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Abstract Between June and August 2020, an agent-based model was used to project rates of COVID-19 infection incidence and cases diagnosed as positive from 15 September to 31 October 2020 for 72 geographic settings. Five scenarios were modelled: a baseline scenario where no future changes were made to existing restrictions, and four scenarios representing small or moderate changes in restrictions at two intervals. Post hoc, upper and lower bounds for number of diagnosed Covid-19 cases were compared with actual data collected during the prediction window. A regression analysis with 17 covariates was performed to determine correlates of accurate projections. It was found that the actual data fell within the lower and upper bounds in 27 settings and out of bounds in 45 settings. The only statistically significant predictor of actual data within the predicted bounds was correct assumptions about future policy changes (OR 15.04; 95% CI 2.20–208.70; p = 0.016). Frequent changes in restrictions implemented by governments, which the modelling team was not always able to predict, in part explains why the majority of model projections were inaccurate compared with actual outcomes and supports revision of projections when policies are changed as well as the importance of modelling teams collaborating with policy experts.

High rates of drug-resistant tuberculosis (DR-TB) continue to threaten public health, especially in Eastern Europe. Costs for treating DR-TB are substantially higher than treating drug-susceptible TB, and higher yet if DR-TB services are delivered in hospital. The WHO recommends that multidrug-resistant (MDR) TB be treated using mainly ambulatory care, shown to have non-inferior health outcomes, however, there has been a delay to transition away from hospital-focused MDR-TB care in certain Eastern European countries. Allocative efficiency analyses were conducted for three countries in Eastern Europe, Belarus, the Republic of Moldova, and Romania, to minimise a combination of TB incidence, prevalence, and mortality by 2035. A primary focus of these studies was to determine the health benefits and financial savings that could be realised if DR-TB service delivery shifted from hospital-focused to ambulatory care. Here we provide a comprehensive assessment of findings from these studies to demonstrate the collective benefit of transitioning from hospital-focused to ambulatory TB care, and to address common regional considerations. We highlight that transitioning from hospital-focused to ambulatory TB care could reduce treatment costs by 20% in Romania, 24% in Moldova, and by as much as 40% in Belarus or almost 35 million US dollars across these three countries by 2035 without affecting quality of care. Improved TB outcomes could be achieved, however, without additional spending by reinvesting these savings in higher-impact TB diagnosis and more efficacious DR-TB treatment regimens. We found commonalities in the large portion of TB cases treated in hospital across these three regional countries, and similar obstacles to transitioning to ambulatory care. National governments in the Eastern European region should examine barriers delaying adoption of ambulatory DR-TB care and consider lost opportunities caused by delays in switching to more efficient treatment modes.

Abstract Background Health resources are limited, which means spending should be focused on the people, places and programs that matter most. Choosing the mix of programs to maximize a health outcome is termed allocative efficiency. Here, we extend the methodology of allocative efficiency to answer the question of how resources should be distributed among different geographic regions. Methods We describe a novel geographical optimization algorithm, which has been implemented as an extension to the Optima HIV model. This algorithm identifies an optimal funding of services and programs across regions, such as multiple countries or multiple districts within a country. The algorithm consists of three steps: (1) calibrating the model to each region, (2) determining the optimal allocation for each region across a range of different budget levels, and (3) finding the budget level in each region that minimizes the outcome (such as reducing new HIV infections and/or HIV-related deaths), subject to the constraint of fixed total budget across all regions. As a case study, we applied this method to determine an illustrative allocation of HIV program funding across three representative oblasts (regions) in Ukraine (Mykolayiv, Poltava, and Zhytomyr) to minimize the number of new HIV infections. Results Geographical optimization was found to identify solutions with better outcomes than would be possible by considering region-specific allocations alone. In the case of Ukraine, prior to optimization (i.e. with status quo spending), a total of 244,000 HIV-related disability-adjusted life years (DALYs) were estimated to occur from 2016 to 2030 across the three oblasts. With optimization within (but not between) oblasts, this was estimated to be reduced to 181,000. With geographical optimization (i.e., allowing reallocation of funds between oblasts), this was estimated to be further reduced to 173,000. Conclusions With the increasing availability of region- and even facility-level data, geographical optimization is likely to play an increasingly important role in health economic decision making. Although the largest gains are typically due to reallocating resources to the most effective interventions, especially treatment, further gains can be achieved by optimally reallocating resources between regions. Finally, the methods described here are not restricted to geographical optimization, and can be applied to other problems where competing resources need to be allocated with constraints, such as between diseases.

