Public Repositories Research Articles

Lessons Learned from a National Initiative Promoting Publicly Available Standards-Based Clinical Decision Support.

Clinical decision support (CDS), which provides tools to assist clinical decision-making, can improve adherence to evidence-based practices, prevent medical errors, and support high-quality and patient-centered care delivery. Publicly available CDS that uses standards to express clinical logic (i.e., standards-based CDS) has the potential to reduce duplicative efforts of translating the same clinical evidence into CDS across multiple health care institutions. Yet development of such CDS is relatively new and its potential only partially explored. This study aimed to describe lessons learned from a national initiative promoting publicly available, standards-based CDS resources, discuss challenges, and report suggestions for improvement. Findings were drawn from an evaluation of the Agency for Healthcare Research and Quality Patient-Centered Outcomes Research CDS Initiative, which aimed to advance evidence into practice through standards-based and publicly available CDS. Methods included literature and program material reviews, key informant interviews, and a web-based survey about a public repository of CDS artifacts and tools for authoring standards-based CDS. The evaluation identified important lessons for developing and implementing standards-based CDS through publicly available repositories such as CDS Connect. Trust is a critical factor in uptake and can be bolstered through transparent information on underlying evidence, collaboration with experts, and feedback loops between users and developers to support continuous improvement. Additionally, while adoption of standards among electronic health record developers will make it easier to implement standards-based CDS, lower-resourced health systems will need extra support to ensure successful implementation and use. Finally, although we found the resources developed by the Initiative to offer valuable prototypes for the field, health systems desire more information about patient-centered, clinical, and cost-related outcomes to help them justify the investment required to implement standards-based, publicly available CDS. While the standards and technology to publicly share standards-based CDS have increased, broad dissemination and implementation remain challenging.

Characterization of Deep Learning-Based Speech-Enhancement Techniques in Online Audio Processing Applications.

Deep learning-based speech-enhancement techniques have recently been an area of growing interest, since their impressive performance can potentially benefit a wide variety of digital voice communication systems. However, such performance has been evaluated mostly in offline audio-processing scenarios (i.e., feeding the model, in one go, a complete audio recording, which may extend several seconds). It is of significant interest to evaluate and characterize the current state-of-the-art in applications that process audio online (i.e., feeding the model a sequence of segments of audio data, concatenating the results at the output end). Although evaluations and comparisons between speech-enhancement techniques have been carried out before, as far as the author knows, the work presented here is the first that evaluates the performance of such techniques in relation to their online applicability. This means that this work measures how the output signal-to-interference ratio (as a separation metric), the response time, and memory usage (as online metrics) are impacted by the input length (the size of audio segments), in addition to the amount of noise, amount and number of interferences, and amount of reverberation. Three popular models were evaluated, given their availability on public repositories and online viability, MetricGAN+, Spectral Feature Mapping with Mimic Loss, and Demucs-Denoiser. The characterization was carried out using a systematic evaluation protocol based on the Speechbrain framework. Several intuitions are presented and discussed, and some recommendations for future work are proposed.

Researchers propose preregistration in taxonomy to enhance transparency and credibility and reduce subjectivity

A new opinion piece published in Megataxa today (Conix et al. 2023) calls for preregistration in the field of taxonomy and discusses its potential benefits and objections. Preregistration is a practice for researchers to document their hypotheses, design and methods of their proposed studies in a public repository before they start the study and collect the data (Nosek et al. 2018). It is believed that preregistration can help reduce bias, increase transparency, and improve study design (especially replicability) of research (e.g. Nosek et al. 2018; Krypotos et al. 2022), including exploratory research such as some aspects of biodiversity discovery and taxonomy (Dirnagl et al. 2020). While preregistration has been already employed in other fields such as social sciences (e.g. Camerer et al. 2016, 2018) and ecology/evolution (e.g. Kelly 2019), it has yet been adopted in taxonomy. Conix et al. (2023) argue that preregistration could be beneficial for taxonomy, given the challenges it faces, such as subjectivity, disagreement, and credibility: 

