Opportunity and accessibility: an environmental scan of publicly available data repositories to address disparities in healthcare decision-making.

BACKGROUND: Health disparities, starkly exposed and exacerbated by coronavirus disease 2019, pose a significant challenge to healthcare system access and health outcomes. Integrating health inequalities into health technology assessment calls for robust analytical methodologies utilizing disaggregated data to investigate and quantify the scope of these disparities. However, a comprehensive summary of population datasets that can be used for this purpose is lacking. The objective of this review was to identify publicly accessible health inequalities data repositories that are potential resources for healthcare decision-making and future health technology assessment submissions. METHODS: An environmental scan was conducted in June of 2023 of six international organizations (World Health Organization, Organisation for Economic Co-operation and Development, Eurostat, United Nations Inter-agency Group for Child Mortality Estimation, the United Nations Sustainable Development Goals, and World Bank) and 38 Organisation for Economic Co-operation and Development countries. The official websites of 42 jurisdictions, excluding non-English websites and those lacking English translations, were reviewed. Screening and data extraction were performed by two reviewers for each data repository, including health indicators, determinants of health, and health inequality metrics. The results were narratively synthesized. RESULTS: The search identified only a limited number of country-level health inequalities data repositories. The World Health Organization Health Inequality Data Repository emerged as the most comprehensive source of health inequality data. Some country-level data repositories, such as Canada's Health Inequality Data Tool and England's Health Inequality Dashboard, offered rich local insights into determinants of health and numerous health status indicators, including mortality. Data repositories predominantly focused on determinants of health such as age, sex, social deprivation, and geography. CONCLUSION: Interactive interfaces featuring data exploration and visualization options across diverse patient populations can serve as valuable tools to address health disparities. The data they provide may help inform complex analytical methodologies that integrate health inequality considerations into healthcare decision-making. This may include assessing the feasibility of transporting health inequality data across borders.

Utilization of publicly available data to summarize spatio-temporal patterns of fish health events of Atlantic salmon (Salmo salar) reported by marine finfish industries in British Columbia (BC), Canada.

Atlantic salmon aquaculture companies in British Columbia (BC) must report fish health events to Fisheries and Oceans Canada (DFO) as part of their licensing conditions. Our study aimed to summarize these fish health events reported by Atlantic salmon sites in BC to identify spatial and spatio-temporal clusters. We conducted descriptive, retrospective global, and local cluster analyses using Moran's I and scan statistics. Between 2016 and 2022, 265 fish health events were reported. The annual incidence ranged from 5.60 (95% CI: 3.90-7.80) to 6.86 (95% CI: 4.70-9.60) health events per 100 active site-months. The most common events were yellow mouth (60.75%; 161/265) and salmonid rickettsial septicaemia (SRS) (15.47%; 41/265). The Moran's I index was positive and significant for yellow mouth, SRS, and overall fish health events at different distance bands. Most of the spatial and spatio-temporal clusters were identified in the west-central and southwestern parts of Vancouver Island. Our study hypothesizes that management practices, environmental conditions, and water quality parameters may have influenced the increased reporting of fish health events in these regions. Overall, the study highlights the potential of publicly available data for practical risk mapping in understanding the patterns of farmed Atlantic salmon diseases in BC.

Using ChatGPT-4 to Create Structured Medical Notes From Audio Recordings of Physician-Patient Encounters: Comparative Study.

