Ratiometric Fluorescent Sensors Illuminate Cellular Magnesium Imbalance in a Model of Acetaminophen-Induced Liver Injury.

Magnesium(II) plays catalytic, structural, regulatory, and signaling roles in living organisms. Abnormal levels of this metal have been associated with numerous pathologies, including cardiovascular disease, diabetes, metabolic syndrome, immunodeficiency, cancer, and, most recently, liver pathologies affecting humans. The role of Mg2+ in the pathophysiology of liver disease, however, has been occluded by concomitant changes in concentration of interfering divalent cations, such as Ca2+, which complicates the interpretation of experiments conducted with existing molecular Mg2+ indicators. Herein, we introduce a new quinoline-based fluorescent sensor, MagZet1, that displays a shift in its excitation and emission wavelengths, affording ratiometric detection of cellular Mg2+ by both fluorescence microscopy and flow cytometry. The new sensor binds the target metal with a submillimolar dissociation constant─well suited for detection of changes in free Mg2+ in cells─and displays a 10-fold selectivity against Ca2+. Furthermore, the fluorescence ratio is insensitive to changes in pH in the physiological range, providing an overall superior performance over existing indicators. We provide insights into the metal selectivity profile of the new sensor based on computational modeling, and we apply it to shed light on a decrease in cytosolic free Mg2+ and altered expression of metal transporters in cellular models of drug-induced liver injury caused by acetaminophen overdose.

Predicting 10-year breast cancer mortality risk in the general female population in England: a model development and validation study.

BACKGROUND: Identifying female individuals at highest risk of developing life-threatening breast cancers could inform novel stratified early detection and prevention strategies to reduce breast cancer mortality, rather than only considering cancer incidence. We aimed to develop a prognostic model that accurately predicts the 10-year risk of breast cancer mortality in female individuals without breast cancer at baseline. METHODS: In this model development and validation study, we used an open cohort study from the QResearch primary care database, which was linked to secondary care and national cancer and mortality registers in England, UK. The data extracted were from female individuals aged 20-90 years without previous breast cancer or ductal carcinoma in situ who entered the cohort between Jan 1, 2000, and Dec 31, 2020. The primary outcome was breast cancer-related death, which was assessed in the full dataset. Cox proportional hazards, competing risks regression, XGBoost, and neural network modelling approaches were used to predict the risk of breast cancer death within 10 years using routinely collected health-care data. Death due to causes other than breast cancer was the competing risk. Internal-external validation was used to evaluate prognostic model performance (using Harrell's C, calibration slope, and calibration in the large), performance heterogeneity, and transportability. Internal-external validation involved dataset partitioning by time period and geographical region. Decision curve analysis was used to assess clinical utility. FINDINGS: We identified data for 11 626 969 female individuals, with 70 095 574 person-years of follow-up. There were 142 712 (1·2%) diagnoses of breast cancer, 24 043 (0·2%) breast cancer-related deaths, and 696 106 (6·0%) deaths from other causes. Meta-analysis pooled estimates of Harrell's C were highest for the competing risks model (0·932, 95% CI 0·917-0·946). The competing risks model was well calibrated overall (slope 1·011, 95% CI 0·978-1·044), and across different ethnic groups. Decision curve analysis suggested favourable clinical utility across all age groups. The XGBoost and neural network models had variable performance across age and ethnic groups. INTERPRETATION: A model that predicts the combined risk of developing and then dying from breast cancer at the population level could inform stratified screening or chemoprevention strategies. Further evaluation of the competing risks model should comprise effect and health economic assessment of model-informed strategies. FUNDING: Cancer Research UK.

Climate analogs can catalyze cross-regional dialogs for US specialty crop adaptation.

Communication theory suggests that interactive dialog rather than information transmission is necessary for climate change action, especially for complex systems like agriculture. Climate analogs-locations whose current climate is similar to a target location's future climate-have garnered recent interest as transmitting more relatable information; however, they have unexplored potential in facilitating meaningful dialogs, and whether the way the analogs are developed could make a difference. We developed climate context-specific analogs based on agriculturally-relevant climate metrics for US specialty crop production, and explored their potential for facilitating dialogs on climate adaptation options. Over 80% of US specialty crop counties had acceptable US analogs for the mid-twenty-first century, especially in the West and Northeast which had greater similarities in the crops produced across target-analog pairs. Western counties generally had analogs to the south, and those in other regions had them to the west. A pilot dialog of target-analog pairs showed promise in eliciting actionable adaptation insights, indicating potential value in incorporating analog-driven dialogs more broadly in climate change communication.

