Search PubMed⌕ Search
Home/Archive/PubMed

PubMed · 16096757

[Virtual microscopy: first applications].

K Glatz-Krieger·D Glatz·M J Mihatsch
View original source ↗Export metadata ↓

Abstract

Only recently fast-paced developments in computer technology allowed for the digitization of complete histologic slides. The resulting virtual slides may be viewed via webbrowser by any number of pathologists or students independent of time and location. Usage of a virtual microscope simply requires a computer workstation with a fast internet connection, which opens this technology to a broad public. A virtual microscopy system consists of three components: acquisition, server and client. Such systems are under development by different commercial and academic bodies worldwide. We have developed a virtual microscope system called vMic (http://www.vmic.unibas.ch) which provides virtual slides of very high image quality. Several successfully held online slide seminars and a histology course for students in dentistry are freely accessible in the internet. With the commercial availability of ultra rapid and easy-to-use slide scanners and the fast improvements of technology virtual microscopy will offer many applications in teaching, research and diagnostics. Thanks to additional functionalities, real microscopes will most likely be replaced by computer workstations in a couple of years.

Explore related subjects

Databases, FactualHumansInternetMicroscopyPathologyUser-Computer InterfaceEnglish AbstractJournal ArticleReview

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

K Glatz-Krieger, D Glatz, M J Mihatsch. 2006. [Virtual microscopy: first applications].. https://doi.org/10.1007/s00292-005-0782-1

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2005

A proposal for a national program reporting beneficial drug responses, analogous to the existing program to detect adverse drug responses.

Many countries have regulatory agencies, such as the Food and Drug Administration in the United States, not only to determine approval of drugs into the marketplace, but also to monitor their safety following the approval process. The logic for the latter role of these agencies is that several important changes occur in the patient population after the approval to market. These changes include the tremendous expansion in the numbers of patients, the removal of many exclusion criteria of co-existing diseases that may have been included in the clinical trials, and the fact that the treatment time for agents used chronically will now exceed any previous exposure time. All of these factors may contribute to the development of adverse drug responses (ADRs) that were not predictable or observable during the clinical trial phases. While the programs to monitor ADRs have important limitations such as the absence of a control group, they are extremely useful at determining possible associations between a drug and an adverse response that can now be used to generate hypotheses to be tested in a more proper and controlled set of trials. A similar system could, in principle, be used to monitor non-predictable serendipitous beneficial drug responses (BDRs) that would otherwise not be seen due to the same limitations of clinical trials in detecting ADRs. The system would expand data collection beyond clinical trials - which often can pick up BDRs of drugs such as minoxidil and sildenafil. This system of monitoring for BDRs would have the tremendous advantages of generating hypotheses derived from data obtained in humans with the disease and a drug-able molecule. The data obtained from this system of monitoring for BDRs could lead to new uses for old drugs as well as provide starting molecules that could be modified to treat diseases unrelated to the original indicated disease for the parent drug. The cost of creating and maintaining a BDR database are in-line with budgets of some of the projects funded by agencies such as the NIH or MRC. One possible feature is that the patient, rather than the doctor, does the majority of BDR reporting. Although lay-reporting would lead to false positives; repeated observations of a beneficial association would be a reasonable basis for further study in formal, professional clinical investigations. It is time to create the largest stage ever for serendipity to appear in biomedical research. After all, her previous appearances have marked some of science's most amazing discoveries.

Databases, Factual↗
PubMed2005

A pilot ontological model of public health indicators.

UNLABELLED: There are various public health databases in the world aiming to provide data to compare health conditions in different countries. Their data sets are more or less overlapping but data from different databases and different countries are hard to compare due to different definitions and interpretations. Our aim was to create a core ontological model that is able to represent public health indicators. We assumed, that by such representation comparability and quality of data could be improved. METHOD: Three sets of indicators were taken, and a core ontology was built from information objects describing their top level entities. The Protégé ontology editor with RDF backend was used for building the ontology. The used indicator sets were the indicators of the Health for All Database of the World Health Organisation (HFA), the OECD Health Data, and the set of indicators proposed by the European Community Health Indicators (ECHI) European project. Then 19 indicators selected from HFA was represented using the core ontology. Strength and weaknesses of the descriptive capability of the model was studied. RESULT: The drafted core model seems to be useful in representing many of the public health indicators. In some cases it really helps improve comparability. However, some of the semantic details cannot be sufficiently expressed by the used ontology representation language. There is a need of merging other domain ontologies to represent indicators related to other domains, such as economy, social and environmental sciences.

Databases, Factual↗