Transcription Errors Research Articles

Assessment of Transcription Errors and their Effects on Medical Approach, Hospitalization Duration, and Financial Costs of Traumatic Patients: How Can We Prevent Them?

Background and aims: Transcription errors pose significant risks to patient well-being, potentially leading to morbidity, mortality, and financial burdens on healthcare systems and society. This study assessed the prevalence of transcription errors and their impacts on medical treatment approaches, hospitalization duration, and associated costs. Methods: Direct observation was used to evaluate and compare all patients’ medical records, identifying errors, costs, and hospitalization duration. Physician orders and patient applications were checked, with intervention and control groups categorized based on the presence or absence of errors, respectively. Data were analyzed using descriptive and quantitative methods such as the chi-square, student’s t-test, Mann-Whitney test, and ANCOVA, to assess statistical significance. Results: No significant differences were observed in gender (P=0.73) and age (P=0.89) between patients. Omission errors and incorrect dosage forms were the most common. Errors were more frequent in patients with underlying diseases (P<0.001), with nurses frequently involved (P<0.001). Errors were more prevalent in intensive care units (ICUs) (P=0.002) and during morning and night shifts (P<0.001). A significant association was found between trauma severity and medication error occurrence (P<0.001). Moreover, patients with transcription errors had higher medical expenses and extended hospital stays (P<0.001). Conclusion: Effective preventive measures are crucial to avoid transcription errors and their adverse consequences. Recommendations include minimizing abbreviations, ensuring complete and explicit orders, rigorous training, double-checking, using technology such as Electronic Prescription and Physician Orders, maintaining a distraction-free, ergonomic work environment, and fostering open communication among healthcare providers and patients to enhance medication safety.

Kidney mRNA-protein expression correlation: what can we learn from the Human Protein Atlas?

Abstract Background The Human Protein Atlas, with more than 10 million immunohistochemical images showing tissue- and cell-specific protein expression levels and subcellular localization information, is widely used in kidney research. The Human Protein Atlas contains comprehensive data on multi-tissue transcript and protein abundance, allowing for comparisons across tissues. However, while visual and intuitive to interpret, immunohistochemistry is limited by its semi-quantitative nature. This can lead to mismatches in protein expression measurements across different platforms. Methods We performed a comparison of the Human Protein Atlas’ kidney-specific RNA sequencing and immunohistochemistry data to determine whether the mRNA and protein abundance levels are concordant. Results Our study shows that there is a discordance between mRNA and protein expression in the kidney based on the Human Protein Atlas data. Using an external validation mass spectrometry dataset, we show that more than 500 proteins undetected by immunohistochemistry are robustly measured by mass spectrometry. The Human Protein Atlas transcriptome data, on the other hand, exhibit similar transcript detection levels as other kidney RNA-seq datasets. Conclusions Discordance in mRNA-protein expression could be due to both biological and technical reasons, such as transcriptional dynamics, translation rates, protein half-lives, and measurement errors. This is further complicated by the heterogeneity of the kidney tissue itself, which can increase the discordance if the cell populations or tissue compartment samples do not match. As such, shedding light on the mRNA-protein relationship of the kidney-specific Human Protein Atlas data can provide context to our scientific inferences on renal gene and protein quantification. Graphical abstract

Transcript errors generate amyloid-like proteins in huwman cells

Aging is characterized by the accumulation of proteins that display amyloid-like behavior. However, the molecular mechanisms by which these proteins arise remain unclear. Here, we demonstrate that amyloid-like proteins are produced in a variety of human cell types, including stem cells, brain organoids and fully differentiated neurons by mistakes that occur in messenger RNA molecules. Some of these mistakes generate mutant proteins already known to cause disease, while others generate proteins that have not been observed before. Moreover, we show that these mistakes increase when cells are exposed to DNA damage, a major hallmark of human aging. When taken together, these experiments suggest a mechanistic link between the normal aging process and age-related diseases.

Automated image transcription for perinatal blood pressure monitoring using mobile health technology.

