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Iodine

Q: Are there different types or variations of iodine?

Yes, there are several different forms of iodine, including potassium iodide, sodium iodide, and organic iodine compounds. These variations can have different bioavailabilty and effects in the body. Researchers may need to consider the specific form of iodine when designing their studies and evaluating the results.

Iodine is an essential trace mineral that plays a critical role in human health.
It is required for the proper functioning of the thyroid gland, which regulates metabolism, growth, and development.
Iodine deficiency can lead to a range of health issues, including hypothyroidism, goiter, and impaired cognitive function.
Conversely, excessive iodine intake can also cause problems, such as thyrotoxicosis.
Researchers studying iodine's physiological effects and therapeutic applications can leverage PubCompare.ai's AI-driven protocol comparisons to optimize their research, locate the best iodine protocols, and enhance reproducibility and accuracy.
Experance the power of AI-driven insights to elevate your iodine research and contribute to the understanding of this vital nutrient.

Most cited protocols related to «Iodine»

Benchmarking Lineage Inference Methods

Simulation parameters. In order to examine the performance of Slingshot and other methods in a wide range of scenarios, we performed a simulation study using the Bioconductor R package splatter [28 (link)] to produce artificial single-cell RNA-Seq datasets. Many parameters can potentially be tuned to generate these datasets, including parameters determining the distribution of mean gene expression, library size, outlier expression, drop-out, and the biological coefficient of variation. In order to make our simulation study as realistic as possible, we used a published dataset [3 (link)] to learn properties of the marginal distributions of the expression measures for both genes and samples.
In the first part of the study, simulated datasets consisted of two branching lineages (Fig. 4a). The number of cells n was varied from 120 to 1500, by increments of 60 cells. Additionally, we adjusted the signal-to-noise ratio by varying the probability of a gene being differentially expressed (DE) along a path between 0.1 (weak signal) and 0.5 (strong signal), by increments of 0.1. For each combination of sample size and DE proportion, we simulated 10 datasets, for a total of 1,200. In the second part, simulated datasets consisted of five branching lineages (Fig. 4c). The number of cells n was varied between 220 and 1,320, by increments of 220. The DE proportion was varied between 0.1 and 0.5, as in the two-lineage setting. Since all methods under consideration can accommodate non-linear paths, the probability of non-linear DE patterns was set to 0.5, meaning that half of all DE genes’ true average expression level varied according to a non-linear function of pseudotime.
Clustering. We examined Slingshot’s robustness to the choice of clustering method by performing hierarchical clustering, k-means clustering, and Gaussian mixture modeling (GMM), to obtain K=3 to 10 clusters on the three-dimensional representation of each simulated dataset obtained by PCA. Fixing the dimensionality reduction technique allows us to focus on the effects of the clustering method for the dimensionality reduction technique used. In order to alleviate the potential impact of outliers, whenever any method identified a cluster consisting of 4 cells or fewer, that cluster was removed and the method was re-run on the remaining cells.
For the purpose of comparing Slingshot with other lineage inference methods, we again used the top three principal components and set the clustering technique to be the Gaussian mixture model which minimizes the Bayesian information criterion (BIC). This is the default behavior of the mclust R package [32 ] and similar to the approach taken by TSCAN, which uses a variable number of principal components inferred from the data.
Evaluation. Methods were evaluated according to the agreement between inferred and true pseudotime variables for each lineage, as measured by the Kendall rank correlation coefficient. The Kendall rank correlation coefficient assesses the ordinal association between inferred pseudotimes and true pseudotimes, making it more robust to outliers and non-linearity than the Pearson correlation coefficient. We use a slight variant of this measure designed to reflect errors in the assignment of cells to lineages. Defining

S_{0}

as the set of cells along a true lineage and

S_{1}

as the set of cells along an inferred lineage, we calculate:

τ \equiv \frac{(# of concordant pairs) - (# of discordant pairs)}{(\binom{| S_{0} \cup S_{1} |}{2})},

where concordant and discordant pairs are defined strictly, not allowing for ties. Hence, only cells belonging to both the true and inferred lineages (i.e., in

S_{0} \cap S_{1}

) contribute to the numerator. Cells which are along the true lineage (i.e., elements of

S_{0}

) and not assigned a pseudotime by the inferred lineage (not in

S_{1}

) will only contribute to the denominator, bringing τ closer to 0. Similarly for extraneous cells which are included in

S_{1}

but not in

S_{0}

.
For each true lineage, we take the maximum τ over all inferred lineages and average these values within a single dataset. This produces a bias in favor of methods that identify many, potentially spurious lineages, as there will be more values over which to take the maximum. We do not correct for this bias, but simply note that Monocle 2 and DPT-Full are the methods which seem to benefit the most from it.