Water Vapor Induced Superionic Conductivity in ZnPS3.

Next-generation batteries based on sustainable multivalent working ions, such as Mg2+, Ca2+, or Zn2+, have the potential to improve the performance, safety, and capacity of current battery systems. Development of such multivalent ion batteries is hindered by a lack of understanding of multivalent ionics in solids, which is crucial for many aspects of battery operation. For instance, multivalent ionic transport was assumed to be correlated with electronic transport; however, we have previously shown that Zn2+ can conduct in electronically insulating ZnPS3 with a low activation energy of 350 meV, albeit with low ionic conductivity. Here, we show that exposure of ZnPS3 to environments with water vapor at different relative humidities results in room-temperature conductivity increases of several orders of magnitude, reaching as high as 1.44 mS cm-1 without decomposition or structural changes. We utilize impedance spectroscopy with ion selective electrodes, ionic transference number measurements, and deposition and stripping of Zn metal, to confirm that both Zn2+ and H+ act as mobile ions. The contribution from Zn2+ to the ionic conductivity in water vapor exposed ZnPS3 is high, representing superionic Zn2+ conduction. The present study demonstrates that it is possible to enhance multivalent ion conduction of electronically insulating solids as a result of water adsorption and highlights the importance of ensuring that increased conductivity in water vapor exposed multivalent ion systems is in fact due to mobile multivalent ions and not solely H+.

Development and internal-external validation of statistical and machine learning models for breast cancer prognostication: cohort study.

OBJECTIVE: To develop a clinically useful model that estimates the 10 year risk of breast cancer related mortality in women (self-reported female sex) with breast cancer of any stage, comparing results from regression and machine learning approaches. DESIGN: Population based cohort study. SETTING: QResearch primary care database in England, with individual level linkage to the national cancer registry, Hospital Episodes Statistics, and national mortality registers. PARTICIPANTS: 141 765 women aged 20 years and older with a diagnosis of invasive breast cancer between 1 January 2000 and 31 December 2020. MAIN OUTCOME MEASURES: Four model building strategies comprising two regression (Cox proportional hazards and competing risks regression) and two machine learning (XGBoost and an artificial neural network) approaches. Internal-external cross validation was used for model evaluation. Random effects meta-analysis that pooled estimates of discrimination and calibration metrics, calibration plots, and decision curve analysis were used to assess model performance, transportability, and clinical utility. RESULTS: During a median 4.16 years (interquartile range 1.76-8.26) of follow-up, 21 688 breast cancer related deaths and 11 454 deaths from other causes occurred. Restricting to 10 years maximum follow-up from breast cancer diagnosis, 20 367 breast cancer related deaths occurred during a total of 688 564.81 person years. The crude breast cancer mortality rate was 295.79 per 10 000 person years (95% confidence interval 291.75 to 299.88). Predictors varied for each regression model, but both Cox and competing risks models included age at diagnosis, body mass index, smoking status, route to diagnosis, hormone receptor status, cancer stage, and grade of breast cancer. The Cox model's random effects meta-analysis pooled estimate for Harrell's C index was the highest of any model at 0.858 (95% confidence interval 0.853 to 0.864, and 95% prediction interval 0.843 to 0.873). It appeared acceptably calibrated on calibration plots. The competing risks regression model had good discrimination: pooled Harrell's C index 0.849 (0.839 to 0.859, and 0.821 to 0.876, and evidence of systematic miscalibration on summary metrics was lacking. The machine learning models had acceptable discrimination overall (Harrell's C index: XGBoost 0.821 (0.813 to 0.828, and 0.805 to 0.837); neural network 0.847 (0.835 to 0.858, and 0.816 to 0.878)), but had more complex patterns of miscalibration and more variable regional and stage specific performance. Decision curve analysis suggested that the Cox and competing risks regression models tested may have higher clinical utility than the two machine learning approaches. CONCLUSION: In women with breast cancer of any stage, using the predictors available in this dataset, regression based methods had better and more consistent performance compared with machine learning approaches and may be worthy of further evaluation for potential clinical use, such as for stratified follow-up.

