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> Disorders > Pathologic Function > Immune Tolerance

Immune Tolerance

Immune Tolerance is a complex biological process in which the immune system becomes unresponsive to specific antigens, preventing harmful immune reactions.
This crucial mechanism helps the body maintain balance and avoid autoimmune disorders.
Researchers can leverage PubCompare.ai's cutting-edge AI tools to streamline their immune tolerance research, rapidly identifying the best protocols from literature, preprints, and patents.
With intelligent comparisons, scientists can pinpoint the optimal approaches to accelerate their discoveries and unlock new insights into this vital physiological process.

Most cited protocols related to «Immune Tolerance»

1
Molecular Dynamics Protocol for Biomolecular Simulations
Initial helical conformations were defined as all amino acids having (φ, ψ)=(−60°, −40°). Initial extended conformations were defined as all (φ, ψ)=(180°, 180°). Native conformations, as appropriate, were defined for each system as below. Explicit solvation was achieved with truncated octahedra of TIP3P water16 with a minimum 8.0 Å buffer between solute atoms and box boundary. All structures were built via the LEaP module of Ambertools. Except where otherwise indicated, equilibration was performed with a weak-coupling (Berendsen) thermostat33 and barostat targeted to 1 bar with isotropic position scaling as follows. With 100 kcal mol−1 Å−2 positional restraints on protein heavy atoms, structures were minimized for up to 10000 cycles and then heated at constant volume from 100 K to 300 K over 100 ps, followed by another 100 ps at 300 K. The pressure was equilibrated for 100 ps and then 250 ps with time constants of 100 fs and then 500 fs on coupling of pressure and temperature to 1 bar and 300 K, and 100 kcal mol−1 Å−2 and then 10 kcal mol−1 Å−2 Cartesian positional restraints on protein heavy atoms. The system was again minimized, with 10 kcal mol−1 Å−2 force constant Cartesian restraints on only the protein main chain N, Cα, and C for up to 10000 cycles. Three 100 ps simulations with temperature and pressure time constants of 500 fs were performed, with backbone restraints of 10 kcal mol−1 Å−2, 1 kcal mol−1 Å−2, and then 0.1 kcal mol−1 Å−2. Finally, the system was simulated unrestrained with pressure and temperature time constants of 1 ps for 500 ps with a 2 fs time step, removing center-of-mass translation and rotation every picosecond.
SHAKE34 was performed on all bonds including hydrogen with the AMBER default tolerance of 10−5 Å for NVT and 10−6 Å for NVE. Non-bonded interactions were calculated directly up to 8 Å. Beyond 8 Å, electrostatic interactions were treated with cubic spline switching and the particle-mesh Ewald approximation35 in explicit solvent, with direct sum tolerances of 10−5 for NVT or 10−6 for NVE. A continuum model correction for energy and pressure was applied to long-range van der Waals interactions. The production timesteps were 2 fs for NVT and 1 fs for NVE.
Maier J.A., Martinez C., Kasavajhala K., Wickstrom L., Hauser K.E, & Simmerling C. (2015). ff14SB: Improving the accuracy of protein side chain and backbone parameters from ff99SB. Journal of chemical theory and computation, 11(8), 3696-3713.
Publication 2015
Amber Amino Acids Buffers Cuboid Bone Debility Electrostatics Helix (Snails) Hydrogen-5 Immune Tolerance nucleoprotein, Measles virus Pressure Proteins Solvents Vertebral Column
2
Transcript Coding Potential Assessment
To assess a transcript's coding potential, we extract six features from the transcript's nucleotide sequence. A true protein-coding transcript is more likely to have a long and high-quality Open Reading Frame (ORF) compared with a non-coding transcript. Thus, our first three features assess the extent and quality of the ORF in a transcript. We use the framefinder software (14 ) to identify the longest reading frame in the three forward frames. Known for its error tolerance, framefinder can identify most correct ORFs even when the input transcripts contain sequencing errors such as point mutations, indels and truncations (14 ,15 (link)). We extract the LOG-ODDS SCORE and the COVERAGE OF THE PREDICTED ORF as the first two features by parsing the framefinder raw output with Perl scripts (available for download from the web site). The LOG-ODDS SCORE is an indicator of the quality of a predicted ORF and the higher the score, the higher the quality. A large COVERAGE OF THE PREDICTED ORF is also an indicator of good ORF quality (14 ). We add a third binary feature, the INTEGRITY OF THE PREDICTED ORF, that indicates whether an ORF begins with a start codon and ends with an in-frame stop codon.
The large and rapidly growing protein sequence databases provide a wealth of information for the identification of protein-coding transcript. We derive another three features from parsing the output of BLASTX (16 (link)) search (using the transcript as query, E-value cutoff 1e-10) against UniProt Reference Clusters (UniRef90) which was developed as a nonredundant protein database with a 90% sequence identity threshold (17 (link)). First, a true protein-coding transcript is likely to have more hits with known proteins than a non-coding transcript does. Thus we extract the NUMBER OF HITS as a feature. Second, for a true protein-coding transcript the hits are also likely to have higher quality; i.e. the HSPs (High-scoring Segment Pairs) overall tend to have lower E-value. Thus we define feature HIT SCORE as follows:

