Deep Learning for Breast Cancer Risk Prediction: Application to a Large Representative UK Screening Cohort.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop an artificial intelligence (AI) deep learning tool capable of predicting future breast cancer risk from a current negative screening mammographic examination and to evaluate the model on data from the UK National Health Service Breast Screening Program. Materials and Methods The OPTIMAM Mammography Imaging Database contains screening data, including mammograms and information on interval cancers, for > 300,000 women who attended screening at three different sites in the UK from 2012 onward. Cancer-free screening examinations from women aged 50-70 years were obtained and classified as risk-positive or risk-negative based on the occurrence of cancer within 3 years of the original examination. Examinations with confirmed cancer and images containing implants were excluded. From the resulting 5264 risk-positive and 191488 risk-negative examinations, training (n = 89285) validation (n = 2106) and test (n = 39351) datasets were produced for model development and evaluation. The AI model was trained to predict future cancer occurrence based on screening mammograms and patient age. Performance was evaluated on the test dataset using the area under the receiver operating characteristic curve (AUC) and compared across subpopulations to assess potential biases. Interpretability of the model was explored, including with saliency maps. Results On the hold-out test set, the AI model achieved an overall AUC of 0.70 (95% CI: 0.69, 0.72). There was no evidence of a difference in performance across the three sites, between patient ethnicities or across age-groups Visualization of saliency maps and sample images provided insights into the mammographic features associated with AI-predicted cancer risk. Conclusion The developed AI tool showed good performance on a multisite, UK-specific dataset. ©RSNA, 2024.

Non-rigid motion-compensated 3D whole-heart T2 mapping in a hybrid 3T PET-MR system.

PURPOSE: Simultaneous PET-MRI improves inflammatory cardiac disease diagnosis. However, challenges persist in respiratory motion and mis-registration between free-breathing 3D PET and 2D breath-held MR images. We propose a free-breathing non-rigid motion-compensated 3D T2 -mapping sequence enabling whole-heart myocardial tissue characterization in a hybrid 3T PET-MR system and provides non-rigid respiratory motion fields to correct also simultaneously acquired PET data. METHODS: Free-breathing 3D whole-heart T2 -mapping was implemented on a hybrid 3T PET-MRI system. Three datasets were acquired with different T2 -preparation modules (0, 28, 55 ms) using 3-fold undersampled variable-density Cartesian trajectory. Respiratory motion was estimated via virtual 3D image navigators, enabling multi-contrast non-rigid motion-corrected MR reconstruction. T2 -maps were computed using dictionary-matching. Approach was tested in phantom, 8 healthy subjects, 14 MR only and 2 PET-MR patients with suspected cardiac disease and compared with spin echo reference (phantom) and clinical 2D T2 -mapping (in-vivo). RESULTS: Phantom results show a high correlation (R2 = 0.996) between proposed approach and gold standard 2D T2 mapping. In-vivo 3D T2 -mapping average values in healthy subjects (39.0 ± 1.4 ms) and patients (healthy tissue) (39.1 ± 1.4 ms) agree with conventional 2D T2 -mapping (healthy = 38.6 ± 1.2 ms, patients = 40.3 ± 1.7 ms). Bland-Altman analysis reveals bias of 1.8 ms and 95% limits of agreement (LOA) of -2.4-6 ms for healthy subjects, and bias of 1.3 ms and 95% LOA of -1.9 to 4.6 ms for patients. CONCLUSION: Validated efficient 3D whole-heart T2 -mapping at hybrid 3T PET-MRI provides myocardial inflammation characterization and non-rigid respiratory motion fields for simultaneous PET data correction. Comparable T2 values were achieved with both 3D and 2D methods. Improved image quality was observed in the PET images after MR-based motion correction.

JNTX-101, a novel albumin-encapsulated gemcitabine prodrug, is efficacious and operates via caveolin-1-mediated endocytosis.