This paper introduces a novel approach to address the challenges associated with transferring blood pressure (BP) data obtained from oscillometric devices used in self-measured BP monitoring systems to integrate this data into medical health records or a proxy database accessible by clinicians, particularly in low literacy populations. To this end, we developed an automated image transcription technique to effectively transcribe readings from BP devices, ultimately enhancing the accessibility and usability of BP data for monitoring and managing BP during pregnancy and the postpartum period, particularly in low-resource settings and low-literate populations. In the designed study, the photos of the BP devices were captured as part of perinatal mobile health (mHealth) monitoring programs, conducted in four studies across two countries. The Guatemala Set 1 and Guatemala Set 2 datasets include the data captured by a cohort of 49 lay midwives from 1697 and 584 pregnant women carrying singletons in the second and third trimesters in rural Guatemala during routine screening. Additionally, we designed an mHealth system in Georgia for postpartum women to monitor and report their BP at home with 23 and 49 African American participants contributing to the Georgia I3 and Georgia IMPROVE projects, respectively. We developed a deep learning-based model which operates in two steps: LCD localization using the You Only Look Once (YOLO) object detection model and digit recognition using a convolutional neural network-based model capable of recognizing multiple digits. We applied color correction and thresholding techniques to minimize the impact of reflection and artifacts. Three experiments were conducted based on the devices used for training the digit recognition model. Overall, our results demonstrate that the device-specific model with transfer learning and the device independent model outperformed the device-specific model without transfer learning. The mean absolute error (MAE) of image transcription on held-out test datasets using the device-independent digit recognition were 1.2 and 0.8 mmHg for systolic and diastolic BP in the Georgia IMPROVE and 0.9 and 0.5 mmHg in Guatemala Set 2 datasets. The MAE, far below the FDA recommendation of 5 mmHg, makes the proposed automatic image transcription model suitable for general use when used with appropriate low-error BP devices.

HIV-1 Reverse Transcriptase Error Rates and Transcriptional Thresholds Based on Single-strand Consensus Sequencing of Target RNA Derived From In Vitro-transcription and HIV-infected Cells

Nucleotide incorporation and lacZ-based forward mutation assays have been widely used to determine the accuracy of reverse transcriptases (RTs) in RNA-dependent DNA polymerization reactions. However, they involve quite complex and laborious procedures, and cannot provide accurate error rates. Recently, NGS-based methods using barcodes opened the possibility of detecting all errors introduced by the RT, although their widespread use is limited by cost, due to the large size of libraries to be sequenced. In this study, we describe a novel and relatively simple NGS assay based on single-strand consensus sequencing that provides robust results with a relatively small number of raw sequences (around 60 Mb). The method has been validated by determining the error rate of HIV-1 (BH10 strain) RT using the HIV-1 protease-coding sequence as target. HIV-1 reverse transcription error rates in standard conditions (37 °C/3 mM Mg2+) using an in vitro-transcribed RNA were around 7.3 × 10−5. In agreement with previous reports, an 8-fold increase in RT’s accuracy was observed after reducing Mg2+ concentration to 0.5 mM. The fidelity of HIV-1 RT was also higher at 50 °C than at 37 °C (error rate 1.5 × 10−5). Interestingly, error rates obtained with HIV-1 RNA from infected cells as template of the reverse transcription at 3 mM Mg2+ (7.4 × 10−5) were similar to those determined with the in vitro-transcribed RNA, and were reduced to 1.8 × 10−5 in the presence of 0.5 mM Mg2+. Values obtained at low magnesium concentrations were modestly higher than the transcription error rates calculated for human cells, thereby suggesting a realistic transcriptional threshold for our NGS-based error rate determinations.