where Eij is the E-value of the j-th HSP in frame i, Si measures the average quality of the HSPs in frame i and HIT SCORE is the average of Si across three frames. The higher the HIT SCORE, the better the overall quality of the hits and the more likely the transcript is protein-coding. Thirdly, for a true protein-coding transcript most of the hits are likely to reside within one frame, whereas for a true non-coding transcript, even if it matches certain known protein sequence segments by chance, these chance hits are likely to scatter in any of the three frames. Thus, we define feature FRAME SCORE to measure the distribution of the HSPs among three reading frames:

The higher the FRAME SCORE, the more concentrated the hits are and the more likely the transcript is protein-coding.
We incorporate these six features into a support vector machine (SVM) machine learning classifier (18 ). Mapping the input features onto a high-dimensional feature space via a proper kernel function, SVM constructs a classification hyper-plane (maximum margin hyper-plane) to separate the transformed data (18 ). Known for its high accuracy and good performance, SVM is a widely used classification tool in bioinformatics analysis such as microarray-based cancer classification (19 (link),20 (link)), prediction of protein function (21 (link),22 (link)) and prediction of subcellular localization (23 (link),24 (link)). We employed the LIBSVM package (25 ) to train a SVM model using the standard radial basis function kernel (RBF kernel). The C and gamma parameters were determined by grid-search in the training dataset. We trained the SVM model using the same training data set as CONC used (13 (link)), containing 5610 protein-coding cDNAs and 2670 noncoding RNAs.
Kong L., Zhang Y., Ye Z.Q., Liu X.Q., Zhao S.Q., Wei L, & Gao G. (2007). CPC: assess the protein-coding potential of transcripts using sequence features and support vector machine. Nucleic Acids Research, 35(Web Server issue), W345-W349.
Publication 2007
Amino Acid Sequence Base Sequence Codon, Initiator Codon, Terminator DNA, Complementary Gamma Rays Immune Tolerance INDEL Mutation Malignant Neoplasms Microarray Analysis Point Mutation Proteins Reading Frames RNA, Untranslated Staphylococcal Protein A
3
Improved AutoDock4 Docking Protocol
Docking experiments were performed with AutoDock4 and compared with docking experiments with AutoDock3. For each complex, 50 docking experiments were performed using the Lamarckian genetic algorithm with the default parameters from AutoDock3. A maximum of 25 million energy evaluations was applied for each experiment. The results were clustered using a tolerance of 2.0 Å.
In the HIV cross dockings, ligand flexibility was limited to 10 torsional degrees of freedom, picking torsions that allowed the fewest number of atoms to move (freezing the core of the molecule). Flexible docking was performed allowing three torsions to rotate in residue ARG8, in both the A and B chains. The structural water (water 301) was included in complexes that included this water in the crystallographic structure, and hydrogen atoms were added in geometry that allowed hydrogen bonding to the flaps.