Albumin is an attractive candidate carrier for the development of novel therapeutic drugs. Gemcitabine has been FDA approved for the treatment of solid tumors; however, new drugs that optimize gemcitabine delivery are not available for clinical use. The aim of this study was to test the efficacy of a novel albumin-encapsulated gemcitabine prodrug, JNTX-101, and investigate whether Cav-1 expression predicts the therapeutic efficacy of JNTX-101. We first determined the treatment efficacy of JNTX-101 in a panel of pancreatic/lung cancer cell lines and found that increases in Cav-1 expression resulted in higher uptake of albumin, while Cav-1 depletion attenuated the sensitivity of cells to JNTX-101. In addition, decreased Cav-1 expression markedly reduced JNTX-101-induced apoptotic cell death in a panel of cells, particularly in low-serum conditions. Furthermore, we tested the therapeutic efficacy of JNTX-101 in xenograft models and the role of Cav-1 in JNTX-101 sensitivity using a Tet-on-inducible tumor model in vivo. Our data suggest that JNTX-101 effectively inhibits cell viability and tumor growth, and that Cav-1 expression dictates optimal sensitivity to JNTX-101. These data indicate that Cav-1 correlates with JNTX-101 sensitivity, especially under nutrient-deprived conditions, and supports a role for Cav-1 as a predictive biomarker for albumin-encapsulated therapeutics such as JNTX-101.

Enantioselective Total Synthesis of (-)-Himalensine A via a Palladium and 4-Hydroxyproline Co-catalyzed Desymmetrization of Vinyl-bromide-tethered Cyclohexanones.

Herein, we describe the convergent enantioselective total synthesis of himalensine A in 18 steps, enabled by a highly enantio- and diastereoselective construction of the morphan core via a palladium/hydroxy proline co-catalyzed desymmetrization of vinyl-bromide-tethered cyclohexanones. The reaction pathway was illuminated by density functional theory calculations, which support an intramolecular Heck reaction of an in situ-generated enamine intermediate, where exquisite enantioselectivity arises from intramolecular carboxylate coordination to the vinyl palladium species in the rate- and enantio-determining carbopalladation steps. The reaction tolerates diverse N-derivatives, all-carbon quaternary centers, and trisubstituted olefins, providing access to molecular scaffolds found in a range of complex natural products. Following large-scale preparation of a key substrate and installation of a ß-substituted enone moiety, the rapid construction of himalensine A was achieved using a highly convergent strategy based on an amide coupling/Michael addition/allylation/ring-closing metathesis sequence which allowed the introduction of three of the five rings in only three synthetic steps (after telescoping). Moreover, our strategy provides a new enantioselective access to a known tetracyclic late-stage intermediate that has been used previously in the synthesis of many Daphniphyllum alkaloids.

Synoptic CT scan reporting of pancreatic adenocarcinoma to align with international consensus guidelines on surgical resectability: a Victorian pilot.

BACKGROUND: Non-metastatic pancreatic ductal adenocarcinoma (PDAC) is classified as resectable (R), borderline resectable (BR) or locally advanced (LA). International Consensus Guidelines on these definitions exist, but have not been integrated into everyday Australian practice. The anatomical features on CT imaging lend themselves to synoptic reporting which should enhance completeness, comparability and consistency. METHODS: We developed and tested a synoptic report for PDAC derived from the International Consensus Guidelines at two metropolitan pancreatic cancer services to standardize CT reporting in the region. Consecutive scans with suspected PDAC discussed at multidisciplinary meetings were reported using the template between October 2020 and September 2021. A purpose-built database captured data regarding resectability and image-quality parameters. RESULTS: Ninety-five scans were reviewed, 57.9% (N = 55) of which conformed to high-quality pancreatic CT protocols. Of suboptimal scans, meaningful synoptic reports were able to be issued for a further 24/40 (due to metastases in 9, and unequivocal resectability status in 15). Of 79 classifiable scans, 20% were metastatic, 51% deemed resectable, 16% locally advanced and 13% borderline resectable. DISCUSSION: PDAC lends itself to synoptic reporting given the specific anatomical considerations that classify resectability. This relies, however, on high-quality CT imaging and it was surprising that over 40% of scans reviewed were of suboptimal quality. Despite this, resectability status according to the International Consensus Guidelines was designated for 83% of scans. Optimal treatment algorithms for LA, BR and resectable disease vary widely underscoring the critical importance of accurately differentiating these anatomic subtypes of PDAC, and thus support further implementation of a synoptic report of this nature.

MR-guided motion-corrected PET image reconstruction for cardiac PET-MR.

