Big data in healthcare can enable unprecedented understanding of diseases and their treatment, particularly in oncology. These data may include electronic health records, medical imaging, genomic sequencing, payor records, and data from pharmaceutical research, wearables, and medical devices. The ability to combine datasets and use data across many analyses is critical to the successful use of big data and is a concern for those who generate and use the data. Interoperability and data quality continue to be major challenges when working with different healthcare datasets. Mapping terminology across datasets, missing and incorrect data, and varying data structures make combining data an onerous and largely manual undertaking. Data privacy is another concern addressed by the Health Insurance Portability and Accountability Act, the Common Rule, and the General Data Protection Regulation. The use of big data is now included in the planning and activities of the FDA and the European Medicines Agency. The willingness of organizations to share data in a precompetitive fashion, agreements on data quality standards, and institution of universal and practical tenets on data privacy will be crucial to fully realizing the potential for big data in medicine.

The analysis of big healthcare data has enormous potential as a tool for advancing oncology drug development and patient treatment, particularly in the context of precision medicine. However, there are challenges in organizing, sharing, integrating, and making these data readily accessible to the research community. This review presents five case studies illustrating various successful approaches to addressing such challenges. These efforts are CancerLinQ, the American Association for Cancer Research Project GENIE, Project Data Sphere, the National Cancer Institute Genomic Data Commons, and the Veterans Health Administration Clinical Data Initiative. Critical factors in the development of these systems include attention to the use of robust pipelines for data aggregation, common data models, data deidentification to enable multiple uses, integration of data collection into physician workflows, terminology standardization and attention to interoperability, extensive quality assurance and quality control activity, incorporation of multiple data types, and understanding how data resources can be best applied. By describing some of the emerging resources, we hope to inspire consideration of the secondary use of such data at the earliest possible step to ensure the proper sharing of data in order to generate insights that advance the understanding and the treatment of cancer.

Abstract Background Individuals with post-acute COVID syndrome (PAC), also known as long-COVID, have new or continuing symptoms lasting more than 1 month from COVID-19 infection. Patients may present with neuropsychiatric (NP) and non-NP symptoms. Whether prior mental health disorders correlate with NP symptoms is unknown. The study aims were to describe the demographics, clinical factors, comorbid conditions, and presenting symptoms of PAC. Methods We included patients evaluated at Baystate Health PAC clinic from January 2021-April 2022, with prior COVID-19 infection and new or persistent symptoms of at least 6 weeks. We defined the primary presenting symptoms as NP (cognitive dysfunction (brain fog), anxiety, or depression) or non-NP (dyspnea, fatigue, dizziness, palpitations). The clinician’s diagnostic impression was recorded and characterized as NP or non-NP. Demographics Results We randomly selected 150 of 250 clinic patients for chart review. Most were women, 117 (78%); mean age was 52. Most 106 (71%) had mild acute COVID illness; 36 (24%) were hospitalized. 49% (n=73) had a pre-existing mental health disorder: mood disorder (depression/bipolar) 26 (17%), anxiety 18 (12%), both anxiety and mood disorder 27 (18%). The most common presenting symptoms in order of frequency were fatigue, dyspnea, brain fog, anxiety, depression, and palpitations. 106 (71%) had at least 1 NP presenting symptom or combination of NP symptoms; cognitive dysfunction with anxiety in 23 (15%), and cognitive dysfunction with anxiety and mood disorder in 13 (9%). The clinician's final diagnostic impression was fatigue (47%), cognitive dysfunction (36%), anxiety (29%) depression (33%) and PTSD (3%). A NP diagnosis was made in 81 (54%). Primary Symptoms Primary Diagnosis Conclusion The majority of PAC clinic patients were female and had mild COVID illness. We noted a high prevalence of pre-existing mental health disorders. More than two-thirds presented with one or more NP symptom. Further studies should explore the association of pre-COVID mental health disorders with PAC. Disclosures All Authors: No reported disclosures

We conducted a retrospective exploratory study evaluating factors associated with selection to receive and infusion with coronavirus disease 2019 monoclonal antibodies. While priority was given to high-risk patients, patients with increased Social Vulnerability Index scores were less likely to present for infusion, raising concern that social factors created barriers to treatment.

