Ten simple rules for managing laboratory information

For many years, the journal PLOS Computational Biology has been publishing a series of "Ten Simple Rules" articles as they relate to data management and sharing in laboratories and research institutes. In this latest installment of the series, Berezin et al. tap into their collective experience to address the management of laboratory data and information, particularly leveraging systems like a laboratory information management system (LIMS). In these rules, the authors examine aspects such common culture development, inventory management, project management, sample management, labeling, and more. They conclude that apart from "[i]mparting a strong organizational structure for your lab information ... the goal of these rules is also to spur conversation about lab management systems both between and within labs as there is no one-size-fits-all solution for lab management." However, the application of these rules isn't exactly a straightforward process; their success "relies on the successful integration of effective software tools, training programs, lab management policies, and the will to abide by these policies."

Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology

In this 2023 paper published in Nature Communications, Signoroni et al. present the results of their efforts towards a system designed for "the global interpretation of diagnostic bacterial culture plates" using deep learning architecture. Noting the many challenges of human-based culture interpretation, the authors present the results of DeepColony," a hierarchical multi-network capable of handling all identification, quantitation, and interpretation stages, from the single colony to the whole plate level." After reviewing the results, the authors conclude that given the "high level of agreement" between DeepColony's results and the interpretation of humans, their system holds significant promise. They add that DeepColony can be viewed as "a unique framework for improving the efficiency and quality of massive routine activities and high-volume decisional procedures in a microbiological laboratory, with great potential to refine and reinforce the critical role of the microbiologist."

Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study

In this 2023 paper published in the journal Digital Health, Čartolovni et al. present the results of their qualitative, multi-stakeholder study regarding the "aspirations, expectations, and critical analysis of the potential for artificial intelligence (AI) to transform the patient–physician relationship." Pulling from hours of semi-structured interviews, the authors developed a number of themes from the interviews and discussed how to reach a consensus on their interpretation and use. The authors focused on four main themes and a variety of subthemes and present the results of their work in the framework of these themes and subthemes. From their work, they conclude a definitive "need to use a critical awareness approach to the implementation of AI in healthcare by applying critical thinking and reasoning, rather than simply relying upon the recommendation of the algorithm." Additionally, they highlight how "it is important not to neglect clinical reasoning and consideration of best clinical practices, while avoiding a negative impact on the existing patient–physician relationship by preserving its core values," strongly urging for the preservation of patient-physician trust in the face of increased AI use.

Judgements of research co-created by generative AI: Experimental evidence

As discussion of artificial intelligence (AI) continues to ramp up in not only academia but also industry and culture, questions abound concerning it's long-term potential and perils. Attitudes about AI and what it means for humanity are diverse, and sometimes controversial. In this 2023 paper published in Economics and Business Review, Niszczota and Conway contribute to the discussion, particularly in regards to how people view research co-created by generative AI. Recruiting more than 440 individuals, the duo conducted a mixed-design experiment using multiple research processes. All said, their "results suggest that people have clear, strong negative views of scientists delegating any aspect of the research process" to generative AI, denoting it "as immoral, untrustworthy, and scientifically unsound." They conclude that "researchers should employ caution when considering whether to incorporate ChatGPT or other [large language models] into their research."

Geochemical biodegraded oil classification using a machine learning approach

The use of chromatography methods in characterizing and interpreting phenomena related to oil has been around for a long time. However, reading chromatograms—particularly large quantities of them—can be time-comsuming. Bispo-Silva et al propose a more rapid process using modern deep learning and artificial intelligence (AI) techniques, increasingly being used in large companies. The authors land on using convolutional neural networks (CNN) for this work, particularly in the use of discriminating biodegraded oils from non-biodegraded oils. After presenting background on the topic, as well as their materials and methods, the authors present the results of using CNN and a variety of algorithms to classify chromatograms with both accurate and misleading training materials. The authors conclude that "CNN can open a new horizon for geochemistry when it comes to analysis by" a variety of gas chromatography techniques, as well as "identification of contaminants (as well as environmental pollutants), identification of analysis defects, and, finally, identification and characterization of origin and oil maturation."

Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study

In this brief study by Mishra et al. in the Journal of Nepal Medical Association, the level of knowledge of internal quality control (IQC) in the Department of Biochemistry, B.P. Koirala Institute of Health Sciences (BPKIHS), a tertiary care center, is explored. Noting the importance of laboratory quality control and knowledge of IQC to patient outcomes, the authors conducted a descriptive cross-sectional study of its laboratory staff (n=20), asking questions related to "the understanding of the purpose of IQC, the types of control materials, various control charts, how and when IQC should be performed, and interpretations of the Levey-Jennings Chart using the Westgard rule." The authors concluded from their results were inline with other studies conducted in similar environments (25% had adequate knowledge of IQC), their facility had work to do in improving IQC knowledge and quality management systems more broadly. They add: "Hence, providing training opportunities on laboratory IQC can be reflected as a necessity in our current laboratory set-up. This could add value to the knowledge of IQC on laboratory personnel to ensure that the reports generated within the laboratory are accurate, reliable, and reproducible."

Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study

In this 2023 article published in the National Journal of Laboratory Medicine, Karaattuthazhathu et al. of KMCT Medical College present the results of a performance assessment of its clinical laboratories' analyte testing parameters using a Six Sigma approach. Examining a six-month period in 2022, the authors looked at 26 parameters in biochemistry and hematology using both internal quality control (IQC) and external quality assurance (EQAS) analyses. After presenting materials and methods used, as well as the results, the authors reviewed their results and concluded that according their sigma metrics analysis, their laboratories are "able to achieve satisfactory results, with world-class performance of many analytes," though recognizing some deficiencies, which were corrected mid-study.

Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems

In this 2023 paper published in Sustainability, Tomich et al. describe the concept of food systems informatics (FSI) within the context of a collection of this and other paper published as a special collection in the journal. Noting many challenges to improving and transforming food systems, as well as the potential for informatics applications to play an important role, the authors describe five use cases of FSI and discuss the potential outcomes and impacts of developing and implementing FSI platforms in these and other use cases. Finally, the authors draw six major conclusions from their work, as well as several caveats about FSI implementation going forward. They argue that FSI definitely has potential as "a tool to enhance equity, sustainability, and resilience of food systems through collaborative, user-driven interaction, negotiation, experimentation, and innovation within food systems." However, the scope of FSI must be expanded to include " food systems security, privacy, and intellectual property considerations" in order to have the greatest impact.

Data management challenges for artificial intelligence in plant and agricultural research

In this 2023 article published in the journal F1000Research, Williamson et al. identify and discuss "eight key challenges in data management that must be addressed to further unlock the potential of [artificial intelligence] in crop and agronomic research." Noting the state of the agricultural research landscape and growing potentials of artificial intelligence and machine learning in the field, the authors perform a literature review that better shapes the nuances of those eight key challenges: data heterogeneity, data selection and digitization, data linkages, standardization and curation of data, sufficient training and ground truth data, system access, data access, and engagement. After examining these challenges in detail, the authors conclude there's a definitive "need for a more systemic change in how research in this domain is conducted, incentivized, supported, and regulated," with aspects such as stronger collaboration, more efficient machine learning methods, improved data curation, and improved data management methods.

A blockchain-driven IoT-based food quality traceability system for dairy products using a deep learning model

In this 2023 paper published in the journal High-Confidence Computing, Manisha and Jagadeeshwar present their custom food quality traceability system, which combines various tools like blockchain, internet of things (IoT) mechanisms, and deep learning architecture for improving traceability about perishable food supply chains (PFSCs). Noting previous works that incorporated some but not all of these elements, each with their own downsides, the authors chose a system model that incorporates blockchain-enabled RFID scans that, upon verification, get added to the overall secure ledger, along with associated IoT-base metadata from sensors gauging humidity and temperature. The duo turns to milk manufacturing and distribution in their case study, applied to their blockchain-enabled deep residual network (BC-DRN) methodology. After examining and comparing their results to other prevalent methodologies used in supply chain management (SCM). The authors conclude that their BC-DRN traceability system, when gauged on metrics like sensitivity, response time, and testing accuracy, beat out other methods. They add "the performance of the devised scheme can be improved by considering better feature extraction techniques. "

Effect of good clinical laboratory practices (GCLP) quality training on knowledge, attitude, and practice among laboratory professionals: Quasi-experimental study

In this brief 2023 article published in Journal of Clinical and Diagnostic Research, Patel et al. provide the results of their survey-based analysis of laboratorians' knowledge, attitude, and practice (KAP) towards laboratory quality through good clinical laboratory practice (GCLP) training. Noting the "ethical obligation to provide accurate and precise results that are cost- and time-effective," the authors state that it's imperative for clinical laboratory personnel to adhere to quality planning and system implementation, while also possessing an understanding of quality management principles as they apply to the lab. The authors describe their survey format and present the results of their survey, concluding that while no statistically significant differences could be found towards staff attitudes towards quality in the lab after GCLP training, laboratorians in their survey acknowledged the benefits of GCLP guidelines and accreditation, as well as the importance of training on such matters. "Such training and assessments would also aid in evaluating the performance of laboratory staff, contributing to improved learning, execution of GLPs, and consistent patient care services."