Clinical Presentation of Mpox in People With and Without HIV in the United Kingdom During the 2022 Global Outbreak.

Early UK surveillance data revealed that people living with HIV were overrepresented among cases of monkeypox (mpox). However, it remains unknown whether mpox infection is more severe in people living with well-controlled HIV. All laboratory-confirmed mpox cases presenting between May and December 2022 to one London hospital service were identified via pathology reporting systems. We extracted demographic and clinical data to allow comparison of clinical presentation and severity of mpox among people with and without HIV. We identified 150 people with mpox (median age 36 years, 99.3% male, 92.7% reporting sex with other men). HIV status was available for 144 individuals, 58 (40.3%) of whom were HIV positive (only 3/58 had CD4 cell counts <200 cells/mm3 and 5/58 had HIV RNA >200 copies/mL). People with HIV had similar clinical presentations to those without HIV, including indicators of more widespread disease, such as extragenital lesions (74.1% vs. 64.0%, p = .20) and nondermatological symptoms (87.9% vs. 82.6%, p = .38). People with HIV also experienced a similar time from onset of symptoms to discharge from all inpatient or outpatient clinical follow-up (p = .63) and total time under follow-up (p = .88) compared with people without HIV. A similar proportion of people with HIV required review in the hospital emergency department (36.2% vs. 25.6%, p = .17) or admission to hospital (19.0% vs. 9.3%, p = .09). There were no recorded deaths. In this cohort of people with mpox, there was a high prevalence of HIV coinfection, the majority of which was well-controlled. We find no evidence that people with well-controlled HIV experienced more severe mpox infection.

Cardiac abnormalities in Long COVID 1-year post-SARS-CoV-2 infection.

BACKGROUND: Long COVID is associated with multiple symptoms and impairment in multiple organs. Cross-sectional studies have reported cardiac impairment to varying degrees by varying methodologies. Using cardiac MR (CMR), we investigated a 12-month trajectory of abnormalities in Long COVID. OBJECTIVES: To investigate cardiac abnormalities 1-year post-SARS-CoV-2 infection. METHODS: 534 individuals with Long COVID underwent CMR (T1/T2 mapping, cardiac mass, volumes, function and strain) and multiorgan MRI at 6 months (IQR 4.3-7.3) since first post-COVID-19 symptoms. 330 were rescanned at 12.6 (IQR 11.4-14.2) months if abnormal baseline findings were reported. Symptoms, questionnaires and blood samples were collected at both time points. CMR abnormalities were defined as ≥1 of low left or right ventricular ejection fraction (LVEF), high left or right ventricular end diastolic volume, low 3D left ventricular global longitudinal strain (GLS), or elevated native T1 in ≥3 cardiac segments. Significant change over time was reported by comparison with 92 healthy controls. RESULTS: Technical success of multiorgan and CMR assessment in non-acute settings was 99.1% and 99.6% at baseline, and 98.3% and 98.8% at follow-up. Of individuals with Long COVID, 102/534 (19%) had CMR abnormalities at baseline; 71/102 had complete paired data at 12 months. Of those, 58% presented with ongoing CMR abnormalities at 12 months. High sensitivity cardiac troponin I and B-type natriuretic peptide were not predictive of CMR findings, symptoms or clinical outcomes. At baseline, low LVEF was associated with persistent CMR abnormality, abnormal GLS associated with low quality of life and abnormal T1 in at least three segments was associated with better clinical outcomes at 12 months. CONCLUSION: CMR abnormalities (left entricular or right ventricular dysfunction/dilatation and/or abnormal T1mapping), occurred in one in five individuals with Long COVID at 6 months, persisting in over half of those at 12 months. Cardiac-related blood biomarkers could not identify CMR abnormalities in Long COVID. TRIAL REGISTRATION NUMBER: NCT04369807.

Estimation of field inhomogeneity map following magnitude-based ambiguity-resolved water-fat separation.

