ICA (N-[4-(2-Pyridinyl)-2-thiazolyl]-2-pyridinamine)
(Synonyms: N-[4-(2-Pyridinyl)-2-thiazolyl]-2-pyridinamine) 目录号 : GC32179
ICA (N-[4-(2-Pyridinyl)-2-thiazolyl]-2-pyridinamine) (N-(pyridin-2-yl)-4-(pyridin-2-yl)thiazol-2-amine) 是具有抗寄生虫活性的 SK 通道抑制剂，IC50 为 2.1 μM.
![ICA (N-[4-(2-Pyridinyl)-2-thiazolyl]-2-pyridinamine) Chemical Structure](/media/struct/GC3/GC32179.png "ICA (N-[4-(2-Pyridinyl)-2-thiazolyl]-2-pyridinamine) Chemical Structure")
Cas No.:3374-88-7
Sample solution is provided at 25 µL, 10mM.
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Quality Control & SDS
- View current batch:
- Purity: >99.50%
- COA (Certificate Of Analysis)
- SDS (Safety Data Sheet)
- Datasheet
ICA (N-(pyridin-2-yl)-4-(pyridin-2-yl)thiazol-2-amine) is a SK channel inhibitor that has antileishmanial activity with an IC50 of 2.1 µM.
The SK channel inhibitor ICA exhibits antiarrhythmic effects. ICA prevents electrically induced runs of atrial fibrillation in the isolated right atrium and induces atrial postrepolarization refractoriness and depolarizes resting membrane potential. ICA at 1 to 10 μM slows conduction velocity. At increased pacing frequencies, SK channel inhibition by ICA (10-30 μM) demonstrates prominent depression of other sodium channel-dependent parameters[2].
[1]. Bhuniya D, et al. Aminothiazoles: Hit to lead development to identify antileishmanial agents. Eur J Med Chem. 2015 Sep 18;102:582-93. [2]. Skibsbye L, et al. Antiarrhythmic Mechanisms of SK Channel Inhibition in the Rat Atrium. J Cardiovasc Pharmacol. 2015 Aug;66(2):165-76.
| Cas No. | 3374-88-7 | SDF | |
| 别名 | N-[4-(2-Pyridinyl)-2-thiazolyl]-2-pyridinamine | ||
| Canonical SMILES | C1(NC2=NC(C3=NC=CC=C3)=CS2)=NC=CC=C1 | ||
| 分子式 | C13H10N4S | 分子量 | 254.31 |
| 溶解度 | DMSO : ≥ 49.5 mg/mL (194.64 mM) | 储存条件 | Store at -20°C |
| General tips | 请根据产品在不同溶剂中的溶解度选择合适的溶剂配制储备液；一旦配成溶液，请分装保存，避免反复冻融造成的产品失效。 储备液的保存方式和期限：-80°C 储存时，请在 6 个月内使用，-20°C 储存时，请在 1 个月内使用。 为了提高溶解度，请将管子加热至37℃，然后在超声波浴中震荡一段时间。 |
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| Shipping Condition | 评估样品解决方案：配备蓝冰进行发货。所有其他可用尺寸：配备RT，或根据请求配备蓝冰。 | ||
| 制备储备液 | |||
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1 mg | 5 mg | 10 mg |
| 1 mM | 3.9322 mL | 19.661 mL | 39.3221 mL |
| 5 mM | 0.7864 mL | 3.9322 mL | 7.8644 mL |
| 10 mM | 0.3932 mL | 1.9661 mL | 3.9322 mL |
| 第一步：请输入基本实验信息（考虑到实验过程中的损耗，建议多配一只动物的药量） | ||||||||||
| 给药剂量 | mg/kg |  |
动物平均体重 | g | |
每只动物给药体积 | ul | |
动物数量 | 只 |
| 第二步：请输入动物体内配方组成（配方适用于不溶于水的药物；不同批次药物配方比例不同，请联系GLPBIO为您提供正确的澄清溶液配方） | ||||||||||
| % DMSO % % Tween 80 % saline | ||||||||||
| 计算重置 | ||||||||||
计算结果:
工作液浓度： mg/ml；
DMSO母液配制方法： mg 药物溶于 μL DMSO溶液（母液浓度 mg/mL，
体内配方配制方法：取 μL DMSO母液，加入 μL PEG300，混匀澄清后加入μL Tween 80，混匀澄清后加入 μL saline，混匀澄清。
1. 首先保证母液是澄清的；
2.