Abstract Background The introduction of highly effective direct-acting antiviral (DAA) therapy for hepatitis C has led to calls to eliminate it as a public health threat through treatment-as-prevention. Recent studies suggest it is possible to develop a vaccine to prevent hepatitis C. Using a mathematical model, we examined the potential impact of a hepatitis C vaccine on the feasibility and cost of achieving the global WHO elimination target of an 80% reduction in incidence by 2030 in the era of DAA treatment. Methods The model was calibrated to 167 countries and included two population groups (people who inject drugs (PWID) and the general community), features of the care cascade, and the coverage of health systems to deliver services. Projections were made for 2018–2030. Results The optimal incidence reduction strategy was to implement test and treat programmes among PWID, and in settings with high levels of community transmission undertake screening and treatment of the general population. With a vaccine available, the optimal strategy was to include vaccination within test and treat programmes, in addition to vaccinating adolescents in settings with high levels of community transmission. Of the 167 countries modelled, between 0 and 48 could achieve an 80% reduction in incidence without a vaccine. This increased to 15–113 countries if a 75% efficacious vaccine with a 10-year duration of protection were available. If a vaccination course cost US$200, vaccine use reduced the cost of elimination for 66 countries (40%) by an aggregate of US$7.4 (US$6.6–8.2) billion. For a US$50 per course vaccine, this increased to a US$9.8 (US$8.7–10.8) billion cost reduction across 78 countries (47%). Conclusions These findings strongly support the case for hepatitis C vaccine development as an urgent public health need, to ensure hepatitis C elimination is achievable and at substantially reduced costs for a majority of countries.

Abstract Background Artemisinin-resistant Plasmodium falciparum has emerged in the Greater Mekong Subregion, an area of relatively low transmission, but has yet to be reported in Africa. A population-based mathematical model was used to investigate the relationship between P. falciparum prevalence, exposure-acquired immunity and time-to-emergence of artemisinin resistance. The possible implication for the emergence of resistance across Africa was assessed. Methods The model included human and mosquito populations, two strains of malaria (“wild-type”, “mutant”), three levels of human exposure-acquired immunity (none, low, high) with two types of immunity for each level (sporozoite/liver stage immunity and blood-stage/gametocyte immunity) and drug pressure based on per-capita treatment numbers. Results The model predicted that artemisinin-resistant strains may circulate up to 10 years longer in high compared to low P. falciparum prevalence areas before resistance is confirmed. Decreased time-to-resistance in low prevalence areas was explained by low genetic diversity and immunity, which resulted in increased probability of selection and spread of artemisinin-resistant strains. Artemisinin resistance was estimated to be established by 2020 in areas of Africa with low ( 25%) prevalence areas, respectively. Conclusions Areas of low transmission and low immunity give rise to a more rapid expansion of artemisinin-resistant parasites, corroborating historical observations of anti-malarial resistance emergence. Populations where control strategies are in place that reduce malaria transmission, and hence immunity, may be prone to a rapid emergence and spread of artemisinin-resistant strains and thus should be carefully monitored.