Magnitude-based PDFF (Proton Density Fat Fraction) and R2∗ mapping with resolved water-fat ambiguity is extended to calculate field inhomogeneity (field map) using the phase images. The estimation is formulated in matrix form, resolving the field map in a least-squares sense. PDFF and R2∗ from magnitude fitting may be updated using the estimated field maps. The limits of quantification of our voxel-independent implementation were assessed. Bland-Altman was used to compare PDFF and field maps from our method against a reference complex-based method on 152 UK Biobank subjects (1.5 T Siemens). A separate acquisition (3 T Siemens) presenting field inhomogeneities was also used. The proposed field mapping was accurate beyond double the complex-based limit range. High agreement was obtained between the proposed method and the reference in UK. Robust field mapping was observed at 3 T, for inhomogeneities over 400 Hz including rapid variation across edges. Field mapping following unambiguous magnitude-based water-fat separation was demonstrated in-vivo and showed potential at 3 T.

Comparison of liver iron concentration calculated from R2* at 1.5 T and 3 T.

PURPOSE: R2*, a measurement obtained using magnetic resonance imaging (MRI) can be used to estimate liver iron concentration (LIC). 3 T and 1.5 T scanners can be used but conversion of 3 T R2* to LIC is less well validated. In this study the aim was to compare 3 T-R2* LIC and 1.5 T-R2* LIC estimations to assess if they can be used interchangeably. METHODS: Thirty participants were scanned at both 1.5 T and 3 T. R2* was measured at both field strengths. 3 T R2* and 1.5 R2* were compared using linear regression and were converted to LIC using different calibration curves. Pearson's rho and Intraclass Correlation Coefficients (ICCs) were used to assess correlation and agreement between 1.5 and 3 T LIC. Bland Altman plots were used to assess bias and limits of agreement. RESULTS: All 1.5 T and 3 T LIC comparisons gave Pearson's rho of 0.99 (p < 0.001). ICC ranged from 0.83 (p = 0.005) to 0.96 (p < 0.001). Biases had magnitude of less than 0.2 mg/g dry weight. CONCLUSION: Agreement and bias between 3 and 1.5 T-R2* LIC depended on the method used for conversion. There were instances when the agreement was excellent and bias was small, indicating that potentially 3 T-R2* LIC can be used alongside or instead of 1.5 T-R2* LIC but care needs to be taken over the conversion methods selected. TRIAL REGISTRATION NUMBER: Clinicaltrials.gov NCT03743272, 16 November 2018.

Free HIV self-test for identification and linkage to care of previously undetected HIV infection in men who have sex with men in England and Wales (SELPHI): an open-label, internet-based, randomised controlled trial.

BACKGROUND: High levels of HIV testing in men who have sex with men remain key to reducing the incidence of HIV. We aimed to assess whether the offer of a single, free HIV self-testing kit led to increased HIV diagnoses with linkage to care. METHODS: SELPHI was an internet-based, open-label, randomised controlled trial that recruited participants via sexual and social networking sites. Eligibility criteria included being a man or trans woman (although trans women are reported separately); being resident in England or Wales, UK; being aged 16 years or older; having had anal intercourse with a man; not having a positive HIV diagnosis; and being willing to provide name, email address, date of birth, and consent to link to national HIV databases. Participants were randomly allocated (3:2) by computer-generated number sequence to receive a free HIV self-test kit (BT group) or to not receive this free kit (nBT group). Online surveys collected data at baseline, 2 weeks after enrolment (BT group only), 3 months after enrolment, and at the end of the study. The primary outcome was confirmed (linked to care) new HIV diagnosis within 3 months of enrolment, analysed by intention to treat. Those assessing the primary outcome were masked to allocation. This study is registered with the ISRCTN Clinical Trials Register, number ISRCTN20312003. FINDINGS: 10 111 participants (6049 in BT group and 4062 in nBT group) enrolled between Feb 16, 2017, and March 1, 2018. The median age of participants was 33 years (IQR 26-44 years); 9000 (89%) participants were White; 8118 (80%) participants were born in the UK; 81 (1%) participants were transgender men; 4706 (47%) participants were university educated; 1537 (15%) participants had never been tested for HIV; and 389 (4%) participants were taking pre-exposure prophylaxis. At enrolment, 7282 (72%) participants reported condomless anal sex with at least one male partner in the previous 3 months. In the BT group, of the 4511 participants for whom HIV testing information was available, 4263 (95%) reported having used the free HIV self-test kit within 3 months.Within 3 months of enrolment there were 19 confirmed new HIV diagnoses (0·31%) in 6049 participants in the BT group and 15 (0·37%) of 4062 in the nBT group (p=0·64). INTERPRETATION: The offer of a single, free HIV self-test did not lead to increased rates of new HIV diagnoses, which could reflect decreasing HIV incidence rates in the UK. Nonetheless, the offer of a free HIV self-testing kit resulted in high HIV testing rates, indicating that self-testing is an attractive testing option for a large group of men who have sex with men. FUNDING: UK National Institute for Health and Care Research.

