	1825 1825	89.3 ± 13.9	0.36 ± 0.13	3.8 ± 2.5	− 0.66	− 3.7	103
j. NVJP-1	388	0	43.2 ± 5.3	0.84 ± 0.13	10.2 ± 2.4	− 0.03	− 0.1	44
k. Randomized	237	0	32.4 ± 6.2	0.28 ± 0.19	2.1 ± 1.1	− 0.12	− 0.7	34

¹Number of amino acid residues.

²The MSA hits from the BFD³ (Big Fantastic Database). The MSA hits include those that match the protein partial segments.

³Mean ± SD for per-residue pLDDT, IUPRED2 and RMSF values.

⁴Two chains of the heterodimer are PAS-A (108 AA) and kinase (287 AA) domain sequences, respectively.

⁵Both chains of the homodimer have the same sequence of 146 AA.

⁶The Pearson’s correlation coefficient (PCC) between pLDDT and RMSF scores, the slope and intercepts of the linear fitting between them are also listed; note that as pLDDT and the AF2 scores in this work are anticorrelated, and the PCC values are the negative of those shown in the Figures.

Guo H.B., Perminov A., Bekele S., Kedziora G., Farajollahi S., Varaljay V., Hinkle K., Molinero V., Meister K., Hung C., Dennis P., Kelley-Loughnane N, & Berry R. (2022). AlphaFold2 models indicate that protein sequence determines both structure and dynamics. Scientific Reports, 12, 10696.

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Publication 2022

43-63 Amino Acids Amino Acid Sequence Phosphotransferases Proteins

Most recents protocols related to «43-63»

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% CO₂at 37 Celsius. Cells were seeded at 5×10³cells/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].

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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

Developing Galactose-Metabolizing Yeast Strains

Not available on PMC !

Example 1

A primary goal of the present invention was to develop yeast strains highly effective at metabolizing galactose. However, galactose-metabolizing yeasts are uncommon; yeasts typically prefer glucose, a carbohydrate source known to strongly suppress the expression of genes needed to metabolize other carbohydrates such as galactose (See Escalante-Chong et al., “Galactose metabolic genes in yeast respond to a ratio of galactose and glucose.” Proc Nat'l Acad Sci, USA 112:1636-41 (2015)). Wild type yeast strains may thus be prevented from utilizing any carbohydrates when glucose is present. Even if a yeast does have the capability to use other carbohydrate sources, it may occur late in the growth process, only after glucose has been completely depleted. Therefore, a particular type of galactose-metabolizing yeast was developed that degrades galactose in the presence of glucose. The present invention provides methods for adaptively evolving yeast according to this process.

To assess the ability of a yeast strain to degrade galactose, its growth was evaluated on media containing galactose in presence or absence of glucose. The following strains were tested: the commercially available strains Saccharomyces cerevisiae (N) (Natureland, Saccharomyces boulardii (SB) (Jarrow, Santa Fe Springs, CA), and Saccharomyces boulardii (B) (Biocodex, Redwood City, CA). Additional strains included in the screening were isolated from food containing large amounts of galactose such as dairy products and legumes stored at room temperature for over two weeks.

Cultures of various strains were initiated from a single colony on agar plates or from glycerol stocks, and grown in liquid YP medium (1% yeast extract, 2% peptone; Teknova) by incubation at 30° C. with agitation at 125 rpm (Murakami & Kaeberlein “Quantifying Yeast Chronological Life Span by Outgrowth of Aged Cells.” J Visual Exp (27) (2009)). Overnight yeast cultures initiated in duplicate in liquid YP medium were used as pre-cultures to initiate growth efficiency experiments in liquid CM (Synthetic Complete Minimal Medium, 0.5% Ammonium Sulfate, Teknova) containing 2% galactose alone as the sole carbon source, 2% glucose alone as the sole carbon source, or galactose and glucose. Culture growth of cultures set at 30° C. under static conditions was monitored over time by measuring optical density (OD) at 600 nm (OD₆₀₀) using a spectrophotometer.

Growth—was evaluated for several strains. As illustrated in Table 1, one of the evolved clone exhibited the lowest doubling time, which remained at the same level independently of the carbohydrate source and growth conditions.

TABLE 1

Doubling Time of Yeast Strains under Static Growth Conditions

in Media Containing Galactose alone, Glucose alone,

and Galactose and Glucose.

Galactose +

GalactoseGlucoseGlucose

Avg.Avg.Avg.

Strains(h)SD(h)SD(h)SD

Y1_Parent4.570.034.430.054.400.01

Evolved Clone4.050.024.170.084.210.02

Strain N6.050.034.460.024.230.01

Strain SB7.790.064.680.004.750.03

Strain B8.850.024.960.024.630.01

Each data point represents the averages (Avg.) and standard deviation (SD) of quadruplicate values obtained for two independent cultures per strain.

US11865151B2. Live biotherapeutics for the treatment of carbohydrate disorders (2024-01-09). GUTSYBIO INC. [US]. Inventors: Genevieve Hansen [US].