Abstract Background Child stunting due to chronic malnutrition is a major problem in low- and middle-income countries due, in part, to inadequate nutrition-related practices and insufficient access to services. Limited budgets for nutritional interventions mean that available resources must be targeted in the most cost-effective manner to have the greatest impact. Quantitative tools can help guide budget allocation decisions. Methods The Optima approach is an established framework to conduct resource allocation optimization analyses. We applied this approach to develop a new tool, ‘Optima Nutrition’, for conducting allocative efficiency analyses that address childhood stunting. At the core of the Optima approach is an epidemiological model for assessing the burden of disease; we use an adapted version of the Lives Saved Tool (LiST). Six nutritional interventions have been included in the first release of the tool: antenatal micronutrient supplementation, balanced energy-protein supplementation, exclusive breastfeeding promotion, promotion of improved infant and young child feeding (IYCF) practices, public provision of complementary foods, and vitamin A supplementation. To demonstrate the use of this tool, we applied it to evaluate the optimal allocation of resources in 7 districts in Bangladesh, using both publicly available data (such as through DHS) and data from a complementary costing study. Results Optima Nutrition can be used to estimate how to target resources to improve nutrition outcomes. Specifically, for the Bangladesh example, despite only limited nutrition-related funding available (an estimated $0.75 per person in need per year), even without any extra resources, better targeting of investments in nutrition programming could increase the cumulative number of children living without stunting by 1.3 million (an extra 5%) by 2030 compared to the current resource allocation. To minimize stunting, priority interventions should include promotion of improved IYCF practices as well as vitamin A supplementation. Once these programs are adequately funded, the public provision of complementary foods should be funded as the next priority. Programmatic efforts should give greatest emphasis to the regions of Dhaka and Chittagong, which have the greatest number of stunted children. Conclusions A resource optimization tool can provide important guidance for targeting nutrition investments to achieve greater impact.

We introduce Viral Phrenology, a new scheme for understanding the genomic composition of spherical viruses based on the locations of their structural protrusions. We used icosahedral point arrays to classify 135 distinct viral capsids collected from over 600 capsids available in the VIPERdb. Using gauge points of point arrays, we found 149 unique structural protrusions. We then show how to use the locations of these protrusions to determine the genetic composition of the virus. We then show that ssDNA, dsDNA, dsRNA and ssRNA viruses use different arrangements for distributing their protrusions. We also found that Triangulation number is also partially dependent on the structural protrusions. This analysis begins to tie together Baltimore Classification and Triangulation number using point arrays.

Abstract Background The high burden of malaria and limited funding means there is a necessity to maximize the allocative efficiency of malaria control programmes. Quantitative tools are urgently needed to guide budget allocation decisions. Methods A geospatial epidemic model was coupled with costing data and an optimization algorithm to estimate the optimal allocation of budgeted and projected funds across all malaria intervention approaches. Interventions included long-lasting insecticide-treated nets (LLINs), indoor residual spraying (IRS), intermittent presumptive treatment during pregnancy (IPTp), seasonal mass chemoprevention in children (SMC), larval source management (LSM), mass drug administration (MDA), and behavioural change communication (BCC). The model was applied to six geopolitical regions of Nigeria in isolation and also the nation as a whole to minimize incidence and malaria-attributable mortality. Results Allocative efficiency gains could avert approximately 84,000 deaths or 15.7 million cases of malaria in Nigeria over 5 years. With an additional US$300 million available, approximately 134,000 deaths or 37.3 million cases of malaria could be prevented over 5 years. Priority funding should go to LLINs, IPTp and BCC programmes, and SMC should be expanded in seasonal areas. To minimize mortality, treatment expansion is critical and prioritized over some LLIN funding, while to minimize incidence, LLIN funding remained a priority. For areas with lower rainfall, LSM is prioritized over IRS but MDA is not recommended unless all other programmes are established. Conclusions Substantial reductions in malaria morbidity and mortality can be made by optimal targeting of investments to the right malaria interventions in the right areas.

