Presentation of the new EUROLAB guidance document on the management of digitalised processes in laboratories accredited to ISO/IEC 17025:2017

Background: Educational science is a reciprocal adjustment process between humans and humans and nature as a regular development and improvement of all moral, intellectual, and physical potentials. In the learning process in the clinical laboratory, lecturers only use textbooks or observation sheets and do not get videos and modules as media in the learning process in the clinical laboratory about nursing skills. Students can choose learning styles through video media and modules to understand nursing materials and skills. This study aims to identify the effectiveness of implementing the clinical skills learning process in the laboratory for nursing students during the COVID-19 pandemic with video media and modules at Universitas Nahdlatul Ulama Surabaya (UNUSA). Methods: This research is a descriptive quantitative study. The selected population is laboratory practicum educators of UNUSA diploma nursing students during the year 2021. The sample was randomly selected, and 60 diploma Nursing students who had used learning media with videos and skill modules made by teaching lecturers during the COVID-19 pandemic were selected. The instrument is designed as a questionnaire regarding various student opinions on online learning with video media and clinical skills modules during the COVID-19 pandemic. Data were collected using a questionnaire and analyzed by t-test and multiple linear regression. Results: The results showed that there was an effect of video learning media on the learning outcomes of skills in the laboratory for diploma nursing students (b=6.64; p=0.023), and there was no effect of learning styles on the learning outcomes of infusion skills (b=6.38; p= 0.111) from the results of multiple linear regression analysis, there is an effect of using video learning media and student learning styles on nursing skills learning outcomes in the laboratory, the effect is statistically significant (b=6.64; p=0.041). Conclusion: This research concludes that using video learning media and modules is very effective in the learning process in clinical laboratories' nursing skills. Therefore, it is recommended that all lecturers make video media and modules about nursing skills per the Standard Operating Procedures from Persatuan Perawat Nasional Indonesia (PPNI) to facilitate the learning process in the laboratory and clinic.

Water recycling is a critical element that supports sustainable development. While the supply of fresh water is limited, both the world’s population and demand for the resource continues to expand rapidly. According to Global Water, Sanitation, and Hygiene (WASH), approximately 2000 children are dying every day as a result of diarrheal diseases due to unsafe drinking water, inadequate availability of water for hygiene, and lack of access to sanitation. On the other hand, distilled water is highly needed due to the demands of wet research laboratories. The distillation process which concurrently generates distilled water and “waste water to drain” is often overlooked for its impact and potential. The massive amounts of drained water as a result of the distillation process in wet laboratories has motivated us to perform this study. Our study includes investigating the amount of water produced due to the water distillation process in a wet laboratory in Universiti Sains Malaysia (USM), Penang. The objectives of this study are to understand the factors contributing to water wastage among distiller users in USM and create awareness about the potential of water recycling from the water distillation process. This study is divided into two parts: the determination of the amount of drained water from the distillation process and survey about water conservation from the distillation process. Validated questionnaires are disseminated to USM residents, particularly to explore user practices and concepts about water conservation from the distillation process. This study found that the water used in producing distilled water is 74065.68 L per day and 2221970.40 L per month, which is equivalent to the basic need of 9875 individuals (according to individual needs stated by WHO). In addition, this study highlighted the overlooked wastage of tremendous amounts of drained water from the distillation process in the laboratory. Efforts and changes shall be imposed to avoid the contribution of the water distillation process to water scarcity. These efforts will support the concepts of Green Lab and also Sustainable Development Goals (SDGs) 2030 Agenda.