Abstract Background COVID-19 monoclonal antibodies (mAb) have been shown to reduce risk of ED visit and hospitalization when administered early in disease course. However, there has been little data evaluating the challenges and barriers to delivering an outpatient mAb infusion. Methods This was a retrospective cohort review of patients referred 12/7/20-5/20/21 to evaluate factors associated with selection and treatment with mAb. Inclusion criteria included symptom onset within 10 days of referral, high-risk for severe disease, not hospitalized or meeting hospitalization criteria, and followed by in-network primary care provider. We collected data on demographics, high risk comorbidities, illness course, infusion selection, and adverse events. Using unadjusted regression models, we estimated effect sizes (mean difference or percentage point difference) and 95% confidence intervals to estimate baseline factors associated with infusion. Results Of the 230 eligible subjects, 179 (78%) were selected for mAb treatment and 119 (66%) were infused. Those with a Social Vulnerability Index > 0.5 were more likely to be selected (86% vs 71%) but among those selected were less likely to be infused (59% vs 74%). Other predictors for selection (Fig 1) included Spanish language preference, 3+ symptoms at the time of referral, shortness of breath, headache, immunosuppressive medications, and chronic respiratory disease. Predictors of mAb infusion (Fig 2) included 3+ symptoms at time of referral, diarrhea, myalgias, male gender, being employed, and history of chronic kidney or respiratory disease. Time of onset of symptoms-to-referral was < 5 days and test-to-referral was 2-3 days (Fig 3). 60 subjects were selected but not infused. 19 (32%) were not infused due to becoming ineligible after referral. Reasons for ineligibility included clinical decompensation or passing eligibility window. 39 (65%) were not infused due to refusal after selection. Most common reasons for refusal included feeling better (12, 20%), concerns about mAb (6, 10%), and lack of caregiver for a family member (4, 7%). Figure 1.Predictors of Selection for mAb InfusionFigure 2.Predictors of mAb Infusion After SelectionFigure 3.Days of Symptoms to Testing and Referral Conclusion Our study identified factors that impact selection and infusion of mAb in a real-world setting. This can help address potential barriers in operationalizing the administration of COVID-19 mAb in the future. Disclosures All Authors: No reported disclosures.

Abstract Objective Assess the effectiveness of providing Logical Observation Identifiers Names and Codes (LOINC®)-to-In Vitro Diagnostic (LIVD) coding specification, required by the United States Department of Health and Human Services for SARS-CoV-2 reporting, in medical center laboratories and utilize findings to inform future United States Food and Drug Administration policy on the use of real-world evidence in regulatory decisions. Materials and Methods We compared gaps and similarities between diagnostic test manufacturers’ recommended LOINC® codes and the LOINC® codes used in medical center laboratories for the same tests. Results Five medical centers and three test manufacturers extracted data from laboratory information systems (LIS) for prioritized tests of interest. The data submission ranged from 74 to 532 LOINC® codes per site. Three test manufacturers submitted 15 LIVD catalogs representing 26 distinct devices, 6956 tests, and 686 LOINC® codes. We identified mismatches in how medical centers use LOINC® to encode laboratory tests compared to how test manufacturers encode the same laboratory tests. Of 331 tests available in the LIVD files, 136 (41%) were represented by a mismatched LOINC® code by the medical centers (chi-square 45.0, 4 df, P < .0001). Discussion The five medical centers and three test manufacturers vary in how they organize, categorize, and store LIS catalog information. This variation impacts data quality and interoperability. Conclusion The results of the study indicate that providing the LIVD mappings was not sufficient to support laboratory data interoperability. National implementation of LIVD and further efforts to promote laboratory interoperability will require a more comprehensive effort and continuing evaluation and quality control.

