Electronic Laboratory Research Articles

Implementation of electronic lab notebooks (ELNs) in science laboratory classes: student response and lessons learned

As data and protocols are increasingly generated, accessed, and manipulated electronically, many labs are shifting to storing data in electronic laboratory notebooks (ELNs). ELNs provide clear advantages in readability, shareability, searchability, accessibility, and data security over their paper counterparts. Our interdisciplinary program implemented ELNs across several sections of our gateway molecular biology laboratory course and analyzed the impact on 384 students (13 sections total from Fall 2016 to Spring 2018) using surveys, focus groups, and grade data. We found that students prefer the electronic format, citing the ease of transitioning to lab reports and the longevity of their data. Course grades overall were not affected by the change. Based on our experience, we offer suggestions for smoothing the transition to an ELN system in undergraduate and graduate laboratory courses, including use during remote teaching.

Solvent flashcards: a visualisation tool for sustainable chemistry

Selecting greener solvents during experiment design is imperative for greener chemistry. While many solvent selection guides are currently used in the pharmaceutical industry, these are often paper-based guides which can make it difficult to identify and compare specific solvents. This work presents a stand-alone version of the solvent flashcards that were developed as part of the AI4Green electronic laboratory notebook. The functionality is an intuitive and interactive interface for the visualisation of data from CHEM21, a pharmaceutical solvent selection guide that categorises solvents according to “greenness”. This open-source software is written in Python, JavaScript, HTML and CSS and allows users to directly contrast and compare specific solvents by generating colour-coded flashcards. It can be installed locally using pip, or alternatively the source code is available on GitHub: https://github.com/AI4Green/solvent_flashcards. The documentation can also be found on GitHub or on the corresponding Python Package Index webpage: https://pypi.org/project/solvent-guide/.Scientific ContributionThis simple and easy-to-use digital tool provides a visualisation of solvent greenness data through a novel intuitive interface and encourages green chemistry. It offers numerous advantages over traditional solvent selection guides, allowing users to directly customise the solvent list and generate side-by-side comparisons of only the most important solvents. The release as a standalone package will maximise the benefit of this software.Graphical

Resistance Assessment as a Strategy to Increase the Adoption of Electronic Laboratory Notebooks

Electronic Lab Notebooks (ELNs) have seen a significant rise in research laboratories. However, realizing the benefits of ELNs requires the active participation of employees within an organization. This study aims to identify the factors contributing to resistance towards adopting an ELN, specifically eLabFTW. A semistructured questionnaire, validated by experts, was administered to record book requesters at the René Rachou Institute (FIOCRUZ MG). The findings revealed that individual decision-making factors include peer influence within the research group and familiarity with traditional physical logbooks. Assessing resistance can serve as a strategic tool to enhance system adoption.

Non-pancreatic hyperlipasemia: A puzzling clinical entity.