Street K., Risso D., Fletcher R.B., Das D., Ngai J., Yosef N., Purdom E, & Dudoit S. (2018). Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics, 19, 477.

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Publication 2018

Biopharmaceuticals cDNA Library Cells Debility Gene Expression Genes Iodine Single-Cell RNA-Seq

Assessing Frailty in Older Adults

The FRAIL scale includes 5 components: Fatigue, Resistance, Ambulation, Illness, and Loss of weight (10 (link)). Frail scale scores range from 0–5 (i.e., 1 point for each component; 0=best to 5=worst) and represent frail (3–5), pre-frail (1–2), and robust (0) health status. For this study, AAH Wave 1 data were used to construct the FRAIL scale. Fatigue was measured by asking respondents how much time during the past 4 weeks they felt tired with responses of “all of the time“ or “most of the time” scored 1 point. Resistance was assessed by asking participants if they had any difficulty walking up 10 steps alone without resting and without aids, and Ambulation by asking if they had any difficulty walking several hundred yards alone and without aids; “yes” responses were each scored as 1 point. Illness was scored 1 for respondents who reported 5 or more illnesses out of 11 total illnesses. Loss of weight was scored 1 for respondents with a weight decline of 5% or greater within the past 12 months based on self-report. A complete description of the AAH FRAIL scale items scoring criteria, and baseline prevalences are provided in Appendix 1.

MORLEY J.E., MALMSTROM T.K, & MILLER D.K. (2012). A SIMPLE FRAILTY QUESTIONNAIRE (FRAIL) PREDICTS OUTCOMES IN MIDDLE AGED AFRICAN AMERICANS. The journal of nutrition, health & aging, 16(7), 601-608.

Publication 2012

Acquired Immunodeficiency Syndrome BAD protein, human Fatigue Feelings Iodine

Defining Network Concepts Across Scales

Analogous to [8] (link), we define a network concept function to be function of a square matrix M = [M_ij] (1≤i,j≤n) and/or a corresponding vector G = (G₁,…,G_n). For example, M could be the adjacency matrix (with diagonal set to 0) and G could be a corresponding gene significance measure.
We make use of the following network concept functions: where the components of matrix B_M in the denominator of the clustering coefficient function are given by b_ij = 1 if i≠j and b_ii = Ind(m_ii>0). Here the indicator function Ind(·) takes on the value 1 if the condition is satisfied and 0 otherwise.
According to our convention, the diagonal elements of the adjacency matrix are set to 1. Therefore, the diagonal elements of A–I (where I denotes the identity matrix) equal 0. Now we are ready to define the (fundamental) network concepts that are studied in this article.
Definition of Fundamental Network Concepts:The fundamental network concepts of a network A are defined by evaluating the network functions (Equation 42) on A–I and the gene significance measure GS, i.e.,
For example, the connectivity is given by
We define an intramodular network conceptNCF(A^(q)−I,GS^(q)) by evaluating the network concept function on the restricted adjacency matrix A^(q) and the restricted gene significance measure GS^(q).
We will now define eigengene-based network concepts. Using the eigengene-based adjacency matrix A_E^(q) = a_e^(q)(a_e^(q))^T (Equation 28) and the eigengene-based gene significance measure GS_E,i^(q) = a_e,i^(q)a_e,t^(q) (Equation 29), we define an eigengene-based network concept as NCF(A_E^(q),GS_E^(q)).
As example, consider the eigengene-based connectivity given by

Horvath S, & Dong J. (2008). Geometric Interpretation of Gene Coexpression Network Analysis. PLoS Computational Biology, 4(8), e1000117.

