Investigating hair cues as a mechanism underlying Black women's intersectional invisibility.

Children psychologically exclude Black women from their representations of women, but the mechanisms underlying this marginalization remain unclear. Across two studies (N = 129; 49 boys, 78 girls, two gender unreported; 79 White, 27 Black, six Latinx, five Asian, and 12 unreported), the present work tests hair texture as one possible perceptual mechanism by which this might occur. In both studies, children gender-categorized Black, White, and Asian men and women using MouseTracker. Children were slower and had more complex patterns in categorizing Black women when they had textured hair (Study 1A), but not when they had straight hair (Study 1B). Implications for the development of gender as a social category are discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A framework for evaluating clinical artificial intelligence systems without ground-truth annotations.

A clinical artificial intelligence (AI) system is often validated on data withheld during its development. This provides an estimate of its performance upon future deployment on data in the wild; those currently unseen but are expected to be encountered in a clinical setting. However, estimating performance on data in the wild is complicated by distribution shift between data in the wild and withheld data and the absence of ground-truth annotations. Here, we introduce SUDO, a framework for evaluating AI systems on data in the wild. Through experiments on AI systems developed for dermatology images, histopathology patches, and clinical notes, we show that SUDO can identify unreliable predictions, inform the selection of models, and allow for the previously out-of-reach assessment of algorithmic bias for data in the wild without ground-truth annotations. These capabilities can contribute to the deployment of trustworthy and ethical AI systems in medicine.

A Machine Learning-Driven Virtual Biopsy System For Kidney Transplant Patients.

In kidney transplantation, day-zero biopsies are used to assess organ quality and discriminate between donor-inherited lesions and those acquired post-transplantation. However, many centers do not perform such biopsies since they are invasive, costly and may delay the transplant procedure. We aim to generate a non-invasive virtual biopsy system using routinely collected donor parameters. Using 14,032 day-zero kidney biopsies from 17 international centers, we develop a virtual biopsy system. 11 basic donor parameters are used to predict four Banff kidney lesions: arteriosclerosis, arteriolar hyalinosis, interstitial fibrosis and tubular atrophy, and the percentage of renal sclerotic glomeruli. Six machine learning models are aggregated into an ensemble model. The virtual biopsy system shows good performance in the internal and external validation sets. We confirm the generalizability of the system in various scenarios. This system could assist physicians in assessing organ quality, optimizing allograft allocation together with discriminating between donor derived and acquired lesions post-transplantation.

The use and timing of angio-embolization in pediatric blunt liver and spleen injury.

BACKGROUND: Nonoperative management (NOM) is the standard of care for the management of blunt liver and spleen injuries (BLSI) in the stable pediatric patient. Angiography with embolization (AE) is utilized as an adjunctive therapy in the management of adult BLSI patients, but it is rarely utilized in the pediatric population. In this planned secondary analysis, we describe the current utilization patterns of AE in the management of pediatric BLSI. METHODS: After obtaining IRB approval at each center, cohort data was collected prospectively for children admitted with BLSI confirmed on CT at 10 Level 1 pediatric trauma centers (PTCs) throughout the United States from April 2013 to January 2016. All patients who underwent angiography with or without embolization for a BLSI were included in this analysis. Data collected included patient demographics, injury details, organ injured and grade of injury, CT finding specifics such as contrast blush, complications, failure of NOM, time to angiography and techniques for embolization. RESULTS: Data were collected for 1004 pediatric patients treated for BLSI over the study period, 30 (3.0%) of which underwent angiography with or without embolization for BLSI. Ten of the patients who underwent angiography for BLSI failed NOM. For patients with embolized splenic injuries, splenic salvage was 100%. Four of 9 patients undergoing embolization of the liver ultimately required an operative intervention, but only one patient required hepatorrhaphy and no patient required hepatectomy after AE. Few angiography studies were obtained early during hospitalization for BLSI, with only 1 patient undergoing angiography within 1 hour of arrival at the PTC, and 7 within 3 hours. CONCLUSIONS: Angioembolization is rarely utilized in the management of BLSI in pediatric trauma patients with blunt abdominal trauma and is generally utilized in a delayed fashion. However, when implemented, angioembolization is associated with 100% splenic salvage for splenic injuries. LEVEL OF EVIDENCE: Level IV, therapeutic/care management.

