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Phenyl benzoate

$43

  • Brand : BIOFRON

  • Catalogue Number : BF-P2001

  • Specification : 98%

  • CAS number : 93-99-2

  • Formula : C13H10O2

  • Molecular Weight : 198.22

  • PUBCHEM ID : 7169

  • Volume : 20mg

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Catalogue Number

BF-P2001

Analysis Method

HPLC,NMR,MS

Specification

98%

Storage

2-8°C

Molecular Weight

198.22

Appearance

White needle-shaped crystal

Botanical Source

synthesis

Structure Type

Others

Category

Standards;Natural Pytochemical;API

SMILES

C1=CC=C(C=C1)C(=O)OC2=CC=CC=C2

Synonyms

Diphenylcarboxylate/Phenyl benzoate/Phenol,benzoate/4-09-00-00303/Phenol, benzoate (7CI)/benzenecarboxylic acid phenyl ester/Benzoic acid phenyl ester/Benzoic acid,phenyl ester/Benzoic acid, phenyl ester

IUPAC Name

phenyl benzoate

Density

1.1±0.1 g/cm3

Solubility

Flash Point

128.9±16.7 °C

Boiling Point

314.0±11.0 °C at 760 mmHg

Melting Point

68-70 °C(lit.)

InChl

InChl Key

WGK Germany

RID/ADR

HS Code Reference

2916310000

Personal Projective Equipment

Correct Usage

For Reference Standard and R&D, Not for Human Use Directly.

Meta Tag

provides coniferyl ferulate(CAS#:93-99-2) MSDS, density, melting point, boiling point, structure, formula, molecular weight etc. Articles of coniferyl ferulate are included as well.>> amp version: coniferyl ferulate

PMID

28815363

Abstract

Venous thromboembolism (VTE) is a potentially life-threatening condition that includes both deep vein thrombosis (DVT) and pulmonary embolism. We sought to improve detection and reporting of children with a new diagnosis of VTE by applying natural language processing (NLP) tools to radiologists’ reports. We validated an NLP tool, Reveal NLP (Health Fidelity Inc, San Mateo, CA) and inference rules engine’s performance in identifying reports with deep venous thrombosis using a curated set of ultrasound reports. We then configured the NLP tool to scan all available radiology reports on a daily basis for studies that met criteria for VTE between July 1, 2015, and March 31, 2016. The NLP tool and inference rules engine correctly identified 140 out of 144 reports with positive DVT findings and 98 out of 106 negative reports in the validation set. The tool’s sensitivity was 97.2% (95% CI 93-99.2%), specificity was 92.5% (95% CI 85.7-96.7%). Subsequently, the NLP tool and inference rules engine processed 6373 radiology reports from 3371 hospital encounters. The NLP tool and inference rules engine identified 178 positive reports and 3193 negative reports with a sensitivity of 82.9% (95% CI 74.8-89.2) and specificity of 97.5% (95% CI 96.9-98). The system functions well as a safety net to screen patients for HA-VTE on a daily basis and offers value as an automated, redundant system. To our knowledge, this is the first pediatric study to apply NLP technology in a prospective manner for HA-VTE identification.

KEYWORDS

Epidemiology; Natural language processing; Pediatrics; Quality improvement; Venous thromboembolism; Venous thrombosis

Title

The use of natural language processing on pediatric diagnostic radiology reports in the electronic health record to identify deep venous thrombosis in children.

Author

Galvez JA1, Pappas JM2, Ahumada L3, Martin JN3, Simpao AF4, Rehman MA4, Witmer C5.

Publish date

2017 Oct

PMID

21457582

Abstract

BACKGROUND:
The determination of coronary flow reserve (CFR) is an essential concept at the moment of decision-making in ischemic heart disease. There are several direct and indirect tests to evaluate this parameter. In this sense, dobutamine stress echocardiography is one of the pharmacological method most commonly used worldwide. It has been previously demonstrated that CFR can be determined by this technique. Despite our wide experience with dobutamine stress echocardiography, we ignored the necessary heart rate to consider sufficient the test for the analysis of CFR. For this reason, our main goal was to determine the velocity of coronary flow in each stage of dobutamine stress echocardiography and the heart rate value necessary to double the baseline values of coronary flow velocity in the territory of the left anterior descending (LAD) coronary artery.

METHODS:
A total of 33 consecutive patients were analyzed. The patients included had low risk for coronary artery disease. All the participants underwent dobutamine stress echocardiography and coronary artery flow velocity was evaluated in the distal segment of LAD coronary artery using transthoracic color-Doppler echocardiography.

RESULTS:
The feasibility of determining CFR in the territory of the LAD during dobutamine stress echocardiography was high: 31/33 patients (94%). Mean CFR was 2.67 at de end of dobutamine test.There was an excellent concordance between delta HR (difference between baseline HR and maximum HR) and the increase in the CFR (correlation coefficient 0.84). In this sense, we found that when HR increased by 50 beats, CFR was ≥ 2 (CI 93-99.2%). In addition, 96.4% of patients reached a CFR ≥ 2 (IC 91.1 – 99%) at 75% of their predicted maximum heart rate.

CONCLUSIONS:
We found that the feasibility of dobutamine stress echocardiography to determine CFR in the territory of the LAD coronary artery was high. In this study, it was necessary to achieve a difference of 50 bpm from baseline HR or at least 75% of the maximum predicted heart rate to consider sufficient the test for the analysis of CFR.

Title

Target heart rate to determine the normal value of coronary flow reserve during dobutamine stress echocardiography.

Author

Forte EH1, Rousse MG, Lowenstein JA.

Publish date

2011 Apr 4

PMID

18332820

Abstract

OBJECTIVES:
To evaluate the ability of paramedics to predict patients requiring a major trauma service. To assess whether paramedic prediction of severity of injury to individual body regions is accurate and could add to overall paramedic prediction of injury severity.

METHODS:
Helicopter paramedics in Victoria prospectively recorded the severity of injury to the head, thoracic, and abdomen regions, and whether the patient required a major trauma service, for primary response adult (>15 years) trauma patients. Paramedic predictions of injuries were compared with patient outcomes. Major trauma was defined as death in hospital; an Injury Severity Score >15; intensive care unit admission >24 hours; and urgent surgery. A severe anatomic injury was defined as an Abbreviated Injury Scale severity >/=3. The sensitivity, specificity, positive predictive value, and negative predictive value were calculated.

RESULTS:
Two hundred and seven patients were enrolled in the study, with 62.3% defined as major trauma. The sensitivity of paramedic predictions ranged from 57.6 (95% confidence interval [CI]; 45.4-68.9) for the head to 38.5 (95% CI; 22.1-57.9) for the abdomen. Specificities ranged from 98.3 (95% CI; 93.5-99.6) for the thorax to 93.5 (95% CI; 87.9-96.6) for the head region. The sensitivity and specificity of paramedic predictions of a major trauma status were 97.7 (95% CI; 93-99.2) and 28.2 (95% CI; 19.3-39.1), respectively. The paramedics correctly categorized all patients who were admitted to an intensive care unit, required urgent surgery or died in hospital as major trauma.

CONCLUSIONS:
Paramedics were unable to reliably identify severe injury to individual body regions. Sensitivity of paramedic judgment of major trauma status was high. Assessment of the severity of injury to individual body regions did not appear to improve accuracy.

Title

Prehospital prediction of the severity of blunt anatomic injury.

Author

Mulholland SA1, Cameron PA, Gabbe BJ, Williamson OD, Young K, Smith KL, Bernard SA.

Publish date

2008 Mar


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