Lean Six Sigma Sample Analysis Process in a Microbiology Laboratory Faced with shrinking budgets, growing volumes, and personnel shortages, clinical laboratories are increasingly moving to automation to maximize output and efficiency. The best tool for improvement is the Lean Six Sigma concept. The concept reaps the full benefits of automation. A Lean process in a laboratory is focused on testing products and materials to deliver results in the most efficient way in terms of cost, speed, or both. The goal of a Lean laboratory is to use less effort, less resources and less time to test incoming samples. On the other hand, the Six Sigma concept provides process workflow and products/services without defects. The Lean Six Sigma approach analyzes laboratory workflow to help identify inefficiencies and uncover opportunities to free capacity, reduce turnaround time and lower costs. The assessment examines the end-to-end process looking closely at workflow as well as overall laboratory efficiency. The proven techniques of Lean and Six Sigma enhance productivity in the laboratory environment and ensure the best outcomes. This article analyzes a particular process, defines the approach, and gives a review of results obtained by deployment of the Lean Six Sigma concept. The article discusses a sample analysis process in a microbiology laboratory. A traditional process that applies standard analysis methods has a number of non-value-added activities, takes too much time, and has opportunities for defects. By mapping an existing process using a SIPOC model, 12 activities were identified. With the use of Lean tools four non-value-adding activities, which are not needed if a new system is used, were identified. Six activities had opportunities for improvement in terms of significant reduction in process time, and saving resources. Only two activities in the existing traditional process, with the use of standard analysis methods were optimally solved, and this did not require redesign or removal. The application of Lean Six Sigma concepts and automated analysis systems on a new process led to only nine activities in the process that now takes much less time and uses less resources. This article presents a description of the main principles, practices, and methods used in Lean and Six Sigma. The Lean tools particularly discussed here are 5s and spaghetti diagram. For Six Sigma, DMAIC methodology is used, and a review of applied quality tools for certain process improvement phases is given.

Chemistry laboratories are an essential and indispensable part of chemistry education; however, the process in the laboratory cannot fully provide the desired gains. Although various scales in the literature address the process in the chemistry laboratory from different perspectives and enable evaluations, a new perspective on the process is student engagement. By determining to what extent and how students engage in the process, the chemistry laboratory can be carried out more effectively, and the efficiency of the teaching process can be increased by making the necessary arrangements. This study aimed to adapt the scale (Smith and Alonso, 2020) from international literature to Turkish and to determine its validity and reliability. For this purpose, the original scale went through the translation phase, and its language validity was checked. The sample of this study consists of 242 students who continue their education in Sakarya University (N= 158) and Gazi University (N=84) Education faculties, Science teaching and Classroom teaching departments. Then its construct validity was ensured by Confirmatory Factor Analysis. Its reliability was studied by determining the internal consistency coefficient. At the same time, the comparison of the data according to some demographic characteristics was also carried out. As a result, The Student Engagement in The General Chemistry Laboratory Scale adapted to our language is a valid and reliable scale consisting of 25 items and six factors.

SummaryBackgroundSample 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.MethodsAn 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.ResultsThe 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).ConclusionsBy 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.

Each lab in the Faculty of Computer Science, Universitas Indonesia was suggested to change its organizational culture into clan type that support knowledge sharing. There are a number of problems regarding to implementtting knowledge management in the Faculty such as lost of knowledge from graduated members, no maximum utilization of knowledge repository for all the members, and low knowledge interdependency. To solve the problems in making a good Knowledge Management(KM) solution, we used Becerra-Frenandez method to measure and identify the knowledge management process characteristic and existing KM process in e-Government laboratory. The assessment of the prototype showed that a gamified prototype is suitable to increase the externalization and sharing processes. However, the use of a competition element of the gamification is not appropriatein supporting knowledge management process in the laboratory.

Background Turnaround time (TAT), defined as time of collection to time of result verification, is monitored monthly for cardiac Troponin (cTnT). Within our STAT laboratory, our TAT goal is 85% of cTnT results verified within 45 min. In October 2021, our lab began observing an increase in TAT. A quality improvement project was launched to identify a root cause and possible solutions. Methods cTnT TAT data from the Hospital Clinical Laboratory (STAT lab) at Mayo Clinic, Rochester, MN was extracted from our Laboratory Information System (LIS) using Tableau (Salesforce Inc., Seattle, WA). The data were categorized into three components: collection time, in lab processing and testing time, and time to result verification. Goal metrics for each component are: collection &amp;lt;10 min, in lab processing and testing &amp;lt;25 min, and result verification to LIS &amp;lt;5 min. The average time for each component and percentage not meeting TAT goals were calculated in Microsoft Excel. TAT while performing manual verification of results (January 2022) and after autoverification was implemented (December 2022) were compared. Results Prior to implementation of cTnT autoverification, total orders for January 2022 were 1408 with 82.0% meeting TAT of 45 min. Of the 18.0% not meeting TAT, 207 (14.7%) orders exceeded goals for one or more components (percentages do not total 100%): collection delays 131 (63.0%), in lab processing and testing delay 175 (85.0%), and delay in result verification to LIS 80 (39.0%). For those orders not meeting the 45 min TAT goal, mean times were 17.6, 40.3, and 8 min for collection time, in lab processing and testing, and result verification to LIS, respectively. Post implementation of autoverification, total orders for December 2022 were 1444 with 88.6% meeting TAT of 45 min. Of the 11.4% not meeting TAT, 93 (6.4%) orders were outside goals for multiple components: collection delays 51 (55.0%), in lab processing and testing delay 67 (72.0%), and result verification to LIS 0 (0.0%) orders. For those orders not meeting the 45 min TAT goal, mean times 16.6, 39.7, and 0 min for collection time, in lab processing and testing, and result verification to LIS, respectively. Conclusion Achieving TAT goals agreed upon by the laboratory and clinical practice is critical to quality patient care. An investigation into our delayed TAT for cTnT highlighted several opportunities for intervention within the control of the lab. We chose to implement autoverification of results to easily and consistently eliminate the time required for manual result verification. TAT was improved by 6.6% (82.0% to 88.6%) through autoverification of results, allowing technologists to focus on other clinical work.