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Publication 2008

Cloning Vectors Conferences Genes Iodine

Summary Data-based Mendelian Randomization

Mendelian randomization is a method that uses genetic variants as instrumental variables to test for causative association between an exposure and an outcome^{9 (link)}. Let z be a genetic variant (e.g., SNP), x be the exposure (e.g., health risk factor) and y be the outcome (e.g., disease). If z is significantly associated with x, the effect of x on y can be estimated using a two-step least squares (2SLS) approach⁵¹

{\hat{b}}_{x y} = {\hat{b}}_{z y} ∕ {\hat{b}}_{z x} with var ({\hat{b}}_{x y}) = var (y) (1 - R_{x y}^{2}) ∕ [n var (x) R_{z x}^{2}],

where n is the sample size,

R_{x y}^{2}

is the variance in y explained by x, and

R_{z x}^{2}

is the variance in x explained z. This analysis requires individual-level data so that the statistical power could be limited if b_xy is small. We have previously proposed an approach that only requires summary-level data to estimate b_xy so that the power can be greatly improved if b_zx and b_zy are estimated from independent studies of large sample size^{17 (link)}, i.e.,

{\hat{b}}_{x y} = {\hat{b}}_{z y} ∕ {\hat{b}}_{z x}

with

var ({\hat{b}}_{x y}) \approx \frac{b_{z y}^{2}}{b_{z x}^{2}} [\frac{var ({\hat{b}}_{z x})}{b_{z x}^{2}} + \frac{var ({\hat{b}}_{z y})}{b_{z y}^{2}}]

. We called this approach a summary data-based Mendelian randomization (SMR) analysis^{17 (link)}. We have also shown previously that a SMR analysis using a single genetic variant is unable to distinguish between causality (the effect of SNP on outcome is mediated by exposure) and pleiotropy (the SNP has distinct effects on exposure and outcome). Here, we extend the SMR method to use all the top associated SNPs at a genome-wide significance level for the exposure as instrumental variables to test for causality. We call this method a generalized SMR (GSMR) analysis. The basic idea of GSMR is that if x is causal for y, any SNP associated with x will have an effect on y, and the expected value of

{\hat{b}}_{x y (i)}

at any SNP i will be identical in the absence of pleiotropy. Let m be the number of GWS top SNPs associated with x after clumping. We have

{\hat{b}}_{x y} = \{{\hat{b}}_{x y (1)}, {\hat{b}}_{x y (2)}, \dots, {\hat{b}}_{x y (m)}\}

with

{\hat{b}}_{x y (i)} = {\hat{b}}_{z y (i)} ∕ {\hat{b}}_{z x (i)}

, and

{\hat{b}}_{x y} ~ N (1 b_{x y}, V)

where 1 is an m × 1 vector of ones and V is the variance-covariance matrix of

{\hat{b}}_{x y}

. We have derived previously that the ij-th element of V is

\begin{matrix} cov ({\hat{b}}_{x y (i)}, {\hat{b}}_{x y (j)}) \approx \frac{r}{b_{z x (i)} b_{z x (j)}} \sqrt{var ({\hat{b}}_{z y (i)}) var ({\hat{b}}_{z y (j)})} + b_{x y (i)} b_{x y (j)} \\ [\frac{r \sqrt{var ({\hat{b}}_{z x (i)}) var ({\hat{b}}_{z x (j)})}}{b_{z x (i)} b_{z x (j)}} - \frac{var ({\hat{b}}_{z x (i)}) var ({\hat{b}}_{z x (j)})}{b_{z x (i)}^{2} b_{z x (j)}^{2}}] \end{matrix}

, where subscripts i and j represent SNP i and j, respectively, r is LD correlation between the two SNPs (not available in the summary data but can be estimated from a reference sample with individual-level genotypes). The i-th diagonal element of V is

var ({\hat{b}}_{x y (i)}) = b_{x y (i)}^{2} [\frac{var ({\hat{b}}_{z x (i)})}{b_{z x (i)}^{2}} + \frac{var ({\hat{b}}_{z y (i)})}{b_{z y (i)}^{2}} - \frac{{var}^{2} ({\hat{b}}_{z x (i)})}{b_{z x (i)}^{4}}]

. Therefore, we can estimate b_xy from all the instruments using the generalized least squares approach as

{\hat{b}}_{x y} = {(1^{'} V^{- 1} 1)}^{- 1} 1^{'} V^{- 1} {\hat{b}}_{x y}

with

var ({\hat{b}}_{x y}) = {(1^{'} V^{- 1} 1)}^{- 1}

. The statistical significance of

{\hat{b}}_{x y}

can be tested by

T_{G S M R} = {\hat{b}}_{x y}^{2} ∕ var ({\hat{b}}_{x y})

which follows a χ² distribution with 1 degree of freedom. Note that because logOR is free of ascertainment bias (i.e., the bias due to a higher proportion of cases in the sample than in the general population), the method can be applied to disease data from case–control studies, and the estimate of b_xy should be interpreted as that of the general population.