COVMOS: A new Monte Carlo approach for galaxy clustering analysis

We validate the COVMOS method introduced in our previous study allowing for the fast simulation of catalogues of different cosmological field tracers (e.g. dark matter particles, halos, galaxies, etc.). The power spectrum and one-point probability distribution function of the underlying tracer’s density field are set as inputs of the method and arbitrarily chosen by the user. To evaluate the validity domain of COVMOS at the level of the produced two-point statistics covariance matrix, we chose to target these two input statistical quantities from realistic N-body simulation outputs. We performed this cloning procedure based on the ΛCDM as well as on a massive neutrino cosmology, for five redshifts in the range of z ∈ [0, 2]. First, we validated the output real-space two-point statistics (both in the configuration and Fourier space) and estimated over 5000 COVMOS realisations per redshift and per cosmology, with a volume of 1 [Gpc/h]3 and 108 particles each. This validation was performed against the corresponding N-body measurements, estimated from 50 simulations. We found the method to be valid up to k ∼ 0.2h Mpc−1 for the power spectrum and down to r ∼ 20 Mpc h−1 for the correlation function. Then, we extended the method by proposing a new modelling of the peculiar velocity distribution, aimed at reproducing the redshift-space distortions both in the linear and mildly non-linear regimes. After validating this prescription, we finally compared and validated the resulting redshift-space two-point statistics covariance matrices in the same range of scales. We released the Python code associated with this method on a public repository, which allows for the production of tens of thousands of realisations in record time. In particular, COVMOS is intended for any study involving large-scale galaxy-survey science that requires a large number of mock realisations.

DeepAngle: Fast calculation of contact angles in tomography images using deep learning

DeepAngle is a machine learning-based method to determine the contact angles of different phases in the tomography images of porous materials. Measurement of angles in 3-D needs to be done within the surface perpendicular to the angle planes, and it could become inaccurate when dealing with the discretized space of the image voxels. A computationally intensive solution is to correlate and vectorize all surfaces using an adaptable grid, and then measure the angles within the desired planes. On the contrary, the present study provides a rapid and low-cost technique powered by deep learning to estimate the interfacial angles directly from images. DeepAngle is tested on both synthetic and realistic images against the direct measurement technique and found to improve the r-squared 5 to 16%, while lowering the computational cost 20 times. This rapid method is especially applicable for processing large tomography data and time-resolved images, which is computationally intensive. The developed code and the dataset are available in a public repository on GitHub (https://www.github.com/ArashRabbani/DeepAngle).

Doubling down on best practices: reflecting on teaching physiology during the COVID-19 pandemic

As we pass the third anniversary of the World Health Organization's declaration of the SARS-CoV-2 global pandemic, it is beneficial to reflect upon how physiology education adapted to the challenges of the pandemic. At the initial stages of the pandemic, many educators were faced with the challenge of quickly transitioning to emergency remote teaching (ERT), requiring shifts in teaching methodology and laboratory structure to adapt to the pandemic normal. In this review, we provide a broad overview of the efforts made by the community of educators associated with the American Physiological Society during the pandemic to encourage best practices in teaching, maintain course and program goals during ERT, and innovate in physiology education. We also highlight diversity, equality, and inclusion work that was produced as the scientific community recommitted to tackling systemic and structural inequalities exacerbated by the pandemic and brought to the forefront by the Black Lives Matter movement. Finally, we examine the potentially long-lasting effects of the pandemic on education from both the student and faculty standpoints and how doubling down on what we learned can be beneficial to the future of physiology education.NEW & NOTEWORTHY This literature review examines and categorizes the repository of publications in physiology education that occurred during and related to the COVID-19 pandemic.

MANSOR: A module alignment method based on neighbor information for scientific workflow

SummaryFinding similar scientific workflow modules that can substitute essential components of the privacy workflows from public repositories to create a personalized workflow is growing in popularity. This is a cost‐effective and error‐free strategy for the scientific community. Currently, module alignment approaches heavily depend on syntactic information such as modules' names and types. However, the contextual semantic information of modules, which encompasses inputs, outputs, and datalinks connecting modules, can also convey their functions. Unfortunately, this information is scarcely utilized during the module alignment process. In this work, we propose a module alignment method based on neighbor information for scientific workflow (MANSOR). Specifically, we present a rule‐based attribute similarity computation approach for calculating initial module‐pair similarity in two workflows. The relation similarity is then employed to iteratively fine‐tune the matching degree with uncertainty of module pairs by considering the contextual semantics until reaching a steady state. After pairwise comparison of workflows, the module alignment results are determined based on their module‐pair similarity. Experimental results on the human‐curated corpus of ratings indicate that MANSOR outperforms the existing state‐of‐the‐art approaches with statistical significance.