BackgroundCost-effectiveness analyses (CEAs) can provide useful information to policymakers concerned with the broad allocation of resources as well as to local decision makers choosing between different options for reducing the burden from a single disease. For the latter, it is important to use country-specific data when possible and to represent cost differences between countries that might make one strategy more or less attractive than another strategy locally. As part of a CEA of cervical cancer screening in five developing countries, we supplemented limited primary cost data by developing other estimation techniques for direct medical and non-medical costs associated with alternative screening approaches using one of three initial screening tests: simple visual screening, HPV DNA testing, and cervical cytology. Here, we report estimation methods and results for three cost areas in which data were lacking.MethodsTo supplement direct medical costs, including staff, supplies, and equipment depreciation using country-specific data, we used alternative techniques to quantify cervical cytology and HPV DNA laboratory sample processing costs. We used a detailed quantity and price approach whose face validity was compared to an adaptation of a US laboratory estimation methodology. This methodology was also used to project annual sample processing capacities for each laboratory type. The cost of sample transport from the clinic to the laboratory was estimated using spatial models. A plausible range of the cost of patient time spent seeking and receiving screening was estimated using only formal sector employment and wages as well as using both formal and informal sector participation and country-specific minimum wages. Data sources included primary data from country-specific studies, international databases, international prices, and expert opinion. Costs were standardized to year 2000 international dollars using inflation adjustment and purchasing power parity.ResultsCervical cytology laboratory processing costs were I$1.57–3.37 using the quantity and price method compared to I$1.58–3.02 from the face validation method. HPV DNA processing costs were I$6.07–6.59. Rural laboratory transport costs for cytology were I$0.12–0.64 and I$0.14–0.74 for HPV DNA laboratories. Under assumptions of lower resource efficiency, these estimates increased to I$0.42–0.83 and I$0.54–1.06. Estimates of the value of an hour of patient time using only formal sector participation were I$0.07–4.16, increasing to I$0.30–4.80 when informal and unpaid labor was also included. The value of patient time for traveling, waiting, and attending a screening visit was I$0.68–17.74. With the total cost of screening for cytology and HPV DNA testing ranging from I$4.85–40.54 and I$11.30–48.77 respectively, the cost of the laboratory transport, processing, and patient time accounted for 26–66% and 33–65% of the total costs. From a payer perspective, laboratory transport and processing accounted for 18–48% and 25–60% of total direct medical costs of I$4.11–19.96 and I$10.57–28.18 respectively.ConclusionCost estimates of laboratory processing, sample transport, and patient time account for a significant proportion of total cervical cancer screening costs in five developing countries and provide important inputs for CEAs of alternative screening modalities.

White mulberry tree (Morus alba L) is cultivated throughout Asia and Europe, including Poland. The leaves and root bark preparations from Morus alba have been used in traditional phytomedicine. The objective of the present study was to compare chemical composition and antioxidative activity of aqueous extracts prepared from Polish variety of Morus alba leaves at the laboratory (L) and pilot plant scale (PP) conditions. Proximate composition, phenolic acids profile (HPLC/MS), flavonol glicosides (HPLC/ MS), polyphenols (Folin-Ciocalteu assay), and the antioxidant activity (ABTS and DPPH assay) of the extracts were determined. The main phenolic compounds were identified as gallic, protocatechuic, phydroxybenzoic, vanillic, chlorogenic, caffeic, p-coumaric, ferulic, and sinapic acids. Chlorogenic acid was the main phenolic constituent of both extracts. The flavonols fraction contained rutin, quercetin 3-β-D-glucoside, and kaempferol 3-β-D- glucopyranoside. Total concentration of phenolic compounds were 7.9 g and 14.4 g gallic acid equivalent/100 g extract, and antioxidant activity was 137.1 and 214.1 μMol Trolox equivalent/g dry weight for the PP and L extracts, respectively. We concluded that current pilot plant process is less efficient than laboratory process at the aqueous extraction of bioactive components from Morus alba dried leaves. Potential improvements may include increasing efficacy of the extraction, decreasing losses of bioactive components during the process, or both.