一定要按照顺序依次将溶剂加入，进行下一步操作之前必须保证上一步操作得到的是澄清的溶液，可采用涡旋、超声或水浴加热等物理方法助溶。
3. 以上所有助溶剂都可在 GlpBio 网站选购。
ICA agenesis with transcavernous anastomosis: a systematic review
Surg Radiol Anat 2023 Mar 11.PMID:36899092DOI:10.1007/s00276-023-03117-8.
Purpose: To present two cases of Internal Carotid Artery (ICA) agenesis and conduct a systematic review to assess for associations with other anomalies and intracranial aneurysms. Methodology: We performed a retrospective review of published cases of patients with ICA agenesis with intercavernous anastomosis in MEDLINE database on August 2022 using search terms "internal carotid artery", "agenesis" and "transcavernous anastomosis". We also included two cases of ICA agenesis with type D collateral that we encountered. Results: Total of 45 studies that included 47 patients and two of our cases resulted in 49 patients. Only 70% of studies reported the location of a collateral vessel of which more than two-thirds were on the floor of sella. More than half of the vessels connected cavernous segments of ICA. A1 segment ipsilateral to the side of ICA agenesis was absent in most of the cases but was not true for all cases. Aneurysm was seen in more than one-quarter of the patients. It can also mimic microadenoma as in prior reported cases as well as in one of our cases. Conclusion: ICA agenesis with type D collateral is a rare anomaly but clinically relevant due to the increased risk of an aneurysm or mimic microadenoma or false alarm for occlusion of ICA but knowledge of this rare variant can help in better management of these patients.
Independent components analysis (ICA) at the "cocktail-party" in analytical chemistry
Talanta 2020 Feb 1;208:120451.PMID:31816793DOI:10.1016/j.talanta.2019.120451.
Independent components analysis (ICA) is a probabilistic method, whose goal is to extract underlying component signals, that are maximally independent and non-Gaussian, from mixed observed signals. Since the data acquired in many applications in analytical chemistry are mixtures of component signals, such a method is of great interest. In this article recent ICA applications for quantitative and qualitative analysis in analytical chemistry are reviewed. The following experimental techniques are covered: fluorescence, UV-VIS, NMR, vibrational spectroscopies as well as chromatographic profiles. Furthermore, we reviewed ICA as a preprocessing tool as well as existing hybrid ICA-based multivariate approaches. Finally, further research directions are proposed. Our review shows that ICA is starting to play an important role in analytical chemistry, and this will definitely increase in the future.
Icariin alleviates uveitis by targeting peroxiredoxin 3 to modulate retinal microglia M1/M2 phenotypic polarization
Redox Biol 2022 Jun;52:102297.PMID:35334248DOI:10.1016/j.redox.2022.102297.