Zhu Z., Zheng Z., Zhang F., Wu Y., Trzaskowski M., Maier R., Robinson M.R., McGrath J.J., Visscher P.M., Wray N.R, & Yang J. (2018). Causal associations between risk factors and common diseases inferred from GWAS summary data. Nature Communications, 9, 224.

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Publication 2018

Cloning Vectors Genetic Diversity Genome Genotype Iodine Single Nucleotide Polymorphism Vanadium

Contrast Staining Protocols for Microscopy

The most broadly useful contrast stains tested so far are inorganic iodine and phosphotungstic acid (PTA)[22 (link)]. The formulations and general procedures used are given in Table 2, and notes on the fixatives used are in Table 3[23 -25 (link)]. The stains and procedures are simple and the procedures are robust. The staining times were found not to be critical, as long as the stain had sufficient time to penetrate the tissues. Inorganic iodine in alcoholic or aqueous solution diffuses rapidly into fixed tissues and was able to stain most specimens in a few hours or less, although staining was generally done overnight. PTA is a much larger molecule [26 (link)], and the solution used here was found to require overnight incubation to penetrate specimens 2–3 mm thick, and longer for larger specimens. PTA is known to bind heavily to various proteins and connective tissue [27 ,28 ], and this property, along with electron-shell energies that match common x-ray source emissions, suggested that it might be a useful stain for x-ray imaging. A few samples were tested with phosphomolybdic acid (PMA) staining, used similarly to PTA. The results (not shown) were generally similar, and PMA was not pursued further here (but see refs. [29 (link)] and [30 ] for successful application of PMA).

, & Metscher B.D. (2009). MicroCT for comparative morphology: simple staining methods allow high-contrast 3D imaging of diverse non-mineralized animal tissues. BMC Physiology, 9, 11.

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Publication 2009

Alcoholics Connective Tissue Electrons Fixatives Iodine phosphomolybdic acid Phosphotungstic Acid Proteins Radiography Stains Tissues

Most recents protocols related to «Iodine»

Synthesis of 5-Iodo-6-Methyl Pyrimidine Dione Derivative

Not available on PMC !

Example 1

10 g (33.09 mmol) of 1-(2-fluoro-6-trifluoromethyl-benzyl)-6-methyl-1H-pyrimidine-2,4-dione (III), 6.8 g (49.62 mmol) of K₂CO₃and 2.4 g (6.6 mmol) of tetrabutylammonium iodide were mixed with 50 mL of acetone at the temperature of about 20° C. Subsequently, 13.6 g (43.12 mmol) of (R)-2-((tert-butoxycarbonyl)amino)-2-phenylethyl methanesulfonate (IVa) were added and the obtained mixture was heated at the temperature of about 55° C. and maintained under stirring for about 16 hours at said temperature.

Once this maintenance was finished, the solvent was vacuum distilled and 50 mL of ethyl acetate and 50 mL of water were added to the residue thus obtained. A 1 M aqueous solution of HCl was slowly added, maintaining the temperature between 20 and 25° C. until achieving a pH of between 7 and 8. The aqueous phase was separated and treated with 3 fractions of 30 mL each of ethyl acetate. All the organic extracts were pooled and the solvent was removed by means of vacuum to obtain a slightly yellowish oily residue to which 45 mL of methanol were added, obtaining complete dissolution of the residue.

Example 2

16.1 g (99.24 mmol) of iodine monochloride (ICI) were dissolved in 40 mL of methanol at the temperature of about 10° C. The methanol solution previously obtained according to the methodology described in Example 1 comprising 3-((R)-2-(tert-butoxycarbonyl)amino-2-phenylethyl)-1-(2-fluoro-6-trifluoromethylbenzyl)-6-methyl-1H-pyrimidine-2,4-dione (II) was added to the iodine monochloride solution, maintaining the temperature between 20 and 25° C. Once the addition was finished, the obtained solution was heated to about 50° C. and was maintained under stirring for 2 hours at the mentioned temperature.