Automatically pre-screening patients for the rare disease aromatic l-amino acid decarboxylase deficiency using knowledge engineering, natural language processing, and machine learning on a large EHR population.

OBJECTIVES: Electronic health record (EHR) data may facilitate the identification of rare diseases in patients, such as aromatic l-amino acid decarboxylase deficiency (AADCd), an autosomal recessive disease caused by pathogenic variants in the dopa decarboxylase gene. Deficiency of the AADC enzyme results in combined severe reductions in monoamine neurotransmitters: dopamine, serotonin, epinephrine, and norepinephrine. This leads to widespread neurological complications affecting motor, behavioral, and autonomic function. The goal of this study was to use EHR data to identify previously undiagnosed patients who may have AADCd without available training cases for the disease. MATERIALS AND METHODS: A multiple symptom and related disease annotated dataset was created and used to train individual concept classifiers on annotated sentence data. A multistep algorithm was then used to combine concept predictions into a single patient rank value. RESULTS: Using an 8000-patient dataset that the algorithms had not seen before ranking, the top and bottom 200 ranked patients were manually reviewed for clinical indications of performing an AADCd diagnostic screening test. The top-ranked patients were 22.5% positively assessed for diagnostic screening, with 0% for the bottom-ranked patients. This result is statistically significant at P < .0001. CONCLUSION: This work validates the approach that large-scale rare-disease screening can be accomplished by combining predictions for relevant individual symptoms and related conditions which are much more common and for which training data is easier to create.

MALDI Mass Spectrometry Imaging and Semi-Quantification of Topically Delivered Lactic Acid.

BACKGROUND: Lactic acid is a common active ingredient in many topical skincare products; however, measuring its delivery into the skin is challenging due to the presence of a large level of endogenous lactic acid. In this study, matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) was used to quantitatively and qualitatively measure the delivery of lactic acid into the skin from a range of topical skincare products. MATERIALS AND METHODS: Porcine skin samples were treated with various skincare products containing lactic acid. After 24 h, skin samples were sectioned and treated via H&E staining or prepared for MALDI-MSI using chemical derivatization. Samples were then analyzed by MALDI-MSI imaging to obtain lactic acid distribution in the entire skin section. RESULTS: Due to the high level of endogenous lactic acid in the skin, a "triple isotope" of lactic acid (L-Lactic acid-13 C3 ), was needed to provide full resolution from the skin's background signal with MALDI-MSI. With this approach, the topically delivered lactic acid could be quantitatively and qualitatively analyzed from a variety of skincare products. CONCLUSIONS: The combination of L-Lactic acid-13 C3 and MALDI-MSI was successfully used to quantitatively and qualitatively measure the topical delivery of lactic acid from a variety of skincare products. This approach could be used in future work to better understand the mode of action of lactic acid as an active ingredient in skincare products.

Approach to machine learning for extraction of real-world data variables from electronic health records.

Background: As artificial intelligence (AI) continues to advance with breakthroughs in natural language processing (NLP) and machine learning (ML), such as the development of models like OpenAI's ChatGPT, new opportunities are emerging for efficient curation of electronic health records (EHR) into real-world data (RWD) for evidence generation in oncology. Our objective is to describe the research and development of industry methods to promote transparency and explainability. Methods: We applied NLP with ML techniques to train, validate, and test the extraction of information from unstructured documents (e.g., clinician notes, radiology reports, lab reports, etc.) to output a set of structured variables required for RWD analysis. This research used a nationwide electronic health record (EHR)-derived database. Models were selected based on performance. Variables curated with an approach using ML extraction are those where the value is determined solely based on an ML model (i.e. not confirmed by abstraction), which identifies key information from visit notes and documents. These models do not predict future events or infer missing information. Results: We developed an approach using NLP and ML for extraction of clinically meaningful information from unstructured EHR documents and found high performance of output variables compared with variables curated by manually abstracted data. These extraction methods resulted in research-ready variables including initial cancer diagnosis with date, advanced/metastatic diagnosis with date, disease stage, histology, smoking status, surgery status with date, biomarker test results with dates, and oral treatments with dates. Conclusion: NLP and ML enable the extraction of retrospective clinical data in EHR with speed and scalability to help researchers learn from the experience of every person with cancer.