We have also changed the default model for the unbound system in the current version of AutoDock. Our previous method calculated internal energies for an extended form of the molecule, mimicking a conformation that might be expected when fully solvated11 (link). Results from beta testers, however, showed that this protocol has severe limitations when used for virtual screening. In cases where the ligand is sterically crowded, the artificial force field used to drive the ligand into an extended conformation tends to lead to conformations with sub-optimal energy. When the difference is calculated between this unbound conformation and the bound conformation, it leads to artificially favorable predictions of the free energy of binding. In response to this problem, we have returned to the default model of assuming that the unbound conformation of the ligand is the same as the bound conformation. Other options in AutoDock allow the user to use an energy-minimized conformation of the ligand as the unbound model.
Morris G.M., Huey R., Lindstrom W., Sanner M.F., Belew R.K., Goodsell D.S, & Olson A.J. (2009). AutoDock4 and AutoDockTools4: Automated Docking with Selective Receptor Flexibility. Journal of computational chemistry, 30(16), 2785-2791.
Publication 2009
Biological Models Crystallography Hydrogen Immune Tolerance Ligands Reproduction Surgical Flaps
4
Quantitative Proteomics of HeLa-E. coli Mixture
An Escherichia coli K12 strain was grown in standard LB medium, harvested, washed in PBS, and lysed in BugBuster (Novagen Merck Chemicals, Schwalbach, Germany) according to the manufacturer's protocol. HeLa S3 cells were grown in standard RPMI 1640 medium supplemented with glutamine, antibiotics, and 10% FBS. After being washed with PBS, cells were lysed in cold modified RIPA buffer (50 mm Tris-HCl, pH 7.5, 1 mm EDTA, 150 mm NaCl, 1% N-octylglycoside, 0.1% sodium deoxycholate, complete protease inhibitor mixture (Roche)) and incubated for 15 min on ice. Lysates were cleared by centrifugation, and after precipitation with chloroform/methanol, proteins were resuspended in 6 m urea, 2 m thiourea, 10 mm HEPES, pH 8.0. Prior to in-solution digestion, 60-μg protein samples from HeLa S3 lysates were spiked with either 10 μg or 30 μg of E. coli K12 lysates based on protein amount (Bradford assay). Both batches were reduced with dithiothreitol and alkylated with iodoacetamide. Proteins were digested with LysC (Wako Chemicals, GmbH, Neuss, Germany) for 4 h and then trypsin digested overnight (Promega, GmbH, Mannheim, Germany). Digestion was stopped by the addition of 2% trifluroacetic acid. Peptides were separated by isoelectric focusing into 24 fractions on a 3100 OFFGEL Fractionator (Agilent, GmbH, Böblingen, Germany) as described in Ref. 45 (link). Each fraction was purified with C18 StageTips (46 (link)) and analyzed via liquid chromatography combined with electrospray tandem mass spectrometry on an LTQ Orbitrap (Thermo Fisher) with lock mass calibration (47 (link)). All raw files were searched against the human and E. coli complete proteome sequences obtained from UniProt (version from January 2013) and a set of commonly observed contaminants. MS/MS spectra were filtered to contain at most eight peaks per 100 mass unit intervals. The initial MS mass tolerance was 20 ppm, and MS/MS fragment ions could deviate by up to 0.5 Da (48 (link)). For quantification, intensities can be determined alternatively as the full peak volume or as the intensity maximum over the retention time profile, and the latter method was used here as the default. Intensities of different isotopic peaks in an isotope pattern are always summed up for further analysis. MaxQuant offers a choice of the degree of uniqueness required in order for peptides to be included for quantification: “all peptides,” “only unique peptides,” and “unique plus razor peptides” (42 (link)). Here we chose the latter, because it is a good compromise between the two competing interests of using only peptides that undoubtedly belong to a protein and using as many peptide signals as possible. The distribution of peptide ions over fractions and samples is shown in supplemental Fig. S1.
