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Regardless of this software, more pressure should be placed on manufacturers of LISs to develop easy mechanisms to log and audit the data related to laboratory errors. Despite many expectations were raised over the past decades, in vivo and non-invasive diagnostics remains an un met target 57 , With limited exceptions, such as in vivo continuous glucose monitoring 59 , it is now undeniable that the blood sample collection will remain an essential part of the total testing process for long.

There is a common saying, that because you have always done something in one way, it does not mean that this way may be right. This actually reflects a human inclination to resist change, and contradicts the notorious concept that »intelligence is the ability to adapt to change« Stephen Hawking; Oxford University graduation.

Technology is taking over many human domains, including health care. It is hence rather obvious that the translation within the preanalytical phase of many promising technological innovations, such as those discussed in this article, holds great promise for decreasing the vulnerability of in vitro diagnostics and ultimately enhancing patient safety.

So, the time has come to start thinking »out of the box«, or as George Bernard Shaw would put it » The reasonable man adapts himself to the world: the unreasonable one persists in trying to adapt the world to himself. Therefore all progress depends on the unreasonable man «. Conflict of interest statement The authors stated that they have no conflicts of interest regarding the publication of this article. J Med Biochem. Published online Oct Giuseppe Lippi.

Author information Article notes Copyright and License information Disclaimer. Received Apr 22; Accepted May Summary The preanalytical phase is crucial for assuring the quality of in vitro diagnostics. Keywords: errors, patient safety, quality, technology.

Introduction The preanalytical phase, encompassing appropriate test selection along with all those activities needed for collection and preparation of biological samples before testing, is a crucial aspect for assuring the quality of in vitro diagnostics 1. Needle-wielding robotic phlebotomy devices Venipuncture is one of the most frequently performed medical procedures in healthcare 11 , Active blood tubes The intrinsic characteristics of the blood collection tubes are essential determinants for obtaining reliable results of in vitro diagnostic testing.

Sample transportation by drones The transportation of biological samples is a big issue in pathology and laboratory diagnostics. Innovative approaches for detecting spurious hemolysis Hemolysis is conventionally defined as injury or complete breakdown of RBCs in blood, a phenomenon that often reflects a more generalized issue of all corpuscular blood elements damage i.

Table I Innovative technologies for detecting hemolysis in whole blood. Hemolysis sensors 2. Integrated systems of plasma separation followed by optical hemoglobin assessment a. Velocity gradient plasma separation using non- positive blood pumps b. High velocity plasma separation within a reagent disc c. Microfluidic-based plasma separation d. Gravity separation of plasma e. Capillary separation of plasma coupled with smartphone camera-based assessment 3. Equations based on routine hematological parameters.

Open in a separate window. Preanalytical errors recording software products Systematic monitoring and recording of errors, either being near misses or adverse events, is one of the mainstays for reducing medical and diagnostic errors Conclusions Despite many expectations were raised over the past decades, in vivo and non-invasive diagnostics remains an un met target 57 , Footnotes Conflict of interest statement The authors stated that they have no conflicts of interest regarding the publication of this article.

References 1. Preanalytical quality improvement. Lippi G. Governance of preanalytical variability: travelling the right path to the bright side of the moon? Clin Chim Acta. Simundic AM. Preanalytical phase - an updated review of the current evidence. Biochem Med Zagreb ; 24 Can Technology Eliminate Human Error? Process Saf Environ Prot. Impact of emerging technologies on medication errors and adverse drug events.

Am J Health Syst Pharm. Overview on patient safety in healthcare and laboratory diagnostics. Biochem Med Zagreb ; 20 — Simundic AM, Lippi G. Preanalytical phase-a continuous challenge for laboratory professionals. Biochem Med Zagreb ; 22 —9. Lippi G, Guidi GC. Risk management in the preanalytical phase of laboratory testing. Clin Chem Lab Med.

Zidel T. A Lean Guide to Transforming Healthcare. Laboratory Diagnostics and Quality of Blood Collection. Ialongo C, Bernardini S. Phlebotomy, a bridge between laboratory and patient. Biochem Med Zagreb ; 26 — Nybo M. Reducing preanalytical laboratory sample errors through educational and technological interventions.

Clin Lab. A research review on clinical needs, technical requirements, and normativity in the design of surgical robots. Int J Med Robot. Portable robot for autonomous venipuncture using 3D near infrared image guidance. Rep U S. Patient and Sample Identification.

