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Abstract Population screening for breast cancer (BC) is currently offered in the UK for women aged 50 to 71 with the aim of reducing mortality. There is additional screening within the national programme for women identified as having a very high risk of BC. There is growing interest in further risk stratification in breast screening, which would require a whole population risk assessment and the subsequent offer of screening tailored to the individual’s risk. Some women would be offered more intensive screening than others or no screening. This might provide a better balance of screening benefits and harms for each individual than the current population age-based programme alone. The UK National Screening Committee (UK NSC) is considering using decision-analytic and other models to evaluate different risk stratification screening strategies and identify remaining gaps in evidence. This paper reports the proceedings of a UK NSC workshop where experts in the field discussed both risk prediction models, as well as decision-analytic models providing a benefit-harm analysis/economic evaluation of risk-stratified screening programmes (see Table 1). The aim of the meeting was to present and discuss the current work of experts, including some data which had not been published at the time of the meeting, to inform the UK NSC. The workshop was not intended to present a balanced evaluation of how to deliver screening in future. Areas for further work identified included methods for comparing models to assess accuracy, the optimum risk assessment tools, the digital screening infrastructure, acceptability of stratification, choice of screening test and reducing inequalities. A move to risk stratification of the whole programme would require a careful phased introduction with continuing assessment of real-world evidence during deployment.

Over 1/3 of the Earth’s human population relies on dryland ecosystems for food and water resources. While these ecosystems are highly sensitive to changes in climate, we lack observational data as to how changes in hydrology influences plant communities. Paleoecological data for southern Arabia show woodland communities transitioned to more dry-adapted herbaceous plants, which suggests rainfall decreased across the Holocene. To assess relationships between hydrology and ecology, we employed leaf wax n-alkane distributions, δ13Cwax, and δDwax records from rock hyrax (Procavia capensis) middens in Dhofar, Oman. The biomarker properties allowed reconstruction of changes in C3/C4 vegetation and local moisture availability, in tandem with community changes represented by a published pollen record. To constrain interpretations, n-alkane analyses were conducted on herbarium specimens of leaves collected in Dhofar. For the modern specimens, xeric plants typically contained longer homologues than mesic plants. Across the fossil middens (4,038–109 cal yrs BP), the proportions of plant-wax homologues do not show major changes, and thus do not suggest a shift between xeric versus mesic plants. Similarly, δ13Cwax values indicate little or no change in the distributions of C3 and C4 vegetation. Limited δDwax data from the middens confirm overall drying occurred into the late Holocene, punctuated by a wetter pulse at ∼1.6 ka. Taken together, plant wax distributions and isotope data indicate changes in moisture availability across the late Holocene did not alter the structural composition of the plant communities and that the proportion of C3/C4 vegetation remained stable. We infer vegetation changes associated with late Holocene drying involved reshuffling of community composition and not major changes in vegetation structure. Additionally, this study demonstrates that leaf wax n-alkanes from rock hyrax middens provide a method to reconstruct changes in climate and vegetation in dryland ecosystems where other archives are scarce.

Microplastic sampling strategies for aquatic systems commonly employ small mesh nets to collect suspended microparticles. These methods work well for marine sampling campaigns; however, complex water systems such as freshwater rivers, effluent discharges, and stormwater ponds characterized by high total suspended solids and fast-moving water can cause the nets to clog, rip, or tear. Published in 2020, ASTM D8332 is an alternative approach to sampling complex water systems for microplastics involving pumping large volumes of water across a cascading stack of sieves to collect suspended particles. Here we show that ASTM D8332 can be applied to sample freshwater rivers for microplastic collection. A high throughput sampling prototype developed in this work is capable of pumping 1500 L of river water in 45 min to collect particles as small as 45 µm. The system is lightweight, modular, and easily transportable. It has a discrete power supply, allowing for the collection of microplastics anywhere along the river, including municipal discharges. The design minimizes the amount of plastic in the flow path and provides a practical way to measure field contamination. Finally, we outline lessons learned through extensive field trials and testing using this system sampling the North Saskatchewan River in Edmonton, Alberta. • Existing small mesh nets face limitations in freshwater rivers, encountering clogging and tearing issues from high suspended solids and fast moving water. • Using a standardized method, ASTM D8332 - a pumping-based approach is efficient for microplastic collection in freshwater rivers. • Lightweight, modular, plastic free prototype system pumps 1500 L of river water in 45 min, collecting particles as small as 45 µm. Successfully tested in the North Saskatchewan River.