Environmental DNA (eDNA) metabarcoding is increasingly being implemented as a non‐invasive and efficient approach for biodiversity research and monitoring across ecosystems. However, accurate detection of species with eDNA requires robust experimental designs as eDNA analysis carries a risk of contamination at every step of the fieldwork and laboratory processes. Several studies focus on rigorous laboratory procedures and processing of sequencing data, but surprisingly, little research investigates the process of background input of DNA in the field. For example, airborne DNA from localities outside the study area could potentially contaminate eDNA samples. Here, we use an experimental setup and eDNA metabarcoding to study the diversity and accumulation of airborne eukaryotic eDNA on exposed surfaces in the field. At two different natural locations, a coastal marine site and a terrestrial grassland site, we placed open containers each filled with 0.5 liters of water, which was then sampled at eight successive time points after exposure to the surroundings. We found an accumulation of detected species richness in the samples, which reached its maximum at the end of the experiment, 24 h after exposure. This result was consistent across both sites and across two markers (COI for eukaryotes and 12S for vertebrates). While many of the detected species were contaminants commonly found in eDNA studies, we also detected several other eukaryotic taxa. Most notable were metazoan species such as birds, fish, and insects, likely originating from airborne transport of eDNA. We also found that increasing the number of PCR cycles tended to have a positive impact on richness for the unfiltered reads but a negative impact on the richness after bioinformatic filtering. Our results add to the sparse evidence that metazoan eDNA can be transported by air, which have wide implications for eDNA research and calls for increased implementation of field control samples.

Simulation as a Tool for Optimizing Automated Laboratory Equipment and Processes?

ABSTRACTIn dry‐grind corn processing, the whole kernel is fermented to produce ethanol and distillers dried grains with solubles (DDGS); the E‐Mill process was developed to generate coproducts in addition to DDGS. Compositions of thin stillage and wet grains obtained from the E‐Mill process will be different from the dry‐grind process. Knowledge of thin stillage compositions will provide information to improve coproducts from both processes. Laboratory dry‐grind and E‐Mill processes that used granular starch hydrolyzing enzymes (GSHE) were compared and process yields determined. Two methods, centrifugation and screening, were used to produce thin stillage and wet grains from the laboratory processes. Compositions of process streams were determined. In the dry‐grind process using GSHE, solids contents of beer, whole stillage, and wet grains were higher compared to the same fractions from the E‐Mill process using GSHE. Solids contents of mash for both processes were similar. Total solids, soluble solids, and ash contents of thin stillage were similar for the two processes. Fat content of thin stillage from E‐Mill was lower than that from the dry‐grind process; protein content of E‐Mill thin stillage was higher than that from dry‐grind thin stillage. Removal of germ and fiber before fermentation changed composition of thin stillage from the E‐Mill process. The screening method produced higher thin stillage and lower wet grains yields than using a centrifugation method. The screening method was less time consuming but resulted in limited wet grains material for additional analyses or processing. The centrifugation method of thin stillage separation removed more solids from thin stillage than the screening method.

