GLORIA

GEOMAR Library Ocean Research Information Access

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • Oxford University Press (OUP)  (2)
  • Struys, Michel M R F  (2)
Material
Publisher
  • Oxford University Press (OUP)  (2)
Language
Years
  • 1
    In: Clinical Chemistry, Oxford University Press (OUP), Vol. 47, No. 2 ( 2001-02-01), p. 281-291
    Abstract: Background: During low-flow or closed-circuit anesthesia with the fluorinated inhalation anesthetic sevoflurane, compound A, an olefinic degradation product with known nephrotoxicity in rats, is generated on contact with alkaline CO2 adsorbents. To evaluate compound A formation and thus potential sevoflurane toxicity, a reliable and reproducible assay for quantitative vapor-phase compound A determination was developed. Methods: Compound A concentrations were measured by fully automated capillary gas chromatography–mass spectrometry with cryofocusing. Calibrators of compound A in the vapor phase were prepared from liquid volumetric dilutions of stock solutions of compound A and sevoflurane in ethyl acetate. 1,1,1-Trifluoro-2-iodoethane was chosen as an internal standard. The resulting quantitative method was fully validated. Results: A linear response over a clinically useful concentration interval (0.3–75 μL/L) was obtained. Specificity, sensitivity, and accuracy conformed with current analytical requirements. The CVs were 4.1–10%, the limit of detection was 0.1 μL/L, and the limit of quantification was 0.3 μL/L. Analytical recoveries were 100.6% ± 10.1%, 102.5% ± 7.3%, and 99.0% ± 4.1% at 0.5, 10, and 75 μL/L, respectively. The method described was used to determine compound A concentrations during simulated closed-circuit conditions. Some of the resulting data are included, illustrating the practical applicability of the proposed analytical approach. Conclusions: A simple, fully automated, and reliable quantitative analytical method for determination of compound A in air was developed. A solution was established for sampling, calibration, and chromatographic separation of volatiles in an area complicated by limited availability of sample volume and low concentrations of the analyte.
    Type of Medium: Online Resource
    ISSN: 0009-9147 , 1530-8561
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2001
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 2
    In: Sleep, Oxford University Press (OUP), Vol. 44, No. 2 ( 2021-02-12)
    Abstract: Dexmedetomidine-induced electroencephalogram (EEG) patterns during deep sedation are comparable with natural sleep patterns. Using large-scale EEG recordings and machine learning techniques, we investigated whether dexmedetomidine-induced deep sedation indeed mimics natural sleep patterns. Methods We used EEG recordings from three sources in this study: 8,707 overnight sleep EEG and 30 dexmedetomidine clinical trial EEG. Dexmedetomidine-induced sedation levels were assessed using the Modified Observer’s Assessment of Alertness/Sedation (MOAA/S) score. We extracted 22 spectral features from each EEG recording using a multitaper spectral estimation method. Elastic-net regularization method was used for feature selection. We compared the performance of several machine learning algorithms (logistic regression, support vector machine, and random forest), trained on individual sleep stages, to predict different levels of the MOAA/S sedation state. Results The random forest algorithm trained on non-rapid eye movement stage 3 (N3) predicted dexmedetomidine-induced deep sedation (MOAA/S = 0) with area under the receiver operator characteristics curve & gt;0.8 outperforming other machine learning models. Power in the delta band (0–4 Hz) was selected as an important feature for prediction in addition to power in theta (4–8 Hz) and beta (16–30 Hz) bands. Conclusions Using a large-scale EEG data-driven approach and machine learning framework, we show that dexmedetomidine-induced deep sedation state mimics N3 sleep EEG patterns. Clinical Trials Name—Pharmacodynamic Interaction of REMI and DMED (PIRAD), URL—https://clinicaltrials.gov/ct2/show/NCT03143972, and registration—NCT03143972.
    Type of Medium: Online Resource
    ISSN: 0161-8105 , 1550-9109
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 2056761-3
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...