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Background After completing university education, biomedical laboratory scientists work in clinical laboratories, in biomedical research laboratories, in biotech, and in pharmaceutical companies. Laboratory diagnostics have undergone rapid development over the recent years, with the pace showing no signs of abatement. This rapid development challenges the competence of the staff and will most certainly influence the education of future staff. This study aimed to examine what was considered the necessary competencies needed to pursue a career as a biomedical laboratory scientist. Methods A modified Delphi technique was used, with the panel of experts expressing their views in a series of three questionnaire. Consensus was defined as the point which 75 % or more of the panel participants agreed that a particular competency was necessary. Results The study highlights the perceived importance of mostly generic competencies that relate to quality, quality assurance, and accuracy, as well as different aspects of safety, respect, trustworthiness (towards patients/clients and colleagues), and communication skills. The results also stress the significance of self-awareness and professionality. Conclusions We identified important competencies for biomedical laboratory scientists. Together with complementary information from other sources, i.e., guidelines, laws, and scientific publications, the competencies identified can be used as learning outcomes in a competency-based education to provide students with all the competencies needed to work as professional biomedical laboratory scientists.

We review the current state of quality assurance in laboratories of the five Central Asia Republics (CARs), focusing on laboratory equipment, and compare quality assurance approaches with CLSI standards. The laboratories of the CARs faced exceptional challenges including highly-structured laboratory systems that retain centralized and outmoded Soviet-era approaches to quality assurance, considerably jeopardizing the validity of laboratory tests. The relative isolation of the CARs, based on geography and almost exclusive use of the Russian language, further hamper change. CARs must make high-level government decisions to widely implement quality assurance programs within their laboratory systems, within which approaches to the management of laboratory equipment will be a prominent part.

Wearable biosensors are garnering substantial interest due to their potential to provide continuous, real-time physiological information via dynamic, noninvasive measurements of biochemical markers in biofluids, such as sweat, tears, saliva and interstitial fluid. Recent developments have focused on electrochemical and optical biosensors, together with advances in the noninvasive monitoring of biomarkers including metabolites, bacteria and hormones. A combination of multiplexed biosensing, microfluidic sampling and transport systems have been integrated, miniaturized and combined with flexible materials for improved wearability and ease of operation. Although wearable biosensors hold promise, a better understanding of the correlations between analyte concentrations in the blood and noninvasive biofluids is needed to improve reliability. An expanded set of on-body bioaffinity assays and more sensing strategies are needed to make more biomarkers accessible to monitoring. Large-cohort validation studies of wearable biosensor performance will be needed to underpin clinical acceptance. Accurate and reliable real-time sensing of physiological information using wearable biosensor technologies would have a broad impact on our daily lives. Assessing progress towards designing reliable wearable biosensors reveals the challenges remaining before the promise of clinical translation can be realized.

Background Cardiovascular diseases kill approximately 17 million people globally every year, and they mainly exhibit as myocardial infarctions and heart failures. Heart failure (HF) occurs when the heart cannot pump enough blood to meet the needs of the body.Available electronic medical records of patients quantify symptoms, body features, and clinical laboratory test values, which can be used to perform biostatistics analysis aimed at highlighting patterns and correlations otherwise undetectable by medical doctors. Machine learning, in particular, can predict patients’ survival from their data and can individuate the most important features among those included in their medical records. Methods In this paper, we analyze a dataset of 299 patients with heart failure collected in 2015. We apply several machine learning classifiers to both predict the patients survival, and rank the features corresponding to the most important risk factors. We also perform an alternative feature ranking analysis by employing traditional biostatistics tests, and compare these results with those provided by the machine learning algorithms. Since both feature ranking approaches clearly identify serum creatinine and ejection fraction as the two most relevant features, we then build the machine learning survival prediction models on these two factors alone. Results Our results of these two-feature models show not only that serum creatinine and ejection fraction are sufficient to predict survival of heart failure patients from medical records, but also that using these two features alone can lead to more accurate predictions than using the original dataset features in its entirety. We also carry out an analysis including the follow-up month of each patient: even in this case, serum creatinine and ejection fraction are the most predictive clinical features of the dataset, and are sufficient to predict patients’ survival. Conclusions This discovery has the potential to impact on clinical practice, becoming a new supporting tool for physicians when predicting if a heart failure patient will survive or not. Indeed, medical doctors aiming at understanding if a patient will survive after heart failure may focus mainly on serum creatinine and ejection fraction.

