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Public Health Public Health

Human exposure pathways to poly- and perfluoroalkyl substances (PFAS) from indoor media: A systematic review protocol.

In Environment international

BACKGROUND : Human exposure to per- and polyfluoroalkyl substances (PFAS) has been primarily attributed to contaminated food and drinking water. However, additional PFAS exposure pathways have been raised by a limited number of studies reporting correlations between commercial and industrial products and PFAS levels in human media and biomonitoring. Systematic review (SR) methodologies have been widely used to evaluate similar questions using an unbiased approach in the fields of clinical medicine, epidemiology, and toxicology, but the deployment in exposure science is ongoing. Here we present a systematic review protocol that adapts existing systematic review methodologies and study evaluation tools to exposure science studies in order to investigate evidence for important PFAS exposure pathways from indoor media including consumer products, household articles, cleaning products, personal care products, plus indoor air and dust.

OBJECTIVES : We will systematically review exposure science studies that present both PFAS concentrations from indoor exposure media and PFAS concentrations in blood serum or plasma. Exposure estimates will be synthesized from the evidence to answer the question, "For the general population, what effect does exposure from PFAS chemicals via indoor media have on blood, serum or plasma concentrations of PFAS?" We adapt existing systematic review methodologies and study evaluation tools from the U.S. EPA's Systematic Review Protocol for the PFBA, PFHxA, PFHxS, PFNA, and PFDA IRIS Assessments and the Navigation Guide for exposure science studies, as well as present innovative developments of exposure pathway-specific search strings for use in artificial intelligence screening software.

DATA SOURCES : We will search electronic databases for potentially relevant literature, including Web of Science, PubMed, and ProQuest. Literature search results will be stored in EPA's Health and Environmental Research Online (HERO) database.

STUDY ELIGIBILITY AND CRITERIA : Included studies will present exposure measures from indoor media including consumer products, household articles, cleaning products, personal care products, plus indoor air and dust, paired with PFAS concentrations in blood, serum or plasma from adults and/or children in the general population. We focus on a subset of PFAS chemicals including perfluorooctanoic acid (PFOA), perfluorooctanesulfonate (PFOS), perfluorobutanoic acid (PFBA), perfluorobutane sulfonate (PFBS), perfluorodecanoic acid (PFDA), perfluorohexanoic acid (PFHxA), perfluorohexanesulfonate (PFHxS), and perfluorononanoic acid (PFNA).

STUDY APPRAISAL AND SYNTHESIS METHODS : Studies will be prefiltered at the title and abstract level using computationally intelligent search strings to expedite the screening process for reviewers. Two independent reviewers will screen the prefiltered studies against inclusion criteria at the title/abstract level and then full-text level, after which the reviewers will assess the studies' risk of bias using an approach modified from established systematic review tools for exposure studies. Exposure estimates will be calculated to investigate the proportion of blood, serum or plasma) PFAS concentrations that can be explained by exposure to PFAS in indoor media.

DeLuca Nicole M, Angrish Michelle, Wilkins Amina, Thayer Kris, Cohen Hubal Elaine A


Exposure pathways, Human exposure, Indoor, PFAS, Systematic review

Public Health Public Health

Artificial Intelligence Model of Drive-Through Vaccination Simulation.

In International journal of environmental research and public health ; h5-index 73.0

Planning for mass vaccination against SARS-Cov-2 is ongoing in many countries considering that vaccine will be available for the general public in the near future. Rapid mass vaccination while a pandemic is ongoing requires the use of traditional and new temporary vaccination clinics. Use of drive-through has been suggested as one of the possible effective temporary mass vaccinations among other methods. In this study, we present a machine learning model that has been developed based on a big dataset derived from 125K runs of a drive-through mass vaccination simulation tool. The results show that the model is able to reasonably well predict the key outputs of the simulation tool. Therefore, the model has been turned to an online application that can help mass vaccination planners to assess the outcomes of different types of drive-through mass vaccination facilities much faster.

Asgary Ali, Valtchev Svetozar Zarko, Chen Michael, Najafabadi Mahdi M, Wu Jianhong


COVID-19 pandemic, artificial intelligence, discrete event simulation, drive-through, mass vaccination

Pathology Pathology

Screening For Bone Marrow Cellularity Changes in Cynomolgus Macaques in Toxicology Safety Studies Using Artificial Intelligence Models.

In Toxicologic pathology

Many compounds affect the cellularity of hematolymphoid organs including bone marrow. Toxicologic pathologists are tasked with their evaluation as part of safety studies. An artificial intelligence (AI) tool could provide diagnostic support for the pathologist. We looked at the ability of a deep-learning AI model to evaluate whole slide images of macaque sternebrae to identify and enumerate bone marrow hematopoietic cells. The AI model was trained and able to differentiate the hematopoietic cells from the other sternebrae tissues. We compared the model to severity scores in a study with decreased hematopoietic cellularity. The mean cells/mm2 from the model was lower for each increase in severity score. The AI model was trained by 1 pathologist, providing proof of concept that AI model generation can be fast and agile, without the need of a cross disciplinary team and significant effort. We see great potential for the role of AI-based bone marrow screening.

Smith Mark A, Westerling-Bui Thomas, Wilcox Angela, Schwartz Julie


artificial intelligence, bone marrow, cellularity, deep learning, digital pathology, image analysis, machine learning, whole slide imaging

General General

Toward Decoding the Relationship between Domain Structure and Functionality in Ferroelectrics via Hidden Latent Variables.