BACKGROUND: Medical documentation plays a crucial role in clinical practice, facilitating accurate patient management and communication among health care professionals. However, inaccuracies in medical notes can lead to miscommunication and diagnostic errors. Additionally, the demands of documentation contribute to physician burnout. Although intermediaries like medical scribes and speech recognition software have been used to ease this burden, they have limitations in terms of accuracy and addressing provider-specific metrics. The integration of ambient artificial intelligence (AI)-powered solutions offers a promising way to improve documentation while fitting seamlessly into existing workflows. OBJECTIVE: This study aims to assess the accuracy and quality of Subjective, Objective, Assessment, and Plan (SOAP) notes generated by ChatGPT-4, an AI model, using established transcripts of History and Physical Examination as the gold standard. We seek to identify potential errors and evaluate the model's performance across different categories. METHODS: We conducted simulated patient-provider encounters representing various ambulatory specialties and transcribed the audio files. Key reportable elements were identified, and ChatGPT-4 was used to generate SOAP notes based on these transcripts. Three versions of each note were created and compared to the gold standard via chart review; errors generated from the comparison were categorized as omissions, incorrect information, or additions. We compared the accuracy of data elements across versions, transcript length, and data categories. Additionally, we assessed note quality using the Physician Documentation Quality Instrument (PDQI) scoring system. RESULTS: Although ChatGPT-4 consistently generated SOAP-style notes, there were, on average, 23.6 errors per clinical case, with errors of omission (86%) being the most common, followed by addition errors (10.5%) and inclusion of incorrect facts (3.2%). There was significant variance between replicates of the same case, with only 52.9% of data elements reported correctly across all 3 replicates. The accuracy of data elements varied across cases, with the highest accuracy observed in the "Objective" section. Consequently, the measure of note quality, assessed by PDQI, demonstrated intra- and intercase variance. Finally, the accuracy of ChatGPT-4 was inversely correlated to both the transcript length (P=.05) and the number of scorable data elements (P=.05). CONCLUSIONS: Our study reveals substantial variability in errors, accuracy, and note quality generated by ChatGPT-4. Errors were not limited to specific sections, and the inconsistency in error types across replicates complicated predictability. Transcript length and data complexity were inversely correlated with note accuracy, raising concerns about the model's effectiveness in handling complex medical cases. The quality and reliability of clinical notes produced by ChatGPT-4 do not meet the standards required for clinical use. Although AI holds promise in health care, caution should be exercised before widespread adoption. Further research is needed to address accuracy, variability, and potential errors. ChatGPT-4, while valuable in various applications, should not be considered a safe alternative to human-generated clinical documentation at this time.

Classification of Fashion Models' Walking Styles Using Publicly Available Data, Pose Detection Technology, and Multivariate Analysis: From Past to Current Trendy Walking Styles.

Understanding past and current trends is crucial in the fashion industry to forecast future market demands. This study quantifies and reports the characteristics of the trendy walking styles of fashion models during real-world runway performances using three cutting-edge technologies: (a) publicly available video resources, (b) human pose detection technology, and (c) multivariate human-movement analysis techniques. The skeletal coordinates of the whole body during one gait cycle, extracted from publicly available video resources of 69 fashion models, underwent principal component analysis to reduce the dimensionality of the data. Then, hierarchical cluster analysis was used to classify the data. The results revealed that (1) the gaits of the fashion models analyzed in this study could be classified into five clusters, (2) there were significant differences in the median years in which the shows were held between the clusters, and (3) reconstructed stick-figure animations representing the walking styles of each cluster indicate that an exaggerated leg-crossing gait has become less common over recent years. Accordingly, we concluded that the level of leg crossing while walking is one of the major changes in trendy walking styles, from the past to the present, directed by the world's leading brands.

Understanding the relationship between the Centers for Medicare and Medicaid Services' Hospital Compare star rating, surgical case volume, and short-term outcomes after major cancer surgery.