Increased lipase level is a serological hallmark of the diagnosis of acute pancreatitis (AP) but can be detected in various other diseases associated with lipase leakage due to inflammation of organs surrounding the pancreas or reduced renal clearance and/or hepatic metabolism. This non-pancreatic hyperlipasemia (NPHL) is puzzling for attending physicians during the diagnostic procedure for AP. It would be clinically beneficial to identify the clinical and laboratory variables that hinder the accuracy of lipase diagnosis with the aim of improve it. A more precise description of the NPHL condition could potentially provide prognostic factors for adverse outcomes which is currently lacking. To perform a detailed clinical and laboratory characterization of NPHL in a large prospective patient cohort with an assessment of parameters determining disease outcomes. A Hungarian patient cohort with serum lipase levels at least three times higher than the upper limit of normal (ULN) was prospectively evaluated over 31 months. Patients were identified using daily electronic laboratory reports developed to support an ongoing observational, multicenter, prospective cohort study called the EASY trial (ISRCTN10525246) to establish a simple, easy, and accurate clinical scoring system for early prognostication of AP. Diagnosis of NPHL was established based on ≥ 3 × ULN serum lipase level in the absence of abdominal pain or abdominal imaging results characteristic of pancreatitis. A total of 808 patients [male, n = 420 (52%); median age (IQR): 65 (51-75) years] were diagnosed with ≥ 3 × ULN serum lipase levels. A total of 392 patients had AP, whereas 401 had NPHL with more than 20 different etiologies. Sepsis and acute kidney injury (AKI) were the most prevalent etiologies of NPHL (27.7% and 33.2%, respectively). The best discriminative cut-off value for lipase was ≥ 666 U/L (sensitivity, 71.4%; specificity, 88.8%). The presence of AKI or sepsis negatively affected the diagnostic performance of lipase. NPHL was associated with a higher in-hospital mortality than AP (22.4% vs 5.1%, P < 0.001). In multivariate binary logistic regression, not lipase but increased amylase level (> 244 U/L) and neutrophil-to-lymphocyte ratio (NLR) (> 10.37, OR: 3.71, 95%CI: 2.006-6.863, P < 0.001), decreased albumin level, age, and presence of sepsis were independent risk factors for in-hospital mortality in NPHL. NPHL is a common cause of lipase elevation and is associated with high mortality rates. Increased NLR value was associated with the highest mortality risk. The presence of sepsis/AKI significantly deteriorates the serological differentiation of AP from NPHL.

NeuralPMG: A Neural Polyphonic Music Generation System Based on Machine Learning Algorithms

The realm of music composition, augmented by technological advancements such as computers and related equipment, has undergone significant evolution since the 1970s. In the field algorithmic composition, however, the incorporation of artificial intelligence (AI) in sound generation and combination has been limited. Existing approaches predominantly emphasize sound synthesis techniques, with no music composition systems currently employing Nicolas Slonimsky’s theoretical framework. This article introduce NeuralPMG, a computer-assisted polyphonic music generation framework based on a Leap Motion (LM) device, machine learning (ML) algorithms, and brain-computer interface (BCI). ML algorithms are employed to classify user’s mental states into two categories: focused and relaxed. Interaction with the LM device allows users to define a melodic pattern, which is elaborated in conjunction with the user’s mental state as detected by the BCI to generate polyphonic music. NeuralPMG was evaluated through a user study that involved 19 students of Electronic Music Laboratory at a music conservatory, all of whom are active in the music composition field. The study encompassed a comprehensive analysis of participant interaction with NeuralPMG. The compositions they created during the study were also evaluated by two domain experts who addressed their aesthetics, innovativeness, elaboration level, practical applicability, and emotional impact. The findings indicate that NeuralPMG represents a promising tool, offering a simplified and expedited approach to music composition, and thus represents a valuable contribution to the field of algorithmic music composition.

A Cost-Effective and Simplified Integration of Simulations and Hands-on Experiments in the Teaching of Electronic Instrumentation and Measurement Laboratory Course

To develop students into effective engineers upon graduation, it is important to bridge the gap between theory and practice. This is achieved through hands-on laboratory experiments, which form an important part of engineering education especially electronic circuits-related courses within the undergraduate level of education. Hence, it is important to develop well-outlined laboratory experiments that reflect the theoretical part of a course. Equally important is comparing simulation and measurement data, to better appreciate the theory. This work presents integrated simulation and hands-on experiments in teaching electronic instrumentation and measurement laboratory course, using low-cost off-the-shelf components. A no-cost circuit simulator software (LTspice) and low-cost breadboard with readily available off-the-shelf circuit components can be used both at home (self-paced) and in the laboratory to motivate students and align results for an improved learning approach. The advantage of this integrated laboratory is that no specialized or expensive teaching kit is required, which improves accessibility and course delivery. In addition, based on the feedback received, students are satisfied with this simplified and low-cost integrated laboratory.

Adoption of Technology by Healthcare Personnel: An Institutional Survey.