Incorrect analysis in "Effects of the application of a food processing-based classification system in obese women: A randomized controlled pilot study" has resulted in incorrect conclusions of demonstrated effects where no such effects have been demonstrated.

In their 2023 Nutrition and Health paper "Effects of the application of a food processing-based classification system in obese women: A randomized controlled pilot study", Giacomello et al. investigated the effects of an educational intervention based on the Dietary Guidelines for the Brazilian Population among obese women. The authors concluded that the intervention significantly improved weight loss, quality of life, components of metabolic syndrome, and pain. However, we believe the statistical analysis employed in the study was flawed. The authors used within-group changes to draw conclusions, which is known as a difference in nominal significance error. This error has the potential to inflate Type I error rates substantially. To address this issue, we re-analyzed the data obtained from the authors. We focused on body mass and hip circumference and replicated the incorrectly chosen within-group analyses, which remained significant. However, to properly evaluate the intervention's effectiveness, it is essential to compare the differences between the groups directly. Therefore, we calculated change scores for each participant and used independent samples t-tests and linear mixed models to compare between-group differences. Both methods yielded similar non-significant p-values, indicating that there is no significant effect of treatment on body mass or hip circumference. The original paper's conclusions regarding the effectiveness of the intervention are not supported by the proper statistical analysis. The data should be re-analyzed using appropriate between-group comparisons, and the corrected results should be published, or the incorrect results and original paper should be retracted.

Reversal of trends in global fine particulate matter air pollution.

Ambient fine particulate matter (PM2.5) is the world's leading environmental health risk factor. Quantification is needed of regional contributions to changes in global PM2.5 exposure. Here we interpret satellite-derived PM2.5 estimates over 1998-2019 and find a reversal of previous growth in global PM2.5 air pollution, which is quantitatively attributed to contributions from 13 regions. Global population-weighted (PW) PM2.5 exposure, related to both pollution levels and population size, increased from 1998 (28.3 µg/m3) to a peak in 2011 (38.9 µg/m3) and decreased steadily afterwards (34.7 µg/m3 in 2019). Post-2011 change was related to exposure reduction in China and slowed exposure growth in other regions (especially South Asia, the Middle East and Africa). The post-2011 exposure reduction contributes to stagnation of growth in global PM2.5-attributable mortality and increasing health benefits per µg/m3 marginal reduction in exposure, implying increasing urgency and benefits of PM2.5 mitigation with aging population and cleaner air.

Deceased Donor Kidney Nonuse: A Systematic Approach to Improvement.

A large number of procured kidneys continue not to be transplanted, while the waiting list remains high. Methods: We analyzed donor characteristics for unutilized kidneys in our large organ procurement organization (OPO) service area in a single year to determine the reasonableness of their nonuse and to identify how we might increase the transplant rate of these kidneys. Five experienced local transplant physicians independently reviewed unutilized kidneys to identify which kidneys they would consider transplanting in the future. Biopsy results, donor age, kidney donor profile index, positive serologies, diabetes, and hypertension were risk factors for nonuse. Results: Two-thirds of nonused kidneys had biopsies with high degree of glomerulosclerosis and interstitial fibrosis. Reviewers identified 33 kidneys as potentially transplantable (12%). Conclusions: Reducing the rate of unutilized kidneys in this OPO service area will be achieved by setting acceptable expanded donor characteristics, identifying suitable well-informed recipients, defining acceptable outcomes, and systematically evaluating the results of these transplants. Because the improvement opportunity will vary by region, to achieve a significant impact on improving the national nonuse rate, it would be useful for all OPOs, in collaboration with their transplant centers, to conduct a similar analysis.

Replication of Real-World Evidence in Oncology Using Electronic Health Record Data Extracted by Machine Learning.

