CAPSO
目录号 : GC66840
CAPSO 是一种两性离子生物缓冲液，其有效 pH 范围为 8.9 ~ 10.3。
Cas No.:73463-39-5
Sample solution is provided at 25 µL, 10mM.
;)
,-1-7$$$37316672.jpg');)
;)
;)
$$$36806353.jpg');)
;33(7)%EF%BC%9A546-561$$$37156877.jpg');)
;)
;)
;)
%201124-1138.$$$35908550.jpg');)
%20766-780.$$$37100057.jpg');)
%20418-432.$$$36893736.jpg');)
%EF%BC%9A-240-255.$$$35108512.jpg');)
533-545$$$36543525.jpg');)
%201-13.$$$36600053.jpg');)
;)
Quality Control & SDS
- View current batch:
- Purity: >98.00%
- COA (Certificate Of Analysis)
- SDS (Safety Data Sheet)
- Datasheet
CAPSO is a biological zwitterionic buffer with the useful pH range from 8.9 to 10.3[1].
[1]. Javid H, et al. Effects of halophilic peptide fusion on solubility, stability, and catalytic performance of D-phenylglycine aminotransferase. J Microbiol Biotechnol. 2014 May;24(5):597-604.
| Cas No. | 73463-39-5 | SDF | Download SDF |
| 分子式 | C9H19NO4S | 分子量 | 237.32 |
| 溶解度 | 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。 |
||
| 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 | |||
 |
1 mg | 5 mg | 10 mg |
| 1 mM | 4.2137 mL | 21.0686 mL | 42.1372 mL |
| 5 mM | 0.8427 mL | 4.2137 mL | 8.4274 mL |
| 10 mM | 0.4214 mL | 2.1069 mL | 4.2137 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。
Cautery-assisted palatal stiffening operation for obstructive sleep apnea: A systematic review and meta-analysis
World J Otorhinolaryngol Head Neck Surg 2018 Sep 25;5(1):49-56.PMID:30775702DOI:10.1016/j.wjorl.2018.05.007.
Objective: To systematically review outcomes for cautery-assisted palatal stiffening operation (CAPSO) as a treatment for adult obstructive sleep apnea (OSA). Methods: Five databases (including PubMed/MEDLINE) were searched through July 12, 2017. Results: Eight studies (307 patients) met criteria. Overall, CAPSO alone (80 patients) improved AHI from a mean ± standard deviation (M ± SD) of (16.8 ± 11.9) to (9.9 ± 10.9) events/h (41.1% decrease). Mixed CAPSO with/without tonsillectomy (92 patients) improved AHI from a M ± SD of (24.8 ± 12.6) to (10.6 ± 9.5) events/h (61.7% decrease). CAPSO with expansion pharyngoplasty (EP), (78 patients) improved AHI from a M ± SD of (26.3 ± 17.7) to (12.6 ± 5.8) events/h (52.1% decrease). CAPSO alone (90 patients) improved lowest oxygen saturation (LSAT) by 5.4 points. Mixed CAPSO with/without tonsillectomy (77 patients) improved LSAT by 10.6 points, and CAPSO with EP (78 patients) improved LSAT by 5.2 points. Sleepiness improved (182 patients) from an Epworth Sleepiness Scale score of 11.8 to 5.1, P < 0.001. Snoring reduced (120 patients) from 7.9 to 2.5 on visual analog scales (0-10 scale), P < 0.001. Conclusions: Apnea-hypopnea index has improved by 41.0% for CAPSO alone, 61.7% for CAPSO with tonsillectomy and 52.1% for CAPSO with expansion pharyngoplasty. Additionally, lowest oxygen saturation, sleepiness and snoring have also improved after CAPSO.
Development and Validation of a Simplified Prehospital Triage Model Using Neural Network to Predict Mortality in Trauma Patients: The Ability to Follow Commands, Age, Pulse Rate, Systolic Blood Pressure and Peripheral Oxygen Saturation (CAPSO) Model
Front Med (Lausanne) 2021 Dec 10;8:810195.PMID:34957169DOI:10.3389/fmed.2021.810195.
Objective: Most trauma scoring systems with high accuracy are difficult to use quickly in field triage, especially in the case of mass casualty events. We aimed to develop a machine learning model for trauma mortality prediction using variables easy to obtain in the prehospital setting. Methods: This was a retrospective prognostic study using the National Trauma Data Bank (NTDB). Data from 2013 to 2016 were used for model training and internal testing, and data from 2017 were used for validation. A neural network model (NN-CAPSO) was developed using the ability to follow commands (whether GCS-motor was <6), age, pulse rate, systolic blood pressure (SBP) and peripheral oxygen saturation, and a new score (the CAPSO score) was developed based on logistic regression. To achieve further simplification, a neural network model with the SBP variable removed (NN-CAPO) was also developed. The discrimination ability of different models and scores was compared based on the area under the receiver operating characteristic curve (AUROC). Furthermore, a reclassification table with three defined risk groups was used to compare NN-CAPSO and other models or scores. Results: The NN-CAPSO had an AUROC of 0.911(95% confidence interval 0.909 to 0.913) in the validation set, which was higher than the other trauma scores available for prehospital settings (all p < 0.001). The NN-CAPO and CAPSO score both reached the AUROC of 0.904 (95% confidence interval 0.902 to 0.906), and were no worse than other prehospital trauma scores. Compared with the NN-CAPO, CAPSO score, and the other trauma scores in reclassification tables, NN-CAPSO was found to more accurately classify patients to the right risk groups. Conclusions: The newly developed CAPSO system simplifies the method of consciousness assessment and has the potential to accurately predict trauma patient mortality in the prehospital setting.
