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Size Matters: Micro v. Nanobubbles in Ultrasound Imaging and Therapy

Use of oxygen-loaded nanobubbles to improve tissue oxygenation: Bone-relevant mechanisms of action and effects on osteoclast differentiation.

Gas-loaded nanobubbles have potential as a method of oxygen delivery to increase tumour oxygenation and therapeutically alleviate tumour hypoxia. However, the mechanism(s) whereby oxygen-loaded nanobubbles increase tumour oxygenation are unknown; with their calculated oxygen-carrying capacity being insufficient to explain this effect. Intra-tumoural hypoxia is a prime therapeutic target, at least partly due to hypoxia-dependent stimulation of the formation and function of bone-resorbing osteoclasts which establish metastatic cells in bone. This study aims to investigate potential mechanism(s) of oxygen delivery and in particular the possible use of oxygen-loaded nanobubbles in preventing bone metastasis via effects on osteoclasts. Lecithin-based nanobubbles preferentially interacted with phagocytic cells (monocytes, osteoclasts) via a combination of lipid transfer, clathrin-dependent endocytosis and phagocytosis. This interaction caused general suppression of osteoclast differentiation via inhibition of cell fusion. Additionally, repeat exposure to oxygen-loaded nanobubbles inhibited osteoclast formation to a greater extent than nitrogen-loaded nanobubbles. This gas-dependent effect was driven by differential effects on the fusion of mononuclear precursor cells to form pre-osteoclasts, partly due to elevated potentiation of RANKL-induced ROS by nitrogen-loaded nanobubbles. Our findings suggest that oxygen-loaded nanobubbles could represent a promising therapeutic strategy for cancer therapy; reducing osteoclast formation and therefore bone metastasis via preferential interaction with monocytes/macrophages within the tumour and bone microenvironment, in addition to known effects of directly improving tumour oxygenation.

Impact of the COVID-19 pandemic on paediatric patients with cancer in low-income, middle-income and high-income countries: protocol for a multicentre, international, observational cohort study.

INTRODUCTION: Childhood cancers are a leading cause of non-communicable disease deaths for children around the world. The COVID-19 pandemic may have impacted on global children's cancer services, which can have consequences for childhood cancer outcomes. The Global Health Research Group on Children's Non-Communicable Diseases is currently undertaking the first international cohort study to determine the variation in paediatric cancer management during the COVID-19 pandemic, and the short-term to medium-term impacts on childhood cancer outcomes. METHODS AND ANALYSIS: This is a multicentre, international cohort study that will use routinely collected hospital data in a deidentified and anonymised form. Patients will be recruited consecutively into the study, with a 12-month follow-up period. Patients will be included if they are below the age of 18 years and undergoing anticancer treatment for the following cancers: acute lymphoblastic leukaemia, Burkitt lymphoma, Hodgkin lymphoma, Wilms tumour, sarcoma, retinoblastoma, gliomas, medulloblastomas and neuroblastomas. Patients must be newly presented or must be undergoing active anticancer treatment from 12 March 2020 to 12 December 2020. The primary objective of the study was to determine all-cause mortality rates of 30 days, 90 days and 12 months. This study will examine the factors that influenced these outcomes. χ2 analysis will be used to compare mortality between low-income and middle-income countries and high-income countries. Multilevel, multivariable logistic regression analysis will be undertaken to identify patient-level and hospital-level factors affecting outcomes with adjustment for confounding factors. ETHICS AND DISSEMINATION: At the host centre, this study was deemed to be exempt from ethical committee approval due to the use of anonymised registry data. At other centres, participating collaborators have gained local approvals in accordance with their institutional ethical regulations. Collaborators will be encouraged to present the results locally, nationally and internationally. The results will be submitted for publication in a peer-reviewed journal.

Exploring the levels of variation, inequality and use of physical activity intervention referrals in England primary care from 2017-2020: a retrospective cohort study.