Meaningful real-world evidence (RWE) generation requires unstructured data found in electronic health records (EHRs) which are often missing from administrative claims; however, obtaining relevant data from unstructured EHR sources is resource-intensive. In response, researchers are using natural language processing (NLP) with machine learning (ML) techniques (i.e., ML extraction) to extract real-world data (RWD) at scale. This study assessed the quality and fitness-for-use of EHR-derived oncology data curated using NLP with ML as compared to the reference standard of expert abstraction. Using a sample of 186,313 patients with lung cancer from a nationwide EHR-derived de-identified database, we performed a series of replication analyses demonstrating some common analyses conducted in retrospective observational research with complex EHR-derived data to generate evidence. Eligible patients were selected into biomarker- and treatment-defined cohorts, first with expert-abstracted then with ML-extracted data. We utilized the biomarker- and treatment-defined cohorts to perform analyses related to biomarker-associated survival and treatment comparative effectiveness, respectively. Across all analyses, the results differed by less than 8% between the data curation methods, and similar conclusions were reached. These results highlight that high-performance ML-extracted variables trained on expert-abstracted data can achieve similar results as when using abstracted data, unlocking the ability to perform oncology research at scale.

Ambient Air Quality Standards and Policies in Eastern Mediterranean Countries: A Review.

Objectives: National ambient air quality standards (NAAQS) are critical tools for controlling air pollution and protecting public health. We designed this study to 1) gather the NAAQS for six classical air pollutants: PM2.5, PM10, O3, NO2, SO2, and CO in the Eastern Mediterranean Region (EMR) countries, 2) compare those with the updated World Health Organizations Air Quality Guidelines (WHO AQGs 2021), 3) estimate the potential health benefits of achieving annual PM2.5 NAAQS and WHO AQGs per country, and 4) gather the information on air quality policies and action plans in the EMR countries. Methods: To gather information on the NAAQS, we searched several bibliographic databases, hand-searched the relevant papers and reports, and analysed unpublished data on NAAQS in the EMR countries reported from these countries to the WHO/Regional office of the Eastern Mediterranean/Climate Change, Health and Environment Unit (WHO/EMR/CHE). To estimate the potential health benefits of reaching the NAAQS and AQG levels for PM2.5, we used the average of ambient PM2.5 exposures in the 22 EMR countries in 2019 from the Global Burden of Disease (GBD) dataset and AirQ+ software. Results: Almost all of the EMR countries have national ambient air quality standards for the critical air pollutants except Djibouti, Somalia, and Yemen. However, the current standards for PM2.5 are up to 10 times higher than the current health-based WHO AQGs. The standards for other considered pollutants exceed AQGs as well. We estimated that the reduction of annual mean PM2.5 exposure level to the AQG level (5 µg m-3) would be associated with a decrease of all natural-cause mortality in adults (age 30+) by 16.9%-42.1% in various EMR countries. All countries would even benefit from the achievement of the Interim Target-2 (25 µg m-3) for annual mean PM2.5: it would reduce all-cause mortality by 3%-37.5%. Less than half of the countries in the Region reported having policies relevant to air quality management, in particular addressing pollution related to sand and desert storms (SDS) such as enhancing the implementation of sustainable land management practices, taking measures to prevent and control the main factors of SDS, and developing early warning systems as tools to combat SDS. Few countries conduct studies on the health effects of air pollution or on a contribution of SDS to pollution levels. Information from air quality monitoring is available for 13 out of the 22 EMR countries. Conclusion: Improvement of air quality management, including international collaboration and prioritization of SDS, supported by an update (or establishment) of NAAQSs and enhanced air quality monitoring are essential elements for reduction of air pollution and its health effects in the EMR.

Exposure to US Cancer Drugs With Lack of Confirmed Benefit After US Food and Drug Administration Accelerated Approval.

This cross-sectional study evaluates patient exposure to oncology drugs withdrawn from the US Food and Drug Administration (FDA) Accelerated Approval program.

Telemedicine use among patients with metastatic breast cancer during the COVID-19 pandemic: Differences by race, age, and region.