Uveitis causes blindness and critical visual impairment in people of all ages, and retinal microglia participate in uveitis progression. Unfortunately, effective treatment is deficient. Icariin (ICA) is a bioactive monomer derived from Epimedium. However, the role of ICA in uveitis remains elusive. Our study indicated that ICA alleviated intraocular inflammation in vivo. Further results showed the proinflammatory M1 microglia could be transferred to anti-inflammatory M2 microglia by ICA in the retina and HMC3 cells. However, the direct pharmacological target of ICA is unknown, to this end, proteome microarrays and molecular simulations were used to identify the molecular targets of ICA. Data showed that ICA binds to peroxiredoxin-3 (PRDX3), increasing PRDX3 protein expression in both a time- and a concentration-dependent manner and promoting the subsequent elimination of H2O2. In addition, GPX4/SLC7A11/ACSL4 pathways were activated accompanied by PRDX3 activation. Functional tests demonstrated that ICA-derived protection is afforded through targeting PRDX3. First, ICA-shifted microglial M1/M2 phenotypic polarization was no longer detected by blocking PRDX3 both in vivo and in vitro. Next, ICA-activated GPX4/SLC7A11/ACSL4 pathways and downregulated H2O2 production were also reversed via inhibiting PRDX3 both in vivo and in vitro. Finally, ICA-elicited positive effects on intraocular inflammation were eliminated in PRDX3-deficient retina from experimental autoimmune uveitis (EAU) mice. Taking together, ICA-derived PRDX3 activation has therapeutic potential for uveitis, which might be associated with modulating microglial M1/M2 phenotypic polarization.
Unique estimation in EEG analysis by the ordering ICA
PLoS One 2022 Oct 24;17(10):e0276680.PMID:36279275DOI:10.1371/journal.pone.0276680.
Independent Component Analysis (ICA) is a method for solving blind source separation problems. Because ICA only needs weak assumptions to estimate the unknown sources from only the observed signals, it is suitable for Electroencephalography (EEG) analysis. A serious disadvantage of the traditional ICA algorithms is that their results often fluctuate and do not converge to the unique and globally optimal solution at each run. It is because there are many local optima and permutation ambiguities. We have recently proposed a new ICA algorithm named the ordering ICA, a simple extension of Fast ICA. The ordering ICA is theoretically guaranteed to extract the independent components in the unique order and avoids the local optima in practice. This paper investigated the usefulness of the ordering ICA in EEG analysis. Experiments showed that the ordering ICA could give unique solutions for the signals with large non-Gaussianity, and the ease of parallelization could reduce computation time.
Evaluating the efficacy of multi-echo ICA denoising on model-based fMRI
Neuroimage 2022 Dec 1;264:119723.PMID:36328274DOI:10.1016/j.neuroimage.2022.119723.
fMRI is an indispensable tool for neuroscience investigation, but this technique is limited by multiple sources of physiological and measurement noise. These noise sources are particularly problematic for analysis techniques that require high signal-to-noise ratio for stable model fitting, such as voxel-wise modeling. Multi-echo data acquisition in combination with echo-time dependent ICA denoising (ME-ICA) represents one promising strategy to mitigate physiological and hardware-related noise sources as well as motion-related artifacts. However, most studies employing ME-ICA to date are resting-state fMRI studies, and therefore we have a limited understanding of the impact of ME-ICA on complex task or model-based fMRI paradigms. Here, we addressed this knowledge gap by comparing data quality and model fitting performance of data acquired during a visual population receptive field (pRF) mapping (N = 13 participants) experiment after applying one of three preprocessing procedures: ME-ICA, optimally combined multi-echo data without ICA-denoising, and typical single echo processing. As expected, multi-echo fMRI improved temporal signal-to-noise compared to single echo fMRI, with ME-ICA amplifying the improvement compared to optimal combination alone. However, unexpectedly, this boost in temporal signal-to-noise did not directly translate to improved model fitting performance: compared to single echo acquisition, model fitting was only improved after ICA-denoising. Specifically, compared to single echo acquisition, ME-ICA resulted in improved variance explained by our pRF model throughout the visual system, including anterior regions of the temporal and parietal lobes where SNR is typically low, while optimal combination without ICA did not. ME-ICA also improved reliability of parameter estimates compared to single echo and optimally combined multi-echo data without ICA-denoising. Collectively, these results suggest that ME-ICA is effective for denoising task-based fMRI data for modeling analyzes and maintains the integrity of the original data. Therefore, ME-ICA may be beneficial for complex fMRI experiments, including voxel-wise modeling and naturalistic paradigms.