OBJECTIVES: In this study, we explore the use of physical activity intervention referrals in primary care in England and compare their use with the rate of cardiovascular disease (CVD) risk factors in England from 2017 to 2020. We also explore variation and inequalities in referrals to these interventions in England across the study period. DESIGN: Retrospective cohort study. SETTING: England primary care via the Royal College of General Practitioners Research Surveillance Centre. PARTICIPANTS: The Royal College of General Practitioners Research Surveillance Centre, a sentinel network across England covering a population of over 15 000 000 registered patients, was used for data analyses covering the 2017-2020 financial years and including patients with long-term conditions indicating CVD risk factors. OUTCOME MEASURES: An existing ontology of primary care codes was used to capture physical activity interventions and a new ontology was designed to cover long-term conditions indicating CVD risk factors. Single factor analysis of variance, paired samples t-test and two-tailed, one proportion z-tests were used to determine the significance of our findings. RESULTS: We observed statistically significant variation in physical activity intervention referrals for people with CVD risk factors from different ethnic groups and age groups across different regions of England as well as a marked decrease during the COVID-19 pandemic. Interestingly, a significant difference was not seen for different socioeconomic groups or sexes. Across all attributes and time periods (with the exception of the 18-39 group, 2017-2019), we observed a statistically significant underuse of physical activity intervention referrals. CONCLUSIONS: Our findings identified statistically significant variation and underuse of physical activity referrals in primary care in England for individuals at risk of CVD for different population subgroups, especially different ethnicities and age groups, across different regions of England and across time, with the COVID-19 pandemic exerting a significant negative impact on referral rates.

Characterisation of progenitor cells in human atherosclerotic vessels.

Recent data from animal models has demonstrated that both endothelial and smooth muscle progenitor cells contribute to the development of atherosclerosis. However, no data exists concerning the presence of progenitor cells in human atherosclerotic vessels. In the present study, a range of normal and atherosclerotic human arteries were collected from patients undergoing coronary artery bypass surgery. Segments of internal mammary artery (normal controls), and segments of proximal ascending aorta with visible fatty streak were analysed. Immunofluorescence was used to detect a panel of progenitor cell markers. A small number of progenitor cells were identified within neointimal lesions and the adventitia with variable expression of CD34, stem cell antigen (Sca-1), c-kit and VEGF receptor 2 (VEGFR2) markers, but no CD133 expression. On average there was a two- to three-fold increase in progenitor cell number in the adventitia of atherosclerotic vessels compared with normal controls, with a significant difference (p<0.05) in the frequency of cells expressing VEGFR2. Thus, we have provided the first evidence that vascular progenitor cells exist within atherosclerotic lesions, and identified an increased number of progenitor cells in the adventitia of human atherosclerotic vessels. These cells might be a source for smooth muscle cells (SMCs), macrophages and endothelial cells (ECs) that form atherosclerotic lesions.

Safe AI-enabled digital health technologies need built-in open feedback.

Author Correction: The IDEAL framework for surgical robotics: development, comparative evaluation and long-term monitoring.

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950-2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021.

BACKGROUND: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020-21 COVID-19 pandemic period. METHODS: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. FINDINGS: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5-65·1] decline), and increased during the COVID-19 pandemic period (2020-21; 5·1% [0·9-9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98-5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50-6·01) in 2019. An estimated 131 million (126-137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7-17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8-24·8), from 49·0 years (46·7-51·3) to 71·7 years (70·9-72·5). Global life expectancy at birth declined by 1·6 years (1·0-2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67-8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4-52·7]) and south Asia (26·3% [9·0-44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. INTERPRETATION: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic. FUNDING: Bill & Melinda Gates Foundation.

Global fertility in 204 countries and territories, 1950-2021, with forecasts to 2100: a comprehensive demographic analysis for the Global Burden of Disease Study 2021.

