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Aim: Biomarker research in Parkinson's lacks a systematic bibliometric review, despite a growing body of bibliometric studies. This study aimed to analyze publications on biomarkers that may play an important role in the diagnosis and treatment of Parkinson's disease. Materials and Methods: In the study, bibliometric methods were used to analyze the related research in depth. Data were obtained from the Web of Science Core Collection database. Specific keywords consisting of MeSH terms were used as search criteria. The search was conducted in English and the type of publication was selected as article. Microsoft Excel, VOS viewer and the visualization and analytical capabilities of the Biblioshiny R package were used in the analysis, providing a versatile approach to achieve the study objectives. Results: A total of 461 articles on Parkinson's disease biomarkers published between 2002 and 2023 in 170 journals met the criteria and were analyzed. Regarding publications and citations, the year 2022 stood out. Each document had an average of 6.28 co-authors. The main contributing countries were the United States, China and Italy, with a global co-authorship rate of 30.8%. Notable institutions included the University of Washington, Ruhr University Bochum and the Paracelsus Elena Clinic. "Journal of Neurochemistry," was the journal with the most publications. Through their total link strength, prominent terms such as "Parkinson's disease", "biomarker" and "alpha-synuclein" showed significant occurrences and thematic relevance. Conclusion: The present study presents the inaugural bibliometric evaluation of biomarkers in Parkinson's disease. The gradual increase of papers since 2012 indicates persistent scholarly interest and foretells continued significance over the following ten years. Future research will be aided by the study's depiction of biomarker patterns, which provides insightful information. The term "Alpha-synuclein" stood out, highlighting its relevance in our research. [ABSTRACT FROM AUTHOR]

Objective: The use of reference change values (RCV) has been advocated as a most appropriate for monitoring individuals in clinical laboratories. The aim of this study was to calculate of RCV of 24 analytes of immun analysis parameters. Methods: Twenty four serum analytes (cortisol, DHEA-S, estradiol, follicle stimulating hormone (FSH), luteinizing hormone (LH), prolactin, testosterone, troponin I, NT-proBNP, parathormone (PTH), insulin, ferritin, folate, thyroid stimulating hormone (TSH), free T3, free T4, thyroglobulin antibody (anti TG), thyroid peroxidase antibody (anti TPO), alpha fetoprotein (AFP), CA 15-3, CA 19-9, CA 125, carcinoembryonic antigen (CEA) and total PSA) were analyzed with Roche kits (Roche Diagnostics, Mannheim, Germany), which were manifactured to use immun analyzer Cobas ®6000 (Roche Diagnostics, Mannheim, Germany). We used analytic coefficient of variation (CVa) from internal quality control programme. Intra-individual biological variation (CVw) were obtained from Ricos current updated published in Westgards website. This database was updated in 2014. We calculated RCV using this formula RCV=21/2xZx(CVa²+CVw²)1/2 (Z=1.65, 95% probability, unidirectional; Z=2.33, 99% probability, unidirectional; Z=1.96, 95% probability, bidirectional; Z=2.58, 99% probability, bidirectional). Results: RCVs (Z=1.96) of these analytes were calculated as 44.30% for cortisol, 31.25% for DHEA-S, 64.34% for estradiol, 35.14% for FSH, 64.58% for LH, 65.24% for prolactin, 27.28% for total testosterone, 39.24% for troponin I, 29.00% for NT-proBNP, 71.93% for PTH, 59.22% for insulin, 40.51% for ferritin, 68.04% for folate, 53.74% for TSH, 23.07% for free T3, 17.38% for free T4, 29.27% for anti TG, 42.52% for anti TPO, 35.42% for AFP, 21.58% for CA 15-3, 44.99% for CA 19-9, 68.92% for CA 125, 36.83% for CEA and 51.00% for total PSA. Conclusion: We suggest to use RCV as well as to use population-based reference interval. RCV could be a valuable tool for clinical decision in especially monitoring individuals. [ABSTRACT FROM AUTHOR]

OBJECTIVES: Calculation of measurement uncertainty is especially important for tests in which clinical decision levels have been determined. In this study, it is aimed to calculate the measurement uncertainty of procalcitonin, which is a sepsis marker, and to compare it with the total allowable error % (TEa%). MATERIALS and METHODS: Procalcitonin was analyzed with elecsys BRAHMS procalcitonin kit (Roche Diagnostics, Mannheim, Germany), which was manufactured to use immune analyzer Cobas e601 (Roche Diagnostics, Mannheim, Germany). The calculation model defined in the Nordtest guide was used to determine the measurement uncertainty. The component of the intermediate precision of the measurement uncertainty was calculated using the last three months of internal quality control data. The component of the u(bias) was obtained from the past 12 months of external quality control data. The expanded uncertainty was calculated according to the 95% confidence interval by combining all components. RESULTS: Measurement uncertainty for procalcitonin was calculated as ± 10.24% at 95% confidence interval and it was not found to be higher than TEa% values (TEa%=20.3% for procalcitonin). CONCLUSIONS: It is thought that giving the result and the measurement uncertainty of procalcitonin used as a risk biomarker in severe sepsis will contribute to the clinicians in clinical decision making. [ABSTRACT FROM AUTHOR]