Since its introduction, the Triangulation number has been the most successful and ubiquitous scheme for classifying spherical viruses. However, despite its many successes, it fails to describe the relative angular orientations of proteins, as well as their radial mass distribution within the capsid. It also fails to provide any critical insight into sites of stability, modifications or possible mutations. We show how classifying spherical viruses using icosahedral point arrays, introduced by Keef and Twarock, unveils new geometric rules and constraints for understanding virus stability and key locations for exterior and interior modifications. We present a modified fitness measure which classifies viruses in an unambiguous and rigorous manner, irrespective of local surface chemistry, steric hinderance, solvent accessibility or Triangulation number. We then use these point arrays to explain the immutable surface loops of bacteriophage MS2, the relative reactivity of surface lysine residues in CPMV and the non-quasi-equivalent flexibility of the HBV dimers. We then explain how point arrays can be used as a predictive tool for site-directed modifications of capsids. This success builds on our previous work showing that viruses place their protruding features along the great circles of the asymmetric unit, demonstrating that viruses indeed adhere to these geometric constraints.

Background The increased adverse cardiac events in women undergoing coronary artery bypass grafting are multifactorial and may include clinical, psychosocial, and biological factors. Potential contributing biological factors could include vascular hyperreactivity of the internal mammary artery (IMA) to endogenous vasoconstrictors in women, resulting in a predilection to myocardial ischemia. This study evaluated sex differences in serotonin and thromboxane A2 dependent vasoconstriction in human isolated IMA, with the mechanistic role of (1) the endothelium, (2) nitric oxide (NO), (3) prostaglandins, and (4) receptor activity investigated for any observed sex difference. Methods and Results Viable isolated human IMA segments were obtained from 116 patients (44 women [mean age, 66.8±12.2 years] and 72 men [mean age, 66.6±10.4 years]) undergoing coronary artery bypass grafting. Cumulative concentration‐response curves for serotonin and thromboxane A2 mimetic, U46619, were determined and revealed an increased sensitivity to serotonin but not U46619 in women. This sex difference to serotonin was further assessed by the following: (1) endothelial denudation, (2) endothelial NO synthase inhibition and NO quantification using electron paramagnetic resonance, (3) cyclooxygenase inhibition and prostaglandin metabolite quantification using mass spectrometry, and (4) quantification of receptor activity status. The female hyperreactivity to serotonin was (1) abolished by endothelial denudation; (2) unaffected by NO synthase inhibition, with no difference in electron paramagnetic resonance–assessed NO levels; (3) abolished by cyclooxygenase inhibition (quantification of prostaglandins in IMA revealed a trend towards reduced 6‐keto prostaglandin F1α in female IMA; P=0.08); and (4) unrelated to receptor activity. Conclusions These data indicate that female IMAs are hyperreactive to serotonin but not U46619, with the former attributable to an endothelium‐dependent cyclooxygenase pathway.

It has been highlighted that the original manuscript [1] contains a typesetting error in the name of Meera Shekar. This had been incorrectly captured as Meera Shekhar in the original article which has since been updated.

In November 1967 the first BBC Local Radio station was launched, following a successful campaign to establish the service by "founding father" Frank Gillard. He had been strongly influenced by what he had seen during a tour of U.S. broadcasters in 1954. Radio historians often cite 1 Pennsylvania station, WVPO, as having particularly impressed him. But little is known about WVPO and less still is known of the other stations, which could share some credit for influencing Gillard. Using local newspaper reports and documents held at the BBC's Written Archive Centre, this paper looks at the American influence on BBC Local Radio. [ABSTRACT FROM AUTHOR]

The body-wave magnitude (mb) is a long-standing network-averaged, amplitude-based magnitude used to estimate the magnitude of seismic sources from teleseismic observations. The U.S. Geological Survey National Earthquake Information Center (NEIC) relies on mb in its global real-time earthquake monitoring mission. Although waveform modeling-based moment magnitudes are the modern standard to characterize earthquake size, mb is important because (1) in many cases, waveform modeling is not possible (e.g., low signal-to-noise events), (2) mb is applicable over a broad range of magnitudes, ∼M 4–7, and (3) there is a many decades-long history of estimating mb magnitudes. We use the NEIC Preliminary Determination of Epicenters earthquake catalog to investigate the uncertainty in NEIC station mb measurements. We show that mb measurements are spatially correlated, which can bias event mb, and we describe an empirical relation between this spatial correlation and station-to-station distance. We further describe an approach to mitigate bias from the spatial correlation. Accounting for the spatial covariance of observations can change the event mb from −0.15 to 0.07 mb units (10th to 90th percentile) for smaller events (mb≤4.5). These smaller events have the largest mb standard deviations ranging from 0.05 to 0.15 mb units (10th to 90th percentile).

