BCI
(Synonyms: (E)-BCI) 目录号 : GC38646
BCI 作为一种选择性双特异性磷酸酶 6 (DUSP6) 抑制剂，可以抑制肿瘤生长和巨噬细胞炎症。
Cas No.:1245792-51-1
Sample solution is provided at 25 µL, 10mM.
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Quality Control & SDS
- View current batch:
- Purity: >98.00%
- COA (Certificate Of Analysis)
- SDS (Safety Data Sheet)
- Datasheet
| Cell experiment [1]: | |
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Cell lines |
MPNST cells |
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Preparation Method |
MPNST cells were starved overnight, incubated with BCI (2 uM) for 60 mins then stimulated with DMEM and 10% FBS for 1 hr. Immunoblot analysis of TP53, p-RB and p-ATM and PARP cleavage and CC3 in ST8814 and S462.TY MPNST cells 24h after treatment with BCI (2 uM). |
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Reaction Conditions |
2 uM; 60 mins |
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Applications |
After 1 hr, p-ERK, p-JNK, p-c-jun and total c-jun were elevated in the BCI-treated MPNST cell lines ST8814 and S462.TY but did not change in iHSC-1λ. Within 24 hours, BCI decreased total PARP and increased cleaved PARP and CC3, indicative of apoptotic cell death in NF1 deficient ST8814 and S462.TY cells. |
| Animal experiment [2]: | |
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Animal models |
Female C57BL/6 mice (8-weeks old) |
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Dosage form |
15 mg/kg or 30 mg/kg; i.p. |
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Preparation method |
Low- or high-concentration (15 mg/kg or 30 mg/kg) BCI was injected intraperitoneally for 8 weeks, and bone loss was evaluated by micro-CT. |
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Applications |
Bone loss was prevented in both the low- and high-concentration BCI groups. Moreover, quantitative results indicated obvious increases in bone volume/total tissue volume (BV/TV), trabecular number (Tb.N), bone mineral density (BMD), and bone surface density (BS/TV) in both BCI-treated groups relative to the OVX group. |
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References: [1] Ramkissoon A, et al. Targeted Inhibition of the Dual Specificity Phosphatases DUSP1 and DUSP6 Suppress MPNST Growth via JNK. Clin Cancer Res. 2019 Jul 1;25(13):4117-4127. [2] Cai C, et al. BCI Suppresses RANKL-Mediated Osteoclastogenesis and Alleviates Ovariectomy-Induced Bone Loss. Front Pharmacol. 2021 Nov 1;12:772540. |
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BCI, as a selective dual-specificity phosphatase 6 (DUSP6) inhibitor, can inhibit tumor growth and macrophage inflammation.[1].
In vitro, at low-dose of ≤2 μM and ≤4 μM, BCI showed no cytotoxic effects on RAW264.7 cells and BMMs, respectively. And at concentrations of ≤4 μM, BCI had no obvious effect on cell cycle progression or apoptosis in BMMs.[1] In vitro experiment it shown that treatment with 1?μM BCI enhanced osteoclastogenesis by inhibiting DUSP6. Moreover, BCI increased the levels of osteoclast-related gene expression such as NFATC1, C-fos, ACP5, and DC-STAMP.[2] In vitro efficacy test it demonstrated that treatment with 4?μm BCI obviously increased the proportion of cells expressing cleaved caspase‐3, 4?μm BCI already elicited extensive cytotoxicity in KELLY and IMR‐32 cells, and only a minority of LAN‐1 and SK‐N‐AS cells remained.[3] In vitro, with 1 μM BCI did not affect total NCC and NCC surface expression as well as ERK1/2 phosphorylation. Treatment with 5 μM BCI can markedly increase ERK1/2 phosphorylation and decrease total NCC and NCC surface expression.[5].
In vivo, mice were treated with 10mg/kg BCI intraperitoneally for five consecutive days per week, suppressed AKT activation and prevents tumor formation.[4] In vivo test it exhibited that treatment with 50, 100, and 200 mg/kg BCI orally in the CPDM animal model obviously increased the number of pNrf2-positive cells in periodontal tissue and mitigated the alveolar bone loss.[6].
References:
[1] Cai C, et al. BCI Suppresses RANKL-Mediated Osteoclastogenesis and Alleviates Ovariectomy-Induced Bone Loss. Front Pharmacol. 2021 Nov 1;12:772540.
[2] Zhang B, et al. DUSP6 expression is associated with osteoporosis through the regulation of osteoclast differentiation via ERK2/Smad2 signaling. Cell Death Dis. 2021 Sep 2;12(9):825.