Cox J., Hein M.Y., Luber C.A., Paron I., Nagaraj N, & Mann M. (2014). Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ. Molecular & Cellular Proteomics : MCP, 13(9), 2513-2526.
Publication 2014
Acids Antibiotics, Antitubercular Biological Assay Buffers Cells Centrifugation Chloroform Cold Temperature Deoxycholic Acid, Monosodium Salt Digestion Dithiothreitol Edetic Acid Escherichia coli Escherichia coli K12 Glutamine HeLa Cells HEPES Homo sapiens Immune Tolerance Iodoacetamide Ions Isotopes Liquid Chromatography Methanol Peptides Promega Protease Inhibitors Proteins Proteome Radioimmunoprecipitation Assay Retention (Psychology) Sodium Chloride Staphylococcal Protein A Tandem Mass Spectrometry Thiourea Tromethamine Trypsin Urea
5
Mass Spectrometry Data Processing Pipeline
Following
data acquisition, Thermo RAW files were processed using
a series of software tools that were developed in-house. First the
RAW files were converted to mzXML using a custom version of ReAdW.exe
(http://sashimi.svn.sourceforge.net/viewvc/sashimi/) that
had been modified to export ion accumulation times and FT peak noise.
During this initial processing we also corrected any erroneous assignments
of monoisotopic m/z. Using Sequest,24 (link) MS2 spectra were searched against the human
UniProt database (downloaded on 08/02/2011), supplemented with the
sequences of common contaminating proteins such as trypsin. This forward
database was followed by a decoy component, which included all target
protein sequences in reversed order.
Searches were performed
using a 50 ppm precursor ion tolerance.25 (link) When searching Orbitrap MS2 data, we used 0.02 Th fragment ion tolerance.
The fragment ion tolerance was set to 1.0 Th when searching ITMS2
data. Only peptide sequences with both termini consistent with the
protease specificity of LysC were considered in the database search,
and up to two missed cleavages were accepted. TMT tags on lysine residues
and peptide N-termini (+ 229.162932 Da) and carbamidomethylation of
cysteine residues (+ 57.02146 Da) were set as static modifications,
while oxidation of methionine residues (+ 15.99492 Da) was treated
as a variable modification. An MS2 spectral assignment false discovery
rate of less than 1% was achieved by applying the target-decoy strategy.26 (link) Filtering was performed using linear discriminant
analysis as described previously27 (link) to create
one composite score from the following peptide ion and MS2 spectra
properties: Sequest parameters XCorr and unique ΔCn, peptide
length and charge state, and precursor ion mass accuracy. The resulting
discriminant scores were used to sort peptides prior to filtering
to a 1% FDR, and the probability that each peptide-spectral-match
was correct was calculated using the posterior error histogram.
Following spectral assignment, peptides were assembled into proteins
and proteins were further filtered based on the combined probabilities
of their constituent peptides to a final FDR of 1%. In cases of redundancy,
shared peptides were assigned to the protein sequence with the most
matching peptides, thus adhering to principles of parsimony.28
McAlister G.C., Nusinow D.P., Jedrychowski M.P., Wühr M., Huttlin E.L., Erickson B.K., Rad R., Haas W, & Gygi S.P. (2014). MultiNotch MS3 Enables Accurate, Sensitive, and Multiplexed Detection of Differential Expression across Cancer Cell Line Proteomes. Analytical Chemistry, 86(14), 7150-7158.
Publication 2014
Amino Acid Sequence Cytokinesis Immune Tolerance Lysine Methionine Peptides Proteins Trypsin tyrosyl-alanyl-glycine