In the current era, technology has a significant influence on healthcare outcomes. Despite that, there are significant barriers and concerns toward the adoption of digital laboratory reporting systems among healthcare professionals in India. The aim of the study was to understand the overall attitude, barriers, and motivators toward the adoption of technology by healthcare personnel. The study was conducted through a single-center and prospective questionnaire survey among physicians and surgeons of various specialities, with 107 participants. The electronic laboratory system at the institution, called "AADI," could be accessed across any computer terminal and through a web-based application that could be downloaded on any mobile device. The results of the study revealed that 98 out of 107 (91.59%) healthcare professionals used the digital platform regularly to access laboratory results, while only 9 (8.4%) did not use it. The mean satisfaction score of the users was 4.62 ± 0.51. The study showed that most users found the digital system to be more secure and reliable, which led to significant time savings compared to the paper-based system. The study also found that age was a determinant of usage, with younger healthcare professionals using the application more frequently. Overall, the study suggests that digital laboratory reporting systems have significant benefits, and further efforts are needed to increase adoption in healthcare establishments in India.

Forecasting acute kidney injury and resource utilization in ICU patients using longitudinal, multimodal models

BackgroundAdvances in artificial intelligence (AI) have realized the potential of revolutionizing healthcare, such as predicting disease progression via longitudinal inspection of Electronic Health Records (EHRs) and lab tests from patients admitted to Intensive Care Units (ICU). Although substantial literature exists addressing broad subjects, including the prediction of mortality, length-of-stay, and readmission, studies focusing on forecasting Acute Kidney Injury (AKI), specifically dialysis anticipation like Continuous Renal Replacement Therapy (CRRT) are scarce. The technicality of how to implement AI remains elusive. ObjectiveThis study aims to elucidate the important factors and methods that are required to develop effective predictive models of AKI and CRRT for patients admitted to ICU, using EHRs in the Medical Information Mart for Intensive Care (MIMIC) database. MethodsWe conducted a comprehensive comparative analysis of established predictive models, considering both time-series measurements and clinical notes from MIMIC-IV databases. Subsequently, we proposed a novel multi-modal model which integrates embeddings of top-performing unimodal models, including Long Short-Term Memory (LSTM) and BioMedBERT, and leverages both unstructured clinical notes and structured time series measurements derived from EHRs to enable the early prediction of AKI and CRRT. ResultsOur multimodal model achieved a lead time of at least 12 h ahead of clinical manifestation, with an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.888 for AKI and 0.997 for CRRT, as well as an Area Under the Precision Recall Curve (AUPRC) of 0.727 for AKI and 0.840 for CRRT, respectively, which significantly outperformed the baseline models. Additionally, we performed a SHapley Additive exPlanation (SHAP) analysis using the expected gradients algorithm, which highlighted important, previously underappreciated predictive features for AKI and CRRT. ConclusionOur study revealed the importance and the technicality of applying longitudinal, multimodal modeling to improve early prediction of AKI and CRRT, offering insights for timely interventions. The performance and interpretability of our model indicate its potential for further assessment towards clinical applications, to ultimately optimize AKI management and enhance patient outcomes.

Assessment of antimicrobial resistance laboratory-based surveillance capacity of hospitals in Zambia: findings and implications for system strengthening