[3] Thompson EM, et al. The cytotoxic action of BCI is not dependent on its stated DUSP1 or DUSP6 targets in neuroblastoma cells. FEBS Open Bio. 2022 Jul;12(7):1388-1405.
[4] Duan S, et al. Loss of FBXO31-mediated degradation of DUSP6 dysregulates ERK and PI3K-AKT signaling and promotes prostate tumorigenesis. Cell Rep. 2021 Oct 19;37(3):109870.
[5] Feng X, et al. Aldosterone modulates thiazide-sensitive sodium chloride cotransporter abundance via DUSP6-mediated ERK1/2 signaling pathway. Am J Physiol Renal Physiol. 2015 May 15;308(10):F1119-27.
[6] Zhu C, et al. The therapeutic role of baicalein in combating experimental periodontitis with diabetes via Nrf2 antioxidant signaling pathway. J Periodontal Res. 2020 Jun;55(3):381-391.
BCI 作为一种选择性双特异性磷酸酶 6 (DUSP6) 抑制剂，可以抑制肿瘤生长和巨噬细胞炎症。[1]。
在体外，在 ≤2 μM 和 ≤4 μM 的低剂量下，BCI 分别对 RAW264.7 细胞和 BMM 没有细胞毒性作用。浓度≤4 μM时，BCI对BMM细胞周期进程或细胞凋亡无明显影响。[1] 体外实验表明，1μM BCI通过抑制DUSP6增强破骨细胞生成。此外，BCI可提高NFATC1、C-fos、ACP5、DC-STAMP等破骨细胞相关基因的表达水平。[2] 体外药效试验表明，4μm BCI处理明显表达裂解的 caspase-3 的细胞比例增加，4μm BCI 已经在 KELLY 和 IMR-32 细胞中引起广泛的细胞毒性，只有少数 LAN-1 和 SK-N-AS 细胞保留下来。[3]< /sup> 在体外，1 μM BCI 不影响总 NCC 和 NCC 表面表达以及 ERK1/2 磷酸化。用 5 μM BCI 处理可显着增加 ERK1/2 磷酸化并降低总 NCC 和 NCC 表面表达。[5]。
在体内，小鼠每周连续 5 天腹腔注射 10mg/kg BCI，抑制 AKT 激活并防止肿瘤形成。[4] 体内试验表明，用 50、 CPDM动物模型口服100、200 mg/kg BCI明显增加牙周组织中pNrf2阳性细胞数量，减轻牙槽骨丢失。[6].
| Cas No. | 1245792-51-1 | SDF | |
| 别名 | (E)-BCI | ||
| Canonical SMILES | O=C1/C(C(NC2CCCCC2)C3=C1C=CC=C3)=C/C4=CC=CC=C4 | ||
| 分子式 | C22H23NO | 分子量 | 317.42 |
| 溶解度 | DMSO: 125 mg/mL (393.80 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 | 3.1504 mL | 15.752 mL | 31.504 mL |
| 5 mM | 0.6301 mL | 3.1504 mL | 6.3008 mL |
| 10 mM | 0.315 mL | 1.5752 mL | 3.1504 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。
Brain-Computer Interface: Advancement and Challenges
Sensors (Basel) 2021 Aug 26;21(17):5746.PMID:34502636DOI:10.3390/s21175746.
Brain-Computer Interface (BCI) is an advanced and multidisciplinary active research domain based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the last decades, several groundbreaking research has been conducted in this domain. Still, no comprehensive review that covers the BCI domain completely has been conducted yet. Hence, a comprehensive overview of the BCI domain is presented in this study. This study covers several applications of BCI and upholds the significance of this domain. Then, each element of BCI systems, including techniques, datasets, feature extraction methods, evaluation measurement matrices, existing BCI algorithms, and classifiers, are explained concisely. In addition, a brief overview of the technologies or hardware, mostly sensors used in BCI, is appended. Finally, the paper investigates several unsolved challenges of the BCI and explains them with possible solutions.
BCI Use and Its Relation to Adaptation in Cortical Networks
IEEE Trans Neural Syst Rehabil Eng 2017 Oct;25(10):1697-1704.PMID:28320670DOI:10.1109/TNSRE.2017.2681963.