Simultaneous PET-MR imaging has shown potential for the comprehensive assessment of myocardial health from a single examination. Furthermore, MR-derived respiratory motion information has been shown to improve PET image quality by incorporating this information into the PET image reconstruction. Separately, MR-based anatomically guided PET image reconstruction has been shown to perform effective denoising, but this has been so far demonstrated mainly in brain imaging. To date the combined benefits of motion compensation and anatomical guidance have not been demonstrated for myocardial PET-MR imaging. This work addresses this by proposing a single cardiac PET-MR image reconstruction framework which fully utilises MR-derived information to allow both motion compensation and anatomical guidance within the reconstruction. Methods: Fifteen patients underwent a 18F-FDG cardiac PET-MR scan with a previously introduced acquisition framework. The MR data processing and image reconstruction pipeline produces respiratory motion fields and a high-resolution respiratory motion-corrected MR image with good tissue contrast. This MR-derived information was then included in a respiratory motion-corrected, cardiac-gated, anatomically guided image reconstruction of the simultaneously acquired PET data. Reconstructions were evaluated by measuring myocardial contrast and noise and compared to images from several comparative intermediate methods using the components of the proposed framework separately. Results: Including respiratory motion correction, cardiac gating, and anatomical guidance significantly increased contrast. In particular, myocardium-to-blood pool contrast increased by 143% on average (p<0.0001) compared to conventional uncorrected, non-guided PET images. Furthermore, anatomical guidance significantly reduced image noise compared to non-guided image reconstruction by 16.1% (p<0.0001). Conclusion: The proposed framework for MR-derived motion compensation and anatomical guidance of cardiac PET data was shown to significantly improve image quality compared to alternative reconstruction methods. Each component of the reconstruction pipeline was shown to have a positive impact on the final image quality. These improvements have the potential to improve clinical interpretability and diagnosis based on cardiac PET-MR images.

Effects of acetate on cerebral blood flow, systemic inflammation, and behavior in alcohol use disorder.

BACKGROUND: Alcohol use disorders (AUDs) are associated with altered regulation of physiological processes in the brain. Acetate, a metabolite of ethanol, has been implicated in several processes that are disrupted in AUDs including transcriptional regulation, metabolism, inflammation, and neurotransmission. To further understand the effects of acetate on brain function in AUDs, we investigated the effects of acetate on cerebral blood flow (CBF), systemic inflammatory cytokines, and behavior in AUD. METHODS: Sixteen participants with AUD were recruited from a nonmedical, clinically managed detoxification center. Each participant received acetate and placebo in a randomly assigned order of infusion and underwent 3T MR scanning using quantitative pseudo-continuous arterial spin labeling. Participants and the study team were blinded to the infusion. CBF values (ml/100 g/min) extracted from thalamus were compared between placebo and acetate using a mixed effect linear regression model accounting for infusion order. Voxel-wise CBF comparisons were set at threshold of p < 0.05 cluster-corrected for multiple comparisons, voxel-level p < 0.0001. Plasma cytokine levels and behavior were also assessed between infusions. RESULTS: Fifteen men and 1 woman were enrolled with Alcohol Use Disorders Identification Test (AUDIT) scores between 13 and 38 with a mean of 28.3 ± 9.1. Compared to placebo, acetate administration increased CBF in the thalamus bilaterally (Left: 51.2 vs. 68.8, p < 0.001; Right: 53.7 vs. 69.6, p = 0.001), as well as the cerebellum, brainstem, and cortex. Older age and higher AUDIT scores were associated with increases in acetate-induced thalamic blood flow. Cytokine levels and behavioral measures did not differ between placebo and acetate infusions. CONCLUSIONS: This pilot study in AUD suggests that during the first week of abstinence from alcohol, the brain's response to acetate differs by brain region and this response may be associated with the severity of alcohol dependence.

The decline of butterflies in Europe: Problems, significance, and possible solutions.

We review changes in the status of butterflies in Europe, focusing on long-running population data available for the United Kingdom, the Netherlands, and Belgium, based on standardized monitoring transects. In the United Kingdom, 8% of resident species have become extinct, and since 1976 overall numbers declined by around 50%. In the Netherlands, 20% of species have become extinct, and since 1990 overall numbers in the country declined by 50%. Distribution trends showed that butterfly distributions began decreasing long ago, and between 1890 and 1940, distributions declined by 80%. In Flanders (Belgium), 20 butterflies have become extinct (29%), and between 1992 and 2007 overall numbers declined by around 30%. A European Grassland Butterfly Indicator from 16 European countries shows there has been a 39% decline of grassland butterflies since 1990. The 2010 Red List of European butterflies listed 38 of the 482 European species (8%) as threatened and 44 species (10%) as near threatened (note that 47 species were not assessed). A country level analysis indicates that the average Red List rating is highest in central and mid-Western Europe and lowest in the far north of Europe and around the Mediterranean. The causes of the decline of butterflies are thought to be similar in most countries, mainly habitat loss and degradation and chemical pollution. Climate change is allowing many species to spread northward while bringing new threats to susceptible species. We describe examples of possible conservation solutions and a summary of policy changes needed to conserve butterflies and other insects.

