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Visualizing Fluorescence: Using a Homemade Fluorescence "Microscope" to View Latent Fingerprints on Paper.

ABSTRACT: We describe an inexpensive handheld fluorescence imager (low-magnification microscope), constructed from poly(vinyl chloride) pipe and other inexpensive components for use as a teaching tool to understand the principles of fluorescence detection. Optical filters are used to select the excitation and emission wavelengths and can be easily interchanged to accommodate different fluorescent samples. As a demonstration, we used the fluorescence imager to view lawsone-dyed fingerprints on paper, which fluoresce red when illuminated with green light. This emission can be seen by viewing the sample through the instrument by eye, or the fluorescence can be captured by a camera. The entire imager can be built for less than $300.

SUBMITTER: Lafratta CN

PROVIDER: S-EPMC2940251 | biostudies-literature | 2010 Oct

REPOSITORIES: biostudies-literature

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Visualizing Fluorescence: Using a Homemade Fluorescence "Microscope" to View Latent Fingerprints on Paper.

Lafratta Christopher N CN Huh Sun Phill SP Mallillin Allistair C AC Riviello Peter J PJ Walt David R DR

Journal of chemical education 20101001 10

We describe an inexpensive handheld fluorescence imager (low-magnification microscope), constructed from poly(vinyl chloride) pipe and other inexpensive components for use as a teaching tool to understand the principles of fluorescence detection. Optical filters are used to select the excitation and emission wavelengths and can be easily interchanged to accommodate different fluorescent samples. As a demonstration, we used the fluorescence imager to view lawsone-dyed fingerprints on paper, which ...[more]

PMID: 20852733

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Project description:BackgroundNowadays, patients with chronic diseases such as diabetes and hypertension have reached alarming numbers worldwide. These diseases increase the risk of developing acute complications and involve a substantial economic burden and demand for health resources. The widespread adoption of Electronic Health Records (EHRs) is opening great opportunities for supporting decision-making. Nevertheless, data extracted from EHRs are complex (heterogeneous, high-dimensional and usually noisy), hampering the knowledge extraction with conventional approaches.MethodsWe propose the use of the Denoising Autoencoder (DAE), a Machine Learning (ML) technique allowing to transform high-dimensional data into latent representations (LRs), thus addressing the main challenges with clinical data. We explore in this work how the combination of LRs with a visualization method can be used to map the patient data in a two-dimensional space, gaining knowledge about the distribution of patients with different chronic conditions. Furthermore, this representation can be also used to characterize the patient's health status evolution, which is of paramount importance in the clinical setting.ResultsTo obtain clinical LRs, we considered real-world data extracted from EHRs linked to the University Hospital of Fuenlabrada in Spain. Experimental results showed the great potential of DAEs to identify patients with clinical patterns linked to hypertension, diabetes and multimorbidity. The procedure allowed us to find patients with the same main chronic disease but different clinical characteristics. Thus, we identified two kinds of diabetic patients with differences in their drug therapy (insulin and non-insulin dependant), and also a group of women affected by hypertension and gestational diabetes. We also present a proof of concept for mapping the health status evolution of synthetic patients when considering the most significant diagnoses and drugs associated with chronic patients.ConclusionOur results highlighted the value of ML techniques to extract clinical knowledge, supporting the identification of patients with certain chronic conditions. Furthermore, the patient's health status progression on the two-dimensional space might be used as a tool for clinicians aiming to characterize health conditions and identify their more relevant clinical codes.

Dataset Information

Visualizing Fluorescence: Using a Homemade Fluorescence "Microscope" to View Latent Fingerprints on Paper.

Publications

Visualizing Fluorescence: Using a Homemade Fluorescence "Microscope" to View Latent Fingerprints on Paper.

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