PURPOSE: Our objective was to describe differences in telemedicine use among women with metastatic breast cancer (mBC) by race, age, and geographic region. METHODS: This was a retrospective cohort study of women with recurrent or de novo mBC treated in US community cancer practices that initiated a new line of therapy between March 2020 and February 2021. Multivariable modified Poisson regression models were used to calculate adjusted rate ratios (RR) and robust 95% confidence intervals (CI) associated with telemedicine visits within 90 days of therapy initiation. RESULTS: Overall, among 3412 women with mBC, 751 (22%) patients had telemedicine visits following therapy initiation, with lower risks observed among older women (<50 years: 24%; 50-64 years: 22%; 65-74 years: 21%; ≥75 years: 20%). Greater telemedicine use was observed among Asian women (35%) compared to White (21%), Black (18%), and Hispanic (21%) women. Fewer telemedicine visits occurred in Southern (12%) and Midwestern (17%) states versus Northeastern (37%) or Western (36%) states. In multivariable models, women ages ≥75 years had significantly lower risks of telemedicine visits (RR = 0.76, 95% CI 0.62-0.95) compared to ages <50 years. Compared to patients in Northeastern states, women in Midwestern (RR = 0.46, 95% CI 0.37-0.57) and Southern (RR = 0.31, 95% CI 0.26-0.37) states had significantly lower risks of telemedicine visits; but not women in Western states (RR = 0.96, 95% CI 0.83-1.12). No statistically significant differences in telemedicine use were found between racial groups in overall multivariable models. CONCLUSIONS: In this study of community cancer practices, older mBC patients and those living in Southern and Midwestern states were less likely to have telemedicine visits. Preferences for communication and delivery of care may have implications for measurement of exposures and endpoints in pharmacoepidemiologic studies of cancer patients.

Apheresis of Deceased Donors as a New Source of Mobilized Peripheral Blood Hematopoietic Stem Cells for Transplant Tolerance.

BACKGROUND: Solid organ transplantation is the therapy of choice for many patients with end-stage organ failure; however, recipients must remain on lifelong immunosuppression, leaving them susceptible to infections and cancer. The study of transplant tolerance to prolong graft survival in the absence of immunosuppression has been restricted to recipients of living donor allografts; however, deceased donors significantly outnumber living donors. Mobilization of hematopoietic stem cells (HSCs) from the bone marrow to peripheral blood (PB) could allow PB-HSCs to be used to induce tolerance in deceased donor kidney recipients; however, a major concern is the well-known concomitant mobilization of immune cells into the liver. METHODS: We mobilized HSCs to the PD using a protocol of 2 doses of granulocyte colony-stimulating factor and 1 dose of plerixafor, followed by the collection of mobilized cells via apheresis in 3 deceased donors. The physiological, laboratory, and radiographic parameters were monitored throughout the procedure. Longitudinal biopsies were performed to assess the potential for ectopic liver mobilization. RESULTS: The use of both agents led to the successful mobilization of peripheral blood CD34+ cells, demonstrating the potential for use in transplant tolerance protocols. Increased immune cell trafficking into the liver was not observed, and apheresis of mobilized cells resulted in a uniform decrease in all liver leukocyte subsets. CONCLUSIONS: HSCs can be mobilized and collected from the PB of brain-dead donors. This new approach may facilitate the dissemination of immune tolerance trials beyond living-donor kidney transplantation to deceased-donor transplantation, without sacrificing the transplantability of the liver.

Hemodilution in pediatric trauma: Defining the expected hemoglobin changes in patients with liver and/or spleen injury: An ATOMAC+ secondary analysis.

BACKGROUND: Many children with blunt liver and/or spleen injury (BLSI) never bleed intraperitoneally. Despite this, decreases in hemoglobin are common. This study examines initial and follow up measured hemoglobin values for children with BLSI with and without evidence of intra-abdominal bleeding. METHODS: Children ≤18 years of age with BLSI between April 2013 and January 2016 were identified from the prospective ATOMAC+ cohort. Initial and follow up hemoglobin levels were analyzed for 4 groups with BLSI: (1) Non bleeding; (2) Bleeding, non transfused (3) Bleeding, transfused, and (4) Bleeding resulting in non operative management (NOM) failure. RESULTS: Of 1007 patients enrolled, 767 were included in one or more of four study cohorts. Of 131 non bleeding patients, the mean decrease in hemoglobin was 0.83 g/dL (+/-1.35) after a median of 6.3 [5.1,7.0] hours, (p = 0.001). Follow-up hemoglobin levels in patients with and without successful NOM were not different. For patients with an initial hemoglobin >9.25 g/dL, the odds ratio (OR) for NOM failure was 14.2 times less, while the OR for transfusion was 11.4 times less (p = 0.001). CONCLUSION: Decreases in hemoglobin are expected after trauma, even if not bleeding. A hemoglobin decrease of 2.15 g/dL [0.8 + 1.35] would still be within one standard deviation of a non bleeding patient. An initial low hemoglobin correlates with failure of NOM as well as transfusion, thereby providing useful information. By contrast, subsequent hemoglobin levels do not appear to guide the need for transfusion, nor correlate with failure of NOM. These results support initial hemoglobin measurement but suggest a lack of utility for routine rechecking of hemoglobin. LEVEL OF EVIDENCE: Level II Prognostic Study.