BackgroundLimited availability of in-person health care services and fear of contracting COVID-19 during the pandemic promoted an increased reliance on telemedicine. However, long-standing inequities in telemedicine due to unequal levels of digital literacy and internet connectivity among different age groups raise concerns about whether the uptake of telemedicine has exacerbated or alleviated those inequities. ObjectiveThe aim of this study is to examine changes in telemedicine and in-person health service use during the COVID-19 pandemic across age groups for Medicaid beneficiaries in the state of Louisiana. MethodsInterrupted time series models were used on Louisiana Medicaid claims data to estimate trends in total, in-person, and telemedicine monthly office visit claims per 1000 Medicaid beneficiaries between January 2018 and December 2020. Changes in care pattern trends and levels were estimated around the infection peaks (April 2020 and July 2020) and for an end-of-year infection leveling off period (December 2020). Four mutually exclusive age categories (0 to 17, 18 to 34, 35 to 49, and 50 to 64 years) were used to compare the differences. ResultsPrior to the COVID-19 pandemic, telemedicine services accounted for less than 1% of total office visit claim volume across the age groups. Each age group followed similar patterns of sharp increases in April 2020, downward trends until sharp increases again in July 2020, followed by flat trends thereafter until December 2020. These sharp increases were most pronounced for older patients, with those aged 50 to 64 years seeing increases of 184.09 telemedicine claims per 1000 Medicaid beneficiaries in April 2020 (95% CI 172.19 to 195.99) and 120.81 in July 2020 (95% CI 101.32 to 140.31) compared with those aged 18 to 34 years, seeing increases of 84.47 (95% CI 78.64 to 90.31) and 57.00 (95% CI 48.21 to 65.79), respectively. This resulted in overall changes from baseline to December 2020 levels of 123.65 (95% CI 112.79 to 134.51) for those aged 50 to 64 years compared with 59.07 (95% CI 53.89 to 64.24) for those aged 18 to 34 years. ConclusionsOlder Medicaid beneficiaries in Louisiana had higher rates of telemedicine claim volume during the COVID-19 pandemic compared with younger beneficiaries.

The fifth annual summer research summit organized by the Center of Excellence (COE) in Health Equity, Training and Research, Baylor College of Medicine (BCM), was held on May 17, 2022. The theme of this year’s summit was ‘Academic-Community Partnerships: Change Agents for Advancing Health Equity.’ Given the ongoing pandemic, the summit was conducted virtually through digital platforms. This program was intended for both BCM and external audiences interested in advancing health equity, diversity, and inclusion in healthcare among healthcare providers and trainees, biomedical scientists, social workers, nurses, and individuals involved in talent acquisition and development, such as hiring managers (HR professionals), supervisors, college and hospital affiliate leadership and administrators, as well as diversity and inclusion excellence practitioners. We had attendees from all regions of the United States as well as from Saudi Arabia. The content in this Book of Abstracts encapsulates a summary of the research efforts by the BCM COE scholars (which includes post-baccalaureate students, medical students, clinical fellows, and junior faculty from BCM) as well as the external summit participants. The range of topics in this year’s summit was quite diverse, encompassing disparities in relation to maternal and child health (MCH), immigrant health, cancers, vaccination uptakes, and COVID-19 infections. Various solutions were ardently presented to address these disparities, including community engagement and partnerships, improvement in health literacy, and the development of novel technologies and therapeutics. With this summit, BCM continues to build on its long history of educational outreach initiatives to promote diversity in medicine by focusing on programs aimed at increasing the number of diverse and highly qualified medical professionals ready to introduce effective and innovative approaches to reduce or eliminate health disparities. These programs will improve information resources, clinical education, curricula, research, and cultural competence as they relate to minority health issues and social determinants of health. The year’s summit was a great success! Copyright © 2022 Dongarwar et al. Published by Global Health and Education Projects, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution License CC BY 4.0.