Abstract Elastic S-wave scattering of a charm meson with a light pseudoscalar meson in J P = 0+ is investigated in the flavour 3 ¯ $$ \overline{\textbf{3}} $$ , 6 and 15 ¯ $$ \overline{\textbf{15}} $$ sectors at the SU(3) f flavour point using lattice QCD, working on three volumes with m π ≈ 700 MeV. Large bases of interpolating operators are employed to extract finite-volume spectra, which are subsequently used with the Lüscher method to provide constraints on infinite-volume scattering amplitudes. Examining the singularities of the amplitudes, the S-wave amplitude in the flavour 3 ¯ $$ \overline{\textbf{3}} $$ sector is found to contain a deeply bound state, strongly coupled to elastic threshold, corresponding to the J P = 0 + D s 0 ∗ 2317 $$ {J}^P={0}^{+}\ {D}_{s0}^{\ast }(2317) $$ . In the exotic flavour 6 sector a virtual bound state is found at s pole $$ \sqrt{s_{\textrm{pole}}} $$ = 2510 – 2610 MeV, roughly 40 – 140 MeV below threshold, whereas the 15 ¯ $$ \overline{\textbf{15}} $$ channel shows weak repulsion.

Abstract Ocular trauma often involves intraocular foreign bodies (IOFBs) that pose challenges in accurate diagnosis due to their size, shape, and material composition. In this study, we proposed a novel whole-eye 3D ophthalmic ultrasound B-scan (3D-UBS) system for automating image acquisition and improved 3D visualization, thereby improving sensitivity for detecting IOFBs. 3D-UBS utilizes 14 MHz Clarius L20 probe, a motorized translation stage, and a surgical microscope for precise placement and movement. The system’s 3D point spread function (PSF) is 0.377 × 0.550 × 0.894 mm3 characterized by the full-width at half-maximum intensity values in the axial, lateral and elevation directions. Digital phantom and ex vivo ocular models were prepared using four types of IOFBs (i.e., plastic, wood, metal, and glass). Ex vivo models were imaged with both 3D-UBS and clinical computed tomography (CT). Image preprocessing was performed on 3D-UBS images to remove uneven illumination and speckle noise. Multiplanar reformatting in 3D-UBS provides optimal plane selection after acquisition, reducing the need for a trained ultrasonographer. 3D-UBS outperforms CT in contrast for wood and plastic, with mean contrast improvement of 2.43 and 1.84 times, respectively. 3D-UBS was able to identify wood and plastic IOFBs larger than 250 µm and 300 in diameter, respectively. CT, with its wider PSF, was only able to detect wood and plastic IOFBs larger than 600 and 550 µm, respectively. Although contrast was higher in CT for metal and glass IOFBs, 3D-UBS provided sufficient contrast to identify those. 3D-UBS provides an easy-to-use, non-expert imaging approach for identifying small IOFBs of different materials and related ocular injuries at the point of care.