Most recents protocols related to «Immune Tolerance»

1
Detailed Phenotypic Characteristics of Brassica Variety 18GG0453L
Not available on PMC !

Example 1

Variety 18GG0453L has shown uniformity and stability for all traits, as described in the following variety description information. The variety has been increased with continued observation for uniformity.

Table 1 provides data on morphological, agronomic, and quality traits for 18GG0453L. When preparing the detailed phenotypic information, plants of the new 18GG0453L variety were observed while being grown using conventional agronomic practices.

TABLE 1
Variety Descriptions based on Morphological,
Agronomic and Quality Trait
CHARACTERSTATE (Score)
Yield (bu/ac)32.94
SEED
Erucic acid content (%)0.01
Glucosinolate content11.37
Seed coat colorBlack (1)
SEEDLING
cotyledon widthWide (7)
seedling growth habitMedium to Upright (6)
Stem anthocyanin intensityAbsent (1)
LEAF
leaf lobesStrong Lobing (7)
number of leaf lobes4
leaf margin indentationMedium (5)
leaf margin shapeSharp (3)
leaf widthMedium (5)
leaf lengthMedium to Long (6)
petiole lengthMedium to Long (6)
PLANT GROWTH AND FLOWER
Time to flowering50.8
(number of days from planting
to 50% of plants showing one
or more open flowers)
Plant height at maturity (cm)125.8
Flower bud locationTouching to Slight Overlap (6)
Petal colorMedium Yellow (3)
Anther fertilityShedding Pollen (9)
Petal spacingTouching to Slight Overlap (6)
PODS AND MATURITY
Pod type
Pod lengthLong (7)
Pod widthMedium (5)
Pod angleHorizontal to Semi-Erect (3)
Pod beak lengthLong (7)
Pedicle lengthLong (7)
Lodging resistanceFair to Good
Time to maturity (no. days97.6
from planting to physiological
maturity)
HERBICIDE TOLERANCE
GlufonsinateTolerant
GlyphosateSusceptible
ImidazolinoneSusceptible
QUALITY CHARACTERISTICS
Oil content % (whole dry seed48.89
basis)
Protein content (percentage,47.24
whole oil-free dry seed basis)
Total saturated fats content6.35
Glucosinolates (μm total11.37
glucosinolates/gram whole
seed, 8.5% moisture basis)
Seed Chlorophyll2% higher than the WCC/RRC checks
Shatter Score (1 = poor;5.5
9 = best)
Acid Detergent Fibre (%)19.24
Total Saturated Fat (%)6.35
Oleic Acid - 18:1 (%)63.1
Linolenic Acid - 18:3 (%)8.89
Sclerotinia tolerance (% of40.16
susceptible check)
Blackleg (% of Westar)29.76

US11856905B2. Canola hybrid 18GG0453L (2024-01-02). PIONEER HI-BRED INTERNATIONAL, INC. [US]. Inventors: Ushan Alahakoon [CA], Igor Falak [CA], Daryoush Fekri [CA], Chadwick Bruce Koscielny [CA], Scott McClinchey [CA], Daniel Joseph Stanton [US].
Full text: Click here
Patent 2024
Acids Anthocyanins Beak Character Chlorophyll Cotyledon Detergents erucic acid Fertility Fibrosis Glucosinolates glyphosate Herbicides Immune Tolerance Linolenic Acid Oleic Acid Phenotype physiology Plant Leaves Plants Pollen Proteins Saturated Fatty Acid Sclerotinia Stem, Plant Tracheophyta
2
Hard Water Tolerance of Chlorinated Cleaner
Not available on PMC !

Example 4

Testing to evaluate hard water tolerance of exemplary formulations of a high-foaming, higher alkaline chlorinated cleaner (with and without PSO) was conducted to determine the impact of the PSO on hard water tolerance. The evaluated formulations are shown below in Table 8 wherein alkaline cleaning compositions including hydroxide alkalinity sources were combined with the PSO adducts and compared to the formulations without the PSO adducts (Control).