A well-established antimicrobial resistance (AMR) laboratory-based surveillance (LBS) is of utmost importance in a country like Zambia which bears a significant proportion of the world's communicable disease burden. This study assessed the capacity of laboratories in selected hospitals to conduct AMR surveillance in Zambia. This cross-sectional exploratory study was conducted among eight purposively selected hospitals in Zambia between August 2023 and December 2023. Data were collected using the self-scoring Laboratory Assessment of Antibiotic Resistance Testing Capacity (LAARC) tool. Of the assessed facilities, none had full capacity to conduct AMR surveillance with varying capacities ranging from moderate (63% (5/8)) to low (38% (3/8)). Some of the barriers of AMR-LBS were the lack of an electronic laboratory information system (63% (5/8)) and the lack of locally generated antibiograms (75% (6/8)). Quality control for antimicrobial susceptibility testing (AST), pathogen identification and media preparation had the lowest overall score among all of the facilities with a score of 14%, 20% and 44%, respectively. The highest overall scores were in specimen processing (79%), data management (78%), specimen collection, transport and management (71%), and safety (70%). Most facilities had standard operating procedures in place but lacked specimen-specific standard operating procedures. The absence of laboratories with full capacity to conduct AMR surveillance hinders efforts to combat AMR and further complicates the treatment outcomes of infectious diseases. Establishing and strengthening LBS systems are essential in quantifying the burden of AMR and supporting the development of local antibiograms and treatment guidelines.

Do Chemformers Dream of Organic Matter? Evaluating a Transformer Model for Multistep Retrosynthesis.

Synthesis planning of new pharmaceutical compounds is a well-known bottleneck in modern drug design. Template-free methods, such as transformers, have recently been proposed as an alternative to template-based methods for single-step retrosynthetic predictions. Here, we trained and evaluated a transformer model, called the Chemformer, for retrosynthesis predictions within drug discovery. The proprietary data set used for training comprised ∼18 M reactions from literature, patents, and electronic lab notebooks. Chemformer was evaluated for the purpose of both single-step and multistep retrosynthesis. We found that the single-step performance of Chemformer was especially good on reaction classes common in drug discovery, with most reaction classes showing a top-10 round-trip accuracy above 0.97. Moreover, Chemformer reached a higher round-trip accuracy compared to that of a template-based model. By analyzing multistep retrosynthesis experiments, we observed that Chemformer found synthetic routes, leading to commercial starting materials for 95% of the target compounds, an increase of more than 20% compared to the template-based model on a proprietary compound data set. In addition to this, we discovered that Chemformer suggested novel disconnections corresponding to reaction templates, which are not included in the template-based model. These findings were further supported by a publicly available ChEMBL compound data set. The conclusions drawn from this work allow for the design of a synthesis planning tool where template-based and template-free models work in harmony to optimize retrosynthetic recommendations.

Antimicrobial Resistance Profile in ICU Patients Across India: A Multicenter, Retrospective, Observational Study.