Brain-computer interfaces (BCIs) carry great potential in the treatment of motor impairments. As a new motor output, BCIs interface with the native motor system, but acquisition of BCI proficiency requires a degree of learning to integrate this new function. In this review, we discuss how BCI designs often take advantage of the brain's motor system infrastructure as sources of command signals. We highlight a growing body of literature examining how this approach leads to changes in activity across cortex, including beyond motor regions, as a result of learning the new skill of BCI control. We discuss the previous research identifying patterns of neural activity associated with BCI skill acquisition and use that closely resembles those associated with learning traditional native motor tasks. We then discuss recent work in animals probing changes in connectivity of the BCI control site, which were linked to BCI skill acquisition, and use this as a foundation for our original work in humans. We present our novel work showing changes in resting state connectivity across cortex following the BCI learning process. We find substantial, heterogeneous changes in connectivity across regions and frequencies, including interactions that do not involve the BCI control site. We conclude from our review and original work that BCI skill acquisition may potentially lead to significant changes in evoked and resting state connectivity across multiple cortical regions. We recommend that future studies of BCIs look beyond motor regions to fully describe the cortical networks involved and long-term adaptations resulting from BCI skill acquisition.
Past, Present, and Future of EEG-Based BCI Applications
Sensors (Basel) 2022 Apr 26;22(9):3331.PMID:35591021DOI:10.3390/s22093331.
An electroencephalography (EEG)-based brain-computer interface (BCI) is a system that provides a pathway between the brain and external devices by interpreting EEG. EEG-based BCI applications have initially been developed for medical purposes, with the aim of facilitating the return of patients to normal life. In addition to the initial aim, EEG-based BCI applications have also gained increasing significance in the non-medical domain, improving the life of healthy people, for instance, by making it more efficient, collaborative and helping develop themselves. The objective of this review is to give a systematic overview of the literature on EEG-based BCI applications from the period of 2009 until 2019. The systematic literature review has been prepared based on three databases PubMed, Web of Science and Scopus. This review was conducted following the PRISMA model. In this review, 202 publications were selected based on specific eligibility criteria. The distribution of the research between the medical and non-medical domain has been analyzed and further categorized into fields of research within the reviewed domains. In this review, the equipment used for gathering EEG data and signal processing methods have also been reviewed. Additionally, current challenges in the field and possibilities for the future have been analyzed.
BCI2000: a general-purpose brain-computer interface (BCI) system
IEEE Trans Biomed Eng 2004 Jun;51(6):1034-43.PMID:15188875DOI:10.1109/TBME.2004.827072.
Many laboratories have begun to develop brain-computer interface (BCI) systems that provide communication and control capabilities to people with severe motor disabilities. Further progress and realization of practical applications depends on systematic evaluations and comparisons of different brain signals, recording methods, processing algorithms, output formats, and operating protocols. However, the typical BCI system is designed specifically for one particular BCI method and is, therefore, not suited to the systematic studies that are essential for continued progress. In response to this problem, we have developed a documented general-purpose BCI research and development platform called BCI2000. BCI2000 can incorporate alone or in combination any brain signals, signal processing methods, output devices, and operating protocols. This report is intended to describe to investigators, biomedical engineers, and computer scientists the concepts that the BC12000 system is based upon and gives examples of successful BCI implementations using this system. To date, we have used BCI2000 to create BCI systems for a variety of brain signals, processing methods, and applications. The data show that these systems function well in online operation and that BCI2000 satisfies the stringent real-time requirements of BCI systems. By substantially reducing labor and cost, BCI2000 facilitates the implementation of different BCI systems and other psychophysiological experiments. It is available with full documentation and free of charge for research or educational purposes and is currently being used in a variety of studies by many research groups.
Toward EEG-Based BCI Applications for Industry 4.0: Challenges and Possible Applications
Front Hum Neurosci 2021 Aug 13;15:705064.PMID:34483868DOI:10.3389/fnhum.2021.705064.
In the last few decades, Brain-Computer Interface (BCI) research has focused predominantly on clinical applications, notably to enable severely disabled people to interact with the environment. However, recent studies rely mostly on the use of non-invasive electroencephalographic (EEG) devices, suggesting that BCI might be ready to be used outside laboratories. In particular, Industry 4.0 is a rapidly evolving sector that aims to restructure traditional methods by deploying digital tools and cyber-physical systems. BCI-based solutions are attracting increasing attention in this field to support industrial performance by optimizing the cognitive load of industrial operators, facilitating human-robot interactions, and make operations in critical conditions more secure. Although these advancements seem promising, numerous aspects must be considered before developing any operational solutions. Indeed, the development of novel applications outside optimal laboratory conditions raises many challenges. In the current study, we carried out a detailed literature review to investigate the main challenges and present criteria relevant to the future deployment of BCI applications for Industry 4.0.