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'''[[Clinical chemistry]]''' (sometimes referred to as '''chemical pathology''') is the area of [[clinical pathology]] that is generally concerned with analysis of bodily fluids. The discipline originated in the late nineteenth century with the use of simple chemical tests for various components of blood and urine. Subsequent to this, other techniques were applied including the use and measurement of enzyme activities, spectrophotometry, [[electrophoresis]], and [[immunoassay]].
'''"[[Journal:Ten simple rules for managing laboratory information|Ten simple rules for managing laboratory information]]"'''


Today [[Clinical laboratory|clinical laboratories]] are now highly automated to accommodate the high workload typical of a hospital laboratory or [[reference laboratory]]. A large clinical laboratory will accept samples for up to about 700 different kinds of tests. Even the largest of laboratories rarely do all these tests themselves, and some must be referred to other labs. This large array of tests can be further sub-categorized into sub-specialties like general or routine chemistry, special chemistry, clinical endocrinology, [[toxicology]], therapeutic drug monitoring, urinalysis, and fecal analysis. ('''[[Clinical chemistry|Full article...]]''')<br />
[[Information]] is the cornerstone of [[research]], from experimental data/[[metadata]] and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging [[laboratory information management system]]s (LIMS) to transform this large information load into useful scientific findings. The development of [[mathematical model]]s that can predict the properties of biological systems is the holy grail of [[computational biology]]. Such models can be used to test biological hypotheses, guide the development of biomanufactured products, engineer new systems meeting user-defined specifications, and much more ... ('''[[Journal:Ten simple rules for managing laboratory information|Full article...]]''')<br />
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Latest revision as of 18:03, 10 June 2024

Fig2 Berezin PLoSCompBio23 19-12.png

"Ten simple rules for managing laboratory information"

Information is the cornerstone of research, from experimental data/metadata and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging laboratory information management systems (LIMS) to transform this large information load into useful scientific findings. The development of mathematical models that can predict the properties of biological systems is the holy grail of computational biology. Such models can be used to test biological hypotheses, guide the development of biomanufactured products, engineer new systems meeting user-defined specifications, and much more ... (Full article...)

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