Abstract Whole-heart coronary calcium Agatston score is a well-established predictor of major adverse cardiovascular events (MACE), but it does not account for individual calcification features related to the pathophysiology of the disease (e.g., multiple-vessel disease, spread of the disease along the vessel, stable calcifications, numbers of lesions, and density). We used novel, hand-crafted calcification features (calcium-omics); Cox time-to-event modeling; elastic net; and up and down synthetic sampling methods for imbalanced data, to assess MACE risk. We used 2457 CT calcium score (CTCS) images enriched for MACE events from our large no-cost CLARIFY program (ClinicalTrials.gov Identifier: NCT04075162). Among calcium-omics features, numbers of calcifications, LAD mass, and diffusivity (a measure of spatial distribution) were especially important determinants of increased risk, with dense calcification (> 1000HU, stable calcifications) associated with reduced risk Our calcium-omics model with (training/testing, 80/20) gave C-index (80.5%/71.6%) and 2-year AUC (82.4%/74.8%). Although the C-index is notoriously impervious to model improvements, calcium-omics compared favorably to Agatston and gave a significant difference (P

Abstract Deciphering past climate variability in the Western Pacific Warm Pool (WPWP), the Earth’s largest heat and moisture centre, is vital for understanding the global climate system. Nevertheless, its long-term evolution remains controversial, largely due to ambiguities in existing proxy interpretations and discrepancies between records. Here, we present a weathering and erosion reconstruction from the WPWP spanning the last 140,000 years, based on the mineralogy and geochemistry of a sediment core from offshore of northern New Guinea. The paleo-weathering reconstruction is consistent with the simulated precipitation evolution on nearby islands, thereby suggesting a close coupling between climate variability and island weathering in a tropical setting. In addition, our combined data-model interpretation of WPWP climate history shows many similarities to the East Asian Summer Monsoon (EASM) variability over orbital timescales. Overall, our study highlights the critical role of precession-paced interhemispheric energy redistribution, via the West Pacific meridional sea-surface pressure gradient, in linking orbital-scale WPWP climate and EASM variability.

Summary: Tumor acidosis is one of the hallmarks indicating the initiation and progression of various cancers. Here, we present a protocol for preparing a hyperpolarized (HP) 13C-bicarbonate tissue pH MRI imaging contrast agent to detect aggressive tumors. We describe the steps for the formulation and polarization of a precursor molecule 13C-glycerol carbonate (13C-GLC), the post-dissolution reaction, and converting HP 13C-GLC to an injectable HP 13C-bicarbonate solution. We then detail procedures for MRI data acquisition to generate tumor pH maps for assessing tumor aggressiveness.For complete details on the use and execution of this protocol, please refer to Mu et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Omega-3 long-chain polyunsaturated fatty acids (n3-LCPUFAs) are produced primarily in aquatic ecosystems and are considered essential nutrients for predators given their structural role in vertebrates’ cerebral tissues. Alarmingly, with urbanization, many aquatic animals now rely on anthropogenic foods lacking n3-LCPUFAs. In this study undertaken in Newfoundland (Canada), we tested whether recent or longer term diet explains the cerebral fatty acid composition of ring-billed gulls (Larus delawarensis), a seabird that now thrives in cities. During the breeding season, cerebral levels of n3-LCPUFAs were significantly higher for gulls nesting in a natural habitat and foraging on marine food (mean ± s.d.: 32 ± 1% of total identified fatty acids) than for urban nesters exploiting rubbish (27 ± 1%). Stable isotope analysis of blood and feathers showed that urban and natural nesters shared similar diets in autumn and winter, suggesting that the difference in cerebral n3-LCPUFAs during the breeding season was owing to concomitant and transient differences in diet. We also experimentally manipulated gulls’ diets throughout incubation by supplementing them with fish oil rich in n3-LCPUFAs, a caloric control lacking n3-LCPUFAs, or nothing, and found evidence that fish oil increased urban nesters’ cerebral n3-LCPUFAs. These complementary analyses provide evidence that the brain of this seabird remains plastic during adulthood and responds to short-term dietary changes.