Non-contact Storage Classification Based on Mechanical Arm in the Context of Prevention and Treatment of COVID-19

COVID-19 has become a global infectious disease, which has a high transmission rate, a wide transmission range, and a variety of transmission modes. Therefore, to reduce the transmission efficiency of COVID-19, human beings try to reduce the manpower ratio to create a non-contact environment in the industry. Therefore, a new model is proposed in this paper. This new model aims to be applied to storage classification in the context of the prevention and treatment of COVID-19 by using the mechanical robot arm. This model will use the UR3 robot arm, Logitech C300 web camera, and OpenCV public repository to implement the computer vision algorithm, and forward and inverse kinematics analysis. This model can successfully implement non-contact storage classification. Through the real experiment, this paper has a good application prospect. It gives a new solution for future research and experiment. In addition, in the summary and outlook part of this paper, new research directions in the future are given.

Celiac Disease Is a Risk Factor for Mature T and NK Cell Lymphoma: A Mendelian Randomization Study.

Celiac disease (CeD) is an immune-mediated disorder triggered by gluten ingestion that damages the small intestine. Although CeD has been associated with a higher risk for cancer, the role of CeD as a risk factor for specific malignancies, such as enteropathy-associated T-cell lymphoma (EATL), remains controversial. Using two-sample Mendelian randomization (2SMR) methods and the summarized results of large genome-wide association studies from public repositories, we addressed the causal relationship between CeD and eight different malignancies. Eleven non-HLA SNPs were selected as instrumental variables (IVs), and causality estimates were obtained using four 2SMR methods: random-effects inverse variance-weighted, weighted median estimation, MR-Egger regression, and MR pleiotropy residual sum and outlier (MR-PRESSO). We identified a significant causal relationship between CeD and mature T/NK cell lymphomas. Under a multivariate Mendelian randomization model, we observed that the causal effect of CeD was not dependent on other known lymphoma risk factors. We found that the most instrumental IV was located in the TAGAP locus, suggesting that aberrant T cell activation might be relevant in the T/NK cell malignization process. Our findings provide new insights into the connection between immune imbalance and the development of severe comorbidities, such as EATL, in patients with CeD.

AnimalAccML: An open-source graphical user interface for automated behavior analytics of individual animals using triaxial accelerometers and machine learning

Automated collection of accelerometer data and subsequent machine learning modeling are prevalent combined methods for animal behavior recognition. However, there is a lack of customized tools for user-friendly machine learning model development. Meanwhile, existing models in previous research could not be directly used for behavior interpretation. The objective of this study was to design and develop a tool for customized machine learning model development and animal behavior analysis using triaxial accelerometer data. A graphical user interface was programmed with Python and saved in a public repository for open access. The interface mainly consists of pages of ‘Manage Project’, ‘Preprocess Data’, ‘Develop Models’, and ‘Analyze Behavior’. An open dataset containing triaxial accelerometer data of six beef cattle was used to test the developed interface. The main results show that users can customize appropriate machine learning models for behavior analytics through several mouse clicks on the interface. A total of 15 models can be selected and trained to determine an optimal one, and model performance can be optimized by adjusting parameters of window size, step size, and training-to-validation ratio. Data imbalance can be solved by merging minority classes into one. The newly developed model has the capacity to analyze overall behavior time budget, statistics (e.g., mean, minimum, maximum, and standard deviation) of each behavior duration, and frequency of behavior sequences. The tool is supportive for automated animal behavior analytics critical to enhancing animal welfare, housing environment, genetics selection, and flock management.