Background The pattern of antimicrobial resistance (AMR) changes with timeand varies in countries and between hospitals within the same country.Physicians might thus benefit from information on regional resistance patterns of clinically significant bacterial isolates when deciding on the best empirical treatment.Numerous nosocomial infections are caused by multidrug-resistant (MDR) strains, notably methicillin-resistant Staphylococcus aureus (MRSA) strains, which are also linked to higher morbidity and death. Aim Evaluation of AMR profile in intensive care unit (ICU) patientsof multiple tertiary care centers across India. Methods This was a multicenter, retrospective studybased on electronic laboratory records of microbial isolates from clinical specimens from ICUs analyzed at microbiology laboratories of identified hospitals.Data of invasive sample records was collected from Microbiology labs of the identified hospitals within India and were aligned to WHO 5 Net standard reporting and as per Clinical & Laboratory Standards Institute (CLSI-2014) Guidelines for assessment. Data from 21556 samples were collectedretrospectively from December 2021 to January 2010. Antibiotic susceptibility testing was done by using both the Kirby Baur disk diffusion method and the automated method (using the Vitek 2 compact system) as per CLSI (2014) guidelines. Results Of 21,556 enrolled patients, the majority (54.12%) were males and adults (62.07%). The median age was 58 years. Of 815 gram-positive bacteria reports, the commonest were S. aureus (552, 67.73%), Coagulase-negative Staphylococci (107, 13.13%), and Enterococcus spp. (105, 12.88%). For Coagulase-negative Staphylococci-positive samples, resistance was to penicillin (79, 73.83%), and erythromycin (73, 68.22%); and for S. aureus was to ciprofloxacin (361, 65.4%), and erythromycin (315,57.07%). Enterococcus spp. showed maximum resistance to erythromycin (73, 69.52%), followed by ampicillin, ciprofloxacin (68,64.76% each). Of 4,183 gram-negative bacteria reports, the commonest were Klebsiella pneumoniae (1,531, 36.6%), Escherichia coli (1,269, 30.34%), and Acinetobacter spp. (589, 14.08%), Pseudomonas aeruginosa (438, 14.08%), other Klebsiella spp. (174, 4.16%) and Enterobacter spp. (161, 3.85%). K. pneumoniae showed resistance to ciprofloxacin (1,001, 65.38%). E. coli showed resistance to ampicillin (918, 72.34%), and ciprofloxacin (798,62.88%); and Acinetobacter spp. to ceftazidime (525, 89.13%), and ciprofloxacin (507, 86.08%), while P. aeruginosa showed resistance to imipenem (234, 53.42%). Enterobacter spp. showed resistance to cefotaxime (129, 80.12%). MRSAsamples showed resistance to phenoxymethylpenicillin (188, 35.54%) and benzylpenicillin (178, 33.46%). Conclusion Gram-negative bacteria were more common than gram-positive bacteria in causing antibiotic-resistant infections in ICU, with beta-lactams, fluoroquinolones, macrolides, and cephalosporins showing varied percentages of resistance. Fluoroquinolones, macrolides, and penicillin were noted to be highly resistant against gram-positive species. This indicates that evaluation based on MDR and antibiotic consumption patterns is imperative.

Using a Minecraft virtual workspace for learning digital electronics

A set of laboratory exercises are presented, which use the video game Minecraft as the environment for teaching combinational digital logic in an introductory electronics lab. Several advantages are noted when students use the virtual Minecraft environment prior to building any real digital circuits.

Sarcopenia is associated with leukopenia in urothelial carcinoma patients who receive tislelizumab combined with gemcitabine and cisplatin therapy

BackgroundIn the era of combination therapy, there has been limited research on body composition. Specific body composition, such as sarcopenia, possesses the potential to serve as a predictive biomarker for toxic effects and clinical response in patients with urothelial carcinoma (UC) undergoing tislelizumab combined with gemcitabine and cisplatin (T + GC).Materials and MethodsA total of 112 UC patients who received T + GC were selected at the Affiliated Hospital of Xuzhou Medical University from April 2020 to January 2023. Baseline patient characteristics and detailed hematological parameters were collected using the electronic medical system and laboratory examinations. The computed tomography images of patients were analyzed to calculate psoas muscle mass index (PMI). We evaluated the association between sarcopenia (PMI < 4.5 cm2/m2 in men; PMI < 3.3 cm2/m2 in women) and both hematological toxicity and tumor response.ResultsOverall, of the 112 patients (65.2% male, median age 56 years), 43 (38.4%) were defined as sarcopenia. Patients with sarcopenia were notably older (p = 0.037), more likely to have hypertension (p = 0.009), and had poorer ECOG-PS (p = 0.027). Patients with sarcopenia were more likely to develop leukopenia (OR 2.969, 95% CI 1.028–8.575, p = 0.044) after receiving at least two cycles of T + GC. However, these significant differences were not observed in thrombocytopenia and anemia. There were no significant differences in the tumor response and grade 3–4 hematological toxicity between patients with sarcopenia and those without sarcopenia.ConclusionsPatients with sarcopenia were more likely to develop leukopenia after receiving T + GC. There were no notable alterations observed in relation to anemia or thrombocytopenia. No significant difference was found between the sarcopenia group and non-sarcopenia group in terms of tumor response and grade 3–4 hematological toxicity.

Laboratory Data Timeliness and Completeness Improves Following Implementation of an Electronic Laboratory Information System in Côte d'Ivoire: Quasi-Experimental Study on 21 Clinical Laboratories From 2014 to 2020.