TABLE 8
EXP 9Control
DI water25-5025-50
NaOH 50%10-3010-30
PSO adducts, 40%1-5 0
Lauryl dimethylamine oxide 30% 5-10 5-10
Sodium Hypochlorite, 10%20-4020-40
Additional Functional Ingredients 5-10 5-10
100.00100

The hardness tolerance testing of the EXP 9 formulation and the control were conducted using 1% solutions in water with varying degrees of synthetic hardness created by adding various amounts of dissolved CaCl2) and MgCl2 to a combination of deionized water and NaHCO3. Once the solutions reached 140° F. they were removed from the heat and let stand for 30 minutes. A failure was characterized by the presence of visible flocculent after the 30 minutes, whereas a passing evaluation was characterized by the absence of visible flocculent after the 30 minutes. The results are shown in Table 9.

TABLE 9
Grains per
Water sourcegallonEXP 9Control
Synthetic hard water16PassPass
Synthetic hard water17PassPass
Synthetic hard water18PassFail
Synthetic hard water19PassFail
Synthetic hard water20Fail—
Synthetic hard water21Fail—
Synthetic hard water22Fail—
Synthetic hard water23Fail—

As shown in Table 10, the exemplary high-foaming formulation (EXP 9) according to the invention containing the PSO adducts had increased hard water tolerance over cleaning compositions not containing the PSO adducts.

The inventions being thus described, it will be obvious that the same may be varied in many ways. Such variations are not to be regarded as a departure from the spirit and scope of the inventions and all such modifications are intended to be included within the scope of the following claims.

US11859155B2. Hard surface cleaning compositions comprising phosphinosuccinic acid adducts and methods of use (2024-01-02). ECOLAB USA INC. [US]. Inventors: Carter M. Silvernail [US], Erik C. Olson [US], Michel M. Lawrence [US], Richard D. Johnson [US], Steven J. Lange [US].
Full text: Click here
Patent 2024
Acids Alkalies Bicarbonate, Sodium Cereals dodecyldimethylamine oxide hydroxide ion Immune Tolerance Magnesium Chloride Sodium Hypochlorite
3
Chronic Effects of GLP1R Agonists on Obesity
Not available on PMC !

Example 15

Diet Induced Obese (DIO) mice were purchased from Charles River and administrated by s.c. route with GLP1R agonist or GLP1R/GCGR dual agonist. Mouse body weight and food intake were monitored daily for 2 weeks, and followed before (5 days in total) and during treatment (5-weeks in total). After 5 weeks, mice were sacrificed and visceral fat mass were taken out and weighed.

Dose dependent weight loss induced by mTA4 or mTA37 (see Table 6) is shown in FIG. 14. Chronic effect on body weight loss with daily administration of mTA4 or mTA37 for 5 weeks and cumulative food intake for 2 weeks are shown in FIG. 15. Increased intra-peritoneal glucose tolerance after daily administration of mTA4 or mTA37 for 5 weeks is shown in FIG. 16. Reduced fasting blood glucose levels after daily administration of mTA4 or mTA37 for 5 weeks is shown in FIG. 17.

US11865160B2. Modified therapeutic agents, stapled peptide lipid conjugates, and compositions thereof (2024-01-09). THE SCRIPPS RESEARCH INSTITUTE [US]. Inventors: Weijun Shen [US], Pengyu Yang [US], Huafei Zou [US], Peter G. Schultz [US].
Full text: Click here
Patent 2024
Blood Glucose Body Weight Diet Eating Glucose Immune Tolerance Injections, Intraperitoneal Mice, Obese Mus Rivers Visceral Fat
4
Herbicide Tolerance in RiceTec IMI Hybrids
Not available on PMC !

Example 1

FIG. 3 Trial Setup:

Trial consisted of 2 treatment regimens with Imazamox applications at approximately the preflood stage with rates of 0.5×, 1×, 2×, or 4×, either alone or 2 weeks following application of 1× Imazethapyr.