Using Collections in Specify to Manage Incoming Exchange Data

The Specify collection management system has been designed to accommodate data from multidisciplinary collecting institutions through a series of administrative units arranged in a hierarchical structure (Specify Collections Consortium 2024). Within the parent Institution, there can be multiple Divisions; within the parent Division there can be multiple Disciplines; and within the parent Discipline there can be multiple Collections (Fig. 1). While these administrative units were originally intended to house distinct collections within an institution, the model allows for flexibility and creativity in how they are implemented. The National Herbarium of Victoria’s (MEL) implementation of Specify utilises a single Discipline with 11 Collections. As well as storing data associated with sub-collections housed within the Herbarium—such as the Victorian Reference Set, which supports Royal Botanic Gardens Victoria’s plant identification service, and the Victorian Conservation Seedbank collection—Specify Collections are used to support curation workflows, including the management of data associated with specimens received as exchange material from other herbaria. The exchange of specimens between herbaria is a long-standing practice that enriches local collections and provides a form of insurance against damage or loss of specimens at the originating herbarium. Most consignments of exchange specimens are now accompanied by digital data, which helps streamline the data entry process and reduces the likelihood of transcription errors in the receiving institution’s collection management system. The National Herbarium of Victoria receives an average of 3000 exchange specimens from Australian and international herbaria each year. Due to resourcing constraints, incoming exchange specimens cannot always be processed upon receipt and are instead relegated to a curation backlog. Using the separate ‘Incoming Exchange’ Collection within Specify as a holding database for exchange data allows herbarium curators to upload the data at the time it is received as a guard against the loss or dissociation of specimen data, while allowing data validation work and the reconciliation of exchange specimens with their data to be postponed until staff resourcing permits, and after the specimens have been mounted. This workflow also allows exchange specimens to be reconciled with their data—and the data to be validated against the MEL data model—before being published into the main ‘National Herbarium of Victoria’ Collection, which forms our contribution to the Australasian Virtual Herbarium and, by extension, the Global Biodiversity Information Facility. Exchange data is uploaded to the ‘Incoming Exchange’ Collection via the Specify Workbench with the sending institution’s catalogue numbers recorded in the ‘Other identifier’ table for each record, but no MEL catalogue numbers applied. Once the exchange data is matched with a physical specimen and validated against the MEL data model, a MEL catalogue number is entered into the ‘collectionObject.CatalogNumber’ field. A stored procedure runs each night, transferring records with a CatalogNumber from the ‘Incoming Exchange’ Collection to the ‘National Herbarium of Victoria’ Collection, where they can be viewed by all user groups (Table 1).

Development and validation of an open-source pipeline for automatic population of case report forms from electronic health records: a pediatric multi-center prospective study

Clinical trials and registry studies are essential for advancing research and developing novel treatments. However, these studies rely on manual entry of thousands of variables for each patient. Repurposing real-world data can significantly simplify the data collection, reduce transcription errors, and make the data entry process more efficient, consistent, and cost-effective. We developed an open-source computational pipeline to collect laboratory and medication information from the electronic health record (EHR) data and populate case report forms. The pipeline was developed and validated with data from two independent pediatric hospitals in the US as part of the Long-terM OUtcomes after Multisystem Inflammatory Syndrome In Children (MUSIC) study. Our pipeline allowed the completion of two of the most time-consuming forms. We compared automatically extracted results with manually entered values in one hospital and applied the pipeline to a second hospital, where the output served as the primary data source for case report forms. We extracted and populated 51,845 laboratory and 4913 medication values for 159 patients in two hospitals participating in a prospective pediatric study. We evaluated pipeline performance against data for 104 patients manually entered by clinicians in one of the hospitals. The highest concordance was found during patient hospitalization, with 91.59% of the automatically extracted laboratory and medication values corresponding with the manually entered values. In addition to the successfully populated values, we identified an additional 13,396 laboratory and 567 medication values of interest for the study. The automatic data entry of laboratory and medication values during admission is feasible and has a high concordance with the manually entered data. By implementing this proof of concept, we demonstrate the quality of automatic data extraction and highlight the potential of secondary use of EHR data to advance medical science by improving data entry efficiency and expediting clinical research. NIH Grant 1OT3HL147154-01, U24HL135691, UG1HL135685.

Assessing the Effectiveness of Automatic Speech Recognition Technology in Emergency Medicine Settings: A Comparative Study of Four AI-powered Engines.