The Ministry of Health in Côte d'Ivoire and the International Training and Education Center for Health at the University of Washington, funded by the United States President's Emergency Plan for AIDS Relief, have been collaborating to develop and implement the Open-Source Enterprise-Level Laboratory Information System (OpenELIS). The system is designed to improve HIV-related laboratory data management and strengthen quality management and capacity at clinical laboratories across the nation. This evaluation aimed to quantify the effects of implementing OpenELIS on data quality for laboratory tests related to HIV care and treatment. This evaluation used a quasi-experimental design to perform an interrupted time-series analysis to estimate the changes in the level and slope of 3 data quality indicators (timeliness, completeness, and validity) after OpenELIS implementation. We collected paper and electronic records on clusters of differentiation 4 (CD4) testing for 48 weeks before OpenELIS adoption until 72 weeks after. Data collection took place at 21 laboratories in 13 health regions that started using OpenELIS between 2014 and 2020. We analyzed the data at the laboratory level. We estimated odds ratios (ORs) by comparing the observed outcomes with modeled counterfactual ones when the laboratories did not adopt OpenELIS. There was an immediate 5-fold increase in timeliness (OR 5.27, 95% CI 4.33-6.41; P<.001) and an immediate 3.6-fold increase in completeness (OR 3.59, 95% CI 2.40-5.37; P<.001). These immediate improvements were observed starting after OpenELIS installation and then maintained until 72 weeks after OpenELIS adoption. The weekly improvement in the postimplementation trend of completeness was significant (OR 1.03, 95% CI 1.02-1.05; P<.001). The improvement in validity was not statistically significant (OR 1.34, 95% CI 0.69-2.60; P=.38), but validity did not fall below pre-OpenELIS levels. These results demonstrate the value of electronic laboratory information systems in improving laboratory data quality and supporting evidence-based decision-making in health care. These findings highlight the importance of OpenELIS in Côte d'Ivoire and the potential for adoption in other low- and middle-income countries with similar health systems.

On-chip bioluminescence biosensor for the detection of microbial surface contamination

Detection of microbial pathogens is important for food safety reasons, and for monitoring sanitation in laboratory environments and health care settings. Traditional detection methods such as culture-based and nucleic acid-based methods are time-consuming, laborious, and require expensive laboratory equipment. Recently, ATP-based bioluminescence methods were developed to assess surface contamination, with commercial products available. In this study, we introduce a biosensor based on a CMOS image sensor for ATP-mediated chemiluminescence detection. The original lens and IR filter were removed from the CMOS sensor revealing a 12 MP periodic microlens/pixel array on an area of 6.5 mm × 3.6 mm. UltraSnap swabs are used to collect samples from solid surfaces including personal electronic devices, and office and laboratory equipment. Samples mixed with chemiluminescence reagents were placed directly on the surface of the image sensor. Close proximity of the sample to the photodiode array leads to high photon collection efficiency. The population of microorganisms can be assessed and quantified by analyzing the intensity of measured chemiluminescence. We report a linear range and limit of detection for measuring ATP in UltraSnap buffer of 10–1000 nM and 225 fmol, respectively. The performance of the CMOS-based device was compared to a commercial luminometer, and a high correlation with a Pearson's correlation coefficient of 0.98589 was obtained. The Bland-Altman plot showed no significant bias between the results of the two methods. Finally, microbial contamination of different surfaces was analyzed with both methods, and the CMOS biosensor exhibited the same trend as the commercial luminometer.