Once the maintenance was finished, the solvent was vacuum distilled and 50 mL of acetone were slowly added to the obtained oily residue at the temperature of between and 25° C. The addition of acetone caused a solid precipitate to appear almost immediately. The obtained mixture was maintained for 1 hour under stirring at the mentioned temperature. The resulting solid was isolated by filtration, washed with two fractions of 25 mL of acetone, and finally dried at the temperature of 50° C. to obtain 15.6 g (80.8% yield) of a white solid corresponding to the 3-((R)-2-(amino-2-phenylethyl)-1-(2-fluoro-6-trifluoromethylbenzyl)-5-iodo-6-methyl-1H-pyrimidine-2,4-dione hydrochloride salt (Ia) (UHPLC purity: 98.9%).

¹H-NMR (d₆-DMSO, 400 MHz) δ (ppm): 8.70 (2H, s broad), 7.65-7.48 (3H, m), 7.40-7.32 (5H, m), 5.40-5.29 (2H, dd), 4.47 (1H, t), 4.25 (2H, dd), 2.65 (3H, s).

¹³C-NMR (d₆-DMSO, 100 MHz) δ (ppm): 161.87, 159.47, 159.41, 154.19, 150.98, 134.70, 129.93, 129.84, 129.01, 128.58, 127.38, 122.61, 122.34, 122.22, 121.34, 121.10, 74.80, 52.26, 45.45, 44.60, 25.66.

The DSC of this compound is shown in FIG. 1 and the XRPD is shown in FIG. 2.

US11858901B2. 3-((R)-2-(amino-2-phenylethyl)-1-(2-fluoro-6 trifluoromethyl benzyl)-5-iodo-6-methyl-1H-pyrimidine-2,4-dione or a salt thereof, process for its preparation, and its use in the synthesis of elagolix (2024-01-02). MOEHS IBERICA, S.L. [ES]. Inventors: Roberto Ballette [ES], Oscar Jiménez Alonso [ES], Elena García García [ES], Alicia Dobarro Rodríguez [ES].

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Patent 2024

1H NMR Acetone Anabolism Carbon-13 Magnetic Resonance Spectroscopy elagolix ethyl acetate Filtration Iodine iodine monochloride methanesulfonate Methanol Oils potassium carbonate Pyrimidines Sodium Chloride Solvents Sulfoxide, Dimethyl TERT protein, human tetrabutylammonium iodide Vacuum

Synthesis of Fluorosulfonyl-functionalized Pyridazinone

Not available on PMC !

Example 6

[Figure (not displayed)]

To a stirred mixture of 2-iodo-6-(3-methoxybenzyl)-4-methyl-4,6-dihydro-5H-thiazolo[5′,4′:4,5]pyrrolo[2,3-d]pyridazin-5-one (90 mg, 0.2 mmol) and CuI (cat.) in DMF (5 mL) was added methyl 2,2-difluoro-2-(fluorosulfonyl)acetate (58 mg, 0.3 mmol). The reaction mixture was stirred at 70° C. for 4 hr. then poured into water and extracted with EtOAc. The combined organic layers were washed with brine, dried over anhydrous Na₂SO₄and concentrated under reduced pressure. The residue was purified by column chromatography on silica gel (eluent: PE/EtOAc=5/1) to give the desired product (10 mg). LCMS: m/z=395 (M+H)⁺. ¹H NMR (400 MHz, DMSO-d₆) δ 8.78 (s, 1H), 7.24 (t, 1H), 6.90-6.82 (m, 3H), 5.34 (s, 2H), 4.32 (s, 3H) 3.72 (s, 3H).

US11872225B2. Pyruvate kinase modulators and use thereof (2024-01-16). Agios Pharmaceuticals, Inc. [US]. Inventors: Giovanni Cianchetta [US], Charles Kung [US], Tao Liu [US], Anil Kumar Padyana [US], Zhihua Sui [US], Zhenwei Cai [CN], Dawei Cui [CN], Jingjing Ji [CN].

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Patent 2024

1H NMR Acetate Anabolism brine Chromatography Iodine Lincomycin Pressure Silica Gel Sulfoxide, Dimethyl

Iodination of 3-(Difluoromethyl)pyrazole

Not available on PMC !