BACKGROUND: Both the Centers for Medicare and Medicaid Services' (CMS) Hospital Compare star rating and surgical case volume have been publicized as metrics that can help patients to identify high-quality hospitals for complex care such as cancer surgery. The current study evaluates the relationship between the CMS' star rating, surgical volume, and short-term outcomes after major cancer surgery. METHODS: National Medicare data were used to evaluate the relationship between hospital star ratings and cancer surgery volume quintiles. Then, multilevel logistic regression models were fit to examine the association between cancer surgery outcomes and both star rankings and surgical volumes. Lastly, a graphical approach was used to compare how well star ratings and surgical volume predicted cancer surgery outcomes. RESULTS: This study identified 365,752 patients undergoing major cancer surgery for 1 of 9 cancer types at 2,550 hospitals. Star rating was not associated with surgical volume (P < .001). However, both the star rating and surgical volume were correlated with 4 short-term cancer surgery outcomes (mortality, complication rate, readmissions, and prolonged length of stay). The adjusted predicted probabilities for 5- and 1-star hospitals were 2.3% and 4.5% for mortality, 39% and 48% for complications, 10% and 15% for readmissions, and 8% and 16% for a prolonged length of stay, respectively. The adjusted predicted probabilities for hospitals with the highest and lowest quintile cancer surgery volumes were 2.7% and 5.8% for mortality, 41% and 55% for complications, 12.2% and 11.6% for readmissions, and 9.4% and 13% for a prolonged length of stay, respectively. Furthermore, surgical volume and the star rating were similarly associated with mortality and complications, whereas the star rating was more highly associated with readmissions and prolonged length of stay. CONCLUSIONS: In the absence of other information, these findings suggest that the star rating may be useful to patients when they are selecting a hospital for major cancer surgery. However, more research is needed before these ratings can supplant surgical volume as a measure of surgical quality. Cancer 2017;123:4259-4267. © 2017 American Cancer Society.

Developing training for Data Safety Monitoring Board members: A National Institute of Allergy and Infectious Diseases case study.

BACKGROUND: Data Safety Monitoring Boards primarily review accumulating data on clinical trials and provide recommendations to sponsors on whether a protocol should continue as planned, be modified, or be terminated. Data Safety Monitoring Boards often provide their recommendations based upon accumulating data to which only their members are given access. Despite the substantial responsibilities assumed by Data Safety Monitoring Board members, there is limited information in the literature about the unique knowledge they must possess and, consequently, the training content needs that are required in order for them to fulfill their obligations. PURPOSE: This article describes how the National Institute of Allergy and Infectious Diseases identified the knowledge that Data Safety Monitoring Board members should acquire and the computer-based training they developed to address the learning needs of the National Institute of Allergy and Infectious Diseases assembled Data Safety Monitoring Board members. METHODS: The National Institute of Allergy and Infectious Diseases conducted a comprehensive literature search and interviewed Data Safety Monitoring Board subject matter experts, including Data Safety Monitoring Board members and chairs from academic institutions, pharmaceutical companies, and the National Institutes of Health to (1) assess whether Data Safety Monitoring Board training is an identified need, (2) evaluate whether Data Safety Monitoring Board training has been developed, and (3) formulate suitable learning objectives. Data Safety Monitoring Board training modules were developed based on the identified learning objectives identified from the interviews. RESULTS: Three Data Safety Monitoring Board training modules were developed and formatted for web-based access, which is free of charge to the public at https://dsmblearningcenter.niaid.nih.gov. The modules include the following: an introduction to the objectives and purpose of Data Safety Monitoring Boards, the organization and responsibilities of Data Safety Monitoring Boards, and a review of statistical topics. LIMITATIONS: The complex concepts that Data Safety Monitoring Board members must apply to their deliberations and decisions require practice and application that come through hands-on experience. To build competency in the Data Safety Monitoring Board member role, not only does a member need to understand these complex concepts but also the member must have the opportunity to practice and apply this knowledge to real-life situations. Additional resources to facilitate practice and application of the complex issues that Data Safety Monitoring Boards deal with should be considered. The computer-based training is targeted to new and inexperienced Data Safety Monitoring Board members. Ongoing learning opportunities should be developed for experienced Data Safety Monitoring Board members. Non-English training must also be considered. CONCLUSION: The National Institute of Allergy and Infectious Diseases identified that training is not widely available for Data Safety Monitoring Board members to build the unique knowledge and skills necessary to serve on Data Safety Monitoring Boards. Consequently, National Institute of Allergy and Infectious Diseases developed publicly available web-based Data Safety Monitoring Board training modules for new or inexperienced members. Additional tools and resources are needed to help Data Safety Monitoring Board members acquire the knowledge and skills to serve their critical function in clinical research and to maximize research participant protections.

Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review.

