Timely and comprehensive analyses of causes of death stratified by age, sex, and location are essential for shaping effective health policies aimed at reducing global mortality. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 provides cause-specific mortality estimates measured in counts, rates, and years of life lost (YLLs). GBD 2023 aimed to enhance our understanding of the relationship between age and cause of death by quantifying the probability of dying before age 70 years (70q0) and the mean age at death by cause and sex. This study enables comparisons of the impact of causes of death over time, offering a deeper understanding of how these causes affect global populations. GBD 2023 produced estimates for 292 causes of death disaggregated by age-sex-location-year in 204 countries and territories and 660 subnational locations for each year from 1990 until 2023. We used a modelling tool developed for GBD, the Cause of Death Ensemble model (CODEm), to estimate cause-specific death rates for most causes. We computed YLLs as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. Probability of death was calculated as the chance of dying from a given cause in a specific age period, for a specific population. Mean age at death was calculated by first assigning the midpoint age of each age group for every death, followed by computing the mean of all midpoint ages across all deaths attributed to a given cause. We used GBD death estimates to calculate the observed mean age at death and to model the expected mean age across causes, sexes, years, and locations. The expected mean age reflects the expected mean age at death for individuals within a population, based on global mortality rates and the population's age structure. Comparatively, the observed mean age represents the actual mean age at death, influenced by all factors unique to a location-specific population, including its age structure. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 250-draw distribution for each metric. Findings are reported as counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2023 include a correction for the misclassification of deaths due to COVID-19, updates to the method used to estimate COVID-19, and updates to the CODEm modelling framework. This analysis used 55 761 data sources, including vital registration and verbal autopsy data as well as data from surveys, censuses, surveillance systems, and cancer registries, among others. For GBD 2023, there were 312 new country-years of vital registration cause-of-death data, 3 country-years of surveillance data, 51 country-years of verbal autopsy data, and 144 country-years of other data types that were added to those used in previous GBD rounds. The initial years of the COVID-19 pandemic caused shifts in long-standing rankings of the leading causes of global deaths: it ranked as the number one age-standardised cause of death at Level 3 of the GBD cause classification hierarchy in 2021. By 2023, COVID-19 dropped to the 20th place among the leading global causes, returning the rankings of the leading two causes to those typical across the time series (ie, ischaemic heart disease and stroke). While ischaemic heart disease and stroke persist as leading causes of death, there has been progress in reducing their age-standardised mortality rates globally. Four other leading causes have also shown large declines in global age-standardised mortality rates across the study period: diarrhoeal diseases, tuberculosis, stomach cancer, and measles. Other causes of death showed disparate patterns between sexes, notably for deaths from conflict and terrorism in some locations. A large reduction in age-standardised rates of YLLs occurred for neonatal disorders. Despite this, neonatal disorders remained the leading cause of global YLLs over the period studied, except in 2021, when COVID-19 was temporarily the leading cause. Compared to 1990, there has been a considerable reduction in total YLLs in many vaccine-preventable diseases, most notably diphtheria, pertussis, tetanus, and measles. In addition, this study quantified the mean age at death for all-cause mortality and cause-specific mortality and found noticeable variation by sex and location. The global all-cause mean age at death increased from 46·8 years (95% UI 46·6-47·0) in 1990 to 63·4 years (63·1-63·7) in 2023. For males, mean age increased from 45·4 years (45·1-45·7) to 61·2 years (60·7-61·6), and for females it increased from 48·5 years (48·1-48·8) to 65·9 years (65·5-66·3), from 1990 to 2023. The highest all-cause mean age at death in 2023 was found in the high-income super-region, where the mean age for females reached 80·9 years (80·9-81·0) and for males 74·8 years (74·8-74·9). By comparison, the lowest all-cause mean age at death occurred in sub-Saharan Africa, where it was 38·0 years (37·5-38·4) for females and 35·6 years (35·2-35·9) for males in 2023. Lastly, our study found that all-cause 70q0 decreased across each GBD super-region and region from 2000 to 2023, although with large variability between them. For females, we found that 70q0 notably increased from drug use disorders and conflict and terrorism. Leading causes that increased 70q0 for males also included drug use disorders, as well as diabetes. In sub-Saharan Africa, there was an increase in 70q0 for many non-communicable diseases (NCDs). Additionally, the mean age at death from NCDs was lower than the expected mean age at death for this super-region. By comparison, there was an increase in 70q0 for drug use disorders in the high-income super-region, which also had an observed mean age at death lower than the expected value. We examined global mortality patterns over the past three decades, highlighting-with enhanced estimation methods-the impacts of major events such as the COVID-19 pandemic, in addition to broader trends such as increasing NCDs in low-income regions that reflect ongoing shifts in the global epidemiological transition. This study also delves into premature mortality patterns, exploring the interplay between age and causes of death and deepening our understanding of where targeted resources could be applied to further reduce preventable sources of mortality. We provide essential insights into global and regional health disparities, identifying locations in need of targeted interventions to address both communicable and non-communicable diseases. There is an ever-present need for strengthened health-care systems that are resilient to future pandemics and the shifting burden of disease, particularly among ageing populations in regions with high mortality rates. Robust estimates of causes of death are increasingly essential to inform health priorities and guide efforts toward achieving global health equity. The need for global collaboration to reduce preventable mortality is more important than ever, as shifting burdens of disease are affecting all nations, albeit at different paces and scales. Gates Foundation.