BACKGROUND: Accurate assessments of current and future fertility-including overall trends and changing population age structures across countries and regions-are essential to help plan for the profound social, economic, environmental, and geopolitical challenges that these changes will bring. Estimates and projections of fertility are necessary to inform policies involving resource and health-care needs, labour supply, education, gender equality, and family planning and support. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 produced up-to-date and comprehensive demographic assessments of key fertility indicators at global, regional, and national levels from 1950 to 2021 and forecast fertility metrics to 2100 based on a reference scenario and key policy-dependent alternative scenarios. METHODS: To estimate fertility indicators from 1950 to 2021, mixed-effects regression models and spatiotemporal Gaussian process regression were used to synthesise data from 8709 country-years of vital and sample registrations, 1455 surveys and censuses, and 150 other sources, and to generate age-specific fertility rates (ASFRs) for 5-year age groups from age 10 years to 54 years. ASFRs were summed across age groups to produce estimates of total fertility rate (TFR). Livebirths were calculated by multiplying ASFR and age-specific female population, then summing across ages 10-54 years. To forecast future fertility up to 2100, our Institute for Health Metrics and Evaluation (IHME) forecasting model was based on projections of completed cohort fertility at age 50 years (CCF50; the average number of children born over time to females from a specified birth cohort), which yields more stable and accurate measures of fertility than directly modelling TFR. CCF50 was modelled using an ensemble approach in which three sub-models (with two, three, and four covariates variously consisting of female educational attainment, contraceptive met need, population density in habitable areas, and under-5 mortality) were given equal weights, and analyses were conducted utilising the MR-BRT (meta-regression-Bayesian, regularised, trimmed) tool. To capture time-series trends in CCF50 not explained by these covariates, we used a first-order autoregressive model on the residual term. CCF50 as a proportion of each 5-year ASFR was predicted using a linear mixed-effects model with fixed-effects covariates (female educational attainment and contraceptive met need) and random intercepts for geographical regions. Projected TFRs were then computed for each calendar year as the sum of single-year ASFRs across age groups. The reference forecast is our estimate of the most likely fertility future given the model, past fertility, forecasts of covariates, and historical relationships between covariates and fertility. We additionally produced forecasts for multiple alternative scenarios in each location: the UN Sustainable Development Goal (SDG) for education is achieved by 2030; the contraceptive met need SDG is achieved by 2030; pro-natal policies are enacted to create supportive environments for those who give birth; and the previous three scenarios combined. Uncertainty from past data inputs and model estimation was propagated throughout analyses by taking 1000 draws for past and present fertility estimates and 500 draws for future forecasts from the estimated distribution for each metric, with 95% uncertainty intervals (UIs) given as the 2·5 and 97·5 percentiles of the draws. To evaluate the forecasting performance of our model and others, we computed skill values-a metric assessing gain in forecasting accuracy-by comparing predicted versus observed ASFRs from the past 15 years (2007-21). A positive skill metric indicates that the model being evaluated performs better than the baseline model (here, a simplified model holding 2007 values constant in the future), and a negative metric indicates that the evaluated model performs worse than baseline. FINDINGS: During the period from 1950 to 2021, global TFR more than halved, from 4·84 (95% UI 4·63-5·06) to 2·23 (2·09-2·38). Global annual livebirths peaked in 2016 at 142 million (95% UI 137-147), declining to 129 million (121-138) in 2021. Fertility rates declined in all countries and territories since 1950, with TFR remaining above 2·1-canonically considered replacement-level fertility-in 94 (46·1%) countries and territories in 2021. This included 44 of 46 countries in sub-Saharan Africa, which was the super-region with the largest share of livebirths in 2021 (29·2% [28·7-29·6]). 47 countries and territories in which lowest estimated fertility between 1950 and 2021 was below replacement experienced one or more subsequent years with higher fertility; only three of these locations rebounded above replacement levels. Future fertility rates were projected to continue to decline worldwide, reaching a global TFR of 1·83 (1·59-2·08) in 2050 and 1·59 (1·25-1·96) in 2100 under the reference scenario. The number of countries and territories with fertility rates remaining above replacement was forecast to be 49 (24·0%) in 2050 and only six (2·9%) in 2100, with three of these six countries included in the 2021 World Bank-defined low-income group, all located in the GBD super-region of sub-Saharan Africa. The proportion of livebirths occurring in sub-Saharan Africa was forecast to increase to more than half of the world's livebirths in 2100, to 41·3% (39·6-43·1) in 2050 and 54·3% (47·1-59·5) in 2100. The share of livebirths was projected to decline between 2021 and 2100 in most of the six other super-regions-decreasing, for example, in south Asia from 24·8% (23·7-25·8) in 2021 to 16·7% (14·3-19·1) in 2050 and 7·1% (4·4-10·1) in 2100-but was forecast to increase modestly in the north Africa and Middle East and high-income super-regions. Forecast estimates for the alternative combined scenario suggest that meeting SDG targets for education and contraceptive met need, as well as implementing pro-natal policies, would result in global TFRs of 1·65 (1·40-1·92) in 2050 and 1·62 (1·35-1·95) in 2100. The forecasting skill metric values for the IHME model were positive across all age groups, indicating that the model is better than the constant prediction. INTERPRETATION: Fertility is declining globally, with rates in more than half of all countries and territories in 2021 below replacement level. Trends since 2000 show considerable heterogeneity in the steepness of declines, and only a small number of countries experienced even a slight fertility rebound after their lowest observed rate, with none reaching replacement level. Additionally, the distribution of livebirths across the globe is shifting, with a greater proportion occurring in the lowest-income countries. Future fertility rates will continue to decline worldwide and will remain low even under successful implementation of pro-natal policies. These changes will have far-reaching economic and societal consequences due to ageing populations and declining workforces in higher-income countries, combined with an increasing share of livebirths among the already poorest regions of the world. FUNDING: Bill & Melinda Gates Foundation.