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Patent 2024

43-63 Agar Carbohydrates Carbon Cellular Senescence Clone Cells Culture Media, Conditioned Dairy Products Fabaceae Food Galactose Gene Expression Genes Glucose Glycerin Growth Disorders hydrocortisone butyrate Natural Springs Parent Peptones Redwood Saccharomyces Saccharomyces boulardii Saccharomyces cerevisiae Strains Sulfate, Ammonium Vision Yeasts

Cracking and Hydrotreating of Arab Light Crude Oil

Not available on PMC !

Example 1

An Arab light crude oil with an API gravity of 33.0 and a sulfur content of 1.6 wt. % was fractionated in a distillation column to form a light stream and a heavy stream. Properties of the feed crude oil stream and the resulting fractions (based on their wt. % composition in the crude oil) are given in Table 1 below.

TABLE 1

Boiling Ni VS N

Stream NameRange(ppm)(ppm)(wt. %)(ppm)

Hydrocarbon4.414.21.6444

Feed

Light StreamLess than <1<10.8136

540° C.

Heavy StreamGreater than4.414.20.8308

540° C.

The same Arab light crude oil used in Example 3 was directly cracked in the same cracking reactor and under the same conditions as was used in Example 3.

TABLE 4

EX-3CE-1

Constituent(wt. %)(wt. %)

H20.680.72

C₁6.476.86

C₂3.103.23

C₂= (ethylene)10.8510.41

C₃1.671.65

C₃= (propylene)18.2016.51

iC₄0.460.42

nC40.410.56

t2C₄=2.221.93

1C₄=1.651.40

iC₄=3.573.09

c2C₄=1.791.54

1,3-BD1.110.99

Butenes9.227.96

Total Gas52.1749.31

Dry Gas10.2410.80

Total Light Olefins38.2734.89

Gasoline27.9224.21

LCO8.439.43

HCO2.043.20

Coke9.4413.86

Total Gas + Coke61.6163.17

As can be seen in Table 4, the yield of total light olefins from the inventive EX-3 is significantly higher than the yield of light olefins in the comparative CE-1. Additionally, EX-3 shows significantly lower coke formation than the comparative CE-1.

Example 2

The heavy stream from Example 1 was hydrotreated in a three-stage hydrotreater. The reaction conditions were: a weighted average bed temperature of 400° C., a pressure of 150 bar, a liquid hourly space velocity (LHSV) of 0.5 h⁻¹, an H₂/oil ratio 1200:1 (v/v), an oil flowrate of 300 ml/h, and an H₂flowrate of 360 L/h.

The first stage of the hydrotreater used a KFR-22 catalyst from Albemarle Co. to accomplish hydro-demetallization (HDM). The second stage of the hydrotreater used a KFR-33 catalyst from Albemarle Co. to accomplish hydro-desulfurization (HDS). The third stage of the hydrotreater used a KFR-70 catalyst from Albemarle Co. to accomplish hydro-dearomatization (HDA). The first, second, and third stages were discrete beds placed atop one another in a single reaction zone. The heavy stream flowed downward to the first stage, then to the second stage, and then to the third stage. Properties of this hydrotreated heavy stream are shown in Table 2 below.

TABLE 2

Kinematic viscosity at 100° C.67.6 mm²/s

Density at 60° C.0.9 g/cm³

Sulfur (wt. %)0.36

Ni (ppm)1

V (ppm)3

Fe (ppm)<1

Na (ppm)<10

Example 3

A catalyst with the composition shown in Table 3 below as used in all of the reactions.

TABLE 3

ComponentWeight %Notes

ZSM-520Phosphorus impregnated at 7.5 wt. % P₂O₅

on zeolite

USY21Lanthanum impregnated at 2.5 wt. % La₂O₃

on zeolite

Alumina8Pural SB from Sasol

Clay49Kaolin

Silica2Added as colloidal silica Ludox TM-40

An Advanced Cracking Evaluation (ACE) unit was used to simulate a down-flow FCC reaction zone with multiple inlet points. The ACE unit emulates commercial FCC process.

Prior to each experiment, the catalyst is loaded into the reactor and heated to the desired reaction temperature. N₂gas is fed through the feed injector from the bottom to keep catalyst particles fluidized. Once the catalyst bed temperature reaches within ±2° C. of the reaction temperature, the reaction can begin. Feed is then injected for a predetermined time (time-on-stream (TOS)). The desired catalyst-to-feed ratio is obtained by controlling the feed pump. The gaseous product is routed to the liquid receiver, where C₅₊ hydrocarbons are condensed and the remaining gases are routed to the gas receiver. After catalyst stripping is over, the reactor is heated to 700° C., and nitrogen was replaced with air to regenerate the catalyst. During regeneration, the released gas is routed to a CO₂analyzer. Coke yield is calculated from the flue gas flow rate and CO2 concentration. The above process was repeated for each of Examples 3(A) and 3(B).

The light stream from Example 1 was combined with the hydrotreated heavy stream from Example 2 to form a combined feed stream. The combined feed stream was fed to the ACE unit. A time-on-stream (TOS) of 75 seconds and a temperature of 675° C. was used. Fresh catalyst was steamed deactivated at 810° C. for 6 hours to resemble the equilibrium catalyst in the actual process. The steam deactivated catalyst was used in this reaction. It should be understood that TOS is directly proportional to residence time.