Sedimentary characteristics of mixed source fine-grained gravity-flow and its significance for shale oil exploration in a lacustrine depression basin: A case study of the Chang 73 Sub-member of the Triassic Yanchang Formation in Ordos Basin, NW China

This study investigates the transport and evolution of fine-grained gravity-flow deposits in lacustrine basins, focusing on the relationship between fine-grained deposits and their respective source–reservoir combinations. Such knowledge is vital for the exploration and development of unconventional oil and gas resources within the source-rock series of continental lake basins in China. We investigated the fine-grained gravity-flow deposits of the Late Triassic Chang73 Sub-member within the Yanchang Formation in the Ordos Basin. Our approach combined core observations, field outcrop evaluations, thin section analysis, scanning electron microscopy, geochemical data, and laboratory measurements. The sedimentary facies, characteristics, controlling factors, and sedimentary model of the fine-grained gravity-flow deposits are investigated. The primary sources of fine-grained sediments are terrestrial clastic and volcanic–hydrothermal fine-grained sediments. These sediments are mainly developed in warm, humid environments with abundant precipitation, favoring a highly reducible hypoxic terrestrial brackish–freshwater environment. Ten lithofacies and eight bed types of fine-grained gravity-flow deposits are categorized. Bed types represent deposits of muddy slide deposits, muddy slump deposits, muddy debris flow deposits, muddy flow deposits, hybrid event beds, fine-grained transitional flow deposits, surge-like turbidity flow deposits, and fine-grained hyperpycnal flow deposits. As a result of paleo-seismicity, volcanic events, and paleogeomorphology, instability and subsequent collapse of the delta front sediments led to the formation of gravity-flow deposits. These deposits formed the “channel–lobe complex” and “sublacustrine fan” sedimentary systems in both the southwestern and northeastern parts of the basin, respectively. Meanwhile, extraordinary flood events formed during the Carnian Pluvial Episode (CPE) played a significant role. It prompted increased fluvial drainage that drove fine-grained hyperpycnal flows through pre-existing valley systems, resulting in the establishment of the “channel–lobe complex” sedimentary system. The Chang 73 Sub-member contains high-quality hydrocarbon source rocks, laying the foundation for shale oil. The introduction of volcanic ash and the influence of hydrothermal activities amplify the enrichment of organic matter within the lacustrine basin. Fine-grained gravity-flow deposits yield siltstone-rich sweet intervals, primarily seen in surge-like turbidity and hyperpycnal flows. The interbedding of sandy and muddy sediments, influenced by hybrid event beds and fine-grained transitional flows, establishes a stratigraphic arrangement in which the upper layer nourishes the underlying reservoir. This structure is particularly beneficial for the development of shale oil and gas sweet intervals.

Development and Evaluation of Surveillance System for Identifying Jail-Associated COVID-19 Cases in Minnesota, USA, 2022.

Confinement facilities are high-risk settings for the spread of infectious disease, necessitating timely surveillance to inform public health action. To identify jail-associated COVID-19 cases from electronic laboratory reports maintained in the Minnesota Electronic Disease Surveillance System (MEDSS), Minnesota, USA, the Minnesota Department of Health developed a surveillance system that used keyword and address matching (KAM). The KAM system used a SAS program (SAS Institute Inc., https://www.sas.com) and an automated program within MEDSS to identify confinement keywords and addresses. To evaluate KAM, we matched jail booking data from the Minnesota Statewide Supervision System by full name and birthdate to the MEDSS records of adults with COVID-19 for 2022. The KAM system identified 2,212 cases in persons detained in jail; sensitivity was 92.40% and specificity was 99.95%. The success of KAM demonstrates its potential to be applied to other diseases and congregate-living settings for real-time surveillance without added reporting burden.

Tracking Cryptococcal Meningitis to Monitor HIV Program Success During the Treat All Era: An Analysis of National Data in Botswana.