Out of the Maze? Swedberg C. Lab-on-a-chip technology for continuous glucose monitoring. J Diabetes Sci Technol. Potentiometric platform for the quantification of cellular potassium efflux. Lab Chip. Lab-ona- chip devices: How to close and plug the lab?

Microelectron Eng. Haemolysis: an overview of the leading cause of unsuitable specimens in clinical laboratories. Quality standards for sample processing, transportation, and storage in hemostasis testing.

Semin Thromb Hemost. Multifunctional, inexpensive, and reusable nanoparticle-printed biochip for cell manipula- tion and diagnosis. Lippi G, Simundic AM. Though Legionella spp. Legionella isolation requires laboratories with long experience in cultivation of the organism, as various factors such as the complex steps necessary for culture and competing microbial flora in the sample can influence culture accuracy [ 4 , 5 , 6 ].

The first differentiation of isolates of Legionella spp. The slow growth of the Legionella spp. Additionally, it was shown that culture according to ISO is not very sensitive to the detection of Legionella non- pneumophila [ 4 , 8 ].

Culture requires long incubation periods of up to ten days, which constitute a problem in time-sensitive cases such as outbreak situations [ 2 , 5 , 7 ]. The high tolerance of Legionella to biocides, heat, and even acid, and its ability to persist, makes the constant monitoring of risk sources, such as cooling towers, hot, and cold-water systems or spa pools, essential [ 9 , 10 ].

All of the above-mentioned difficulties lead various scientists to the development of new methods for the detection and quantification of Legionella in water samples, one of the techniques suggested by numerous scientists being quantitative polymerase chain reaction qPCR [ 2 , 11 , 12 ].

Different assays were developed for this purpose, with some relying on intercalating fluorescent dyes such as SYBR Green for quantification, and others using molecular hybridization probe-based detection methods, such as TaqMan assays [ 13 ]. These methods enable the detection and quantification via the total DNA isolated from the sample, thus allowing precise quantification of low amounts of target gene [ 13 ].

In order to improve the detection in the culture-based method, an updated version of the standard method ISO Water quality and Enumeration of Legionella , referenced in most guidelines for drinking water, cooling towers, etc. This reference method for detection of Legionella proposes three different methods for analyzing water samples, depending on the accompanying microbial flora in the water samples.

One method, described as Matrix A, was recommended for the analysis of samples with low accompanying microbial flora, such as potable water. Matrix B was recommended for the analysis of samples with high accompanying microbial flora, such as cooling towers, cooling water, etc. Matrix C was recommended for samples with extremely high levels of accompanying flora, such as sewage. Our study aimed at comparing the results for the detection of Legionella in water samples detection of Legionella spp.

All were analyzed for possible Legionella contamination by culture, as well as by qPCR. Samples were collected in multiple, sterile mL plastic bottles VWR International, Vienna, Austria , mL aliquots were used for quantification by culture, and 50— mL aliquots, depending on the amount of sample sent to the water laboratory, were used for quantification by qPCR.

For DNA extraction, aliquots were filtered through a 45 mm polycarbonate membrane with a 0. The quantification of L. Sample handling was performed according to the decision matrix described in the standard method ISO see Table 1 , with the exception of applying Matrix A as well as Matrix B, regardless of presumed accompanying microbial flora to compare both. No samples requiring Matrix C were included in this study.

For water supply samples and water circuit samples presumed low burden of accompanying microbial flora , samples were analyzed using Matrix A as well as Matrix B. In brief, for Matrix A, 1 mL as well as mL of the sample were filtered through a 47 mm mixed cellulose esters filter with a 0. For Method B, 1 mL and mL of the samples were filtered and microorganisms were subsequently recovered from the membrane filters using 5 mL of 2. For Matrix B, mL of the sample were filtered through a mm polycarbonate filter with a 0.

A total of 0. For heat treatment, another 0. For acid treatment, another 0. Afterwards, filters were rinsed with 20 mL of 2. For the cooling tower, cooling water, car wash facility water, system water, and bath water samples presumed high burden of accompanying microbial flora , the samples were also analyzed using Matrix A and B, with the following modifications.

For Matrix A, 0. Matrix B was performed in the same manner as for the other sample types. Five or more presumptive Legionella -colonies were confirmed to be Legionella spp. The colonies were defined as Legionella spp. For the final enumeration of Legionella , plates with the highest count of confirmed Legionella colonies were used. All gene targets occurred as a single copy in the Legionella genome.

A positive control of L. Legionella spp. Samples were analyzed in duplicates. Samples that tested positive were repeated. For the positive control, L. The extracted genomic DNA was then diluted to One sample sample A contained 1. The other sample sample B contained 3. Experimental procedure was performed the same as described above for all samples.