In ACS applied materials & interfaces ; h5-index 147.0

Polarization switching mechanisms in ferroelectric materials are fundamentally linked to local domain structure and the presence of the structural defects, which both can act as nucleation and pinning centers and create local electrostatic and mechanical depolarization fields affecting wall dynamics. However, the general correlative mechanisms between domain structure and polarization dynamics are only weakly explored, precluding insight into the associated physical mechanisms. Here, the correlation between local domain structures and switching behavior in ferroelectric materials is explored using convolutional encoder-decoder networks, enabling image to spectral (im2spec) and spectral to image (spec2im) translations via encoding of latent variables. The latter reflect the assumption that the relationship between domain structure and polarization switching is parsimonious, i.e., is based upon a small number of local mechanisms. The analysis of latent variables distributions and their real-space representations provides insight into the predictability of the local switching behavior and hence associated physical mechanisms. We further pose that the regions where these correlative relationships are violated, i.e., predictability of the polarization dynamics from domain structure is reduced, represent the obvious target for detailed studies, e.g., in the context of automated experiments. This approach provides a workflow to establish the presence of correlation between local spectral responses and local structure and can be universally applied to spectral imaging techniques such as piezoresponse force microscopy (PFM), scanning tunneling microscopy (STM) and spectroscopy, and electron energy loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM).

Kalinin Sergei V, Kelley Kyle, Vasudevan Rama K, Ziatdinov Maxim


ferroelectrics, latent space, machine learning, neural networks, scanning probe microscopy

Ophthalmology Ophthalmology

Evaluating the Effect of Topical Atropine Use for Myopia Control on Intraocular Pressure by Using Machine Learning.

In Journal of clinical medicine

Atropine is a common treatment used in children with myopia. However, it probably affects intraocular pressure (IOP) under some conditions. Our research aims to analyze clinical data by using machine learning models to evaluate the effect of 19 important factors on intraocular pressure (IOP) in children with myopia treated with topical atropine. The data is collected on 1545 eyes with spherical equivalent (SE) less than -10.0 diopters (D) treated with atropine for myopia control. Four machine learning models, namely multivariate adaptive regression splines (MARS), classification and regression tree (CART), random forest (RF), and eXtreme gradient boosting (XGBoost), were used. Linear regression (LR) was used for benchmarking. The 10-fold cross-validation method was used to estimate the performance of the five methods. The main outcome measure is that the 19 important factors associated with atropine use that may affect IOP are evaluated using machine learning models. Endpoint IOP at the last visit was set as the target variable. The results show that the top five significant variables, including baseline IOP, recruitment duration, age, total duration and previous cumulative dosage, were identified as most significant for evaluating the effect of atropine use for treating myopia on IOP. We can conclude that the use of machine learning methods to evaluate factors that affect IOP in children with myopia treated with topical atropine is promising. XGBoost is the best predictive model, and baseline IOP is the most accurate predictive factor for endpoint IOP among all machine learning approaches.

Wu Tzu-En, Chen Hsin-An, Jhou Mao-Jhen, Chen Yen-Ning, Chang Ting-Jen, Lu Chi-Jie


abbreviations and acronyms, intraocular pressure (IOP), machine learning, myopia, topical atropine

General General

Increasing the Density of Laboratory Measures for Machine Learning Applications.

In Journal of clinical medicine

BACKGROUND : The imputation of missingness is a key step in Electronic Health Records (EHR) mining, as it can significantly affect the conclusions derived from the downstream analysis in translational medicine. The missingness of laboratory values in EHR is not at random, yet imputation techniques tend to disregard this key distinction. Consequently, the development of an adaptive imputation strategy designed specifically for EHR is an important step in improving the data imbalance and enhancing the predictive power of modeling tools for healthcare applications.

METHOD : We analyzed the laboratory measures derived from Geisinger's EHR on patients in three distinct cohorts-patients tested for Clostridioides difficile (Cdiff) infection, patients with a diagnosis of inflammatory bowel disease (IBD), and patients with a diagnosis of hip or knee osteoarthritis (OA). We extracted Logical Observation Identifiers Names and Codes (LOINC) from which we excluded those with 75% or more missingness. The comorbidities, primary or secondary diagnosis, as well as active problem lists, were also extracted. The adaptive imputation strategy was designed based on a hybrid approach. The comorbidity patterns of patients were transformed into latent patterns and then clustered. Imputation was performed on a cluster of patients for each cohort independently to show the generalizability of the method. The results were compared with imputation applied to the complete dataset without incorporating the information from comorbidity patterns.

RESULTS : We analyzed a total of 67,445 patients (11,230 IBD patients, 10,000 OA patients, and 46,215 patients tested for C. difficile infection). We extracted 495 LOINC and 11,230 diagnosis codes for the IBD cohort, 8160 diagnosis codes for the Cdiff cohort, and 2042 diagnosis codes for the OA cohort based on the primary/secondary diagnosis and active problem list in the EHR. Overall, the most improvement from this strategy was observed when the laboratory measures had a higher level of missingness. The best root mean square error (RMSE) difference for each dataset was recorded as -35.5 for the Cdiff, -8.3 for the IBD, and -11.3 for the OA dataset.

CONCLUSIONS : An adaptive imputation strategy designed specifically for EHR that uses complementary information from the clinical profile of the patient can be used to improve the imputation of missing laboratory values, especially when laboratory codes with high levels of missingness are included in the analysis.

Abedi Vida, Li Jiang, Shivakumar Manu K, Avula Venkatesh, Chaudhary Durgesh P, Shellenberger Matthew J, Khara Harshit S, Zhang Yanfei, Lee Ming Ta Michael, Wolk Donna M, Yeasin Mohammed, Hontecillas Raquel, Bassaganya-Riera Josep, Zand Ramin


C. difficile infection, EHR, complex diseases, electronic health records, imputation, inflammatory bowel disease, laboratory measures, machine learning, medical informatics, osteoarthritis