Greater ectopic fat deposition and liver fibroinflammation and lower skeletal muscle mass in people with type 2 diabetes.

OBJECTIVE: Type 2 diabetes (T2D) is associated with significant end-organ damage and ectopic fat accumulation. Multiparametric magnetic resonance imaging (MRI) can provide a rapid, noninvasive assessment of multiorgan and body composition. The primary objective of this study was to investigate differences in visceral adiposity, ectopic fat accumulation, body composition, and relevant biomarkers between people with and without T2D. METHODS: Participant demographics, routine biochemistry, and multiparametric MRI scans of the liver, pancreas, visceral and subcutaneous adipose tissue, and skeletal muscle were analyzed from 266 participants (131 with T2D and 135 without T2D) who were matched for age, gender, and BMI. Wilcoxon and χ2 tests were performed to calculate differences between groups. RESULTS: Participants with T2D had significantly elevated liver fat (7.4% vs. 5.3%, p = 0.011) and fibroinflammation (as assessed by corrected T1 [cT1]; 730 milliseconds vs. 709 milliseconds, p = 0.019), despite there being no differences in liver biochemistry, serum aspartate aminotransferase (p = 0.35), or alanine transaminase concentration (p = 0.11). Significantly lower measures of skeletal muscle index (45.2 cm2 /m2 vs. 50.6 cm2 /m2 , p = 0.003) and high-density lipoprotein cholesterol (1.1 mmol/L vs. 1.3 mmol/L, p < 0.0001) were observed in participants with T2D. CONCLUSIONS: Multiparametric MRI revealed significantly elevated liver fat and fibroinflammation in participants with T2D, despite normal liver biochemistry. This study corroborates findings of significantly lower measures of skeletal muscle and high-density lipoprotein cholesterol in participants with T2D versus those without T2D.

Pancreas MRI Segmentation Into Head, Body, and Tail Enables Regional Quantitative Analysis of Heterogeneous Disease.

BACKGROUND: Quantitative imaging studies of the pancreas have often targeted the three main anatomical segments, head, body, and tail, using manual region of interest strategies to assess geographic heterogeneity. Existing automated analyses have implemented whole-organ segmentation, providing overall quantification but failing to address spatial heterogeneity. PURPOSE: To develop and validate an automated method for pancreas segmentation into head, body, and tail subregions in abdominal MRI. STUDY TYPE: Retrospective. SUBJECTS: One hundred and fifty nominally healthy subjects from UK Biobank (100 subjects for method development and 50 subjects for validation). A separate 390 UK Biobank triples of subjects including type 2 diabetes mellitus (T2DM) subjects and matched nondiabetics. FIELD STRENGTH/SEQUENCE: A 1.5 T, three-dimensional two-point Dixon sequence (for segmentation and volume assessment) and a two-dimensional axial multiecho gradient-recalled echo sequence. ASSESSMENT: Pancreas segments were annotated by four raters on the validation cohort. Intrarater agreement and interrater agreement were reported using Dice overlap (Dice similarity coefficient [DSC]). A segmentation method based on template registration was developed and evaluated against annotations. Results on regional pancreatic fat assessment are also presented, by intersecting the three-dimensional parts segmentation with one available proton density fat fraction (PDFF) image. STATISTICAL TEST: Wilcoxon signed rank test and Mann-Whitney U-test for comparisons. DSC and volume differences for evaluation. A P value < 0.05 was considered statistically significant. RESULTS: Good intrarater (DSC mean, head: 0.982, body: 0.940, tail: 0.961) agreement and interrater (DSC mean, head: 0.968, body: 0.905, tail: 0.943) agreement were observed. No differences (DSC, head: P = 0.4358, body: P = 0.0992, tail: P = 0.1080) were observed between the manual annotations and our method's segmentations (DSC mean, head: 0.965, body: 0.893, tail: 0.934). Pancreatic body PDFF was different between T2DM and nondiabetics matched by body mass index. DATA CONCLUSION: The developed segmentation's performance was no different from manual annotations. Application on type 2 diabetes subjects showed potential for assessing pancreatic disease heterogeneity. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 3.