In research and clinical genomics laboratories today, sample preparation is the bottleneck of experiments, particularly when it comes to high-throughput next generation sequencing (NGS). More genomics laboratories are now considering liquid-handling automation to make the sequencing workflow more efficient and cost effective. The question remains as to its suitability and return on investment. A number of points need to be carefully considered before introducing robots into biological laboratories. Here, we describe the state-of-the-art technology of both sophisticated and do-it-yourself (DIY) robotic liquid-handlers and provide a practical review of the motivation, implications and requirements of laboratory automation for genome sequencing experiments.

Routine biomarker results from hospital laboratory information systems, covering hospitals and general practitioners, in Denmark are available to researchers through access to the regional Clinical Laboratory Information System Research Database at Aarhus University and the nationwide Register of Laboratory Results for Research. This review describes these two data sources. The laboratory databases have different geographical and temporal coverage. They both include individual-level biomarker results that are electronically transferred from laboratory information systems. The biomarker results can be linked to all other Danish registries at the individual level, using the unique identifier, the CPR number. The databases include variables such as the CPR number, date and time (hour and minute) of sampling, NPU code, and name of the biomarker, identification code for the laboratory and the requisitioner, the test result with the corresponding unit, and the lower and upper reference limits. Access to the two databases differs since they are hosted by two different institutions. Data cannot be transferred outside Denmark, and direct access is provided only to Danish institutions. It is concluded that access to data on routine biomarkers expands the detailed biological and clinical information available on patients in the Danish healthcare system. The full potential is enabled through linkage to other Danish healthcare registries. Keywords: biomarkers, database, data resource, laboratory information systems

CSF and PET biomarkers of amyloid β and tau accurately detect Alzheimer's disease pathology, but the invasiveness, high cost, and poor availability of these detection methods restrict their widespread use as clinical diagnostic tools. CSF tau phosphorylated at threonine 181 (p-tau181) is a highly specific biomarker for Alzheimer's disease pathology. We aimed to assess whether blood p-tau181 could be used as a biomarker for Alzheimer's disease and for prediction of cognitive decline and hippocampal atrophy. We developed and validated an ultrasensitive blood immunoassay for p-tau181. Assay performance was evaluated in four clinic-based prospective cohorts. The discovery cohort comprised patients with Alzheimer's disease and age-matched controls. Two validation cohorts (TRIAD and BioFINDER-2) included cognitively unimpaired older adults (mean age 63–69 years), participants with mild cognitive impairment (MCI), Alzheimer's disease, and frontotemporal dementia. In addition, TRIAD included healthy young adults (mean age 23 years) and BioFINDER-2 included patients with other neurodegenerative disorders. The primary care cohort, which recruited participants in Montreal, Canada, comprised control participants from the community without a diagnosis of a neurological condition and patients referred from primary care physicians of the Canadian National Health Service for specialist care. Concentrations of plasma p-tau181 were compared with established CSF and PET biomarkers and longitudinal measurements using Spearman correlation, area under the curve (AUC), and linear regression analyses. We studied 37 individuals in the discovery cohort, 226 in the first validation cohort (TRIAD), 763 in the second validation cohort (BioFINDER-2), and 105 in the primary care cohort (n=1131 individuals). In all cohorts, plasma p-tau181 showed gradual increases along the Alzheimer's disease continuum, from the lowest concentrations in amyloid β-negative young adults and cognitively unimpaired older adults, through higher concentrations in the amyloid β-positive cognitively unimpaired older adults and MCI groups, to the highest concentrations in the amyloid β-positive MCI and Alzheimer's disease groups (p<0·001, Alzheimer's disease vs all other groups). Plasma p-tau181 distinguished Alzheimer's disease dementia from amyloid β-negative young adults (AUC=99·40%) and cognitively unimpaired older adults (AUC=90·21–98·24% across cohorts), as well as other neurodegenerative disorders, including frontotemporal dementia (AUC=82·76–100% across cohorts), vascular dementia (AUC=92·13%), progressive supranuclear palsy or corticobasal syndrome (AUC=88·47%), and Parkinson's disease or multiple systems atrophy (AUC=81·90%). Plasma p-tau181 was associated with PET-measured cerebral tau (AUC=83·08–93·11% across cohorts) and amyloid β (AUC=76·14–88·09% across cohorts) pathologies, and 1-year cognitive decline (p=0·0015) and hippocampal atrophy (p=0·015). In the primary care cohort, plasma p-tau181 discriminated Alzheimer's disease from young adults (AUC=100%) and cognitively unimpaired older adults (AUC=84·44%), but not from MCI (AUC=55·00%). Blood p-tau181 can predict tau and amyloid β pathologies, differentiate Alzheimer's disease from other neurodegenerative disorders, and identify Alzheimer's disease across the clinical continuum. Blood p-tau181 could be used as a simple, accessible, and scalable test for screening and diagnosis of Alzheimer's disease. Alzheimer Drug Discovery Foundation, European Research Council, Swedish Research Council, Swedish Alzheimer Foundation, Swedish Dementia Foundation, Alzheimer Society Research Program.

