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1.
Pharmaceuticals (Basel) ; 15(12)2022 Dec 19.
Article in English | MEDLINE | ID: mdl-36559034

ABSTRACT

The aim of Quantitative mass spectrometry imaging (Q-MSI) is to provide distribution analysis and quantitation from one single mass-spectrometry-based experiment, and several quantitation methods have been devised for Q-MSI. Mimetic tissue models based on spiked tissue homogenates are considered one of the most accurate ways to perform Q-MSI, since the analyte is present in a well-defined concentration in a sample matrix highly similar to the one of the unknown sample to be analyzed. The delivery of drugs in skin is among the most frequent types of pharmaceutical MSI studies. Here, a mimetic tissue model is extended for use on the skin, which, due to its high collagen content, is different from most other tissue as the homogenates become extremely viscous. A protocol is presented which overcomes this by the addition of water and the handling of the homogenate at an elevated temperature where the viscosity is lower. Using a mimetic tissue model, a method was developed for the quantitative imaging of bleomycin in skin. To compensate for the signal drift and the inhomogeneities in the skin, an internal standard was included in the method. The method was tested on skin from a pig which had had an electropneumatic injection of bleomycin into the skin. Quantification was made at several regions in a cross section of the skin at the injection site, and the results were compared to the results of a quantitative LC-MS on a neighboring tissue biopsy from the same animal experiment. The overall tissue concentration determined by the LC-MS was within the range of the different regions quantified by the Q-MSI. As the model provides the results of the same order of magnitude as a LC-MS, it can either be used to replace LC-MS in skin studies where MSI and LC-MS are today carried out in combination, or it can add quantitative information to skin studies which are otherwise carried out by MSI alone.

2.
J Health Organ Manag ; 28(3): 422-36, 2014.
Article in English | MEDLINE | ID: mdl-25080653

ABSTRACT

PURPOSE: The purpose of this paper is to develop a framework for health care performance evaluation that enables decision makers to identify areas indicative of corrective actions. The framework should provide information on strategic pro-/regress in an operational context that justifies the need for organizational adjustments. DESIGN/METHODOLOGY/APPROACH: The study adopts qualitative methods for constructing the framework, subsequently implementing the framework in a Danish magnetic resonance imaging (MRI) unit. Workshops and interviews form the basis of the qualitative construction phase, and two internal and five external databases are used for a quantitative data collection. FINDINGS: By aggregating performance outcomes, collective measures of performance are achieved. This enables easy and intuitive identification of areas not strategically aligned. In general, the framework has proven helpful in an MRI unit, where operational decision makers have been struggling with extensive amounts of performance information. RESEARCH LIMITATIONS/IMPLICATIONS: The implementation of the framework in a single case in a public and highly political environment restricts the generalizing potential. The authors acknowledge that there may be more suitable approaches in organizations with different settings. PRACTICAL IMPLICATIONS: The strength of the framework lies in the identification of performance problems prior to decision making. The quality of decisions is directly related to the individual decision maker. The only function of the framework is to support these decisions. ORIGINALITY/VALUE: The study demonstrates a more refined and transparent use of performance reporting by combining strategic weight assignment and performance aggregation in hierarchies. In this way, the framework accentuates performance as a function of strategic progress or regress, thus assisting decision makers in exerting operational effort in pursuit of strategic alignment.


Subject(s)
Delivery of Health Care/standards , Quality Assurance, Health Care/methods , Quality Indicators, Health Care , Algorithms , Decision Making , Denmark , Humans , Magnetic Resonance Imaging , Qualitative Research , Quality Indicators, Health Care/statistics & numerical data
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