Here we report on initial efforts to evaluate enhanced darkfield microscopy (EDFM) and light scattering Vis-NIR hyperspectral imaging (HSI) as a rapid screening tool for the offline analysis of mixed cellulose ester (MCE) filter media used to collect airborne nanoparticulate from work environments. For this study, the materials of interest were nanoscale titanium dioxide (TiO2 ) and silicon dioxide (SiO2 ; silica), chosen for their frequent use in consumer products. TiO2 and SiO2 nanoscale particles (NPs) were collected on MCE filter media and were imaged and analyzed via EDFM-HSI. When visualized by EDFM, TiO2 and SiO2 NPs were readily apparent as bright spherical structures against a dark background. Moreover, TiO2 and SiO2 NPs were identified in hyperspectral images. EDFM-HSI images and data were compared to scanning transmission electron microscopy (STEM), a NIST-traceable technique for particle size analysis, and the current gold standard for offline analysis of filter media. As expected, STEM provided more accurate sizing and morphology data when compared to EDFM-HSI, but is not ideal for rapid screening of the presence of NPs of interest since it is a costly, low-throughput technique. In this study, we demonstrate the utility of EDFM-HSI in rapidly visualizing and identifying TiO2 and SiO2 NPs on MCE filters. This screening method may prove useful in expediting time-to-knowledge compared to electron microscopy. Future work will expand this evaluation to other industrially relevant NPs, other filter media types, and real-world filter samples from occupational exposure assessments.

Enhanced darkfield microscopy (EDFM) and hyperspectral imaging (HSI) are being evaluated as a potential rapid screening modality to reduce the time-to-knowledge for direct visualisation and analysis of filter media used to sample nanoparticulate from work environments, as compared to the current analytical gold standard of transmission electron microscopy (TEM). Here, we compare accuracy, specificity, and sensitivity of several hyperspectral classification models and data preprocessing techniques to determine how to most effectively identify multiwalled carbon nanotubes (MWCNTs) in hyperspectral images. Several classification schemes were identified that are capable of classifying pixels as MWCNT(+) or MWCNT(-) in hyperspectral images with specificity and sensitivity over 99% on the test dataset. Functional principal component analysis (FPCA) was identified as an appropriate data preprocessing technique, testing optimally when coupled with a quadratic discriminant analysis (QDA) model with forward stepwise variable selection and with a support vector machines (SVM) model. The success of these methods suggests that EDFM-HSI may be reliably employed to assess filter media exposed to MWCNTs. Future work will evaluate the ability of EDFM-HSI to quantify MWCNTs collected on filter media using this classification algorithm framework using the best-performing model identified here - quadratic discriminant analysis with forward stepwise selection on functional principal component data - on an expanded sample set.

Nanotechnology involves the discovery and fabrication of nanoscale materials possessing unique physicochemical properties that are being employed in industry and medicine. Infectious Diseases clinicians and public health scientists utilize nanotechnology applications to diagnose, treat, and prevent infectious diseases. However, fundamental principles of nanotechnology are often presented in technical formats that presuppose an advanced knowledge of chemistry, physics, and engineering, thereby limiting the clinician's grasp of the underlying science. While nanoscience is technically complex, it need not be out of reach of the clinical practitioner. The aim of this review is to introduce fundamental principles of nanotechnology in an accessible format, describe examples of current clinical infectious diseases and public health applications, and provide a foundation that will aid understanding of and appreciation for this burgeoning and important field of science.

A significant hurdle in conducting effective health and safety hazard analysis and risk assessment for the nanotechnology workforce is the lack of a rapid method for the direct visualization and analysis of filter media used to sample nanomaterials from work environments that represent potential worker exposure. Current best-known methods include transmission electron microscopy (TEM) coupled with energy dispersive x-ray spectroscopy (EDS) for elemental identification. TEM-EDS is considerably time-, cost-, and resource-intensive, which may prevent timely health and safety recommendations and corrective actions. A rapid screening method is currently being explored using enhanced darkfield microscopy with hyperspectral imaging (EDFM-HSI). For this approach to be effective, rapid, and easy, sample preparation that is amenable to the analytical technique is needed. Here, we compare the sample preparation steps for mixed cellulose ester (MCE) filter media specified in NIOSH Method 7400-Asbestos and Other Fibers by Phase Contrast Microscopy (PCM)-against a new method, which involves saturation of the filter media with acetone. NIOSH Method 7400 was chosen as a starting point since it is an established technique for preparing transparent MCE filters for optical microscopy. Limitations in this method led to the development and comparison of a new method. The new method was faster, easier, and rendered filters more transparent, resulting in improved visualization and analysis of nanomaterials via EDFM-HSI. This new method is suitable for a rapid screening protocol due to its speed, ease of use, and the improvement in image acquisition and analysis.