Bootstrap-Optimised Regularised Image Reconstruction for Emission Tomography.

In emission tomography, iterative image reconstruction from noisy measured data usually results in noisy images, and so regularisation is often used to compensate for noise. However, in practice, an appropriate, automatic and precise specification of the strength of regularisation for image reconstruction from a given noisy measured dataset remains unresolved. Existing approaches are either empirical approximations with no guarantee of generalisation, or else are computationally intensive cross-validation methods requiring full reconstructions for a limited set of preselected regularisation strengths. In contrast, we propose a novel methodology embedded within iterative image reconstruction, using one or more bootstrapped replicates of the measured data for precise optimisation of the regularisation. The approach uses a conventional unregularised iterative update of a current image estimate from the noisy measured data, and then also uses the bootstrap replicate to obtain a bootstrap update of the current image estimate. The method then seeks the regularisation hyperparameters which, when applied to the bootstrap update of the image, lead to a best fit of the regularised bootstrap update to the conventional measured data update. This corresponds to estimating the degree of regularisation needed in order to map the noisy update to a model of the mean of an ensemble of noisy updates. For a given regularised objective function (e.g. penalised likelihood), no hyperparameter selection or tuning is required. The method is demonstrated for positron emission tomography (PET) data at different noise levels, and delivers near-optimal reconstructions (in terms of reconstruction error) without any knowledge of the ground truth, nor any form of training data.

Intercomparison of MR-informed PET image reconstruction methods.

PURPOSE: Numerous image reconstruction methodologies for positron emission tomography (PET) have been developed that incorporate magnetic resonance (MR) imaging structural information, producing reconstructed images with improved suppression of noise and reduced partial volume effects. However, the influence of MR structural information also increases the possibility of suppression or bias of structures present only in the PET data (PET-unique regions). To address this, further developments for MR-informed methods have been proposed, for example, through inclusion of the current reconstructed PET image, alongside the MR image, in the iterative reconstruction process. In this present work, a number of kernel and maximum a posteriori (MAP) methodologies are compared, with the aim of identifying methods that enable a favorable trade-off between the suppression of noise and the retention of unique features present in the PET data. METHODS: The reconstruction methods investigated were: the MR-informed conventional and spatially compact kernel methods, referred to as KEM and KEM largest value sparsification (LVS) respectively; the MR-informed Bowsher and Gaussian MR-guided MAP methods; and the PET-MR-informed hybrid kernel and anato-functional MAP methods. The trade-off between improving the reconstruction of the whole brain region and the PET-unique regions was investigated for all methods in comparison with postsmoothed maximum likelihood expectation maximization (MLEM), evaluated in terms of structural similarity index (SSIM), normalized root mean square error (NRMSE), bias, and standard deviation. Both simulated BrainWeb (10 noise realizations) and real [18 F] fluorodeoxyglucose (FDG) three-dimensional datasets were used. The real [18 F]FDG dataset was augmented with simulated tumors to allow comparison of the reconstruction methodologies for the case of known regions of PET-MR discrepancy and evaluated at full counts (100%) and at a reduced (10%) count level. RESULTS: For the high-count simulated and real data studies, the anato-functional MAP method performed better than the other methods under investigation (MR-informed, PET-MR-informed and postsmoothed MLEM), in terms of achieving the best trade-off for the reconstruction of the whole brain and PET-unique regions, assessed in terms of the SSIM, NRMSE, and bias vs standard deviation. The inclusion of PET information in the anato-functional MAP method enables the reconstruction of PET-unique regions to attain similarly low levels of bias as unsmoothed MLEM, while moderately improving the whole brain image quality for low levels of regularization. However, for low count simulated datasets the anato-functional MAP method performs poorly, due to the inclusion of noisy PET information in the regularization term. For the low counts simulated dataset, KEM LVS and to a lesser extent, HKEM performed better than the other methods under investigation in terms of achieving the best trade-off for the reconstruction of the whole brain and PET-unique regions, assessed in terms of the SSIM, NRMSE, and bias vs standard deviation. CONCLUSION: For the reconstruction of noisy data, multiple MR-informed methods produce favorable whole brain vs PET-unique region trade-off in terms of the image quality metrics of SSIM and NRMSE, comfortably outperforming the whole image denoising of postsmoothed MLEM.