Example 2

Sulfuric acid (98%; 64.37 g) was dropwise added to a mixture of 3-(difluoromethyl)-1-methyl-1H-pyrazole (73.5 g, 0.56 mol), Iodine (67.5 g, 0.27 mol), potassium iodate (31 g, 0.14 mol) and acetic acid (816 g) at about 45° C. in about 20 minutes. The temperature of the reaction mixture was raised to a temperature of about 60° C. and maintained at the same temperature for an hour. The reaction mixture was quenched with water (500 ml) at about 25° C. to about 30° C., the mixture was neutralized with an aqueous solution of sodium bisulfite (100 ml). The mixture was extracted with dichloromethane (200 ml). The layers were separated and washed twice with water (500 ml). The organic layers were combined and concentrated to give 3-(difluoromethyl)-4-iodo-1-methyl-1H-pyrazole.

Yield: 90%; Purity: 96%

US11873289B2. Pyrazole compounds and preparation thereof (2024-01-16). MYOKARDIA, INC. [US]. Inventors: Sunil Sharma [IN], Dinesh Jangid [IN], Priyanka Dhaka [IN], Siddharth Madhwal [IN], Bharat Kumar [IN], Kapil Kumar [IN], Rajdeep Anand [IN], Anurag Jain [IN].

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Patent 2024

4-iodopyrazole Acetic Acid Fever Iodine Methylene Chloride potassium iodate pyrazole sodium bisulfite sulfuric acid

Synthesis of 6-cyclopropoxy-2-((1s,4s)-4-hydroxycyclohexyl)-N-(imidazo[1,2-b]pyridazin-3-yl)-2H-indazole-5-carboxamide

Not available on PMC !

Example 85

[Figure (not displayed)]

To crude (1s,4s)-4-(6-cyclopropoxy-5-iodo-2H-indazol-2-yl)cyclohexan-1-ol (Int IV-1) (70 mg, 0.2 mmol), dppp (7 mg, 0.02 mmol), TEA (98 μL, 0.7 mmol), and imidazo[1,2-b]pyridazin-3-amine (54 mg, 0.4 mmol) in MeCN (5 mL) was added Pd(OAc)₂(5 mg, 0.02 mmol) and the resulting mixture was stirred under a 15 atm CO atmosphere at 90° C. After 12 h the reaction mixture was allowed to cool to rt and the solvent was removed in vacuo. The resulting residue was purified using C18-flash chromatography (eluting with 0% to 100% MeCN in water (0.1% FA)) to afford 6-cyclopropoxy-2-((1s,4s)-4-hydroxycyclohexyl)-N-(imidazo[1,2-b]pyridazin-3-yl)-2H-indazole-5-carboxamide (7 mg, 9%) as a pale-yellow solid. ¹H NMR (300 MHz, DMSO-d₆) δ 10.94 (s, 1H), 8.65 (dd, 1H), 8.63 (s, 1H), 8.62 (s, 1H), 8.15 (dd, 1H), 8.07 (s, 1H), 7.55 (s, 1H), 7.22 (dd, 1H), 4.43-4.59 (m, 2H), 4.20-4.29 (m, 1H), 3.86-3.93 (m, 1H), 2.22-2.39 (m, 2H), 1.72-1.95 (m, 4H), 1.57-1.72 (m, 2H), 1.07-1.15 (m, 2H), 0.97-1.07 (m, 2H). m/z (ESI+), [M+H]⁺=433.

US11866405B2. Substituted indazoles as IRAK4 inhibitors (2024-01-09). AstraZeneca AB [SE]. Inventors: Ina Terstiege [SE], Stefan Schiesser [SE], Yafeng Xue [SE], Hui-Fang Chang [SE], Anna Ingrid Kristina Berggren [SE].

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Patent 2024

1H NMR Amines Atmosphere Chromatography Indazoles inhibitors Iodine IRAK4 protein, human Solvents Sulfoxide, Dimethyl

Azide-Alkyne Click Conjugation of Polymer and DNA Oligo

Not available on PMC !

Example 47

Azide Polymer Synthesis for Click Conjugation to Alkyne Terminated DNA Oligo

A solution of azidohexanoic acid NHS ester (2.5 mg) in anhydrous DMF (100 μL) was added to a solution of the amine-functional polymer (9.9 mg) in anhydrous DMF (100 μL) under argon. Diisopropylethylamine (2 μL) was then added. The reaction was agitated at room temperature for 15 hours. Water was then added (0.8 mL) and the azide-modified polymer was purified over a NAP-10 column. The eluent was freeze dried overnight. Yield 9.4 mg, 95%.