US11866663B1. Multi-zone catalytic cracking of crude oils (2024-01-09). Saudi Arabian Oil Company [SA]. Inventors: Mansour Ali Al-Herz [SA], Ali Al Jawad [SA], Qi Xu [SA], Aaron Akah [SA], Musaed Salem Al-Ghrami [SA].

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Patent 2024

43-63 Adjustment Disorders Alkenes Arabs BD-38 butylene Catalysis Clay Cocaine Distillation ethylene Gravity Hydrocarbons Kaolin Lanthanum Light Neoplasm Metastasis Nitrogen Oxide, Aluminum Petroleum phosphoric anhydride Phosphorus Pressure propylene Regeneration Silicon Dioxide Steam Sulfur Viscosity Vision Zeolites

Synthesis and Characterization of Compound 373

Not available on PMC !

Example 227

[Figure (not displayed)]

Analysis: LCMS m/z=349 (M+1). ¹H NMR (DMSO-d₆) δ: 8.82 (d, 2H, J=6.0 Hz), 8.25 (d, 2H, J=6.8 Hz), 7.88 (d, 1H, J=2.0 Hz), 7.82 (m, 1H), 7.03 (d, 1H, J=8.5 Hz), 4.08 (m, 2H), 3.43-3.63 (br m, 2H), 3.03-3.18 (m, 1H), 2.87 (m, 2H), 1.95-2.07 (m, 1H), 1.62-1.93 (m, 5H), 1.55 (m, 1H), 0.62-0.80 (m, 4H).

US11878982B2. Spiropiperidine derivatives (2024-01-23). 89BIO LTD [IL]. Inventors: Robert L Hudkins [US], David B. Whitman [US], Craig A. Zificsak [US], Allison L. Zulli [US], Melody McWherter [US].

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Patent 2024

1H NMR 43-63 Lincomycin piperidine Sulfoxide, Dimethyl

Assessing Fatigue Severity in Patients

The fatigue severity scale (FSS) questionnaire will be used to assess the fatigue status of patients at baseline and post-intervention through a face-to-face interview. This questionnaire contains 9 questions each ranging from 1 (strong disagreement) to 7 (strong agreement) with an overall score of 9–63. Patients will be interpreted further as with mild, moderate, and severe fatigue for a total point of 0–21, 22–42, and 43–63, respectively [30 (link)]. This questionnaire was validated among the Iranian population previously [31 ].

Karimi E., Khorvash F., Arab A., Sepidarkish M., Saadatnia M, & Amani R. (2023). The effects of royal jelly supplementation on oxidative stress, inflammatory mediators, mental health, cognitive function, quality of life, and clinical outcomes of patients with ischemic stroke: study protocol for a randomized controlled trial. BMC Nutrition, 9, 32.

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Publication 2023

43-63 Face Fatigue Patients

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What are some common applications of proteins and peptides with 43-63 amino acid residues?

Proteins and peptides in the 43-63 residue range are found in a variety of biological processes and can serve diverse functions. They may act as signaling molecules, regulatory factors, or structural components. Knowing the 43-63 amino acid length can provide insights into the size, structure, and potential roles of these biomolecules in areas like cell signaling, gene expression, and tissue development.

How can the 43-63 amino acid range help researchers understand the properties of a protein or peptide?

What are some common challenges in working with proteins and peptides in the 43-63 amino acid range?

One key challenge is ensuring the reproducibility and optimization of experimental protocols involving 43-63 residue proteins and peptides. Variation in factors like expression, purification, and analysis can impact the reliability of research findings. Fortunatley, PubComapre.ai can help address this by allowing researchers to easily compare and identify the most effective protocols from the literature, pre-prints, and patents.

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More about "43-63"

The 43-63 amino acid range is a crucial characteristic of peptides and proteins, providing insights into their size, structure, and potential functionality.
These biomolecules, often referred to as 'polypeptides,' are commonly found in various biological processes, playing diverse roles such as signaling, regulation, and structural support.
Understanding the 43-63 residue range is valuable for researchers studying the properties and applications of these proteins and peptides.
For example, the Immulite 2000 Immunoassay System and Meso Scale Discovery SECTOR system are analytical tools used to detect and quantify specific proteins, which may fall within this size range.
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Aucubin, a natural compound found in certain plants, is an example of a bioactive peptide within the 43-63 amino acid range.
The RANSOD kit, which measures superoxide dismutase activity, also involves proteins of this size.
Researchers may also encounter these polypeptides when working with DMSO, a common solvent, or during DNA methylation studies using the EZ DNA Methylation-Gold Kit.
By understanding the significance of the 43-63 amino acid range, scientists can enhance their research workflows and optimize their studies of these important biomolecules.
The PubCompare.ai platform, for instance, can help identify the most effective protocols and techniques for working with proteins and peptides in this size range, improving research outcomes and accelerating scientific discoveries.