Spatially-Compact MR-Guided Kernel EM for PET Image Reconstruction.

Positron emission tomography (PET) is a highly sensitive functional and molecular imaging modality which can measure picomolar concentrations of an injected radionuclide. However, the physical sensitivity of PET is limited, and reducing the injected dose leads to low count data and noisy reconstructed images. A highly effective way of reducing noise is to reparameterise the reconstruction in terms of MR-derived spatial basis functions. Spatial basis functions derived using the kernel method have demonstrated excellent noise reduction properties and maintain shared PET-MR detailed structures. However, as previously shown in the literature, the MR-guided kernel method may lead to excessive smoothing of structures that are only present in the PET data. This work makes two main contributions in order to address this problem: first, we exploit the potential of the MR-guided kernel method to form more spatially-compact basis functions which are able to preserve PET-unique structures, and secondly, we consider reconstruction at the native MR resolution. The former contribution notably improves the recovery of structures which are unique to the PET data. These adaptations of the kernel method were compared to the conventional implementation of the MR-guided kernel method and also to MLEM, in terms of ability to recover PET unique structures for both simulated and real data. The spatially-compact kernel method showed clear visual and quantitative improvements in the reconstruction of the PET unique structures, relative to the conventional kernel method for all sizes of PET unique structures investigated, whilst maintaining to a large extent the impressive noise mitigating and detail preserving properties of the conventional MR-guided kernel method. We therefore conclude that a spatially-compact parameterisation of the MR-guided kernel method, should be the preferred implementation strategy in order to obviate unnecessary losses in PET-unique details.

Enantioselective Silver-Catalyzed Cascade Synthesis of Fused Lactone and Lactam Oxazolines.

A new and highly stereoselective cascade reaction between isocyanoacetate esters and α-hydroxy and α-amino ketones has been developed. A cinchona alkaloid derived aminophosphine/silver(I) catalyst complex promoted the reaction and enabled the ready synthesis of fused bicyclic γ-lactone and γ-lactam oxazolines with high enantiocontrol (up to 99% ee).

Multi-Tracer Guided PET Image Reconstruction.

Multi-tracer positron emission tomography (PET) has the potential to enhance PET imaging by providing complementary information from different physiological processes. However, one or more of the images may present high levels of noise. Guided image reconstruction methods transfer information from a guide image into the PET image reconstruction to encourage edge-preserving noise reduction. In this work we aim to reduce noise in poorer quality PET datasets via guidance from higher quality ones by using a weighted quadratic penalty approach. In particular, we applied this methodology to [18F]fluorodeoxyglucose (FDG) and [11C]methionine imaging of gliomas. 3D simulation studies showed that guiding the reconstruction of methionine datasets using pre-existing FDG images reduced reconstruction errors across the whole-brain (-8%) and within a tumour (-36%) compared to maximum likelihood expectation-maximisation (MLEM). Furthermore, guided reconstruction outperformed a comparable non-local means filter, indicating that regularising during reconstruction is preferable to post-reconstruction approaches. Hyperparameters selected from the 3D simulation study were applied to real data, where it was observed that the proposed FDG-guided methionine reconstruction allows for better edge preservation and noise reduction than standard MLEM. Overall, the results in this work demonstrate that transferring information between datasets in multi-tracer PET studies improves image quality and quantification performance.

MR-Guided Kernel EM Reconstruction for Reduced Dose PET Imaging.

PET image reconstruction is highly susceptible to the impact of Poisson noise, and if shorter acquisition times or reduced injected doses are used, the noisy PET data become even more limiting. The recent development of kernel expectation maximisation (KEM) is a simple way to reduce noise in PET images, and we show in this work that impressive dose reduction can be achieved when the kernel method is used with MR-derived kernels. The kernel method is shown to surpass maximum likelihood expectation maximisation (MLEM) for the reconstruction of low-count datasets (corresponding to those obtained at reduced injected doses) producing visibly clearer reconstructions for unsmoothed and smoothed images, at all count levels. The kernel EM reconstruction of 10% of the data had comparable whole brain voxel-level error measures to the MLEM reconstruction of 100% of the data (for simulated data, at 100 iterations). For regional metrics, the kernel method at reduced dose levels attained a reduced coefficient of variation and more accurate mean values compared to MLEM. However, the advances provided by the kernel method are at the expense of possible over-smoothing of features unique to the PET data. Further assessment on clinical data is required to determine the level of dose reduction that can be routinely achieved using the kernel method, whilst maintaining the diagnostic utility of the scan.