Advancements in digital pathology and computing resources have made a significant impact in the field of computational pathology for breast cancer diagnosis and treatment. However, access to high-quality labeled histopathological images of breast cancer is a big challenge that limits the development of accurate and robust deep learning models. In this scoping review, we identified the publicly available datasets of breast H&E-stained whole-slide images (WSIs) that can be used to develop deep learning algorithms. We systematically searched 9 scientific literature databases and 9 research data repositories and found 17 publicly available datasets containing 10 385 H&E WSIs of breast cancer. Moreover, we reported image metadata and characteristics for each dataset to assist researchers in selecting proper datasets for specific tasks in breast cancer computational pathology. In addition, we compiled 2 lists of breast H&E patches and private datasets as supplementary resources for researchers. Notably, only 28% of the included articles utilized multiple datasets, and only 14% used an external validation set, suggesting that the performance of other developed models may be susceptible to overestimation. The TCGA-BRCA was used in 52% of the selected studies. This dataset has a considerable selection bias that can impact the robustness and generalizability of the trained algorithms. There is also a lack of consistent metadata reporting of breast WSI datasets that can be an issue in developing accurate deep learning models, indicating the necessity of establishing explicit guidelines for documenting breast WSI dataset characteristics and metadata.

A low-cost cure for CUREs: An undergraduate microbiology course engaging students in authentic research using publicly available datasets.

Undergraduate research experiences are key to preparing STEM students for a range of careers and graduate programs, and to impacting retention in STEM. Providing undergraduate research experiences can be challenging for institutions due to the high cost associated with equipment and reagents, lab space, and research mentors. In this study, we present an upper-level microbiology seminar course that does not require these resources, as each student chooses and performs their own research project using data obtained from publicly available datasets. The faculty member provides hands-on instruction and regular feedback to mentor the cohort of students through all stages of their research projects, from honing a research question, to choosing a dataset, to data analysis and visualization. Students build science communication skills through each writing a scientific paper, and creating and presenting a scientific poster. These papers and presentations, along with results from student pre- and post-surveys, demonstrate that students built research and communication skills, while also building their confidence and interest in science careers. To access this research experience, students only need to register for this course; no application or selection is required, and no prior research experience is expected. The use of publicly available data makes this course a low-cost way to integrate authentic research projects into the college curriculum, and can be adapted to courses in any discipline. Such "low-cost CUREs" (course-based undergraduate research experiences) can be used to build capacity for undergraduate research experiences that are so crucial to preparing students for opportunities in and beyond college.

Availability of information needed to evaluate algorithmic fairness - A systematic review of publicly accessible critical care databases.

BACKGROUND: Machine learning (ML) may improve clinical decision-making in critical care settings, but intrinsic biases in datasets can introduce bias into predictive models. This study aims to determine if publicly available critical care datasets provide relevant information to identify historically marginalized populations. METHOD: We conducted a review to identify the manuscripts that report the training/validation of ML algorithms using publicly accessible critical care electronic medical record (EMR) datasets. The datasets were reviewed to determine if the following 12 variables were available: age, sex, gender identity, race and/or ethnicity, self-identification as an indigenous person, payor, primary language, religion, place of residence, education, occupation, and income. RESULTS: 7 publicly available databases were identified. Medical Information Mart for Intensive Care (MIMIC) reports information on 7 of the 12 variables of interest, Sistema de Informação de Vigilância Epidemiológica da Gripe (SIVEP-Gripe) on 7, COVID-19 Mexican Open Repository on 4, and eICU on 4. Other datasets report information on 2 or fewer variables. All 7 databases included information about sex and age. Four databases (57%) included information about whether a patient identified as native or indigenous. Only 3 (43%) included data about race and/or ethnicity. Two databases (29%) included information about residence, and one (14%) included information about payor, language, and religion. One database (14%) included information about education and patient occupation. No databases included information on gender identity and income. CONCLUSION: This review demonstrates that critical care publicly available data used to train AI algorithms do not include enough information to properly look for intrinsic bias and fairness issues towards historically marginalized populations.

Underestimating attacks: comparing two sources of publicly-available data about attacks on health care in 2017.