GitHub as an open electronic laboratory notebook for real-time sharing of knowledge and collaboration

In this 2023 paper published in the journal Digital Discover, Scroggie et al. of the University of Sydney present their efforts towards utilizing the open developer platform GitHub as an electronic laboratory notebook (ELN) for chemistry research. Noting a lack of open-source ELNs with a focus on non-organic chemistry that have a wealth of collaboration tools, as well as problems with expensive and inflexible commercial ELNs, the authors turned to the many open facets of GitHub to repurpose its workings for synthetic chemistry projects. After a brief discussion of GitHub, the authors explain how they used the various facets of GitHub for ELN-related tasks, including notebooks, data and metadata management, and collaborative tools. They also acknowledged several shortcomings of their approach, including learning Markdown, dealing with data storage limitations, and integrating discipline-specific applications. The authors conclude that some of GitHub's features "are undeniably more oriented towards coders, such as the Actions tab in which users can set up workflows using code, these features do not detract from GitHub's usefulness as an ELN, which lies mainly in its adaptability and capacity for knowledge-sharing and collaboration."

SODAR: Managing multiomics study data and metadata

In this 2023 paper published in the journal GigaScience, Nieminen et al. of the Berlin Institute of Health at Charité–Universitätsmedizin Berlin present SODAR (System for Omics Data Access and Retrieval), an open-source scientific data management system (SDMS) with a focus on omics data management during multiassay research studies. Noting numeroud data management challenges and dearth of open-source options, the authors describe the software framework, features, and limitations of the system. Highlighting SODAR's "programmable application programming interfaces (APIs) and command-line access for metadata and file storage," the authors conclude that SODAR can readily "support multiple technologies such as whole genome sequencing, single-cell sequencing, proteomics, and mass spectrometry," though some aspects such as automated data export and "data commons" access are currently not available.

Benefits of information technology in healthcare: Artificial intelligence, internet of things, and personal health records

In this 2023 paper published in Healthcare Informatics Research, Chang et al. perform a literature review of current research in order to better characterize the currently viewed benefits of of artificial intelligence (AI), the internet of things (IoT), and personal health records (PHR) in the healthcare setting. Noting "limited empirical evidence regarding the benefits of information technology in healthcare settings," the authors examined 24 reviews and meta-analysis studies on these three technologies, with a strong focus on four outcome domains of clinical, psycho-behavioral, managerial, and socioeconomic implications. After detailing their findings, the authors conclude that "AI and PHRs can enhance clinical outcomes, while IoT holds promise for boosting managerial efficiency," though further research is required to address "the organizational and socioeconomic benefits of PHR," as well as the greater role of IoT in the healthcare setting.

A quality assurance discrimination tool for the evaluation of satellite laboratory practice excellence in the context of European regulatory meat inspection for Trichinella spp.

Like many other parts of the food and beverage industry, the meat processing industry is guided by regulations that mandate the safety of the final product to the consumer. This includes laboratory testing for Trichinella spp., a nematode parasite responsible for the disease trichinosis. In order to ensure timely and accurate results, laboratories in the meat industry must act with purpose, implementing quality assurance (QA) practices that incorporate quality management systems (QMSs). In this 2023 journal article, Villegas-Pérez et al. examine the state of QA and QMSs in the in-house laboratories of Southern Spain's slaughterhouses and game-handling establishments, using canonical discriminant analysis (CDA) to gauge the effectiveness of those labs' practices. After lengthy review and discussion, the authors conclude that their CDA-based tools were able to discover "deficiencies in processes and procedures, necessitating measures for result reliability, due to facilities’ unfamiliarity with extensive QMS documentation." The authors offer several recommendations to fix those deficiencies.