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021.

BACKGROUND: Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS: The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS: The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION: Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING: Bill & Melinda Gates Foundation.

Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021.

BACKGROUND: Understanding the health consequences associated with exposure to risk factors is necessary to inform public health policy and practice. To systematically quantify the contributions of risk factor exposures to specific health outcomes, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 aims to provide comprehensive estimates of exposure levels, relative health risks, and attributable burden of disease for 88 risk factors in 204 countries and territories and 811 subnational locations, from 1990 to 2021. METHODS: The GBD 2021 risk factor analysis used data from 54 561 total distinct sources to produce epidemiological estimates for 88 risk factors and their associated health outcomes for a total of 631 risk-outcome pairs. Pairs were included on the basis of data-driven determination of a risk-outcome association. Age-sex-location-year-specific estimates were generated at global, regional, and national levels. Our approach followed the comparative risk assessment framework predicated on a causal web of hierarchically organised, potentially combinative, modifiable risks. Relative risks (RRs) of a given outcome occurring as a function of risk factor exposure were estimated separately for each risk-outcome pair, and summary exposure values (SEVs), representing risk-weighted exposure prevalence, and theoretical minimum risk exposure levels (TMRELs) were estimated for each risk factor. These estimates were used to calculate the population attributable fraction (PAF; ie, the proportional change in health risk that would occur if exposure to a risk factor were reduced to the TMREL). The product of PAFs and disease burden associated with a given outcome, measured in disability-adjusted life-years (DALYs), yielded measures of attributable burden (ie, the proportion of total disease burden attributable to a particular risk factor or combination of risk factors). Adjustments for mediation were applied to account for relationships involving risk factors that act indirectly on outcomes via intermediate risks. Attributable burden estimates were stratified by Socio-demographic Index (SDI) quintile and presented as counts, age-standardised rates, and rankings. To complement estimates of RR and attributable burden, newly developed burden of proof risk function (BPRF) methods were applied to yield supplementary, conservative interpretations of risk-outcome associations based on the consistency of underlying evidence, accounting for unexplained heterogeneity between input data from different studies. Estimates reported represent the mean value across 500 draws from the estimate's distribution, with 95% uncertainty intervals (UIs) calculated as the 2·5th and 97·5th percentile values across the draws. FINDINGS: Among the specific risk factors analysed for this study, particulate matter air pollution was the leading contributor to the global disease burden in 2021, contributing 8·0% (95% UI 6·7-9·4) of total DALYs, followed by high systolic blood pressure (SBP; 7·8% [6·4-9·2]), smoking (5·7% [4·7-6·8]), low birthweight and short gestation (5·6% [4·8-6·3]), and high fasting plasma glucose (FPG; 5·4% [4·8-6·0]). For younger demographics (ie, those aged 0-4 years and 5-14 years), risks such as low birthweight and short gestation and unsafe water, sanitation, and handwashing (WaSH) were among the leading risk factors, while for older age groups, metabolic risks such as high SBP, high body-mass index (BMI), high FPG, and high LDL cholesterol had a greater impact. From 2000 to 2021, there was an observable shift in global health challenges, marked by a decline in the number of all-age DALYs broadly attributable to behavioural risks (decrease of 20·7% [13·9-27·7]) and environmental and occupational risks (decrease of 22·0% [15·5-28·8]), coupled with a 49·4% (42·3-56·9) increase in DALYs attributable to metabolic risks, all reflecting ageing populations and changing lifestyles on a global scale. Age-standardised global DALY rates attributable to high BMI and high FPG rose considerably (15·7% [9·9-21·7] for high BMI and 7·9% [3·3-12·9] for high FPG) over this period, with exposure to these risks increasing annually at rates of 1·8% (1·6-1·9) for high BMI and 1·3% (1·1-1·5) for high FPG. By contrast, the global risk-attributable burden and exposure to many other risk factors declined, notably for risks such as child growth failure and unsafe water source, with age-standardised attributable DALYs decreasing by 71·5% (64·4-78·8) for child growth failure and 66·3% (60·2-72·0) for unsafe water source. We separated risk factors into three groups according to trajectory over time: those with a decreasing attributable burden, due largely to declining risk exposure (eg, diet high in trans-fat and household air pollution) but also to proportionally smaller child and youth populations (eg, child and maternal malnutrition); those for which the burden increased moderately in spite of declining risk exposure, due largely to population ageing (eg, smoking); and those for which the burden increased considerably due to both increasing risk exposure and population ageing (eg, ambient particulate matter air pollution, high BMI, high FPG, and high SBP). INTERPRETATION: Substantial progress has been made in reducing the global disease burden attributable to a range of risk factors, particularly those related to maternal and child health, WaSH, and household air pollution. Maintaining efforts to minimise the impact of these risk factors, especially in low SDI locations, is necessary to sustain progress. Successes in moderating the smoking-related burden by reducing risk exposure highlight the need to advance policies that reduce exposure to other leading risk factors such as ambient particulate matter air pollution and high SBP. Troubling increases in high FPG, high BMI, and other risk factors related to obesity and metabolic syndrome indicate an urgent need to identify and implement interventions. FUNDING: Bill & Melinda Gates Foundation.