Oligo Synthesis with Pendant Alkyne (Hexyne) for Click Conjugation to Azide Polymer

The 3′ propanol oligo A8885 (sequence YATTTTACCCTCTGAAGGCTCCP, where Y=hexynyl group and P=propanol group) was synthesized using 3′ spacer SynBase™ CPG 1000 column on an Applied Biosystems 394 automated DNA/RNA synthesizer. A standard 1.0 mole phosphoramidite cycle of acid-catalyzed detritylation, coupling, capping and iodine oxidation was used. The coupling time for the standards monomers was 40 s, and the coupling time for the 5′ alkyne monomer was 10 min.

The oligo was cleaved from the solid support and deprotected by exposure to concentrated aqueous ammonia for 60 min at room temperature, followed by heating in a sealed tube for 5 h at 55° C. The oligo was then purified by RP-HPLC under standard conditions. Yield 34 OD.

Solution Phase Click Conjugation: Probe Synthesis

A solution of degassed copper sulphate pentahydrate (0.063 mg) in aqueous sodium chloride (0.2 M, 2.5 μL) was added to a degassed solution of tris-benzo triazole ligand (0.5 mg) and sodium ascorbate (0.5 mg) in aqueous sodium chloride (0.2 M, 12.5 μL). Subsequently, a degassed solution of oligo A8885 (50 nmole) in aqueous sodium chloride (0.2 M, 30 μL) and a degassed solution of azide polymer (4.5 mg) in anhydrous DMF (50 μL) were added, respectively. The reaction was degassed once more with argon for 30 s prior to sealing the tube and incubating at 55° C. for 2 h. Water (0.9 mL) was then added and the modified oligo was purified over a NAP-10 column. The eluent was freeze-dried overnight. The conjugate was isolated as a distinct band using PAGE purification and characterized by mass spectrometry. Yield estimated at 10-20%.

Fluorescence Studies

The oligo-polymer conjugate was used as a probe in fluorescence studies. The probe was hybridized with the target A8090 (sequence GGAGCCTTCAGAGGGTAAAAT-Dabcyl), which was labeled with dabcyl at the 3′ end to act as a fluorescence quencher. The target and probe were hybridized, and fluorescence monitored in a Peltier-controlled variable temperature fluorimeter. The fluorescence was scanned every 5° C. over a temperature range of 30° C. to 80° C. at a rate of 2° C./min. FIG. 25 shows increasing fluorescence intensity or emission with increasing temperature, indicating that as the probe-target pair melt, the polymer and quencher separate and fluorescence is recovered.

Polymer conjugation to nucleic acids can also be performed using methods adapted from the protocols described in Examples 14, 45 and 46.

US11874278B2. Reagents for directed biomarker signal amplification (2024-01-16). SIRIGEN II LIMITED [GB]. Inventors: Brent S. Gaylord [US], Glenn P. Bartholomew [US], Russell A. Baldocchi [US], Janice W. Hong [US], William H. Huisman [US], Yongchao Liang [US], Trung Nguyen [US], Lan T. Tran [US], Jean M. Wheeler [US], Adrian Charles Vernon Palmer [GB], Frank Peter Uckert [US].

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Patent 2024

4-(4-dimethylaminophenylazo)benzoic acid Acids Alkynes Amines Ammonia Anabolism Argon Azides DNA Replication Esters Fluorescence Freezing High-Performance Liquid Chromatographies Iodine Ligands Mass Spectrometry Moles Nucleic Acids Oligonucleotides phosphoramidite Polymers Propanols Sodium Ascorbate Sodium Chloride Spacer DNA Sulfate, Copper Triazoles Tromethamine

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Omnipaque by GE Healthcare

Sourced in United States, China, Norway, Ireland, United Kingdom, Germany, Italy, France, New Zealand, Spain

Omnipaque is a radiographic contrast agent developed by GE Healthcare. It is used to enhance the visibility of internal structures during medical imaging procedures, such as computed tomography (CT) scans and angiography. Omnipaque contains the active ingredient iohexol, which is an iodinated compound that temporarily increases the absorption of X-rays, allowing for better visualization of the target tissues or structures.

Propidium iodine by Merck Group

Sourced in United States

Propidium iodide is a fluorescent dye commonly used in molecular biology and cell biology to identify dead cells in a population. It is membrane-impermeant and generally excluded from viable cells, making it useful for distinguishing between live and dead cells.