Cutting-edge automatic speech recognition (ASR) technology holds significant promise in transcribing and recognizing medical information during patient encounters, thereby enabling automatic and real-time clinical documentation, which could significantly alleviate care clinicians' burdens. Nevertheless, the performance of current-generation ASR technology in analyzing conversations in noisy and dynamic medical settings, such as prehospital or Emergency Medical Services (EMS), lacks sufficient validation. This study explores the current technological limitations and future potential of deploying ASR technology for clinical documentation in fastpaced and noisy medical settings such as EMS. In this study, we evaluated four ASR engines, including Google Speech-to-Text Clinical Conversation, OpenAI Speech-to-Text, Amazon Transcribe Medical, and Azure Speech-to-Text engine. The empirical data used for evaluation were 40 EMS simulation recordings. The transcribed texts were analyzed for accuracy against 23 Electronic Health Records (EHR) categories of EMS. The common types of errors in transcription were also analyzed. Among all four ASR engines, Google Speech-to-Text Clinical Conversation performed the best. Among all EHR categories, better performance was observed in categories "mental state" (F1 = 1.0), "allergies" (F1 = 0.917), "past medical history" (F1 = 0.804), "electrolytes" (F1 = 1.0), and "blood glucose level" (F1 = 0.813). However, all four ASR engines demonstrated low performance in transcribing certain critical categories, such as "treatment" (F1 = 0.650) and "medication" (F1 = 0.577). Current ASR solutions fall short in fully automating the clinical documentation in EMS setting. Our findings highlight the need for further improvement and development of automated clinical documentation technology to improve recognition accuracy in time-critical and dynamic medical settings.

Prevalence and causes of medication transcription errors among hospitalized patients: An observational study and survey of nurses at a faith-based hospital in Cameroon

Medication errors are a leading cause of death globally; with the burden on resource-limited countries more than double that in high-income countries. Errors can occur at all stages of the medication use process; however, there is limited information on medication transcription errors (MTEs) among hospitalized patients in Africa. This study sought to determine the prevalence and types of MTEs and to assess nurses’ perceptions of the likely causes of MTEs within the pediatric and general wards at a faith-based hospital in Cameroon. A prospective chart review of medication orders transcribed for patients admitted from April 1 to April 30, 2021, was conducted. Data analyzed included patient demographics, prevalence and types of MTEs, medication classes involved in MTEs, and nurse’s perceptions of the causes of MTEs. A total of 578 medication orders were reviewed for 75 patients, of whom 40 (53%) were female, and 30 were admitted to the pediatric ward. The results revealed that 38 MTEs were recorded, with 47% occurring on the medication administration sheet and dispensing cards. The most frequent MTEs were the omission of new and stop medication orders, involved in 28.9% and 26% of all MTEs, respectively. Antibacterials for systemic use were involved in the majority (55.3%) of MTEs. Illegible prescriptions (90.7%), distractions (87.0%), and high workload (77.8%) were ascribed as the most likely causes of MTEs. In conclusion, MTEs are common and pose a potential risk of patient harm in hospitalized patients. The adoption of a computerized provider order entry system could eliminate illegible prescriptions, reduce transcription processes, and alleviate workload, thereby improving patient safety.

External quality assurance experience with Royal College of Pathologists of Australasia Program at an academic hospital in South Africa.

Laboratories use their performance in external quality assurance (EQA) to establish quality planning strategies and to assess whether testing processes require improvement. The EQA performance of the hematology and coagulation test parameters on the Royal College of Pathologists of Australasia EQA program was evaluated over a 4-year cycle at an academic hospital in Johannesburg, South Africa. The test performance was determined from analytical quality specification (APS) and/or z-scores. Bias and imprecision were used to calculate sigma (σ) metric scores. Specifications from European Federation of Laboratory Medicine and/or biological variation were applied. The laboratory achieved a mean testing score of 98.7±4.0%. There were 103 (10.7%) unacceptable results. On investigation, root causes included: presurvey issues (83%), transcription errors (9%), random errors (6%), and test performance errors (3%). All test parameters evaluated achieved an acceptable median APS during the study period. The mean z-scores, however, were >2 and unacceptable for mean cell hemoglobin concentration and hematocrit. On investigation, this was attributed to significant delay in transport and storage of full blood count samples. White cell count and d-dimer achieved a σ ≥ 6. EQA participation assisted the laboratory in maintaining a quality system. Close monitoring is necessary for international laboratories to avoid sample delays that can affect result quality.