Penalized maximum likelihood simultaneous longitudinal PET image reconstruction with difference-image priors.

PURPOSE: Many clinical contexts require the acquisition of multiple positron emission tomography (PET) scans of a single subject, for example, to observe and quantitate changes in functional behaviour in tumors after treatment in oncology. Typically, the datasets from each of these scans are reconstructed individually, without exploiting the similarities between them. We have recently shown that sharing information between longitudinal PET datasets by penalizing voxel-wise differences during image reconstruction can improve reconstructed images by reducing background noise and increasing the contrast-to-noise ratio of high-activity lesions. Here, we present two additional novel longitudinal difference-image priors and evaluate their performance using two-dimesional (2D) simulation studies and a three-dimensional (3D) real dataset case study. METHODS: We have previously proposed a simultaneous difference-image-based penalized maximum likelihood (PML) longitudinal image reconstruction method that encourages sparse difference images (DS-PML), and in this work we propose two further novel prior terms. The priors are designed to encourage longitudinal images with corresponding differences which have (a) low entropy (DE-PML), and (b) high sparsity in their spatial gradients (DTV-PML). These two new priors and the originally proposed longitudinal prior were applied to 2D-simulated treatment response [18 F]fluorodeoxyglucose (FDG) brain tumor datasets and compared to standard maximum likelihood expectation-maximization (MLEM) reconstructions. These 2D simulation studies explored the effects of penalty strengths, tumor behaviour, and interscan coupling on reconstructed images. Finally, a real two-scan longitudinal data series acquired from a head and neck cancer patient was reconstructed with the proposed methods and the results compared to standard reconstruction methods. RESULTS: Using any of the three priors with an appropriate penalty strength produced images with noise levels equivalent to those seen when using standard reconstructions with increased counts levels. In tumor regions, each method produces subtly different results in terms of preservation of tumor quantitation and reconstruction root mean-squared error (RMSE). In particular, in the two-scan simulations, the DE-PML method produced tumor means in close agreement with MLEM reconstructions, while the DTV-PML method produced the lowest errors due to noise reduction within the tumor. Across a range of tumor responses and different numbers of scans, similar results were observed, with DTV-PML producing the lowest errors of the three priors and DE-PML producing the lowest bias. Similar improvements were observed in the reconstructions of the real longitudinal datasets, although imperfect alignment of the two PET images resulted in additional changes in the difference image that affected the performance of the proposed methods. CONCLUSION: Reconstruction of longitudinal datasets by penalizing difference images between pairs of scans from a data series allows for noise reduction in all reconstructed images. An appropriate choice of penalty term and penalty strength allows for this noise reduction to be achieved while maintaining reconstruction performance in regions of change, either in terms of quantitation of mean intensity via DE-PML, or in terms of tumor RMSE via DTV-PML. Overall, improving the image quality of longitudinal datasets via simultaneous reconstruction has the potential to improve upon currently used methods, allow dose reduction, or reduce scan time while maintaining image quality at current levels.

Methyldopa blocks MHC class II binding to disease-specific antigens in autoimmune diabetes.

Major histocompatibility (MHC) class II molecules are strongly associated with many autoimmune disorders. In type 1 diabetes (T1D), the DQ8 molecule is common, confers significant disease risk, and is involved in disease pathogenesis. We hypothesized that blocking DQ8 antigen presentation would provide therapeutic benefit by preventing recognition of self-peptides by pathogenic T cells. We used the crystal structure of DQ8 to select drug-like small molecules predicted to bind structural pockets in the MHC antigen-binding cleft. A limited number of the predicted compounds inhibited DQ8 antigen presentation in vitro, with 1 compound preventing insulin autoantibody production and delaying diabetes onset in an animal model of spontaneous autoimmune diabetes. An existing drug with a similar structure, methyldopa, specifically blocked DQ8 in patients with recent-onset T1D and reduced inflammatory T cell responses to insulin, highlighting the relevance of blocking disease-specific MHC class II antigen presentation to treat autoimmunity.