Cryptococcal meningitis (CM) causes substantial mortality in African countries with a high prevalence of human immunodeficiency virus (HIV), despite advances in disease management and increasing antiretroviral therapy (ART) coverage. Reliable diagnosis of CM is cheap and more accessible than other indicators of advanced HIV disease burden such as CD4 testing or investigation for disseminated tuberculosis; therefore, monitoring CM incidence has the potential to serve as a valuable metric of HIV programmatic success. Botswana national meningitis surveillance data from 2015 to 2022 were obtained from electronic health records. All electronic laboratory records from cerebrospinal fluid samples analyzed within government healthcare facilities in Botswana were extracted from a central online repository. Adjustments for missing data were made through triangulation with prospective cohort study datasets. CM case frequency was enumerated using a case definition and incidence calculated using national census data. A total of 1744 episodes of CM were identified; incidence declined from 15.0 (95% confidence interval [CI], 13.4-16.7) cases/100 000 person-years in 2015 to 7.4 (95% CI, 6.4-8.6) cases/100 000 person-years in 2022. However, the rate of decline slowed following the introduction of universal treatment in 2016. The highest incidence was observed in men and individuals aged 40-44 years. The proportion of cases diagnosed through cryptococcal antigen testing increased from 35.5% to 86.3%. CM incidence has decreased in Botswana following expansion of ART coverage but persists at a stubbornly high incidence. Most cases are now diagnosed through the cheap and easy-to-use cryptococcal antigen test, highlighting the potential of using CM as key metric of program success in the Treat All era.

Use of OneNote class notebook as a combined electronic laboratory notebook and content delivery tool in an introductory biochemistry laboratory course.

The COVID-19 pandemic has forced a shift in thinking regarding the safe delivery of wet laboratory courses. While we were fortunate to have the capacity to continue delivering wet laboratory experiments with physical distancing and other measures in place, modifications to the mechanisms of delivery within courses were necessary to minimize risk to students and teaching staff. One such modification was introduced in BCH370H, an introductory biochemistry laboratory course, where a OneNote Class Notebook (ONCN) was used as an electronic laboratory notebook (ELN) in place of the traditional hardbound paper laboratory notebook (PLN) used prior to the pandemic. The initial reasoning for switching to an ELN was around safety-allowing course staff and students to maintain physical distancing whenever possible and eliminating the need for teaching assistants to handle student notebooks; however, the benefits of the ONCN proved to be significantly more. OneNote acted not only as a place for students to record notes but the Class Notebook's unique features allowed easy integration of other important aspects of the course, including delivery of laboratory manuals, posting of student results, notetaking feedback, sharing of instructional materials with teaching assistants, and more. Student and teacher experiences with the ONCN as used within a fully in person biochemistry laboratory course, as well as learned best practices, are reviewed.

An interactive augmented reality framework to enhance the user experience and operational skills in electronics laboratories

Augmented reality (AR) stands as a widely embraced technology that significantly enhances learning experiences for students. AR offers an instructional approach supported by technological design, thereby fostering enriched learning interactions. This research proposes an interactive AR framework, intended to create an augmented reality learning environment (ARLE) for the specific needs of electronics engineering laboratory hardware operations. The ARLE functions as an active learning system designed using a user-centered methodology. It offers interactive 3D models of laboratory equipment, providing learners with preliminary training in equipment operation. The real-time connection between the laboratory apparatus and the AR environment is established using the Arduino board. This interface empowers users to control the AR simulation through the laboratory equipment seamlessly. An experimental study involving 80 engineering students was conducted to evaluate the impact of AR intervention on user experience, usability, and operational skills. The participants were divided into two groups: the experimental group (N = 40) and the control group (N = 40). The experimental group underwent electronics equipment training using ARLE, while the control group followed instructions from a standard instrument handbook. To assess the usability and user experience of ARLE, the system usability scale (SUS) and the user experience questionnaire (UEQ) were employed (N = 40). The findings revealed an SUS score of 80.9 for ARLE, categorizing it as “good” according to SUS ratings. Additionally, the UEQ results demonstrated significantly favorable scores across the six scales when compared to the benchmark dataset. The study's outcomes demonstrate that AR intervention offers learners significant pedagogical value, resulting in a substantial positive impact on operational skills in electronics laboratories.