The higher throughput and lower per-base cost of next-generation sequencing (NGS) as compared to Sanger sequencing has led to its rapid adoption in clinical testing. The number of laboratories offering NGS-based tests has also grown considerably in the past few years, despite the fact that specific Clinical Laboratory Improvement Amendments of 1988/College of American Pathologists (CAP) laboratory standards had not yet been developed to regulate this technology. To develop a checklist for clinical testing using NGS technology that sets standards for the analytic wet bench process and for bioinformatics or \"dry bench\" analyses. As NGS-based clinical tests are new to diagnostic testing and are of much greater complexity than traditional Sanger sequencing-based tests, there is an urgent need to develop new regulatory standards for laboratories offering these tests. To develop the necessary regulatory framework for NGS and to facilitate appropriate adoption of this technology for clinical testing, CAP formed a committee in 2011, the NGS Work Group, to deliberate upon the contents to be included in the checklist. Results . -A total of 18 laboratory accreditation checklist requirements for the analytic wet bench process and bioinformatics analysis processes have been included within CAP's molecular pathology checklist (MOL). This report describes the important issues considered by the CAP committee during the development of the new checklist requirements, which address documentation, validation, quality assurance, confirmatory testing, exception logs, monitoring of upgrades, variant interpretation and reporting, incidental findings, data storage, version traceability, and data transfer confidentiality.

The quantification of phosphorylated tau in biofluids, either cerebrospinal fluid (CSF) or plasma, has shown great promise in detecting Alzheimer’s disease (AD) pathophysiology. Tau phosphorylated at threonine 231 (p-tau231) is one such biomarker in CSF but its usefulness as a blood biomarker is currently unknown. Here, we developed an ultrasensitive Single molecule array (Simoa) for the quantification of plasma p-tau231 which was validated in four independent cohorts (n = 588) in different settings, including the full AD continuum and non-AD neurodegenerative disorders. Plasma p-tau231 was able to identify patients with AD and differentiate them from amyloid-β negative cognitively unimpaired (CU) older adults with high accuracy (AUC = 0.92–0.94). Plasma p-tau231 also distinguished AD patients from patients with non-AD neurodegenerative disorders (AUC = 0.93), as well as from amyloid-β negative MCI patients (AUC = 0.89). In a neuropathology cohort, plasma p-tau231 in samples taken on avergae 4.2 years prior to post-mortem very accurately identified AD neuropathology in comparison to non-AD neurodegenerative disorders (AUC = 0.99), this is despite all patients being given an AD dementia diagnosis during life. Plasma p-tau231 was highly correlated with CSF p-tau231, tau pathology as assessed by [18F]MK-6240 positron emission tomography (PET), and brain amyloidosis by [18F]AZD469 PET. Remarkably, the inflection point of plasma p-tau231, increasing as a function of continuous [18F]AZD469 amyloid-β PET standardized uptake value ratio, was shown to be earlier than standard thresholds of amyloid-β PET positivity and the increase of plasma p-tau181. Furthermore, plasma p-tau231 was significantly increased in amyloid-β PET quartiles 2–4, whereas CSF p-tau217 and plasma p-tau181 increased only at quartiles 3–4 and 4, respectively. Finally, plasma p-tau231 differentiated individuals across the entire Braak stage spectrum, including Braak staging from Braak 0 through Braak I–II, which was not observed for plasma p-tau181. To conclude, this novel plasma p-tau231 assay identifies the clinical stages of AD and neuropathology equally well as plasma p-tau181, but increases earlier, already with subtle amyloid-β deposition, prior to the threshold for amyloid-β PET positivity has been attained, and also in response to early brain tau deposition. Thus, plasma p-tau231 is a promising novel biomarker of emerging AD pathology with the potential to facilitate clinical trials to identify vulnerable populations below PET threshold of amyloid-β positivity or apparent entorhinal tau deposition.