The egfp gene was selected as an internal amplification control IAC , as described by Bliem et al. A pJET1. Furthermore, the software automatically calculated mean crossing point cp values for replicates, which were used for the final calculations. The cp value of the last detectable standard was set as the limit of detection LOD of the qPCR, as the non-template control was not detectable 1 [ 1 ].

Predictive values were calculated considering the culture method as the reference method for the detection of Legionella in environmental water samples. Cp values for Legionella spp. No unspecific amplification for the NTC was observed.

Measured values obtained for sample A left graph , containing defined concentrations of 1. Measured values for sample B right graph showed correct amplification for all three assays, corresponding to the target values of 3.

As shown in Figure 1 , sample A showed no amplification for L. Measured values for sample B showed correct amplification for all three assays, corresponding to the target value of 3. For this purpose, the samples coming from the cooling waters were used, as they represent a difficult sample matrix, due to the potential use of biocides. Water systems that produce aerosols are especially under inspection for Legionella contamination, e.

Target specificity control for all three qPCR assays. Mean cp values for the IAC were Samples were considered to be inhibited if the ct values shifted higher than 2 cycles, as compared to the IAC in the NTC, in this case, the samples were repeated diluted. Quantification of Legionella spp. LOD was 2. No amplification of the NTC was observed.

The LOD of the culture methods was dependent on the volume filtrated for either of the Matrix procedures, which was 1 as well as mL for Matrix A; or for Matrix B, from a filtration volume of mL that was recovered in 5 mL, 0. Thirteen water supply samples In combination, two samples Of the analyzed samples in this study, 31 samples There were eight culture positive-qPCR negative samples This results for samples with an expected low burden of accompanying microbial flora in a PPV of Positive samples with low and high burden of accompanying microbial flora from qPCR and culture for L.

Predictive values of qPCR for L. This paper aims to identify those parameters of realistic occupants-related heat gains that actually cause this gap. The investigation therefore, systematically distinguishes the occupant behavior using three behavior parameters, namely: the occupancy behavior , the appliance use behavior and the family size.

The effect of these parameters is investigated on a building for two different insulation standards using heat pump as energy supply system. Results further show that variation in household sizes is an important parameter to understand the variation in the actual energy use for similar buildings. Sensitivity of findings is tested against building thermal mass and condensing gas boiler. Analysis shows no significant variations in the conclusions.

The study therefore concludes that using identified parameters in modeling practices can contribute to improve the prediction of actual energy use of buildings. This is a preview of subscription content, access via your institution. Rent this article via DeepDyve. Compilation of diversity factors and schedules for energy and cooling load calculations.

Google Scholar. Synthetically derived profiles for representating occupant-drive electrical loads in Canadian housing. Journal of Building Performance Simulation , 2: 15— Andersen R Occupant behaviour with regard to control of the indoor environment.

Bourgeois D Detailed occupancy predictions, occupancysensing control and advanced behavioural modelling within whole building-energy simulation. Brundrett GW Ventilation: A behavioural approach. International Journal of Energy Research , 1: — A bottom-up approach to residential load modeling. CEN a. EN Air conditioning, liquid chilling packages and heat pump with electrically driven compressors for space heating and cooling.

CEN b. EN Heating systems in buildings—Method for calculation of system energy requirements and system efficiencies. CEN EN Energy performance of buildings—Overall energy use and definition of energy ratings. Comparison between predicted and actual energy performance for winter heating in high-performance residential buildings in the Lombardy region Italy. Energy and Buildings , — The gap between predicted and measured energy performance of buildings: A framework for investigation.

Automation in Construction , 40— Building and Environment , — Grinden B, Feilberg N Technical Report. Haldi F, Robinson D Adaptive actions on shading devices in response to local visual stimuli. Journal of Building Performance Simulation , 3: — Haldi F, Robinson D a. Journal of Building Performance Simulation , 4: — Haldi F, Robinson D b.

Modelling occupants personal characteristics for thermal comfort prediction. International Journal of Biometeorology , — Towards a model of user behaviour regarding the manual control of windows in office buildings. User behavior in whole building simulation. Time use survey—Documentation of data collection, analysis and data quality. ISO ISO Energy performance of buildings—Calculation of energy use for space heating and cooling.

Jordan U, Vajen K Realistic domestic hot-water profiles indifferent time scales. Technical Report, Marburg University. Kampf J On the modelling and optimization of urban energy fluxes. Hovedunderskelse for elektrisitetsbruk i husholdningene.

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