Emerging and novel technologies, materials, and information integrated into increasingly automated and networked manufacturing processes or into traditional manufacturing settings are enhancing the efficiency and productivity of manufacturing. Globally, there is a move toward a new era in manufacturing that is characterized by: (1) the ability to create and deliver more complex designs of products; (2) the creation and use of materials with new properties that meet a design need; (3) the employment of new technologies, such as additive and digital techniques that improve on conventional manufacturing processes; and (4) a compression of the time from initial design concept to the creation of a final product. Globally, this movement has many names, but "advanced manufacturing" has become the shorthand for this complex integration of material and technology elements that enable new ways to manufacture existing products, as well as new products emerging from new technologies and new design methods. As the breadth of activities associated with advanced manufacturing suggests, there is no single advanced manufacturing industry. Instead, aspects of advanced manufacturing can be identified across a diverse set of business sectors that use manufacturing technologies, ranging from the semiconductors and electronics to the automotive and pharmaceutical industries. The breadth and diversity of advanced manufacturing may change the occupational and environmental risk profile, challenge the basic elements of comprehensive health and safety (material, process, worker, environment, product, and general public health and safety), and provide an opportunity for development and dissemination of occupational and environmental health and safety (OEHS) guidance and best practices. It is unknown how much the risk profile of different elements of OEHS will change, thus requiring an evolution of health and safety practices. These changes may be accomplished most effectively through multi-disciplinary, multi-sector, public-private dialogue that identifies issues and offers solutions.

Petrochemical and oil & gas industries are crucial for global economy while great attention is needed for the related contamination and its impact on the environment. Papers reviewed herein represent the recent research and development on petrochemical wastewater and produced water from oil & gas industry, published in 2017 and beginning of 2018 globally. In the petrochemical wastewater, progresses were made in characterization, physicochemical treatment and biological treatment. In the oil & gas produced water, efforts were made on the characterization, the environmental impact and treatment options.

Hyperspectral imaging (HSI) and classification are established methods that are being applied in new ways to the analysis of nanoscale materials in a variety of matrices. Typically, enhanced darkfield microscopy (EDFM)-based HSI data (also known as image datacubes) are collected in the wavelength range of 400-1000 nm for each pixel in a datacube. Utilising different spectral library (SL) creation methods, spectra from pixels in the datacube corresponding to known materials can be collected into reference spectral libraries (RSLs), which can be used to classify materials in datacubes of experimental samples using existing classification algorithms. In this study, EDFM-HSI was used to visualise and analyse industrial cerium oxide (CeO2; ceria) nanoparticles (NPs) in rat lung tissues and in aqueous suspension. Rats were exposed to ceria NPs via inhalation, mimicking potential real-world occupational exposures. The lung tissues were histologically prepared: some tissues were stained with hematoxylin and eosin (H&E) and some were left unstained. The goal of this study was to determine how HSI and classification results for ceria NPs were influenced by (1) the use of different RSL creation and classification methods and (2) the application of those methods to samples in different matrices (stained tissue, unstained tissue, or aqueous solution). Three different RSL creation methods - particle filtering (PF), manual selection, and spectral hourglass wizard (SHW) - were utilised to create the RSLs of known materials in unstained and stained tissue, and aqueous suspensions, which were then used to classify the NPs in the different matrices. Two classification algorithms - spectral angle mapper (SAM) and spectral feature fitting (SFF) - were utilised to determine the presence or absence of ceria NPs in each sample. The results from the classification algorithms were compared to determine how each influenced the classification results for samples in different matrices. The results showed that sample matrix and sample preparation significantly influenced the NP classification thresholds in the complex matrices. Moreover, considerable differences were observed in the classification results when utilising each RSL creation and classification method for each type of sample. Results from this study illustrate the importance of appropriately selecting HSI algorithms based on specific material and matrix characteristics in order to obtain optimal classification results. As HSI is increasingly utilised for NP characterisation for clinical, environmental and health and safety applications, this investigation is important for further refining HSI protocols while ensuring appropriate data collection and analysis.