Analyzing the Effects of Transcription Errors on Summary Generation of Bengali Spoken Documents

Automatic speech recognition (ASR) has become an indispensable part of the AI domain, with various speech technologies reliant on it. The quality of speech recognition depends on the amount of annotated data used to train an ASR system, among other factors. For a low-resourced language, this is a severe constraint and thus ASR quality is often poor. Humans can read through text containing ASR-errors, provided the context of the sentence is preserved. Yet in cases of transcripts generated by ASR systems of low-resource languages, multiple important words are misrecognized and the context is mostly lost; discerning such a text becomes nearly impossible. This paper analyzes the types of transcription errors that occur while generating ASR transcripts of spoken documents in Bengali, an under-resourced language predominantly spoken in India and Bangladesh. The transcripts of the Bengali spoken document are generated using the ASR of Google Cloud Speech. The paper also explores if there is an effect of such transcription errors in generating speech summaries of these spoken documents. Summarization is carried out extractively; sentences are selected from the ASR-generated text of the spoken document. Speech summaries are created by aggregating the speech-segments from the original speech of the selected sentences. Subjective evaluation shows the ‘readability’ of the spoken summaries are not degraded by ASR errors, but the quality is affected due to the reliance on intermediate text-summary containing transcription errors.

Automatic transcription system for parliamentary debates in the context of assembly of the republic of Portugal

The transcription of parliamentary proceedings is essential for democratic governance. Traditional methods are manual and time-consuming. This work introduces an Automatic Transcription System for the Assembly of the Republic of Portugal (STAAR) that uses an automatic speech recognition model and speaker diarization technologies. STAAR was developed after analyzing existing technologies and the Assembly’s specific needs, leading to an effective solution that integrates with current processes. STAAR stands out for its efficiency in transcribing debates and adapting to parliamentary language nuances. It significantly exceeded expectations by presenting a low transcription error rate, ranging from 1.7 to 11.3%, depending on the context and speech style, reducing the time required to produce the official parliamentary debates journal, and improving overall transcription efficiency. Additionally, STAAR enabled the transcription of previously undocumented parliamentary committee meetings, enhancing the documentation of parliamentary activities. This achievement marks a significant step in modernizing parliamentary processes, increasing transparency and accessibility of political information, and positions the Portuguese Parliament at the forefront of technological innovation in parliamentary debates transcription.

Normalization of Web of Science Institution Names Based on Deep Learning

Academic evaluation is a process of assessing and measuring researchers, institutions, or disciplinary fields. Its goal is to evaluate their contributions and impact in the academic community, as well as to determine their reputation and status within specific disciplinary domains. Web of Science (WOS), being the most renowned global academic citation database, provides crucial data for academic evaluation. However, due to factors such as institutional changes, translation discrepancies, transcription errors in databases, and authors’ individual writing habits, there exist ambiguities in the institution names recorded in the WOS literature, which in turn affect the scientific evaluation of researchers and institutions. To address the issue of data reliability in academic evaluation, this paper proposes a WOS institution name synonym recognition framework that integrates multi-granular embeddings and multi-contextual information.

Implementation of hospital electronic medical record Patient Friendly Medication Lists and Interim Medication Administration Charts

AbstractPatients discharged from Australian hospitals require a list of current medications at the point of discharge, which is commonly in the form of a Patient Friendly Medication List (PFML). Furthermore, the provision of an Interim Medication Administration Chart (IMAC) reduces the number of medication administration delays and omissions for patients discharged to residential aged‐care facilities. To increase the adoption of PFMLs and IMACs, a new process was developed for creating PFMLs and IMACs directly from the discharge prescription in the Electronic Medical Record (EMR). This retrospective pre‐ and post‐intervention audit aimed to evaluate 1 year of PFML and IMAC generation from three acute metropolitan hospitals, prior to and after transitioning from pharmacy dispensing software to EMR‐generated documents. Despite similar hospital activity, the transition to EMR‐generated PFMLs and IMACs was associated with a 157% increase in PFML provision (7930 vs 20 373), a 220% increase in IMAC provision (1569 vs 5022) and a 99% reduction in the number of items typed into the pharmacy dispensing software that did not require supply (−59 171/year). Discharge dispensary turnaround times were lower in the post‐intervention period (36 min vs 30 min, p < 0.01). In conclusion, the transition to EMR‐generated PFMLs and IMACs was associated with increased provision of these documents, reduced data entry for items not required to be supplied, decreased risk of transcription errors and shortened time taken for the hospital pharmacy to process discharge items. This project was exempt due to the local policy requirements that constitute research by the Eastern Health Office of Research and Ethics (Reference no: QA21‐094). The justification for this ethics exemption was as follows: the project complies with the National Health and Medical Research Council's Ethical considerations in quality assurance and evaluation activities and met local requirements for an audit or quality assurance activity.