Is mpox an STI? The societal aspects and healthcare implications of a key question.

This letter explores the societal aspects and healthcare implications that underlie thinking about mpox (formerly known was monkeypox), in the 2022 outbreak, as a sexually transmitted infection (STI). The authors examine what underlies this question, exploring what is an STI, what is sex, and what is the role of stigma in sexual health promotion. The authors argue that, in this specific outbreak, mpox is an STI among men who have sex with men (MSM). The authors highlight the need of critically thinking about how to communicate effectively, the role of homophobia and other inequalities, and the importance of the social sciences.

"What ELSE about this job?" Teaching Job Decision-Making to College Students With Intellectual and Developmental Disabilities.

Decision-making is the central element of self-determination, requiring targeted, systematic instruction to learn. In this study, researchers developed a multicomponent intervention, "What ELSE about this job?", to teach job decision-making skills to college students with intellectual and developmental disabilities. The intervention coupled remote audio coaching (RAC) with a mnemonic device, 'ELSE', to guide students to make decisions about whether certain jobs would be a good fit for them. The study aimed to investigate the effectiveness of the intervention and determine whether the skills would maintain once the intervention was removed. All students who received the intervention substantially increased their ability to make job decisions. Additionally, all participants maintained the skills upon removal of the intervention, and showed signs of generalizing their skills to novel job coaches and web-based job search apps. Implications and future research are discussed.

Impacts of irrigation efficiency on water-dependent sectors are heavily controlled by region-specific institutions and infrastructures.

Farmers' investment in more efficient irrigation systems represents a primary adaptation strategy when confronting climate change. However, the regional benefits of these investments and their influence on the conflicting demands among different water dependent stakeholders for intensely irrigated regions remains an open question. Using the Pacific Northwest of the United States as an illustrative region of focus, we show that higher irrigation efficiency has diverse effects across stakeholders that are contingent on many local climatic, institutional and infrastructural factors such as the availability of water storage, the location of hydropower generators, and water rights. These complexities limit simple abstractions of irrigation efficiency as broader policy challenge and are central to its inclusion within the class of "wicked problems". Additionally, we argue that the widely used rebound effect concept, which implicitly discourages irrigation efficiency supporting policies, should not be assumed to fully capture the nuances of the complex suite of regional impacts that emerge from irrigation efficiency investments. Consequently, the evaluation of irrigation efficiency investments requires a broader framing across a diversity of perspectives. policies and actions that are pluralistic, context-specific, and closely engage various groups of stakeholders in the policymaking process.

Fair shares: building and benefiting from healthcare AI with mutually beneficial structures and development partnerships.

Artificial intelligence (AI) algorithms are used in an increasing range of aspects of our lives. In particular, medical applications of AI are being developed and deployed, including many in image analysis. Deep learning methods, which have recently proved successful in image classification, rely on large volumes of clinical data generated by healthcare institutions. Such data is collected from their served populations. In this opinion article, using digital mammographic screening as an example, we briefly consider the background to AI development and some issues around its deployment. We highlight the importance of high quality clinical data as fundamental to these technologies, and question how the ownership of resultant tools should be defined. Though many of the ethical issues concerning the development and use of medical AI technologies continue to be discussed, the value of the data on which they rely remains a subject that is seldom considered. This potentially controversial issue can and should be addressed in a way which is beneficial to all parties, particularly the population in general and the patients we serve.

A simple, open and extensible gating Control unit for cardiac and respiratory synchronisation control in small animal MRI and demonstration of its robust performance in steady-state maintained CINE-MRI.