A systematic literature review on long‐term localization and mapping for mobile robots

AbstractLong‐term operation of robots creates new challenges to Simultaneous Localization and Mapping (SLAM) algorithms. Long‐term SLAM algorithms should adapt to recent changes while preserving older states, when dealing with appearance variations (lighting, daytime, weather, or seasonal) or environment reconfiguration. When also operating robots for long periods and trajectory lengths, the map should readjust to environment changes but not grow indefinitely. The map size should depend only on updating the map with new information of interest, not on the operation time or trajectory length. Although several studies in the literature review SLAM algorithms, none of the studies focus on the challenges associated to lifelong SLAM. Thus, this paper presents a systematic literature review on long‐term localization and mapping following the Preferred Reporting Items for Systematic reviews and Meta‐Analysis guidelines. The review analyzes 142 works covering appearance invariance, modeling the environment dynamics, map size management, multisession, and computational topics such as parallel computing and timing efficiency. The analysis also focus on the experimental data and evaluation metrics commonly used to assess long‐term autonomy. Moreover, an overview over the bibliographic data of the 142 records provides analysis in terms of keywords and authorship co‐occurrence to identify the terms more used in long‐term SLAM and research networks between authors, respectively. Future studies can update this paper thanks to the systematic methodology presented in the review and the public GitHub repository with all the documentation and scripts used during the review process.

Cross-verification and benchmarking analysis of electrodynamic tether simulators

Five electrodynamic tether simulators (BETsMA v2.0, DYNATETHER, EDTSim, FLEX, and TeMPEST) have been cross-verified by running and analysing simulations of increasing complexity. A set of ten simulations without any tether was run to test the orbital propagators and the implementation of the perturbation force due to the non-sphericity of the Earth and its non-homogeneous mass distribution. The environmental models of the five codes and their implementation were then cross-verified by analysing the evolution of the magnetic field and the plasma and atmospheric densities. The electric modules of the simulators for electrodynamic tethers working in the passive mode, i.e., the routines in charge of computing the current and the voltage profiles along the tethers as well as the Lorentz force, were compared by running simulations with bare tethers. Configurations with an ideal electron emitter (zero potential drop), a real emitter, and a resistor and an ideal emitter were considered, as well as round and tape tethers. The electric models of BETsMA, EDTSim, FLEX, and TeMPEST, which assumed a straight tether, were also compared with the result of DYNATETHER for curved tethers. The consistency of the five simulators as a whole was tested by preparing performance maps with the deorbit time versus orbit inclination for a reference scenario and considering tape and round electrodynamic tethers. Although they implement different models and make different assumptions, the results of the five codes are consistent for the full simulation campaign. The simulation data and the software to visualize them are available in a public repository.

Using a Bayesian-Inference Approach to Calibrating Models for Simulation in Robotics

AbstractIn robotics, simulation has the potential to reduce design time and costs, and lead to a more robust engineered solution and a safer development process. However, the use of simulators is predicated on the availability of good models. This contribution is concerned with improving the quality of these models via calibration, which is cast herein in a Bayesian framework. First, we discuss the Bayesian machinery involved in model calibration. Then, we demonstrate it in one example: calibration of a vehicle dynamics model that has low degree-of-freedom (DOF) count and can be used for state estimation, model predictive control, or path planning. A high fidelity simulator is used to emulate the “experiments” and generate the data for the calibration. The merit of this work is not tied to a new Bayesian methodology for calibration, but to the demonstration of how the Bayesian machinery can establish connections among models in computational dynamics, even when the data in use is noisy. The software used to generate the results reported herein is available in a public repository for unfettered use and distribution.

Hyd1d and hydflood - R packages to compute water levels and annual flood durations along the rivers Rhine and Elbe, Germany

Floodplains are environments characterized by fluctuating water levels. Spatial and temporal heterogeneity of habitat conditions driven by hydrology are the key factor for their high biodiversity because strong environmental filtering either creates favorable conditions for specialized organisms or eliminates competing generalists.hyd1d and hydflood are two R packages for simplified hydrological modelling in two large central European floodplains – along the rivers Rhine and Elbe in Germany – that enable users to simulate the dynamics of water level changes, flood extents and durations with high spatial and temporal resolution over long time periods and vast areas. They are simplified in the sense of not being based on physical equations, but they implement fast computing interpolation of 1d hydraulic data and GIS extrapolation algorithms. Annual and long-term products provide the possibility to predict the distribution of floodplain habitats.All input datasets are highly integrated into the packages and automatically updated. Computationally expensive products such as annual flood durations between 1960 and 2022 are provided as open data. The software itself is provided as open source software through CRAN and in public code repositories. With minimal efforts users can apply the software within the given conditions or integrate other river systems through data and source code contributions.