A Comprehensive Analysis of Medication Errors on Antimicrobials in a Tertiary Care Hospital of Northern India

Background: Medication errors, resulting from incomplete understanding, pose significant risks to patient safety. Antimicrobial medication error is a global concern further exacerbated by underreporting. This study aims to determine the prevalence of antimicrobial medication errors at a tertiary care medical facility. Methodology: A retrospective study was conducted over six months. Antimicrobial medication errors were analyzed and categorized as prescription, transcription, indenting, dispensing, and administration errors. A total of 208 antimicrobial medication errors were analyzed using the National Coordinating Council for Medication Error Reporting and Prevention (NCCMERP) risk index. Results: Among 6,439 medication charts assessed, 558 (9%) medication errors were identified, with antimicrobial errors accounting for 208 (37.2%). Prescription errors were the most prevalent (61%), followed by transcription errors (16%) and administration errors (11%). Dispensing, indenting, and documentation errors occurred at rates of 8%, 2%, and 2%, respectively. The primary causes of errors were incorrect doses (47.2%) and frequencies (30%). Approximately 55.3% of antimicrobial medication errors were classified as Category C according to the NCCMERP risk assessment. Conclusion: Prescription errors represent the majority of antimicrobial medication errors, underscoring the need for enhanced vigilance among consultant physicians during prescription writing and drug schedule checks.

DeTELpy: Python package for high-throughput detection of amino acid substitutions in mass spectrometry datasets.

Errors in the processing of genetic information during protein synthesis can lead to phenotypic mutations, such as amino acid substitutions, e.g. by transcription or translation errors. While genetic mutations can be readily identified using DNA sequencing, and mutations due to transcription errors by RNA sequencing, translation errors can only be identified proteome-wide using mass spectrometry. Here, we provide a Python package implementation of a high-throughput pipeline to detect amino acid substitutions in mass spectrometry datasets. Our tools enable users to process hundreds of mass spectrometry datasets in batch mode to detect amino acid substitutions and calculate codon-specific and site-specific translation error rates. deTELpy will facilitate the systematic understanding of amino acid misincorporation rates (translation error rates), and the inference of error models across organisms and under stress conditions, such as drug treatment or disease conditions. deTELpy is implemented in Python 3 and is freely available with detailed documentation and practical examples at https://git.mpi-cbg.de/tothpetroczylab/detelpy and https://pypi.org/project/deTELpy/ and can be easily installed via pip install deTELpy.

Interactive Electronic Pegboard for Enhancing Manual Dexterity and Cognitive Abilities: Instrument Usability Study.

Strokes pose a substantial health burden, impacting 1 in 6 people globally. One-tenth of patients will endure a second, often more severe, stroke within a year. Alarmingly, a younger demographic is being affected due to recent lifestyle changes. As fine motor and cognitive issues arise, patient disability as well as the strain on caregivers and health care resources is exacerbated. Contemporary occupational therapy assesses manual dexterity and cognitive functions through object manipulation and pen-and-paper recordings. However, these assessments are typically isolated, which makes it challenging for therapists to comprehensively evaluate specific patient conditions. Furthermore, the reliance on one-on-one training and assessment approaches on manual documentation is inefficient and prone to transcription errors. This study examines the feasibility of using an interactive electronic pegboard for stroke rehabilitation in clinical settings. A total of 10 patients with a history of stroke and 10 healthy older individuals were recruited. With a limit of 10 minutes, both groups of participants underwent a series of challenges involving tasks related to manual operation, shape recognition, and color discrimination. All participants underwent the Box and Block Test and the Purdue Pegboard Test to assess manual dexterity, as well as an array of cognitive assessments, including the Trail Making Test and the Mini-Mental Status Examination, which served as a basis to quantify participants' attention, executive functioning, and cognitive abilities. The findings validate the potential application of an interactive electronic pegboard for stroke rehabilitation in clinical contexts. Significant statistical differences (P<.01) were observed across all assessed variables, including age, Box and Block Test results, Purdue Pegboard Test outcomes, Trail Making Test-A scores, and Mini-Mental Status Examination performance, between patients with a history of stroke and their healthy older counterparts. Functional and task testing, along with questionnaire interviews, revealed that patients with a history of stroke demonstrated prolonged completion times and slightly inferior performance. Nonetheless, most patients perceived the prototype as user-friendly and engaging. Thus, in the context of patient rehabilitation interventions or the evaluation of patient cognition, physical functioning, or manual dexterity assessments, the developed pegboard could potentially serve as a valuable tool for hand function, attention, and cognitive rehabilitation, thereby mitigating the burden on health care professionals. Health care professionals can use digital electronic pegboards not only as a precise one-on-one training tool but also as a flexible system that can be configured for online or offline, single-player or multiplayer use. Through data analysis, a more informed examination of patients' cognitive and functional issues can be conducted. Importantly, patient records will be fully retained throughout practices, exercises, or tests, and by leveraging the characteristics of big data, patients can receive the most accurate rehabilitation prescriptions, thereby assisting them in obtaining optimal care.