Background This research emphasizes the profound impact of intestinal parasitic infections on developing countries, particularly in regions such as sub-Saharan Africa, South and Central America, China, and East Asia. With over 1.5 billion people globally affected and 450 million facing serious illness and a mortality rate of 155,000 cases per year, the socio-economic hindrances posed by these infections necessitate innovative approaches for control and eradication. The integration of artificial intelligence (AI) and machine learning (ML) into parasitology, exemplified by the “Automated Diagnosis of Intestinal Parasites” (ADIP), from brazilian Portuguese “Diagnóstico Automatizado de Parasitas Intestinais” (DAPI) system, showcases a paradigm shift. This advanced system, combining simple and complex decision-making mechanisms, demonstrates promising results, achieving high agreement compared to TF-Test (three fecal test). Methods Data were extracted through reports from the laboratory information system (LIS) from January-December 2023. TF-test was replaced by ADIP system in October 2023. Data were analyzed using Microsoft Excel software, with a bibliographic survey in national and international repositories. Results The parasitology laboratory of AFIP implemented artificial intelligence (AI) with ADIP equipment associated with system LIS automation, automating the analysis of approximately 38,000 monthly stool samples. The ADIP classification, image analysis and subsequent algorithm allowed a reduced examination time, since approximately 90% of samples could be released automatically, optimizing the whole process. In addition, after a three-month period using only ADIP, positivity results increased from 7,63% (TF-test) to 9,53%. Our database is periodically reviewed by specialized professionals who validate positive results, ensuring the security of information obtained by artificial intelligence, bringing efficiency, safety, and quality to the laboratory processes. Conclusions Artificial intelligence (AI) in parasite identification and the automated results system promises a bright future. Collaboration between the scientific community and data scientists is crucial to enhance laboratory processes. Through these tools, it is possible to focus on continuous improvements, increased efficiency, and, most importantly, without sacrificing accuracy and result quality. With new technologies associated with the parasitology field, detailed records can be ensured, previously challenging to obtain, showcasing each step from result acquisition to issued report. Process improvement in parasitology brings optimizations, suggesting the potential for team reduction without compromising quality and improving the takt time.

(a) Situation faced: Recent developments in the medical and industrial laboratory market have increased the need for highly flexible laboratory processes. This pressure results from new requirements that have accompanied the internationalization of laboratories and the digitalization of paper-based, bureaucratic work practices. The execution of laboratory processes is supported by laboratory information systems (LISs), which handle the control and information flow of incoming orders end-to-end. State-of-the-art LISs do not feature sufficient flexibility-to-use and flexibility-to-change capabilities. To prepare medical and industrial laboratories for the challenges ahead, LISs require more advanced flexibility capabilities that meet the need for flexibility in complex laboratory processes. (b) Action taken: To address the challenges of medical and industrial laboratories, MELOS, a leading German LIS provider, and the Project Group BISE of the Fraunhofer FIT conducted the LIS4FUTURE project. The project team compiled requirements on the flexibility of laboratory processes and derived corresponding requirements for the LIS’s flexibility-to-use and flexibility-to-change. The lack of configuration capabilities and modularity across all layers of the software architecture was identified as a major inhibitor of flexible laboratory processes. Following an agile development process and grounded on extant knowledge, the project team developed the LIS4FUTURE demonstrator, a process-aware LIS with a modular architecture and a rule-based configuration mechanism. (c) Results achieved: Based on identified requirements, the project team iteratively developed and evaluated the modular architecture and the rule-based configuration mechanism as part of the development of the LIS4FUTURE demonstrator. The modular architecture allows for the complete replacement of process steps at build time, while the rule-based configuration mechanism makes it possible to meet the ever-increasing demands for flexibility at runtime. The LIS4FUTURE demonstrator, which shows the applicability of the developed concepts in real-world scenarios, will help MELOS develop an innovative release of their LIS. (d) Lessons learned: During the LIS4FUTURE project, the project team learned that: (1) advanced flexibility-to-use and flexibility-to-change IS capabilities are needed to prepare for flexibility demands on the process level; (2) radical redesign of existing processes and systems should be preferred over incremental improvement in order to tap the disruptive potential of innovation opportunities; (3) the LIS architecture must be aligned with the process paradigm if it is to be flexible; (4) discussions among academics and practitioners are more effective if they are based on running prototypes rather than on theoretical concepts; and (5) project results improve if project team members work a substantial fraction of their time at the same location.

Pathologists play an important role in the final diagnosis for the patient and help the clinician in appropriate treatment planning. Both clinicians and pathologists have a crucial role in the laboratory process and it should be monitored properly for accurate and timely reporting.There are many steps in the laboratory process, where errors can occur which can affect the final diagnosis and prognosis for the patient. Errors can be major or minor but it eventually affects the patient's outcome. The laboratory process is broadly classified into three categories- pre-analytic, analytic and post-analytic phases. The errors that could frequently occur in each phase and the troubleshooting methods are broadly discussed in this article. Errors that have occurred should be investigated properly by identifying the main source of error and preventive action should be taken to avoid it in the future.