Nicotinic Signaling Stimulates Glucagon Secretion in Mouse and Human Pancreatic α-Cells.

Smoking is widely regarded as a risk factor for type 2 diabetes because nicotine contributes to insulin resistance by desensitizing the insulin receptors in muscle, liver, or fat. Little is known, however, about the immediate regulation of islet hormonal output by nicotine, an agonist of ionotropic cholinergic receptors. We investigated this by imaging cytosolic Ca2+ dynamics in mouse and human islets using confocal microscopy and measuring glucagon secretion in response to the alkaloid from isolated mouse islets. Nicotine acutely stimulated cytosolic Ca2+ in glucagon-secreting α-cells but not in insulin-secreting β-cells. The 2.8- ± 0.5-fold (P < 0.05) increase in Ca2+, observed in >70% of α-cells, correlated well with a 2.5- ± 0.3-fold stimulation of glucagon secretion. Nicotine-induced elevation of cytosolic Ca2+ relied on influx from the extracellular compartment rather than release of the cation from intracellular depots. Metabotropic cholinergic signaling, monitored at the level of intracellular diacylglycerol, was limited to 69% of α-cells versus 94% of β-cells. We conclude that parasympathetic regulation of pancreatic islet hormone release uses different signaling pathways in β-cells (metabotropic) and α-cells (metabotropic and ionotropic), resulting in the fine-tuning of acetylcholine-induced glucagon exocytosis. Sustained nicotinic stimulation is, therefore, likely to attenuate insulin sensitivity by increasing glucagon release.

Corrigendum for “The global birth prevalence of clubfoot: a systematic review and meta-analysis” (eClinicalMedicine (2023) 63, (S2589537023003553), (10.1016/j.eclinm.2023.102178))

In Table 2 (Study characteristics and estimated birth prevalence) and Fig. 2 (Pooled global birth prevalence of clubfoot), the estimate was noted as 3.57 (2.93–4.34) per 1000 births. However, Zhou et al. (2020) report this as per 10,000 births. The correct estimate is therefore 0.36 (0.29–0.43) per 1000 births. The error occurred during transcription and we have since confirmed with the authors that the denominator is 10,000 births. The correction of this estimate does not alter the main results or conclusions of the paper. The pooled global prevalence of clubfoot should be 1.10 (95%CI 0.93–1.28) instead of 1.18 per 1000 births (95% CI: 1.00–1.36), and the subgroup analysis of the WHO Western Pacific Region should be 0.62 (95% CI 0.38–0.86) instead of 0.86 (95% CI 0.60–1.11) per 1000 births. The authors apologise for any confusion this error may have caused and appreciate the understanding of the readers.

Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI.

A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico, evaluation, but few have yet demonstrated real benefit to patient care. Early stage clinical evaluation is important to assess an AI system’s actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use, and pave the way to further large scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multistakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two round, modified Delphi process to collect and analyse expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 predefined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. 123 experts participated in the first round of Delphi, 138 in the second, 16 in the consensus meeting, and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI specific reporting items (made of 28 subitems) and 10 generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we have developed a guideline comprising key items that should be reported in early stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings.

Genomic Evolution and Transcriptional Changes in the Evolution of Prostate Cancer into Neuroendocrine and Ductal Carcinoma Types.

Prostate cancer is typically of acinar adenocarcinoma type but can occasionally present as neuroendocrine and/or ductal type carcinoma. These are associated with clinically aggressive disease, and the former often arises on a background of androgen deprivation therapy, although it can also arise de novo. Two prostate cancer cases were sequenced by exome capture from archival tissue. Case 1 was de novo small cell neuroendocrine carcinoma and ductal adenocarcinoma with three longitudinal samples over 5 years. Case 2 was a single time point after the development of treatment-related neuroendocrine prostate carcinoma. Case 1 showed whole genome doubling in all samples and focal amplification of AR in all samples except the first time point. Phylogenetic analysis revealed a common ancestry for ductal and small cell carcinoma. Case 2 showed 13q loss (involving RB1) in both adenocarcinoma and small cell carcinoma regions, and 3p gain, 4p loss, and 17p loss (involving TP53) in the latter. By using highly curated samples, we demonstrate for the first time that small-cell neuroendocrine and ductal prostatic carcinoma can have a common ancestry. We highlight whole genome doubling in a patient with prostate cancer relapse, reinforcing its poor prognostic nature.

Conscientious objection: a global health perspective.

Conscientious objection is a critical topic that has been sparsely discussed from a global health perspective, despite its special relevance to our inherently diverse field. In this Analysis paper, we argue that blanket prohibitions of a specific type of non-discriminatory conscientious objection are unjustified in the global health context. We begin both by introducing a nuanced account of conscience that is grounded in moral psychology and by providing an overview of discriminatory and non-discriminatory forms of objection. Next, we point to the frequently neglected but ubiquitous presence of moral uncertainty, which entails a need for epistemic humility-that is, an attitude that acknowledges the possibility one might be wrong. We build two arguments on moral uncertainty. First, if epistemic humility is necessary when dealing with values in theory (as appears to be the consensus in bioethics), then it will be even more necessary when these values are applied in the real world. Second, the emergence of global health from its colonial past requires special awareness of, and resistance to, moral imperialism. Absolutist attitudes towards disagreement are thus incompatible with global health's dual aims of reducing inequity and emerging from colonialism. Indeed, the possibility of global bioethics (which balances respect for plurality with the goal of collective moral progress) hinges on appropriately acknowledging moral uncertainty when faced with inevitable disagreement. This is incompatible with blanket prohibitions of conscientious objection. As a brief final note, we distinguish conscientious objection from the problem of equitable access to care. We note that conflating the two may actually lead to a less equitable picture on the whole. We conclude by recommending that international consensus documents, such as the Universal Declaration on Bioethics and Human Rights, be amended to include nuanced guidelines regarding conscientious objection that can then be used as a template by regional and national policymaking bodies.

Clinical ischemia-reperfusion injury: Driven by reductive rather than oxidative stress? A narrative review.

Ischemia-reperfusion (IR) injury remains a major contributor to organ dysfunction following transient ischemic insults. Although numerous interventions have been found effective to reduce IR injury in preclinical models, none of these therapies have been successfully translated to the clinical setting. In the context of the persistent translational gap, we systematically investigated the mechanisms implicated in IR injury using kidney donation and transplantation as a clinical model of IR. Whilst our results do not implicate traditional culprits such as reactive oxygen species, complement activation or inflammation as triggers of IR injury, they reveal a clear metabolic signature for renal IR injury. This discriminatory signature of IR injury is consistent with a post-reperfusion metabolic paralysis and involves high-energy phosphate depletion, tricarboxylic acid cycle defects, and a compensatory activation of catabolic routes. Against this background, the picture emerges that clinical IR injury is driven by reductive stress. In this article, we therefore wish to elaborate on the processes contributing to reductive stress in the context of clinical IR injury and provide a better insight in potential clinical therapeutic strategies that might be helpful in restoring the redox balance.

