Bachground: Sample classification and registration have been recognized as important and time-consuming processes in laboratories. There is increasing pressure on laboratories to automate processes due to intense workload and reduce manual procedures and errors. The aim of the present study was to evaluate the positive effects of an automatic tube registration and sorting system on specimen processing.
Methods: An automatic tube registration and sorting system (HCTS2000 MK2, m-u-t AG, Wedel, Germany) was evaluated. Turnaround time (TAT), rate of sample rejection and unrealized tests were examined 12 months pre- and post-implementation of the automatic tube sorting and registration system.
Results: The mean TAT of routine chemistry immunoassay, complete blood cell count (CBC) and coagulation samples were significantly improved (P<0.001). The number of rejected samples and unrealized tests was insignificantly decreased post-implementation of the system (0.4% to 0.2% and 4.5% to 1.4%, respectively) (P>0.05).
Conclusions: By reducing delays and errors in the preanalytical processing and sorting of samples, significant improvements in specimen processing were observed after implementation of the system. These results suggest that an automatic tube registration and sorting system may also be used to improve specimen processing in a higher-volume core laboratory.
Diabetes mellitus is a heterogeneous group of disorders in which particular disease phenotypes can be characterized by a specific etiology and/or pathogenesis of the disease, but in many cases its classification is greatly impeded due to significant phenotype overlapping. Diabetes is a wordwide epidemic with significant health and economic consequences. The frequency of type 2 diabetes (T2D) is much higher than type 1 diabetes (T1D). In adults, around 285 million people suffer from T2DM with a projected rise to 438 million in the next 20 years. A variety of pharmacological treatments exist for patients with T2D, in addition to dietary and physical activity. Pharmacologically, diabetes is treated with nine major classes of approved drugs, including insulin and its analogues, sulfonylureas, biguanides, thiazolidinediones (TZDs), meglitinides, a-glucosidase inhibitors, amylin analogues, incretin hormone mimetics, and dipeptidyl peptidase 4 (DPP4) inhibitors. Treatment strategy for T2D is based mostly on oral hypoglycemic drug (OHD) efficacy assessed usually by HbA1c and/or fasting plasma glucose. The patients are often treated with more than one OHD in combination with the purpose to receive more effective treatment. Characterization of drug response is expected to substantially increase the ability to provide patients with the most effective treatment strategy. If pharmacogenetic testing for diabetes drugs could be used to predict treatment outcome, appropriate measures could be taken to treat T2D more efficiently. To date, major pharmacogenetic studies have focused on response to sulfonylureas, biguanides, and TZDs, the most used OHD. A comprehensive review of the pharmacogenetic studies of specific OHD is presented in this article. Understanding the pharmacogenetics of these drugs will provide critical baseline information for the development and implementation of a genetic screening program into therapeutic decision making, enabling a personalized medicine approach for T2D patients.
Genomics is a comprehensive study of the whole genome, genetic products, and their interactions. Human genome project has identified around 25,000-30,000 genes, and prevailing presence in tumor pathogenesis, high number of mutations, epigenetic changes, and other gene disorders have been identified. Microarrays technology is used for the analysis of these changes. Postgenome age has begun, and the initial results ensure the improvement of molecular tumor diagnostics and the making of a new taxonomic tumor classification, as well as the improvement, optimization and individualization of anti-tumor therapy. First genomic classifications have been made of leukemias, non-Hodgkin lymphoma, and many solid tumors. For example, 4 molecular types of breast carcinoma, three types of diffuse B cell lymphoma, two types of chromophobic renal carcinoma have been identified. Also, gene structures for favorable and unfavorable outcome in leukemia, breast cancer, prostate, bronchi, and other tumors have been identified. It is absolutely possible to diagnose the primary outcome of tumors with which standard tumor position may not be proved using standard diagnostic tools. Pharmacogenomic profiles have ensured better definition of interindividual differences during therapy using antineoplastic drugs and the decrease of their toxicity, as well as individual treatment approach and patient selection with which favorable clinical outcome is expected. Pharmacogenomics has impacted the accelerated development of target drugs, which have showed to be useful in practice. New genomic markers mtDNA, meDNA, and miRNA have been identified, which, with great certainty, help the detection and diagnostics of carcinoma. In the future, functional genomics in clinical oncology provides to gain knowledge about tumor pathogenesis; it will improve diagnostics and prognosis, and open up new therapeutic options.
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