For more than three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has provided a framework to quantify health loss due to diseases, injuries, and associated risk factors. This paper presents GBD 2023 findings on disease and injury burden and risk-attributable health loss, offering a global audit of the state of world health to inform public health priorities. This work captures the evolving landscape of health metrics across age groups, sexes, and locations, while reflecting on the remaining post-COVID-19 challenges to achieving our collective global health ambitions. The GBD 2023 combined analysis estimated years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 375 diseases and injuries, and risk-attributable burden associated with 88 modifiable risk factors. Of the more than 310 000 total data sources used for all GBD 2023 (about 30% of which were new to this estimation round), more than 120 000 sources were used for estimation of disease and injury burden and 59 000 for risk factor estimation, and included vital registration systems, surveys, disease registries, and published scientific literature. Data were analysed using previously established modelling approaches, such as disease modelling meta-regression version 2.1 (DisMod-MR 2.1) and comparative risk assessment methods. Diseases and injuries were categorised into four levels on the basis of the established GBD cause hierarchy, as were risk factors using the GBD risk hierarchy. Estimates stratified by age, sex, location, and year from 1990 to 2023 were focused on disease-specific time trends over the 2010-23 period and presented as counts (to three significant figures) and age-standardised rates per 100 000 person-years (to one decimal place). For each measure, 95% uncertainty intervals [UIs] were calculated with the 2·5th and 97·5th percentile ordered values from a 250-draw distribution. Total numbers of global DALYs grew 6·1% (95% UI 4·0-8·1), from 2·64 billion (2·46-2·86) in 2010 to 2·80 billion (2·57-3·08) in 2023, but age-standardised DALY rates, which account for population growth and ageing, decreased by 12·6% (11·0-14·1), revealing large long-term health improvements. Non-communicable diseases (NCDs) contributed 1·45 billion (1·31-1·61) global DALYs in 2010, increasing to 1·80 billion (1·63-2·03) in 2023, alongside a concurrent 4·1% (1·9-6·3) reduction in age-standardised rates. Based on DALY counts, the leading level 3 NCDs in 2023 were ischaemic heart disease (193 million [176-209] DALYs), stroke (157 million [141-172]), and diabetes (90·2 million [75·2-107]), with the largest increases in age-standardised rates since 2010 occurring for anxiety disorders (62·8% [34·0-107·5]), depressive disorders (26·3% [11·6-42·9]), and diabetes (14·9% [7·5-25·6]). Remarkable health gains were made for communicable, maternal, neonatal, and nutritional (CMNN) diseases, with DALYs falling from 874 million (837-917) in 2010 to 681 million (642-736) in 2023, and a 25·8% (22·6-28·7) reduction in age-standardised DALY rates. During the COVID-19 pandemic, DALYs due to CMNN diseases rose but returned to pre-pandemic levels by 2023. From 2010 to 2023, decreases in age-standardised rates for CMNN diseases were led by rate decreases of 49·1% (32·7-61·0) for diarrhoeal diseases, 42·9% (38·0-48·0) for HIV/AIDS, and 42·2% (23·6-56·6) for tuberculosis. Neonatal disorders and lower respiratory infections remained the leading level 3 CMNN causes globally in 2023, although both showed notable rate decreases from 2010, declining by 16·5% (10·6-22·0) and 24·8% (7·4-36·7), respectively. Injury-related age-standardised DALY rates decreased by 15·6% (10·7-19·8) over the same period. Differences in burden due to NCDs, CMNN diseases, and injuries persisted across age, sex, time, and location. Based on our risk analysis, nearly 50% (1·27 billion [1·18-1·38]) of the roughly 2·80 billion total global DALYs in 2023 were attributable to the 88 risk factors analysed in GBD. Globally, the five level 3 risk factors contributing the highest proportion of risk-attributable DALYs were high systolic blood pressure (SBP), particulate matter pollution, high fasting plasma glucose (FPG), smoking, and low birthweight and short gestation-with high SBP accounting for 8·4% (6·9-10·0) of total DALYs. Of the three overarching level 1 GBD risk factor categories-behavioural, metabolic, and environmental and occupational-risk-attributable DALYs rose between 2010 and 2023 only for metabolic risks, increasing by 30·7% (24·8-37·3); however, age-standardised DALY rates attributable to metabolic risks decreased by 6·7% (2·0-11·0) over the same period. For all but three of the 25 leading level 3 risk factors, age-standardised rates dropped between 2010 and 2023-eg, declining by 54·4% (38·7-65·3) for unsafe sanitation, 50·5% (33·3-63·1) for unsafe water source, and 45·2% (25·6-72·0) for no access to