Pruning unimportant parameters can allow deep neural networks (DNNs) to reduce their heavy computation and memory requirements. A saliency metric estimates which parameters can be safely pruned with little impact on the classification performance of the DNN. Many saliency metrics have been proposed, each within the context of a wider pruning algorithm. The result is that it is difficult to separate the effectiveness of the saliency metric from the wider pruning algorithm that surrounds it. Similar-looking saliency metrics can yield very different results because of apparently minor design choices. We propose a novel taxonomy of saliency metrics based on four mostly-orthogonal principal components. We show that a broad range of metrics from the pruning literature can be grouped according to these components. Our taxonomy serves as a guide to prior work, and allows us to construct new saliency metrics by exploring novel combinations of our taxonomic components. We perform the first in-depth experimental investigation of more than 300 saliency metrics made up of existing techniques and new combinations of components. Our results provide decisive answers to open research questions. In particular, we demonstrate the importance of reduction and scaling when pruning groups of weights. We also propose a novel scaling method based on the number of weights transitively removed. We find that some of our constructed metrics can outperform the best existing state-of-the-art metrics for convolutional neural network channel pruning. We find further that our novel scaling method improves existing saliency metrics.

Abstract “In what circumstances would you want this AI to make decisions on your behalf?” We have been investigating how to enable a user of an Artificial Intelligence‐powered system to answer questions like this through a series of empirical studies, a group of which we summarize here. We began the series by (a) comparing four explanation configurations of saliency explanations and/or reward explanations. From this study we learned that, although some configurations had significant strengths, no one configuration was a clear “winner.” This result led us to hypothesize that one reason for the low success rates Explainable AI (XAI) research has in enabling users to create a coherent mental model is that the AI itself does not have a coherent model. This hypothesis led us to (b) build a model‐based agent, to compare explaining it with explaining a model‐free agent. Our results were encouraging, but we then realized that participants' cognitive energy was being sapped by having to create not only a mental model, but also a process by which to create that mental model. This realization led us to (c) create such a process (which we term After‐Action Review for AI or “AAR/AI”) for them, integrate it into the explanation environment, and compare participants' success with AAR/AI scaffolding vs without it. Our AAR/AI studies' results showed that AAR/AI participants were more effective assessing the AI than non‐AAR/AI participants, with significantly better precision and significantly better recall at finding the AI's reasoning flaws.

Background Obesity is a continuing national epidemic, and the condition can have a physical, psychological, as well as social impact on one’s well-being. Consequently, it is critical to diagnose and document obesity accurately in the patient’s electronic medical record (EMR), so that the information can be used and shared to improve clinical decision making and health communication and, in turn, the patient’s prognosis. It is therefore worthwhile identifying the various factors that play a role in documenting obesity diagnosis and the methods to improve current documentation practices. Method We used a retrospective cross-sectional design to analyze outpatient EMRs of patients at an academic outpatient clinic. Obese patients were identified using the measured body mass index (BMI; ≥30 kg/m 2 ) entry in the EMR, recorded at each visit, and checked for documentation of obesity in the EMR problem list. Patients were categorized into two groups (diagnosed or undiagnosed) based on a documented diagnosis (or omission) of obesity in the EMR problem list and compared. Results A total of 10,208 unique patient records of obese patients were included for analysis, of which 4119 (40%) did not have any documentation of obesity in their problem list. Chi-square analysis between the diagnosed and undiagnosed groups revealed significant associations between documentation of obesity in the EMR and patient characteristics. Conclusion EMR designers and developers must consider employing automated decision support techniques to populate and update problem lists based on the existing recorded BMI in the EMR in order to prevent omissions occurring from manual entry.

Abstract Background Label-retaining cancer cells (LRCC) have been proposed as a model of slowly cycling cancer stem cells (CSC) which mediate resistance to chemotherapy, tumor recurrence, and metastasis. The molecular mechanisms of chemoresistance in LRCC remain to-date incompletely understood. This study aims to identify molecular targets in LRCC that can be exploited to overcome resistance to gemcitabine, a standard chemotherapy agent for the treatment of pancreas cancer. Methods LRCC were isolated following Cy5-dUTP staining by flow cytometry from pancreatic cancer cell lines. Gene expression profiles obtained from LRCC, non-LRCC (NLRCC), and bulk tumor cells were used to generate differentially regulated pathway networks. Loss of upregulated targets in LRCC on gemcitabine sensitivity was assessed via RNAi experiments and pharmacological inhibition. Expression patterns of PDPK1, one of the upregulated targets in LRCC, was studied in patients’ tumor samples and correlated with pathological variables and clinical outcome. Results LRCC are significantly more resistant to gemcitabine than the bulk tumor cell population. Non-canonical EGF (epidermal growth factor)-mediated signal transduction emerged as the top upregulated network in LRCC compared to non-LRCC, and knock down of EGF signaling effectors PDPK1 (3-phosphoinositide dependent protein kinase-1), BMX (BMX non-receptor tyrosine kinase), and NTRK2 (neurotrophic receptor tyrosine kinase 2) or treatment with PDPK1 inhibitors increased growth inhibition and induction of apoptosis in response to gemcitabine. Knockdown of PDPK1 preferentially increased growth inhibition and reduced resistance to induction of apoptosis upon gemcitabine treatment in the LRCC vs non-LRCC population. These findings are accompanied by lower expression levels of PDPK1 in tumors compared to matched uninvolved pancreas in surgical resection specimens and a negative association of membranous localization on IHC with high nuclear grade (p