Hyperspectral imaging (HSI) and mapping are increasingly used for visualization and identification of nanoparticles (NPs) in a variety of matrices, including aqueous suspensions and biological samples. Reference spectral libraries (RSLs) contain hyperspectral data collected from materials of known composition and are used to detect the known materials in experimental samples through a one-to-one pixel mapping process. In some HSI studies, RSLs created from raw NPs were used to map NPs in experimental samples in a different matrix; for example, RSLs created from NPs in suspension to map NPs in biological tissue. Others have utilized RSLs created from NPs in the same matrix. However, few studies have systematically compared hyperspectral data as a function of the matrix in which the NPs are found and its impact on mapping results. The objective of this study is to compare RSLs created from metal oxide NPs in aqueous suspensions to RSLs created from the same NPs in rat tissues following in vivo inhalation exposure, and to investigate the differences in mapping that result from the use of each RSL. Results demonstrate that the spectral profiles of these NPs are matrix dependent: RSLs created from NPs in positive control tissues mapped to experimental tissues more appropriately than RSLs created from NPs in suspension. Aqueous suspension RSLs mapped 0-602 out of 500,424 pixels per tissue image while tissue RSLs mapped 689-18,435 pixels for the same images. This study underscores the need for appropriate positive controls for the creation of RSLs for mapping NPs in experimental samples.

Best-known methods for ENM exposure assessment are based on using real-time direct-reading instruments (DRIs) to measure airborne particle counts and filter-based methods collected in tandem for off-line direct visualization by transmission electron microscopy (TEM) and for elemental analysis. 1 Direct visualization by TEM is based on methods developed two decades ago for micron-sized asbestos 2,3 and is low-throughput, expensive, and time- and resource-intensive. Therefore, the overarching goal of this project is to rapidly advance exposure assessment for the nanotechnology workforce by developing and testing a new protocol using enhanced darkfield microscopy (EDFM) with hyperspectral imaging (HSI) and mapping. EDFM-HSI combines imaging and spectrophotometry to identify, locate, and map ENMs within a sample based on the unique spectral response. Preliminary work has explored the use of EDFM-HSI for the analysis of silicon dioxide nanoparticles (NPs) captured on mixed cellulose ester (MCE) filter media.

The most significant hurdle holding back exposure science, risk assessment, and the development of safety guidelines for the nanotechnology workforce is the lack of validated analytical techniques that accurately identify and characterize engineered nanomaterials (ENMs) captured in occupational settings. The associated costs, time, and lack of standardization of existing methods make it impossible for industries to implement exposure assessment programs or comply with new or forthcoming recommended exposure limits for ENMs. This research team seeks to advance the state of the science by developing and testing a new protocol for analysis of ENMs on filters by: further developing a novel hyperspectral imaging (HSI) method for highthroughput screening; evolving best-known methods for direct visualization of filter-captured ENMs by developing and incorporating advanced techniques into the new protocol; and testing the new protocol on real-world samples obtained during occupational exposure scenarios.

CeO2 nanoparticles with various characteristics find an increasing number of applications in the electronic, medical, and other industries and are therefore likely released in the environment. This calls for investigations linking the physicochemical properties of these particles with their potential environmental impacts. In this study, CeO2 nanoparticle powders were prepared using three different precursors [Ce(NO3)3, CeCl3, and Ce(CH3COO)3] and annealing temperatures (300, 500, and 700 °C). This procedure resulted in nine different types of nanoparticles with differing size (5–90 nm), morphology, surface Ce3+/Ce4+ ratio, and slightly different crystal structures as characterized using transmission electron microscopy, dynamic light scattering, X-ray photoelectron spectroscopy, and X-ray diffraction measurements with Rietveld refinement. These CeO2 nanoparticles underwent toxicity testing at concentrations up to 64 mg L−1 using Daphnia magna. Toxic effects were observed for three particle types with EC50 values between 5 and 64 mg L−1. No clear correlation was observed between the physicochemical properties (size, shape, oxygen occupancy, Ce3+/Ce4+ ratio) of the nanoparticles and their toxicity. However, toxicity was correlated with the amount of Ce remaining suspended in the test medium after 24 h. This indicated that toxic effects may depend on the colloidal stability of CeO2 nanoparticles during the first day of exposure. Therefore, being readily suspended and remaining stable for several days in the aquatic media increases the likelihood that CeO2 nanoparticles will cause unwanted adverse effects.