Prospective cardiac gating during MRI is hampered by electromagnetic induction from the rapidly switched imaging gradients into the ECG detection circuit. This is particularly challenging in small animal MRI, as higher heart rates combined with a smaller myocardial mass render routine ECG detection challenging. We have developed an open-hardware system that enables continuously running MRI scans to be performed in conjunction with cardio-respiratory gating such that the relaxation-weighted steady state magnetisation is maintained throughout the scan. This requires that the R-wave must be detected reliably even in the presence of rapidly switching gradients, and that data previously acquired that were corrupted by respiratory motion re-acquired. The accurately maintained steady-state magnetisation leads to an improvement in image quality and removes alterations in intensity that may otherwise occur throughout the cardiac cycle and impact upon automated image analysis. We describe the hardware required to enable this and demonstrate its application and robust performance using prospectively cardio-respiratory gated CINE imaging that is operated at a single, constant TR. Schematics, technical drawings, component listing and assembly instructions are made publicly available.

RICE: A method for quantitative mammographic image enhancement.

We introduce Region of Interest Contrast Enhancement (RICE) to identify focal densities in mammograms. It aims to help radiologists: 1) enhancing the contrast of mammographic images; and 2) detecting regions of interest (such as focal densities) that are candidate masses potentially masked behind dense parenchyma. Cancer masking is an unsolved issue, particularly in breast density categories BI-RADS C and D. RICE suppresses normal breast parenchyma in order to highlight focal densities. Unlike methods that enhance mammograms by modifying the dynamic range of an image; RICE relies on the actual tissue composition of the breast. It segments Volumetric Breast Density (VBD) maps into smaller regions and then applies a recursive mechanism to estimate the 'neighbourhood' for each segment. The method then subtracts and updates the neighbourhood, or the encompassing tissue, from each piecewise constant component of the breast image. This not only enhances the appearance of a candidate mass but also helps in estimating the mass density. In extensive experiments, RICE enhances focal densities in all breast density types including the most challenging category BI-RADS D. Suitably adapted, RICE can be used as a precursor to any computer-aided diagnostics and detection system.

Multiorgan impairment in low-risk individuals with post-COVID-19 syndrome: a prospective, community-based study.

OBJECTIVE: To assess medium-term organ impairment in symptomatic individuals following recovery from acute SARS-CoV-2 infection. DESIGN: Baseline findings from a prospective, observational cohort study. SETTING: Community-based individuals from two UK centres between 1 April and 14 September 2020. PARTICIPANTS: Individuals ≥18 years with persistent symptoms following recovery from acute SARS-CoV-2 infection and age-matched healthy controls. INTERVENTION: Assessment of symptoms by standardised questionnaires (EQ-5D-5L, Dyspnoea-12) and organ-specific metrics by biochemical assessment and quantitative MRI. MAIN OUTCOME MEASURES: Severe post-COVID-19 syndrome defined as ongoing respiratory symptoms and/or moderate functional impairment in activities of daily living; single-organ and multiorgan impairment (heart, lungs, kidneys, liver, pancreas, spleen) by consensus definitions at baseline investigation. RESULTS: 201 individuals (mean age 45, range 21-71 years, 71% female, 88% white, 32% healthcare workers) completed the baseline assessment (median of 141 days following SARS-CoV-2 infection, IQR 110-162). The study population was at low risk of COVID-19 mortality (obesity 20%, hypertension 7%, type 2 diabetes 2%, heart disease 5%), with only 19% hospitalised with COVID-19. 42% of individuals had 10 or more symptoms and 60% had severe post-COVID-19 syndrome. Fatigue (98%), muscle aches (87%), breathlessness (88%) and headaches (83%) were most frequently reported. Mild organ impairment was present in the heart (26%), lungs (11%), kidneys (4%), liver (28%), pancreas (40%) and spleen (4%), with single-organ and multiorgan impairment in 70% and 29%, respectively. Hospitalisation was associated with older age (p=0.001), non-white ethnicity (p=0.016), increased liver volume (p<0.0001), pancreatic inflammation (p<0.01), and fat accumulation in the liver (p<0.05) and pancreas (p<0.01). Severe post-COVID-19 syndrome was associated with radiological evidence of cardiac damage (myocarditis) (p<0.05). CONCLUSIONS: In individuals at low risk of COVID-19 mortality with ongoing symptoms, 70% have impairment in one or more organs 4 months after initial COVID-19 symptoms, with implications for healthcare and public health, which have assumed low risk in young people with no comorbidities. TRIAL REGISTRATION NUMBER: NCT04369807; Pre-results.