Effects of the glyphosate-based herbicide roundup on C. elegans and S. cerevisiae mortality, reproduction, and transcription fidelity

Glyphosate-based weed killers such as Roundup have been implicated in detrimental effects on single- and multicellular eukaryotic model organism health and longevity. However, the mode(s) of action for these effects are currently unknown. In this study, we investigate the impact of exposure to Roundup on two model organisms: Saccharomyces cerevisiae and Caenorhabditis elegans and test the hypothesis that exposure to Roundup decreases transcription fidelity. Population growth assays and motility assays were performed in order to determine the phenotypic effects of Roundup exposure. We also used Rolling-Circle Amplification RNA sequencing to quantify the impact of exposure to Roundup on transcription fidelity in these two model organisms. Our results show that exposure to the glyphosate-based herbicide Roundup increases mortality, reduces reproduction, and increases transcription error rates in C. elegans and S. cerevisiae. We suggest that these effects may be due in part to the involvement of inflammation and oxidative stress, conditions which may also contribute to increases in transcription error rates.

Sources of variability in Luminex bead-based cytokine assays: Evidence from twelve years of multi-site proficiency testing

Bead array assays, such as those sold by Luminex, BD Biosciences, Sartorius, Abcam and other companies, are a well-established platform for multiplexed quantification of cytokines and other biomarkers in both clinical and discovery research environments. In 2011, the National Institute of Allergy and Infectious Diseases (NIAID)-funded External Quality Assurance Program Oversight Laboratory (EQAPOL) established a proficiency assessment program to monitor participating laboratories performing multiplex cytokine measurements using Luminex bead array technology. During every assessment cycle, each site was sent an assay kit, a protocol, and blinded samples of human sera spiked with recombinant cytokines. Site results were then evaluated for performance relative to peer laboratories. After over a decade of biannual assessments, the cumulative dataset contained over 15,500 bead array observations collected at more than forty laboratories in twelve countries. These data were evaluated alongside post-assessment survey results to empirically test factors that may contribute to variability and accuracy in Luminex bead-based cytokine assays. Bead material, individual technical ability, analyte, analyte concentration, and assay kit vendor were identified as significant contributors to assay performance. In contrast, the bead reader instrument model and the use of automated plate washers were found not to contribute to variability or accuracy, and sample results were found to be highly-consistent between assay kit-manufacturing lots and over time. In addition to these statistical analyses, subjective evaluations identified technical ability, instrument failure, protocol adherence, and data transcription errors as the most common causes of poor performance in the proficiency program. The findings from the EQAPOL multiplex program were then used to develop recommended best practices for bead array monitoring of human cytokines. These included collecting samples to assay as a single batch, centralizing analysis, participating in a quality assurance program, and testing samples using paramagnetic-bead kits from a single manufacturer using a standardized protocol.

Fair or Fare? Understanding Automated Transcription Error Bias in Social Media and Videoconferencing Platforms

Fair or Fare? Understanding Automated Transcription Error Bias in Social Media and Videoconferencing Platforms