Developing a framework for open and FAIR data management practices for next generation risk- and benefit assessment of fish and seafood

In this brief paper published in the EFSA Journal (from the European Food Safety Authority), Pineda-Pampliega et al. of the Norwegian Scientific Committee for Food and Environment (VKM) discuss the specifics of their work-related project to see "if existing commonly used [food safety] databases in risk assessment are in line with the FAIR principles" (which ensure data and information is findable, accessible, interoperable, and reusable), as well as to determine how to improve deficiencies in meeting FAIR principles. The authors describe their approach using data from both the EFSA's Chemical Hazards Database, OpenFoodTox, and the Institute of Marine Research's Seafood database. After evaluating these databases and describing the necessary framework activities to make them more FAIR, the authors conclude that their methods of using the R programming language, Shiny, GitHub, Zenodo, and appropriate file formats "is an essential step to ensuring the success of the future risk–benefit assessment [towards food safety], by offering more timely results with adequate spending of human and economic resources."

An extract-transform-load process design for the incremental loading of German real-world data based on FHIR and OMOP CDM: Algorithm development and validation

In this 2023 paper published in JMIR Medical Informatics, Henke et al. of Technische Universität Dresden present the results of their effort to add "incremental loading" to the Medical Informatics in Research and Care in University Medicine's (MIRACUM's) clinical trial recruitment support systems (CTRSSs). Those CTRSSs already allows bulk loading of German-based FHIR data, supporting "the possibilities for multicentric and even international studies," but MIRACUM needed greater efficiencies when updating such data on a daily, incremental basis. The paper presents their literature review and approach to adding incremental loading to their systems. They conclude that the extract-transfer-load (ETL) "process no longer needs to be executed as a bulk load every day" with the change, instead being able to rely on "using bulk load for an initial load and switching to incremental load for daily updates." They add that this process has international applicability and is not limited to German FHIR data.

Potency and safety analysis of hemp-derived delta-9 products: The hemp vs. cannabis demarcation problem

In this 2023 paper published in the Journal of Cannabis Research, Johnson et al. examine the current state of hemp-derived delta-9-tetrahydrocannabinol (Δ9-THC) products on the U.S. market after the passage of the Agriculture Improvement Act of 2018. Noting discrepancies and loopholes in the legislation, the authors performed laboratory analyses on 53 hemp-derived Δ9-THC products from 48 brands, while also examining aspects such as age verification, labeling consistency, and comparison of reported company values vs. analyzed values. After describing their methodology and results, the authors conclude that "the legal status of hemp-derived Δ9-THC products in America essentially permits their open sale while placing very few requirements on the companies selling them." The end result includes finding, for example, products "that have 3.7 times the THC content of edibles in adult-use states," as well as inaccurately labeled products.

The NOMAD Artificial Intelligence Toolkit: Turning materials science data into knowledge and understanding

"Artificial intelligence" (AI) may seem like a buzzword akin to the "nanotechnology" craze of the 2000s, but it is inevitably finding its way into scientific applications, including in the materials sciences. In this December 2022 article published in npj Computational Materials, Sbailò et al. present their AI-driven Novel Materials Discovery (NOMAD) toolkit as an extension of their NOMAD Repository & Archive, focused on making materials science data FAIR (findable, accessible, interoperable, and reusable), as well as AI-ready. After introducing the details of their workspace and goals towards adding notebook-based tools to the NOMAD Repository & Archive, the authors dive into the details of NOMAD AI Toolkit. They close by noting their toolkit "offers a selection of notebooks demonstrating such [AI-based] workflows, so that users can understand step by step what was done in publications and readily modify and adapt the workflows to their own needs." They add that the system "will allow for enhanced reproducibility of data-driven materials science papers and dampen the learning curve for newcomers to the field."

Quality control in the clinical biochemistry laboratory: A glance

In this 2023 article published in Journal of Clinical and Diagnostic Research, Naphade et al. provide a brief introductory-level review of the importance of quality control (QC) to the clinical laboratory. After a brief introduction on clinical lab testing, the authors analyze the wide variety of sources for laboratory errors, covering the pre-analytical, analytical. and post-analytical phases. They then introduce the concept of quality control, followed by explaining how QC is implemented in the laboratory, including through the use of QC materials, statistical control charts, and shifts and trends. They conclude this review by stating that "reliable and confident laboratory testing avoids misdiagnosis, delayed treatment, and unnecessary costing of repeat testing." They add that given these benefits, "the individual laboratory should assess and analyze their own QC process to find out the possible root cause of any digressive test results which are not correlating with patients' clinical presentation or expected response to treatment."
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