FIG. 4 Representative Pots 4 Weeks Post Application of Imazamox.

Summary:

The results of this trial indicates that RiceTec (RT) IMI hybrids show high levels of tolerance to Imazamox herbicide. No injury was observed in any of the RT IMI hybrids in any treatment. However, injury was observed in the single trait mutant lines. This was expected, because it is known that the G654E mutation only confers a weak level of tolerance to IMI herbicides. Due to the trial being conducted in the greenhouse during the difficult winter season the injury response may not have shown exactly as that of an optimal field trial, but given the consistency of response across all treatments the data indicates that RT IMI hybrids have equal or higher levels of tolerance to Imazamox than current commercial IMI hybrids.

US11877553B2. Effects of a plurality of mutations to improve herbicide resistance/tolerance in rice (2024-01-23). RICETEC, INC. [US]. Inventors: Dario Bernacchi [US], Caleb Knepper [US].
Full text: Click here
Patent 2024
Debility Herbicides Hybrids imazamox imazethapyr Immune Tolerance Injuries Marijuana Abuse Mutation Treatment Protocols
5
Herbicide Tolerance Evaluation in Soybean
Not available on PMC !

Example 2

Trial:

single herbicide application at the 3-4 leaf stage using the half-step log sprayer at rates from 16× to 1/128×.

Plot Images 3 Weeks Post Application.

FIG. 5A Control, FIG. 5B 8×, FIG. 5C 1×, FIG. 5D 0.25×. Planting order L to R: P1003A205V, R0146G654E, P1003, P1003, LF2-RTC2/LM1-RTC1, LF3-RTC2\LM4-RTC1.

Summary:

FIG. 6 shows % of injury 3 weeks post 3-4 leaf application. The homozygous A205V (RTC1) mutant line (P1003A205V) had very good tolerance to even high rates of Imazethapyr herbicide. Observed injury in the P1003A205V was below 10% at all tested rates 8× (48 oz/acre) and lower. Recorded injury in the G654E (RTC2) homozygous line was lower than expected, but this may be partially due to the later application. The G654E/A205V hybrids showed very strong levels of tolerance in this trial, with no observed injury by three weeks post application in either hybrid.

US11877553B2. Effects of a plurality of mutations to improve herbicide resistance/tolerance in rice (2024-01-23). RICETEC, INC. [US]. Inventors: Dario Bernacchi [US], Caleb Knepper [US].
Full text: Click here
Patent 2024
Herbicides Homozygote Hybrids imazethapyr Immune Tolerance Injuries Plant Leaves

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More about "Immune Tolerance"

Immune tolerance is a critical biological process in which the immune system becomes unresponsive to specific antigens, preventing harmful immune reactions.
This crucial mechanism helps the body maintain balance and avoid autoimmune disorders.
Researchers can leverage cutting-edge AI tools like PubCompare.ai to streamline their immune tolerance research, rapidly identifying the best protocols from literature, preprints, and patents.
With intelligent comparisons, scientists can pinpoint the optimal approaches to accelerate their discoveries and unlock new insights into this vital physiological process.
Immune tolerance is closely related to other key biological concepts and tools used in research.
Proteome Discoverer and Mascot are software platforms used for protein identification and quantification, which can provide valuable insights into the molecular mechanisms underlying immune tolerance.
Humulin R is a form of insulin used to manage diabetes, a condition that can be impacted by immune system dysfunction.
D-glucose, the primary sugar molecule in the body, also plays a crucial role in immune system regulation and metabolic processes.
By leveraging the power of AI-driven platforms like PubCompare.ai, researchers can enhance their understanding of immune tolerance and accelerate their discoveries.
These tools enable rapid identification of the best protocols from a vast array of literature, preprints, and patents, helping scientists pinpoint the optimal approaches to advance their research.
With Mascot 2.4 and Proteome Discoverer 2.2, researchers can further analyze and quantify the proteins and molecules involved in immune tolerance, unlocking new insights and accelerating their progress.