Abstract Thin-cap fibroatheroma (TCFA) is a prominent risk factor for plaque rupture. Intravascular optical coherence tomography (IVOCT) enables identification of fibrous cap (FC), measurement of FC thicknesses, and assessment of plaque vulnerability. We developed a fully-automated deep learning method for FC segmentation. This study included 32,531 images across 227 pullbacks from two registries (TRANSFORM-OCT and UHCMC). Images were semi-automatically labeled using our OCTOPUS with expert editing using established guidelines. We employed preprocessing including guidewire shadow detection, lumen segmentation, pixel-shifting, and Gaussian filtering on raw IVOCT (r,θ) images. Data were augmented in a natural way by changing θ in spiral acquisitions and by changing intensity and noise values. We used a modified SegResNet and comparison networks to segment FCs. We employed transfer learning from our existing much larger, fully-labeled calcification IVOCT dataset to reduce deep-learning training. Postprocessing with a morphological operation enhanced segmentation performance. Overall, our method consistently delivered better FC segmentation results (Dice: 0.837 ± 0.012) than other deep-learning methods. Transfer learning reduced training time by 84% and reduced the need for more training samples. Our method showed a high level of generalizability, evidenced by highly-consistent segmentations across five-fold cross-validation (sensitivity: 85.0 ± 0.3%, Dice: 0.846 ± 0.011) and the held-out test (sensitivity: 84.9%, Dice: 0.816) sets. In addition, we found excellent agreement of FC thickness with ground truth (2.95 ± 20.73 µm), giving clinically insignificant bias. There was excellent reproducibility in pre- and post-stenting pullbacks (average FC angle: 200.9 ± 128.0°/202.0 ± 121.1°). Our fully automated, deep-learning FC segmentation method demonstrated excellent performance, generalizability, and reproducibility on multi-center datasets. It will be useful for multiple research purposes and potentially for planning stent deployments that avoid placing a stent edge over an FC.

Abstract The emergence of long COVID during the ongoing COVID-19 pandemic has presented considerable challenges for healthcare professionals and researchers. The task of identifying relevant literature is particularly daunting due to the rapidly evolving scientific landscape, inconsistent definitions, and a lack of standardized nomenclature. This paper proposes a novel solution to this challenge by employing machine learning techniques to classify long COVID literature. However, the scarcity of annotated data for machine learning poses a significant obstacle. To overcome this, we introduce a strategy called medical paraphrasing, which diversifies the training data while maintaining the original content. Additionally, we propose a Data-Reweighting-Based Multi-Level Optimization Framework for Domain Adaptive Paraphrasing, supported by a Meta-Weight-Network (MWN). This innovative approach incorporates feedback from the downstream text classification model to influence the training of the paraphrasing model. During the training process, the framework assigns higher weights to the training examples that contribute more effectively to the downstream task of long COVID text classification. Our findings demonstrate that this method substantially improves the accuracy and efficiency of long COVID literature classification, offering a valuable tool for physicians and researchers navigating this complex and ever-evolving field.

Abstract This is the protocol for a Campbell Collaboration systematic review. Our objective is to synthesize what is known about the effectiveness of strategies for reducing community violence, focusing on those strategies that have been subjected to a systematic review. We aim to answer the following questions in this review: what strategies to reduce community violence have been rigorously evaluated through systematic reviews; which have sufficient evidence of effectiveness, which seem promising, and which appear ineffective; and what implications for practice and policy can be drawn from this large body of research? We anticipate categorizing the results of our review similarly to the original review by Abt and Winship (2016). That is, categorizing reviews by people‐based approaches, place‐based approaches, and behavior‐based approaches. However, given that this is an updated review and we will be incorporating additional studies, we may find that an alternative or additional categorization is warranted and update our categorization accordingly. Implications for policy and practice as they relate to these categories will be discussed.