Urologists and social media: pitfalls and perils and what to think about before hitting 'send'.

OBJECTIVE: To summarise current guidelines from professional bodies relevant to urologists on social media, and to discusses a range of risks associated with social media use. These include the risk of a fitness to practise investigation, breaking the law, loss of employment, and personal risk in the form of harassment and doxxing. METHODS: Review of guidelines and recommendations published by professional bodies revelant to urologists and review of relevant case examples in the medical profession and other relevant professions. RESULTS: This article finds whereas most doctors will be aware of the risks of posting on public social media platforms, the recent case studies in medical and other contexts have highlighted the risks of disciplinary action from regulators and even criminal investigation in the use of private social media. CONCLUSION: Although the majority of urologists are unlikely to violate ethical and good medical practice principles in their use of social media, this article serves as a reminder of the potential consequences based on real-life case examples.

Tumour purity assessment with deep learning in colorectal cancer and impact on molecular analysis.

Tumour content plays a pivotal role in directing the bioinformatic analysis of molecular profiles such as copy number variation (CNV). In clinical application, tumour purity estimation (TPE) is achieved either through visual pathological review [conventional pathology (CP)] or the deconvolution of molecular data. While CP provides a direct measurement, it demonstrates modest reproducibility and lacks standardisation. Conversely, deconvolution methods offer an indirect assessment with uncertain accuracy, underscoring the necessity for innovative approaches. SoftCTM is an open-source, multiorgan deep-learning (DL) model for the detection of tumour and non-tumour cells in H&E-stained slides, developed within the Overlapped Cell on Tissue Dataset for Histopathology (OCELOT) Challenge 2023. Here, using three large multicentre colorectal cancer (CRC) cohorts (N = 1,097 patients) with digital pathology and multi-omic data, we compare the utility and accuracy of TPE with SoftCTM versus CP and bioinformatic deconvolution methods (RNA expression, DNA methylation) for downstream molecular analysis, including CNV profiling. SoftCTM showed technical repeatability when applied twice on the same slide (r = 1.0) and excellent correlations in paired H&E slides (r > 0.9). TPEs profiled by SoftCTM correlated highly with RNA expression (r = 0.59) and DNA methylation (r = 0.40), while TPEs by CP showed a lower correlation with RNA expression (r = 0.41) and DNA methylation (r = 0.29). We show that CP and deconvolution methods respectively underestimate and overestimate tumour content compared to SoftCTM, resulting in 6-13% differing CNV calls. In summary, TPE with SoftCTM enables reproducibility, automation, and standardisation at single-cell resolution. SoftCTM estimates (M = 58.9%, SD ±16.3%) reconcile the overestimation by molecular data extrapolation (RNA expression: M = 79.2%, SD ±10.5, DNA methylation: M = 62.7%, SD ±11.8%) and underestimation by CP (M = 35.9%, SD ±13.1%), providing a more reliable middle ground. A fully integrated computational pathology solution could therefore be used to improve downstream molecular analyses for research and clinics. © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Characterising borderline areas in bladder tumour grading with Bayesian graph neural networks

Urothelial carcinoma is the most common bladder cancer whose grading is critical to clinical decision-making. The WHO 2004 grading system classifies urothelial carcinoma into either low grade or high grade, but sometimes cases sit on the border between grades. This makes assessment by the pathologist challenging but could potentially lead to under-treatment or overtreatment. The aim of this study was to use deep learning methods to identify and characterise borderline areas in whole slide images (WSIs) from bladder tumour cases. We constructed graphs on WSIs to accelerate computation, where positive unlabeled learning was utilized, accommodating the partial annotation strategy deployed in clinics. We used Bayesian deep learning for carcinoma classification, where we modeled the borderline as prediction uncertainty quantified by Bayesian graph neural networks. Our experiments showed promising performance of our approach in carcinoma detection and classification, with a potential use case to highlight and better characterise areas on the border for high grade and low grade to pathologists.