Simple stupid insecure practices and GitHub’s code search: A looming threat?

Insecure coding practices are a known, long-standing problem in open-source development, which takes on a new dimension with the current capabilities for mining open-source software repositories through version control systems. Although most insecure practices require a sequence of interlinked behaviour, prior work also determined that simpler, one-liner coding practices can introduce vulnerabilities in the code. Such simple stupid insecure practices (SSIPs) can have severe security implications for package-based software systems, as they are easily spread over version-control systems. Moreover, GitHub is piloting regular-expression-based code searches across public repositories through its “Code Search Technology”, potentially simplifying unearthing SSIPs. As an exploratory case study, we focused on popular PyPi packages and analysed their source code using regular expressions (as done by GitHub’s incoming search engine). The goal was to explore how detectable these simple vulnerabilities are and how exploitable “Code Search” technology is. Results show that packages on lower versions are more vulnerable, that “code injection” is the most scattered issue, and that about 20% of the scouted packages have at least one vulnerability. Most concerningly, malicious use of this engine was straightforward, raising severe concerns about the implications of a publicly available “Code Search”.

A survey on antimicrobial resistance genes of frequently used probiotic bacteria, 1901 to 2022.

BackgroundAntimicrobial resistance (AMR) is caused by AMR determinants, mainly genes (ARGs) in the bacterial genome. Bacteriophages, integrative mobile genetic elements (iMGEs) or plasmids can allow ARGs to be exchanged among bacteria by horizontal gene transfer (HGT). Bacteria, including bacteria with ARGs, can be found in food. Thus, it is conceivable that in the gastrointestinal tract, bacteria from the gut flora could take up ARGs from food.AimThe study objective was to gain insight into the ARG set carried by commonly used probiotic bacteria that may enter the human body with non-fermented foods, fermented foods, or probiotic dietary supplements (FFPs) and to assess ARG mobility.MethodsNext generation sequencing whole genome data from 579 isolates of 12 commonly employed probiotic bacterial species were collected from a public repository. Using bioinformatical tools, ARGs were analysed and linkage with mobile genetic elements assessed.ResultsResistance genes were found in eight bacterial species. The ratios of ARG positive/negative samples per species were: Bifidobacterium animalis (65/0), Lactiplantibacillus plantarum (18/194), Lactobacillus delbrueckii (1/40), Lactobacillus helveticus (2/64), Lactococcus lactis (74/5), Leucoconstoc mesenteroides (4/8), Levilactobacillus brevis (1/46), Streptococcus thermophilus (4/19). In 66% (112/169) of the ARG-positive samples, at least one ARG could be linked to plasmids or iMGEs. No bacteriophage-linked ARGs were found.ConclusionThe finding of potentially mobile ARGs in probiotic strains for human consumption raises awareness of a possibility of ARG HGT in the gastrointestinal tract. In addition to existing recommendations, screening FFP bacterial strains for ARG content and mobility characteristics might be considered.

Prospective Genomic Surveillance Reveals Cryptic MRSA Outbreaks with Local to International Origins among NICU Patients.

Methicillin-resistant Staphylococcus aureus (MRSA) infections cause substantive morbidity and mortality in neonates. Using publicly available resources from the National Center of Biotechnology Information (NCBI) and Food and Drug Administration's (FDA) GalaxyTrakr pipeline, we illustrate the dynamics of MRSA colonization and infection in neonates. Over 217 days of prospective surveillance, analyses revealed concurrent MRSA transmission chains affecting 11 of 17 MRSA-colonized patients (65%), with two clusters that demonstrated intervals of more than a month among the appearance of isolates. All MRSA infected neonates (n = 3) showed previous colonization with the infecting strain. GalaxyTrakr clustering of the NICU strains, in the context of 21,521 international isolates deposited in NCBI's Pathogen Detection Resource, revealed NICU isolates to be distinct from adult MRSA strains seen locally and internationally. Clustering of the NICU strains within an international context enhanced the resolution of strain clusters and supported the rule-out of suspected, local transmission events within the NICU. Analyses also identified sequence type 1535 isolates, emergent in the Middle East, carrying a unique SCCmec with fusC and aac(6')-Ie/aph(2'')-1a that provided a multidrug-resistant phenotype. NICU genomic pathogen surveillance, leveraging public repositories and outbreak detection tools, supports rapid identification of cryptic MRSA clusters, and can inform infection prevention interventions for this vulnerable patient population. Results demonstrate that sporadic infections in the NICU may be indicative of hidden chains of asymptomatic transmission best identified with sequenced-based approaches.