BACKGROUND: Attacks on health care represent an area of growing international concern. Publicly available data are important in documenting attacks, and are often the only easily accessible data source. Data collection processes about attacks on health and their implications have received little attention, despite the fact that datasets and their collection processes may result in differing numbers. Comparing two separate datasets compiled using publicly-available data revealed minimal overlap. This article aims to explain the reasons for the lack of overlap, to better understand the gaps and their implications. METHODS: We compared the data collection processes for datasets comprised of publicly-reported attacks on health care from the World Health Organization (WHO) and Insecurity Insight's Security in Numbers Database (SiND). We compared each individual event to compile a comparable dataset and identify unique and matched events in order to determine the overlap between them. We report descriptive statistics for this comparison. RESULTS: We identified a common dataset of 287 events from 2017, of which only 33 appeared in both datasets, resulting in a mere 12.9% (n = 254) overlap. Events affecting personnel and facilities appeared most often in both, and 22 of 31 countries lacked any overlap between datasets. CONCLUSIONS: We conclude that the minimal overlap suggests significant underreporting of attacks on health care, and furthermore, that dataset definitions and parameters affect data collection. Source variation appears to best explain the discrepancies and closer comparison of the collection processes reveal weaknesses of both automated and manual data collection that rely on hidden curation processes. To generate more accurate datasets compiled from public sources requires systematic work to translate definitions into effective online search mechanisms to better capture the full range of events, and to increase the diversity of languages and local sources to better capture events across geographies.

A scoping review finds that guides to authors of protocols for observational epidemiological studies varied highly in format and content.

OBJECTIVE: To identify, characterize, and explore author guides on the role, format, and content of protocols for observational epidemiological studies, particularly cohort and case-control studies. STUDY DESIGN AND SETTING: Scoping review. We searched for guides in Medline, Embase, Google Scholar, 10 general medical and epidemiological/public health journals, and 10 major funders' websites. Two review authors extracted data. We classified guides as "main" based on word count and number of protocol items, described such guides more comprehensively and analyzed number of citations as an indicator of uptake. RESULTS: Thirty-nine protocol guides were included intended for cohort studies (n = 3), case-control studies (n = 1), or epidemiological studies in general (n = 35). Content and format were highly variable. Several guides had a broader focus than protocol development, e.g., also including study conduct and reporting. The guideline developmental process was often reported sparsely. One guide, intended for interventional studies, combined a systematic preparatory process with a primary focus on protocol development. We categorized seven guides as 'main'. In general the guides were cited infrequently, indicating limited uptake. CONCLUSION: Guides for authors of protocols for observational epidemiological studies varied highly in format and content. We suggest that such guides should routinely be based on a systematic preparatory process.

How Many Private Data Are Needed for Deep Learning in Lung Nodule Detection on CT Scans? A Retrospective Multicenter Study.

Early detection of lung nodules is essential for preventing lung cancer. However, the number of radiologists who can diagnose lung nodules is limited, and considerable effort and time are required. To address this problem, researchers are investigating the automation of deep-learning-based lung nodule detection. However, deep learning requires large amounts of data, which can be difficult to collect. Therefore, data collection should be optimized to facilitate experiments at the beginning of lung nodule detection studies. We collected chest computed tomography scans from 515 patients with lung nodules from three hospitals and high-quality lung nodule annotations reviewed by radiologists. We conducted several experiments using the collected datasets and publicly available data from LUNA16. The object detection model, YOLOX was used in the lung nodule detection experiment. Similar or better performance was obtained when training the model with the collected data rather than LUNA16 with large amounts of data. We also show that weight transfer learning from pre-trained open data is very useful when it is difficult to collect large amounts of data. Good performance can otherwise be expected when reaching more than 100 patients. This study offers valuable insights for guiding data collection in lung nodules studies in the future.

The global regulatory framework for the commercialization of nutrient enriched biofortified foods.