Aminoglycoside-induced lipotoxicity and its reversal in kidney on chip.

Aminoglycosides are an important class of antibiotics that play a critical role in the treatment of life-threatening infections, but their use is limited by their toxicity. In fact, gentamicin causes severe nephrotoxicity in 17% of hospitalized patients. The kidney proximal tubule is particularly vulnerable to drug-induced nephrotoxicity due to its role in drug transport. In this work, we developed a perfused vascularized model of human kidney tubuloids integrated with tissue-embedded microsensors that track the metabolic dynamics of aminoglycoside-induced renal toxicity in real time. Our model shows that gentamicin disrupts proximal tubule polarity at concentrations 20-fold below its TC50, leading to a 3.2-fold increase in glucose uptake, and reverse TCA cycle flux culminating in a 40-fold increase in lipid accumulation. Blocking glucose reabsorption using the SGLT2 inhibitor empagliflozin significantly reduced gentamicin toxicity by 10-fold. These results demonstrate the utility of sensor-integrated kidney-on-chip platforms to rapidly identify new metabolic mechanisms that may underly adverse drug reactions. The results should improve our ability to modulate the toxicity of novel aminoglycosides.

Health burden and economic loss attributable to ambient PM2.5 in Iran based on the ground and satellite data.

We estimated mortality and economic loss attributable to PM2·5 air pollution exposure in 429 counties of Iran in 2018. Ambient PM2.5-related deaths were estimated using the Global Exposure Mortality Model (GEMM). According to the ground-monitored and satellite-based PM2.5 data, the annual mean population-weighted PM2·5 concentrations for Iran were 30.1 and 38.6 µg m-3, respectively. We estimated that long-term exposure to ambient PM2.5 contributed to 49,303 (95% confidence interval (CI) 40,914-57,379) deaths in adults ≥ 25 yr. from all-natural causes based on ground monitored data and 58,873 (95% CI 49,024-68,287) deaths using satellite-based models for PM2.5. The crude death rate and the age-standardized death rate per 100,000 population for age group ≥ 25 year due to ground-monitored PM2.5 data versus satellite-based exposure estimates was 97 (95% CI 81-113) versus 116 (95% CI 97-135) and 125 (95% CI 104-145) versus 149 (95% CI 124-173), respectively. For ground-monitored and satellite-based PM2.5 data, the economic loss attributable to ambient PM2.5-total mortality was approximately 10,713 (95% CI 8890-12,467) and 12,792.1 (95% CI 10,652.0-14,837.6) million USD, equivalent to nearly 3.7% (95% CI 3.06-4.29) and 4.3% (95% CI 3.6-4.5.0) of the total gross domestic product in Iran in 2018.

Testing a filtering strategy for systematic reviews: evaluating work savings and recall.

Systematic reviews are extremely time-consuming. The goal of this work is to assess work savings and recall for a publication type filtering strategy that uses the output of two machine learning models, Multi-Tagger and web RCT Tagger, applied retrospectively to 10 systematic reviews on drug effectiveness. Our filtering strategy resulted in mean work savings of 33.6% and recall of 98.3%. Of 363 articles finally included in any of the systematic reviews, 7 were filtered out by our strategy, but 1 "error" was actually an article using a publication type that the SR team had not pre-specified as relevant for inclusion. Our analysis suggests that automated publication type filtering can potentially provide substantial work savings with minimal loss of included articles. Publication type filtering should be personalized for each systematic review and might be combined with other filtering or ranking methods to provide additional work savings for manual triage.