Facscalibur by BD

Sourced in United States, Germany, United Kingdom, China, Canada, Japan, Italy, France, Belgium, Switzerland, Singapore, Uruguay, Australia, Spain, Poland, India, Austria, Denmark, Netherlands, Jersey, Finland, Sweden

The FACSCalibur is a flow cytometry system designed for multi-parameter analysis of cells and other particles. It features a blue (488 nm) and a red (635 nm) laser for excitation of fluorescent dyes. The instrument is capable of detecting forward scatter, side scatter, and up to four fluorescent parameters simultaneously.

Discovery ct750 hd by GE Healthcare

Sourced in United States, Germany, Japan, China, United Kingdom

The Discovery CT750 HD is a computed tomography (CT) scanner developed by GE Healthcare. It is designed to provide high-quality medical imaging for a variety of clinical applications. The core function of this product is to generate detailed cross-sectional images of the body using advanced X-ray technology.

Ultravist by Bayer

Sourced in Germany, China, France, Italy, United States, United Kingdom

Ultravist is a diagnostic imaging agent used in radiology procedures. It is an iodinated contrast medium that enhances the visibility of organs and structures within the body during medical imaging tests such as computed tomography (CT) scans.

Ultravist 370 by Bayer

Sourced in Germany, United States, Italy, United Kingdom, Switzerland

Ultravist 370 is a non-ionic, water-soluble contrast medium used for radiographic examinations. It contains the active ingredient iopromide and has a concentration of 370 mg iodine per milliliter.

Somatom force by Siemens

Sourced in Germany, United States, Japan

The SOMATOM Force is a high-performance computed tomography (CT) system developed by Siemens. It is designed to deliver fast, precise, and efficient imaging capabilities for a wide range of clinical applications. The SOMATOM Force features advanced technologies that enable high-quality imaging while minimizing radiation exposure.

Propidium iodine by Thermo Fisher Scientific

Sourced in United States

Propidium iodide is a fluorescent dye used to stain nucleic acids. It has the ability to bind to DNA and RNA, making it a useful tool for various applications in cell and molecular biology, such as cell cycle analysis and cell viability assays.

What are the main benefits of adequate iodine intake?

Iodine is an essential trace mineral that plays a critical role in human health. It is required for the proper functioning of the thyroid gland, which regulates metabolism, growth, and development. Adequate iodine intake helps prevent hypothyroidism, goiter, and impaired cognitive function. Iodine is also important for fetal development and avoiding cretinism, a condition characterized by physical and mental impairment.

What are the potential consequences of iodine deficiency or excess?

How can PubCompare.ai assist with iodine research?

Researchers studying iodine's physiological effects and therapeutic applications can leverage PubCompare.ai's AI-driven protocol comparisons to optimize their research. The platform allows you to screen protocol literature more efficiently, leveraging AI to pinpoint critical insights. PubCompare.ai can help researchers identify the most effective protocols related to iodine for their specific research goals, highlighting key differences in protocol effectiveness and enabling them to choose the best option for reproducibility and accuracy.

Are there different types or variations of iodine?

What are some common applications of iodine?

Iodine has a variety of applications in healthcare and beyond. It is commonly used to treat or prevent iodine deficiency disorders, such as hypothyroidism and goiter. Iodine is also used as a disinfectant, in water purification, and in certain medical procedures, such as radioiodine therapy for thyroid conditions. Additionally, some people take iodine supplements for general health and wellness purposes, though it's important to consult a healthcare provider before doing so.

More about "Iodine"

Iodine is a vital trace mineral that plays a crucial role in human health.
It is essential for the proper functioning of the thyroid gland, which regulates metabolism, growth, and development.
Iodine deficiency can lead to a range of health issues, including hypothyroidism, goiter, and impaired cognitive function.
Conversely, excessive iodine intake can also cause problems, such as thyrotoxicosis.
Researchers studying iodine's physiological effects and therapeutic applications can leverage PubCompare.ai's AI-driven protocol comparisons to optimize their research, locate the best iodine protocols, and enhance reproducibility and accuracy.
This powerful tool can help researchers explore the various forms and applications of iodine, such as SOMATOM Definition Flash, Omnipaque, Propidium iodine, FACSCalibur, Discovery CT750 HD, Ultravist, and Ultravist 370, as well as the SOMATOM Force.
By utilizing PubCompare.ai's AI-driven insights, researchers can elevate their iodine research and contribute to the deeper understanding of this vital nutrient.
The platform's ability to compare protocols and locate the most effective approaches can lead to groundbreaking discoveries and advancements in the field of iodine research and its therapeutic applications.