Abstract 5993: Dissecting the role of CDK4 in liposarcoma

Abstract Background: Liposarcoma (LPS) encompass one of most common soft tissue sarcoma (STS) histological subtypes accounting for 15% of all cases. They can be divided into four entities including atypical lipomatous tumor or well-differentiated LPS (ALT/WDLPS), dedifferentiated LPS (DDLPS), myxoid LPS (MLPS) and pleomorphic LPS (PLS). Their heterogeneity is reflected in their morphology, molecular landscape, prognosis and clinical behavior. Current treatment options for localized disease include surgery, (neo)adjuvant radiotherapy and chemotherapy; for the metastatic setting chemotherapy represents the cornerstone but its role needs to be elucidated. The unavailability of predictive biomarkers makes the management of these mesenchymal diseases even more challenging. Recent evidences have shed light on the key role of cyclin-dependent kinase 4 (CDK4) in cancer cells proliferation. Methods: This study involved 21 adult patients affected by liposarcoma (n= 5 ALT/WDLPS and n= 16 DDLPS). IHC analyses of CDK4 and MDM2 were performed on FFPE surgically resected specimens by experienced sarcoma pathologists. Moreover, a patient-derived DDLPS cell line was established and pharmacological profiling was performed. Next, standard and innovative drugs currently used for LPS management were assessed in both 2D and 3D culture systems. Finally, in silico analyses based on public repositories on STS were carried out including 260 sarcoma patients. Results: IHC results highlighted an higher expression of CDK4 and MDM2 biomarkers in DDLPS compared to ALT/WDLSP. Moreover, although CDK4 and MDM2 are codified by the same genomic region, their expression did not correlate (e.g. 100% of CDK4 and 2% of MDM2 in the same patient). Therefore we hypothesized that even exhibiting a low MDM2 expression, a patient could benefit from CDK4 inhibition. Furthermore, our data showed that in DDLPS cases CDK4 expression ranged from 90% to 100% in spermatic cord, skin and abdomen while it ranged from 5% to 50% in the retroperitoneum. The above observations prompted us to hypothesize a correlation of CDK4 expression with specific anatomical sites. Moreover, pharmacological profiling showed a synergistic effect exerted by sequential treatment with palbociclib and some chemotherapeutics including trabectedin, dacarbazine, eribulin and lenvatinib. Indeed we observed a 10% increase in cell proliferation inhibition compared to standard treatment. In addition, in silico analyses revealed the role of CDK4 as negative prognostic biomarker for PFS and OS in STS. Conclusions: Our study highlighted the promising role of CDK4 in the management of LPS patients. In particular, we pointed out encouraging results of sequential treatment combining CDK4 inhibitor palbociclib and chemotherapy. Furthermore, preliminary analyses highlighted the involvement of this biomarker as a potential prognostic tool for STS. Citation Format: Silvia Vanni, Graziana Gallo, Valentina Fausti, Giacomo Miserocchi, Chiara Liverani, Chiara Spadazzi, Claudia Cocchi, Chiara Calabrese, Giovanni De Luca, Massimo Bassi, Manlio Gessaroli, Angelo Campobassi, Federica Pieri, Giorgio Ercolani, Davide Cavaliere, Lorena Gurrieri, Nada Riva, Giovanni Martinelli, Laura Mercatali, Alessandro De Vita. Dissecting the role of CDK4 in liposarcoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5993.