handwashing facility, and by 44·9% (37·3-53·5) for child growth failure. The three leading level 3 risk factors for which age-standardised attributable DALY rates rose were high BMI (10·5% [0·1 to 20·9]), drug use (8·4% [2·6 to 15·3]), and high FPG (6·2% [-2·7 to 15·6]; non-significant). Our findings underscore the complex and dynamic nature of global health challenges. Since 2010, there have been large decreases in burden due to CMNN diseases and many environmental and behavioural risk factors, juxtaposed with sizeable increases in DALYs attributable to metabolic risk factors and NCDs in growing and ageing populations. This long-observed consequence of the global epidemiological transition was only temporarily interrupted by the COVID-19 pandemic. The substantially decreasing CMNN disease burden, despite the 2008 global financial crisis and pandemic-related disruptions, is one of the greatest collective public health successes known. However, these achievements are at risk of being reversed due to major cuts to development assistance for health globally, the effects of which will hit low-income countries with high burden the hardest. Without sustained investment in evidence-based interventions and policies, progress could stall or reverse, leading to widespread human costs and geopolitical instability. Moreover, the rising NCD burden necessitates intensified efforts to mitigate exposure to leading risk factors-eg, air pollution, smoking, and metabolic risks, such as high SBP, BMI, and FPG-including policies that promote food security, healthier diets, physical activity, and equitable and expanded access to potential treatments, such as GLP-1 receptor agonists. Decisive, coordinated action is needed to address long-standing yet growing health challenges, including depressive and anxiety disorders. Yet this can be only part of the solution. Our response to the NCD syndemic-the complex interaction of multiple health risks, social determinants, and systemic challenges-will define the future landscape of global health. To ensure human wellbeing, economic stability, and social equity, global action to sustain and advance health gains must prioritise reducing disparities by addressing socioeconomic and demographic determinants, ensuring equitable health-care access, tackling malnutrition, strengthening health systems, and improving vaccination coverage. We live in times of great opportunity. Gates Foundation and Bloomberg Philanthropies.
To perform a systematic review and meta-analysis to estimate diagnostic performance, category-wise malignancy rates, and inter-observer reliability of Node Reporting and Data System 1.0 (Node-RADS). Five electronic databases were systematically searched for primary studies on the use of Node-RADS to report the possibility of cancer involvement of lymph nodes on CT and MRI from January 1, 2021, until April 15, 2024. The study quality was assessed by modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Quality Appraisal of Diagnostic Reliability (QAREL) tools. The diagnostic accuracy was estimated with bivariate random-effects model, while the pooled category-wise malignancy rates were obtained with random-effects model. Six Node-RADS-CT studies and three Node-RADS-MRI studies covering nine types of cancer were included. The study quality was mainly damaged by inappropriate index test and unknown timing according to QUADAS-2, and unclear blindness during the rating process according to QAREL. The area under hierarchical summary receiver operating characteristic curve (95% conventional interval) was 0.92 (0.89-0.94) for Node-RADS ≥ 3 as positive and 0.91 (0.88-0.93) for Node-RADS ≥ 4 as positive, respectively. The pooled malignancy rates (95% CIs) of Node-RADS 1 to 5 were 4% (0-10%), 31% (9-58%), 55% (34-75%), 89% (73-99%), and 100% (97-100%), respectively. The inter-observer reliability of five studies was interpreted as fair to substantial. Node-RADS presented a promising diagnostic performance with an increasing probability of malignancy along higher category. However, the evidence for inter-observer reliability of Node-RADS is insufficient, and may hinder its implementation in clinical practice for lymph node assessment. Question Node-RADS is designed for structured reporting of the possibility of cancer involvement of lymph nodes, but the evidence supporting its application has not been summarized. Findings Node-RADS presented diagnostic performance with AUC of 0.92, and malignancy rates for categories 1-5 ranged from 4% to 100%, while the inter-observer reliability was unclear. Clinical relevance Node-RADS is a useful tool for structured reporting of the possibility of cancer involvement of lymph nodes with high diagnostic performance and appropriate malignancy rate for each category, but unclear inter-observer reliability may hinder its implementation in clinical practice.