Nutrient enriched crops (NECs) were developed through biofortification as a tool to reach the world's most vulnerable. The delivery model developed by HarvestPlus for the scaling of NECs relies on commercial demand from food businesses and consumers, coupled with the ability to promote and market foods that comply with legislation. This review of standards, regulations, and laws across the value chain in 20 countries demonstrates that existing provisions for food labeling are sufficient to carry out sales and marketing of foods made from conventionally bred NECs. The term biofortification is not necessary to create demand and, potentially, is counterproductive. Promoting the natural source of vitamins and minerals and relevant nutrition claims is the most effective and simple way to signpost healthier products to consumers. Until 2021, it was not possible to distinguish NECs at the grain level from the market standard. The development of a globally relevant Publicly Available Specification allows traders to demand grains that offer a substantial increase in zinc, iron, or vitamin A. Addressing this gap at the grain level ensures that standards and regulations are available end-to-end in the food supply chain providing the enabling environment for the rapid scale of NECs.

Incidences of Involuntary Psychiatric Detentions in 25 U.S. States.

OBJECTIVE: Characterizing commitment as an involuntary psychiatric emergency detention that possibly extends into a longer-term detention, the authors aimed to calculate population rates of detentions and chart interstate differences since 2011 by means of publicly available state counts. METHODS: Searches of state health and court websites yielded counts from 38 U.S. states. Usable counts from 25 states were classified as emergency or longer-term detentions and converted to crude rates per 100,000 people by using Census Bureau figures. RESULTS: All-ages rates (per 100,000 people) of emergency detentions ranged from 29 in Connecticut to 966 in Florida. In 22 states with continuous 2012-2016 data, the average rate increased from 273 to 309. In four of five states with separate counts for adults and minors, rates over time for both were nearly parallel. In eight states that provided relevant data, the mean longer-term detention rate was 42% of a state's average emergency detention rate. Only one state provided length-of-stay data, and one counted both detentions and persons detained. In 24 states-accounting for 51.9% of the U.S. population-591,402 emergency involuntary detentions were recorded in 2014, the most recent year with most states reporting, a crude rate of 357 per 100,000. CONCLUSIONS: Incidences of involuntary psychiatric detentions between 2011 and 2018 varied 33-fold across 25 states, and the mean state rate increased by three times the mean state population increase. Omissions in most states' counts clouded interpretation. More valid incidences obtained from standardized national data would improve analysis of the controversial yet opaque procedure of involuntary inpatient civil commitment.

Assessing the Contribution of Relative Macrophage Frequencies to Subcutaneous Adipose Tissue.

Background: Macrophages play an important role in regulating adipose tissue function, while their frequencies in adipose tissue vary between individuals. Adipose tissue infiltration by high frequencies of macrophages has been linked to changes in adipokine levels and low-grade inflammation, frequently associated with the progression of obesity. The objective of this project was to assess the contribution of relative macrophage frequencies to the overall subcutaneous adipose tissue gene expression using publicly available datasets. Methods: Seven publicly available microarray gene expression datasets from human subcutaneous adipose tissue biopsies (n = 519) were used together with TissueDecoder to determine the adipose tissue cell-type composition of each sample. We divided the subjects in four groups based on their relative macrophage frequencies. Differential gene expression analysis between the high and low relative macrophage frequencies groups was performed, adjusting for sex and study. Finally, biological processes were identified using pathway enrichment and network analysis. Results: We observed lower frequencies of adipocytes and higher frequencies of adipose stem cells in individuals characterized by high macrophage frequencies. We additionally studied whether, within subcutaneous adipose tissue, interindividual differences in the relative frequencies of macrophages were reflected in transcriptional differences in metabolic and inflammatory pathways. Adipose tissue of individuals with high macrophage frequencies had a higher expression of genes involved in complement activation, chemotaxis, focal adhesion, and oxidative stress. Similarly, we observed a lower expression of genes involved in lipid metabolism, fatty acid synthesis, and oxidation and mitochondrial respiration. Conclusion: We present an approach that combines publicly available subcutaneous adipose tissue gene expression datasets with a deconvolution algorithm to calculate subcutaneous adipose tissue cell-type composition. The results showed the expected increased inflammation gene expression profile accompanied by decreased gene expression in pathways related to lipid metabolism and mitochondrial respiration in subcutaneous adipose tissue in individuals characterized by high macrophage frequencies. This approach demonstrates the hidden strength of reusing publicly available data to gain cell-type-specific insights into adipose tissue function.