Simultaneous maximum a posteriori longitudinal PET image reconstruction.

Positron emission tomography (PET) is frequently used to monitor functional changes that occur over extended time scales, for example in longitudinal oncology PET protocols that include routine clinical follow-up scans to assess the efficacy of a course of treatment. In these contexts PET datasets are currently reconstructed into images using single-dataset reconstruction methods. Inspired by recently proposed joint PET-MR reconstruction methods, we propose to reconstruct longitudinal datasets simultaneously by using a joint penalty term in order to exploit the high degree of similarity between longitudinal images. We achieved this by penalising voxel-wise differences between pairs of longitudinal PET images in a one-step-late maximum a posteriori (MAP) fashion, resulting in the MAP simultaneous longitudinal reconstruction (SLR) method. The proposed method reduced reconstruction errors and visually improved images relative to standard maximum likelihood expectation-maximisation (ML-EM) in simulated 2D longitudinal brain tumour scans. In reconstructions of split real 3D data with inserted simulated tumours, noise across images reconstructed with MAP-SLR was reduced to levels equivalent to doubling the number of detected counts when using ML-EM. Furthermore, quantification of tumour activities was largely preserved over a variety of longitudinal tumour changes, including changes in size and activity, with larger changes inducing larger biases relative to standard ML-EM reconstructions. Similar improvements were observed for a range of counts levels, demonstrating the robustness of the method when used with a single penalty strength. The results suggest that longitudinal regularisation is a simple but effective method of improving reconstructed PET images without using resolution degrading priors.

Access to a Guanacastepene and Cortistatin-Related Skeleton via Ethynyl Lactone Ireland-Claisen Rearrangement and Transannular (4 + 3)-Cycloaddition of an Azatrimethylenemethane Diyl.

Heating a 2,5-furanocyclic (2-azidoethyl)allene initiates a cascade reaction comprising azide-allene cycloaddition, loss of nitrogen, and azatrimethylenemethane (ATMM) diyl-furan transannular (4 + 3)-cycloaddition. The major product of this reaction contains the pentacyclic core common to guanacastepenes D and H and radianspenes J-L; in addition, the central oxa-bridged cycloheptene ring, flanked by two carbocyclic rings, is structurally related to the ABC-ring system found in the cortistatins. This is the first reported synthetic application of a "free" (nonconjugated) ATMM. The cyclization precursors were prepared via the first reported examples of the Ireland-Claisen rearrangement of an ethynyl lactone.

Decline of a Rare Moth at Its Last Known English Site: Causes and Lessons for Conservation.

The conditions required by rare species are often only approximately known. Monitoring such species over time can help refine management of their protected areas. We report population trends of a rare moth, the Dark Bordered Beauty Epione vespertaria (Linnaeus, 1767) (Lepidoptera: Geometridae) at its last known English site on a protected lowland heath, and those of its host-plant, Salix repens (L.) (Malpighiales: Salicaceae). Between 2007 and 2014, adult moth density reduced by an average of 30-35% annually over the monitored area, and its range over the monitored area contracted in concert. By comparing data from before this decline (2005) with data taken in 2013, we show that the density of host-plants over the monitored area reduced three-fold overall, and ten-fold in the areas of highest host-plant density. In addition, plants were significantly smaller in 2013. In 2005, moth larvae tended to be found on plants that were significantly larger than average at the time. By 2013, far fewer plants were of an equivalent size. This suggests that the rapid decline of the moth population coincides with, and is likely driven by, changes in the host-plant population. Why the host-plant population has changed remains less certain, but fire, frost damage and grazing damage have probably contributed. It is likely that a reduction in grazing pressure in parts of the site would aid host-plant recovery, although grazing remains an important site management activity. Our work confirms the value of constant monitoring of rare or priority insect species, of the risks posed to species with few populations even when their populations are large, of the potential conflict between bespoke management for species and generic management of habitats, and hence the value of refining our knowledge of rare species' requirements so that their needs can be incorporated into the management of protected areas.

Lessons learned from a lipid lowering trial in adolescents with type 1 diabetes.

Herein, we describe recruitment efforts for a trial of lipid-lowering medications in adolescents with type 1 diabetes, age 12-21 years. Based on our experience, future studies will require multiple centers to enroll a sufficient number of participants for adequate data to direct dyslipidemia medication treatment guidelines for adolescents with type 1 diabetes.