Nutritional immunity describes the host-driven manipulation of essential micronutrients, including iron, zinc and manganese. To withstand nutritional immunity and proliferate within their hosts, pathogenic microbes must express efficient micronutrient uptake and homeostatic systems. Here we have elucidated the pathway of cellular zinc assimilation in the major human fungal pathogen Candida albicans. Bioinformatics analysis identified nine putative zinc transporters: four cytoplasmic-import Zip proteins (Zrt1, Zrt2, Zrt3 and orf19.5428) and five cytoplasmic-export ZnT proteins (orf19.1536/Zrc1, orf19.3874, orf19.3769, orf19.3132 and orf19.52). Only Zrt1 and Zrt2 are predicted to localise to the plasma membrane and here we demonstrate that Zrt2 is essential for C. albicans zinc uptake and growth at acidic pH. In contrast, ZRT1 expression was found to be highly pH-dependent and could support growth of the ZRT2-null strain at pH 7 and above. This regulatory paradigm is analogous to the distantly related pathogenic mould, Aspergillus fumigatus, suggesting that pH-adaptation of zinc transport may be conserved in fungi and we propose that environmental pH has shaped the evolution of zinc import systems in fungi. Deletion of C. albicans ZRT2 reduced kidney fungal burden in wild type, but not in mice lacking the zinc-chelating antimicrobial protein calprotectin. Inhibition of zrt2Δ growth by neutrophil extracellular traps was calprotectin-dependent. This suggests that, within the kidney, C. albicans growth is determined by pathogen-Zrt2 and host-calprotectin. As well as serving as an essential micronutrient, zinc can also be highly toxic and we show that C. albicans deals with this potential threat by rapidly compartmentalising zinc within vesicular stores called zincosomes. In order to understand mechanistically how this process occurs, we created deletion mutants of all five ZnT-type transporters in C. albicans. Here we show that, unlike in Saccharomyces cerevisiae, C. albicans Zrc1 mediates zinc tolerance via zincosomal zinc compartmentalisation. This novel transporter was also essential for virulence and liver colonisation in vivo. In summary, we show that zinc homeostasis in a major human fungal pathogen is a multi-stage process initiated by Zrt1/Zrt2-cellular import, followed by Zrc1-dependent intracellular compartmentalisation.

The meaning of most words in language depends on their context. Understanding how the human brain extracts contextualized meaning, and identifying where in the brain this takes place, remain important scientific challenges. But technological and computational advances in neuroscience and artificial intelligence now provide unprecedented opportunities to study the human brain in action as language is read and understood. Recent contextualized language models seem to be able to capture homonymic meaning variation ("bat", in a baseball vs. a vampire context), as well as more nuanced differences of meaning--for example, polysemous words such as "book", which can be interpreted in distinct but related senses ("explain a book", information, vs. "open a book", object) whose differences are fine-grained. We study these subtle differences in lexical meaning along the concrete/abstract dimension, as they are triggered by verb-noun semantic composition. We analyze functional magnetic resonance imaging (fMRI) activations elicited by Italian verb phrases containing nouns whose interpretation is affected by the verb to different degrees. By using a contextualized language model and human concreteness ratings, we shed light on where in the brain such fine-grained meaning variation takes place and how it is coded. Our results show that phrase concreteness judgments and the contextualized model can predict BOLD activation associated with semantic composition within the language network. Importantly, representations derived from a complex, nonlinear composition process consistently outperform simpler composition approaches. This is compatible with a holistic view of semantic composition in the brain, where semantic representations are modified by the process of composition itself. When looking at individual brain areas, we find that encoding performance is statistically significant, although with differing patterns of results, suggesting differential involvement, in the posterior superior temporal sulcus, inferior frontal gyrus and anterior temporal lobe, and in motor areas previously associated with processing of concreteness/abstractness.