Cautery-assisted palatal stiffening operation
Otolaryngol Head Neck Surg 2000 Apr;122(4):547-56.PMID:10740176DOI:10.1067/mhn.2000.106475.
Outpatient surgical therapy of habitual snoring and mild obstructive sleep apnea has evolved significantly in recent years. We introduce the cautery-assisted palatal stiffening operation (CAPSO) and detail its important advantages over uvulopalatopharyngoplasty, laser-assisted uvulopalatoplasty, and palatal radiofrequency ablation. CAPSO is critically analyzed with regard to extent of surgery, need for repetition of procedure, results, complications, predictors of success, and cost analysis. CAPSO is a mucosal surgery that induces a midline palatal scar that stiffens the floppy palate. Two hundred six consecutive patients underwent CAPSO over an 18-month period, followed by office examination and telephone evaluation. The success rate was initially 92% and dipped to 77% after 1 year. CAPSO eliminates excessive snoring caused by palatal flutter and has success rates that were comparable with those of traditional palatal surgery. CAPSO is a simple and safe office procedure that avoids the need for multiple-stage operations and does not rely on expensive laser systems or radiofrequency generators and hand pieces.
Cautery-assisted palatal stiffening operation for the treatment of obstructive sleep apnea syndrome
Otolaryngol Head Neck Surg 2000 Jul;123(1 Pt 1):55-60.PMID:10889482DOI:10.1067/mhn.2000.105184.
Cautery-assisted palatal stiffening operation (CAPSO) is a recently developed single office-based procedure performed with local anesthesia for the treatment of palatal snoring. A midline strip of soft palate mucosa is removed, and the wound is allowed to heal by secondary intention. The flaccid palate is stiffened, and palatal snoring ceases. This prospective study evaluated the ability of CAPSO to treat obstructive sleep apnea syndrome (OSAS). Twenty-five consecutive patients with OSAS underwent CAPSO. Responders were defined as patients who had a reduction in apnea-hypopnea index (AHI) of 50% or more and an AHI of 10 or less after surgery. By these strict criteria, 40% of patients were considered to have responded to CAPSO. Mean AHI improved from 25.1+/-12.9 to 16.6+/-15.0 (P = 0.010). The Epworth Sleepiness Scale, a subjective measure of daytime sleepiness, improved from 12.7+/-5.6 to 8.8+/-4.6 (P<0.001). These results indicate that CAPSO is as effective as other palatal surgeries in the management of OSAS.
Prediction Model of Organic Molecular Absorption Energies based on Deep Learning trained by Chaos-enhanced Accelerated Evolutionary algorithm
Sci Rep 2019 Nov 21;9(1):17261.PMID:31754116DOI:10.1038/s41598-019-53206-1.
As an important physical property of molecules, absorption energy can characterize the electronic property and structural information of molecules. Moreover, the accurate calculation of molecular absorption energies is highly valuable. Present linear and nonlinear methods hold low calculation accuracies due to great errors, especially irregular complicated molecular systems for structures. Thus, developing a prediction model for molecular absorption energies with enhanced accuracy, efficiency, and stability is highly beneficial. By combining deep learning and intelligence algorithms, we propose a prediction model based on the chaos-enhanced accelerated particle swarm optimization algorithm and deep artificial neural network (CAPSO BP DNN) that possesses a seven-layer 8-4-4-4-4-4-1 structure. Eight parameters related to molecular absorption energies are selected as inputs, such as a theoretical calculating value Ec of absorption energy (B3LYP/STO-3G), molecular electron number Ne, oscillator strength Os, number of double bonds Ndb, total number of atoms Na, number of hydrogen atoms Nh, number of carbon atoms Nc, and number of nitrogen atoms NN; and one parameter representing the molecular absorption energy is regarded as the output. A prediction experiment on organic molecular absorption energies indicates that CAPSO BP DNN exhibits a favourable predictive effect, accuracy, and correlation. The tested absolute average relative error, predicted root-mean-square error, and square correlation coefficient are 0.033, 0.0153, and 0.9957, respectively. Relative to other prediction models, the CAPSO BP DNN model exhibits a good comprehensive prediction performance and can provide references for other materials, chemistry and physics fields, such as nonlinear prediction of chemical and physical properties, QSAR/QAPR and chemical information modelling, etc.