Introduction/rationale Protein biomarkers may help enable the prediction of incident interstitial features on chest CT.Methods We identified which protein biomarkers in a cohort of smokers (COPDGene) differed between those with and without objectively measured interstitial features at baseline using a univariate screen (t-test false discovery rate, FDR p

Abstract Background Drug occupancy studies with positron emission tomography imaging are used routinely in early phase drug development trials. Recently, our group introduced the Lassen Plot Filter, an extended version of the standard Lassen plot to estimate voxel-level occupancy images. Occupancy images can be used to create an EC50 image by applying an E max model at each voxel. Our goal was to apply functional clustering of occupancy images via a clustering algorithm and produce a more precise EC50 image while maintaining accuracy. Method A digital brain phantom was used to create 10 occupancy images (corresponding to 10 different plasma concentrations of drug) that correspond to a ground truth EC50 image containing two bilateral local “hot spots” of high EC50 (region-1: 25; region-2: 50; background: 6–10 ng/mL). Maximum occupancy was specified as 0.85. An established noise model was applied to the simulated occupancy images and the images were smoothed. Simple Linear Iterative Clustering, an existing k-means clustering algorithm, was modified to segment a series of occupancy images into K clusters (which we call “SLIC-Occ”). EC50 images were estimated by nonlinear estimation at each cluster (post SLIC-Occ) and voxel (no clustering). Coefficient of variation images were estimated at each cluster and voxel, respectively. The same process was also applied to human occupancy data produced for a previously published study. Results Variability in EC50 estimates was reduced by more than 80% in the phantom data after application of SLIC-Occ to occupancy images with only minimal loss of accuracy. A similar, but more modest improvement was achieved in variability when SLIC-Occ was applied to human occupancy images. Conclusions Our results suggest that functional segmentation of occupancy images via SLIC-Occ could produce more precise EC50 images and improve our ability to identify local “hot spots” of high effective affinity of a drug for its target(s).

Abstract It can be difficult/impossible to fully expand a coronary artery stent in a heavily calcified coronary artery lesion. Under-expanded stents are linked to later complications. Here we used machine/deep learning to analyze calcifications in pre-stent intravascular optical coherence tomography (IVOCT) images and predicted the success of vessel expansion. Pre- and post-stent IVOCT image data were obtained from 110 coronary lesions. Lumen and calcifications in pre-stent images were segmented using deep learning, and lesion features were extracted. We analyzed stent expansion along the lesion, enabling frame, segmental, and whole-lesion analyses. We trained regression models to predict the post-stent lumen area and then computed the stent expansion index (SEI). Best performance (root-mean-square-error = 0.04 ± 0.02 mm2, r = 0.94 ± 0.04, p 80% were classified as “well-expanded;” others were “under-expanded.” Under-expansion classification results (e.g., AUC = 0.85 ± 0.02) were significantly improved over a previous, simple calculation, as well as other machine learning solutions. Promising results suggest that such methods can identify lesions at risk of under-expansion that would be candidates for intervention lesion preparation (e.g., atherectomy).

Abstract In this work, stenting in non-calcified and heavily calcified coronary arteries was quantified in terms of diameter-pressure relationships and load transfer from the balloon to the artery. The efficacy of post-dilation in non-calcified and heavily calcified coronary arteries was also characterized in terms of load sharing and the changes in tissue mechanics. Our results have shown that stent expansion exhibits a cylindrical shape in non-calcified lesions, while it exhibits a dog bone shape in heavily calcified lesions. Load-sharing analysis has shown that only a small portion of the pressure load (1.4 N, 0.8% of total pressure load) was transferred to the non-calcified lesion, while a large amount of the pressure load (19 N, 12%) was transferred to the heavily calcified lesion. In addition, the increasing inflation pressure (from 10 to 20 atm) can effectively increase the minimal lumen diameter (from 1.48 to 2.82 mm) of the heavily calcified lesion, the stress (from 1.5 to 8.4 MPa) and the strain energy in the calcification (1.77 mJ to 26.5 mJ), which are associated with the potential of calcification fracture. Results indicated that increasing inflation pressure can be an effective way to improve the stent expansion if a dog bone shape of the stenting profile is observed. Considering the risk of a balloon burst, our results support the design and application of the high-pressure balloon for post-dilation. This work also sheds some light on the stent design and choice of stent materials for improving the stent expansion at the dog bone region and mitigating stresses on arterial tissues.