Abstract 5637: Path to the holy grail of spatial biology: Spatial single-cell whole transcriptomes using 6000-plex spatial molecular imaging on FFPE tissue

Abstract Cancer research across drug development, molecular biomarkers, and patient response depends on understanding biology that is dependent on complex interactions between malignant, immune, and stromal cells. To survive clearance mechanisms, a tumor can rely on a myriad of escape strategies, and the microenvironment is architected around the current path of escape. To enable a more comprehensive picture of tumor biology, we have developed the CosMx™ Spatial Molecular Imager (SMI) technology to capture a snapshot of thousands of RNA species resolved subcellularly from a single, standard histopathology slide. Building upon the previously released panels, this study tests a new 6,000-plex panel, the highest RNA plex measured in situ within human tissue, allowing the imputation of a spatial whole transcriptome in the tissue. We performed an ultra-high-plex RNA assay to detect 6,000 targets simultaneously in situ on an FFPE human liver cancer tissue (~1 cm2 area) using the CosMx SMI. This RNA panel covers broad biological areas with special emphasis on oncology, immunology, and signal transduction, such that all cancer researchers can benefit from the direct detection of targets of interest (sans imputation) in intact tissue. Analysis algorithms were developed to allow robust assessments of cell types, cell states, cell-cell interactions, and pathway activation. Imputation based on reference profiles from HCA, TCGA, and other public repositories allows estimation of non-measured transcripts at a ratio of approximately 1:3, compared to the approximate 1:20-1:70 imputations performed previously for spatial data.Thousands of transcripts were simultaneously detected with high sensitivity and specificity on the FFPE liver cancer tissue section at single-cell subcellular resolution. We were able to accurately map known reference profiles from scRNA-seq into this sample while identifying cancer-specific malignant, immune, and stromal cells in this tissue sample using this ultra-high plex RNA panel. In addition, we constructed sample-specific spatial neighborhoods, defined by cell types, cell states, and nearly unlimited sets of biological pathways through the imputed whole transcriptome. Finally, we measured >1,000 ligand-receptor interactions between key cell types of adjacent cells in the tissue, identifying mechanisms for tumor-mediated escape as well as reactive re-architecting of the native stroma which defines the trajectory of cancer’s evolution. Single-cell spatial measurements of gene expression at 6,000 plex from a single FFPE slide has the potential to transform our understanding of tumor biology and facilitate the next advances in cancer research by extracting the highest data density possible from rare specimens collected during patient treatment. Citation Format: Shanshan He, Michael Patrick, Jason W. Reeves, Patrick Danaher, Julian Preciado, Joseph Phan, Erin Piazza, Zachary Reitz, Lidan Wu, Rustem Khafizov, Haiyan Zhai, Michael Rhodes, David Ruff, Joseph Beechem. Path to the holy grail of spatial biology: Spatial single-cell whole transcriptomes using 6000-plex spatial molecular imaging on FFPE tissue. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5637.

Commercializing Personal Health Information: A Critical Qualitative Content Analysis of Documents Describing Proprietary Primary Care Databases in Canada.

Commercial data brokers have amassed large collections of primary care patient data in proprietary databases. Our study objective was to critically analyze how entities involved in the collection and use of these records construct the value of these proprietary databases. We also discuss the implications of the collection and use of these databases. We conducted a critical qualitative content analysis using publicly available documents describing the creation and use of proprietary databases containing Canadian primary care patient data. We identified relevant commercial data brokers, as well as entities involved in collecting data or in using data from these databases. We sampled documents associated with these entities that described any aspect of the collection, processing, and use of the proprietary databases. We extracted data from each document using a structured data tool. We conducted an interpretive thematic content analysis by inductively coding documents and the extracted data. We analyzed 25 documents produced between 2013 and 2021. These documents were largely directed at the pharmaceutical industry, as well as shareholders, academics, and governments. The documents constructed the value of the proprietary databases by describing extensive, intimate, detailed patient-level data holdings. They provided examples of how the databases could be used by pharmaceutical companies for regulatory approval, marketing and understanding physician behaviour. The documents constructed the value of these data more broadly by claiming to improve health for patients, while also addressing risks to privacy. Some documents referred to the trade-offs between patient privacy and data utility, which suggests these considerations may be in tension. Documents in our analysis positioned the proprietary databases as socially legitimate and valuable, particularly to pharmaceutical companies. The databases, however, may pose risks to patient privacy and contribute to problematic drug promotion. Solutions include expanding public data repositories with appropriate governance and external regulatory oversight.