Face Mask Policies in South Korea in Response to COVID-19.

All over the world, the critical shortage of face masks has been evident during the COVID-19 outbreak. No specific policy to solve the shortage has been shared among public health scholars and practitioners. Recently, the Korean government implemented noteworthy policies to stabilize the face mask market. This article examines the three government initiatives (Emergency Stabilization Policies) using participant observation, and what the effects of the Emergency Stabilization Policies are.

Urgent call for a global enforcement of the public sharing of health emergencies data: lesson learned from serious arboviral disease epidemics in Sudan.

One crucial element of the timely detection and identification of the causative agent(s) of a health emergency is access to live and historical data about the health risks in the area of concern. Therefore, sharing data on health emergencies is essential to the early investigation and detection teams. Although, theoretically, there is a global agreement on sharing data rapidly, in practice this is not always the case, particularly in developing countries such as Sudan, where there is continuous failure in making epidemics-related data publicly available. An alternative model for information and data sharing is suggested.

Centromeric cohesion failure invokes a conserved choreography of chromosomal mis-segregations in pancreatic neuroendocrine tumor.

BACKGROUND: Pancreatic neuroendocrine tumors (PANETs) are rare, slow growing cancers that often present with local and distant metastasis upon detection. PANETS contain distinct karyotypes, epigenetic dysregulation, and recurrent mutations in MEN1, ATRX, and DAXX (MAD+); however, the molecular basis of disease progression remains uncharacterized. METHODS: We evaluated associations between aneuploidy and the MAD+ mutational state of 532 PANETs from 11 published genomic studies and 19 new cases using a combination of exome, targeted panel, shallow WGS, or RNA-seq. We mapped the molecular timing of MAD+ PANET progression using cellular fractions corrected for inferred tumor content. RESULTS: In 287 PANETs with mutational data, MAD+ tumors always exhibited a highly recurrent signature of loss of heterozygosity (LOH) and copy-number alterations affecting 11 chromosomes, typically followed by genome doubling upon metastasis. These LOH chromosomes substantially overlap with those that undergo non-random mis-segregation due to ectopic CENP-A localization to flanking centromeric regions in DAXX-depleted cell lines. Using expression data from 122 PANETs, we found decreased gene expression in the regions immediately adjacent to the centromere in MAD+ PANETs. Using 43 PANETs from AACR GENIE, we inferred this signature to be preceded by mutations in MEN1, ATRX, and DAXX. We conducted a meta-analysis on 226 PANETs from 8 CGH studies to show an association of this signature with metastatic incidence. Our study shows that MAD+ tumors are a genetically diverse and aggressive subtype of PANETs that display extensive chromosomal loss after MAD+ mutation, which is followed by genome doubling. CONCLUSIONS: We propose an evolutionary model for a subset of aggressive PANETs that is initiated by mutation of MEN1, ATRX, and DAXX, resulting in defects in centromere cohesion from ectopic CENP-A deposition that leads to selective loss of chromosomes and the LOH phenotype seen in late-stage metastatic PANETs. These insights aid in disease risk stratification and nominate potential therapeutic vulnerabilities to treat this disease.

Effective automated method for detection and suppression of muscle artefacts from single-channel EEG signal.

This Letter proposes an automated method for the detection and suppression of muscle artefacts (MAs) in the single-channel electroencephalogram (EEG) signal based on variational mode decomposition (VMD) and zero crossings count threshold criterion without the use of reference electromyogram (EMG). The proposed method involves three major steps: decomposition of the input EEG signal into two modes using VMD; detection of MAs based on zero crossings count thresholding in the second mode; retention of the first mode as MAs-free EEG signal only after detection of MAs in the second mode. The authors evaluate the robustness of the proposed method on a variety of EEG and EMG signals taken from publicly available databases, including Mendeley database, epileptic Bonn database and EEG during mental arithmetic tasks database (EEGMAT). Evaluation results using different objective performance metrics depict the superiority of the proposed method as compared to existing methods while preserving the clinical features of the reconstructed EEG signal.