Comprehensive, comparable, and timely estimates of demographic metrics-including life expectancy and age-specific mortality-are essential for evaluating, understanding, and addressing trends in population health. The COVID-19 pandemic highlighted the importance of timely and all-cause mortality estimates for being able to respond to changing trends in health outcomes, showing a strong need for demographic analysis tools that can produce all-cause mortality estimates more rapidly with more readily available all-age vital registration (VR) data. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) is an ongoing research effort that quantifies human health by estimating a range of epidemiological quantities of interest across time, age, sex, location, cause, and risk. This study-part of the latest GBD release, GBD 2023-aims to provide new and updated estimates of all-cause mortality and life expectancy for 1950 to 2023 using a novel statistical model that accounts for complex correlation structures in demographic data across age and time. We used 24 025 data sources from VR, sample registration, surveys, censuses, and other sources to estimate all-cause mortality for males, females, and all sexes combined across 25 age groups in 204 countries and territories as well as 660 subnational units in 20 countries and territories, for the years 1950-2023. For the first time, we used complete birth history data for ages 5-14 years, age-specific sibling history data for ages 15-49 years, and age-specific mortality data from Health and Demographic Surveillance Systems. We developed a single statistical model that incorporates both parametric and non-parametric methods, referred to as OneMod, to produce estimates of all-cause mortality for each age-sex-location group. OneMod includes two main steps: a detailed regression analysis with a generalised linear modelling tool that accounts for age-specific covariate effects such as the Socio-demographic Index (SDI) and a population attributable fraction (PAF) for all risk factors combined; and a non-parametric analysis of residuals using a multivariate kernel regression model that smooths across age and time to adaptably follow trends in the data without overfitting. We calibrated asymptotic uncertainty estimates using Pearson residuals to produce 95% uncertainty intervals (UIs) and corresponding 1000 draws. Life expectancy was calculated from age-specific mortality rates with standard demographic methods. For each measure, 95% UIs were calculated with the 25th and 975th ordered values from a 1000-draw posterior distribution. In 2023, 60·1 million (95% UI 59·0-61·1) deaths occurred globally, of which 4·67 million (4·59-4·75) were in children younger than 5 years. Due to considerable population growth and ageing since 1950, the number of annual deaths globally increased by 35·2% (32·2-38·4) over the 1950-2023 study period, during which the global age-standardised all-cause mortality rate declined by 66·6% (65·8-67·3). Trends in age-specific mortality rates between 2011 and 2023 varied by age group and location, with the largest decline in under-5 mortality occurring in east Asia (67·7% decrease); the largest increases in mortality for those aged 5-14 years, 25-29 years, and 30-39 years occurring in high-income North America (11·5%, 31·7%, and 49·9%, respectively); and the largest increases in mortality for those aged 15-19 years and 20-24 years occurring in Eastern Europe (53·9% and 40·1%, respectively). We also identified higher than previously estimated mortality rates in sub-Saharan Africa for all sexes combined aged 5-14 years (87·3% higher in GBD 2023 than GBD 2021 on average across countries and territories over the 1950-2021 period) and for females aged 15-29 years (61·2% higher), as well as lower than previously estimated mortality rates in sub-Saharan Africa for all sexes combined aged 50 years and older (13·2% lower), reflecting advances in our modelling approach. Global life expectancy followed three distinct trends over the study period. First, between 1950 and 2019, there were considerable improvements, from 51·2 (50·6-51·7) years for females and 47·9 (47·4-48·4) years for males in 1950 to 76·3 (76·2-76·4) years for females and 71·4 (71·3-71·5) years for males in 2019. Second, this period was followed by a decrease in life expectancy during the COVID-19 pandemic, to 74·7 (74·6-74·8) years for females and 69·3 (69·2-69·4) years for males in 2021. Finally, the world experienced a period of post-pandemic recovery in 2022 and 2023, wherein life expectancy generally returned to pre-pandemic (2019) levels in 2023 (76·3 [76·0-76·6] years for females and 71·5 [71·2-71·8] years for males). 194 (95·1%) of 204 countries and territories experienced at least partial post-pandemic recovery in age-standardised mortality rates by 2023, with 61·8% (126 of 204) recovering to or falling below pre-pandemic levels. There were several mortality trajectories during and following the pandemic across countries and territories. Long-term mortality trends also varied considerably between age groups and locations, demonstrating the diverse landscape of health outcomes globally. This analysis identified several key differences in mortality trends from previous estimates, including higher rates of adolescent mortality, higher rates of young adult mortality in females, and lower rates of mortality in older age groups in much of sub-Saharan Africa. The findings also highlight stark differences across countries and territories in the timing and scale of changes in all-cause mortality trends during and following the COVID-19 pandemic (2020-23). Our estimates of evolving trends in mortality and life expectancy across locations, ages, sexes, and SDI levels in recent years as well as over the entire 1950-2023 study period provide crucial information for governments, policy makers, and the public to ensure that health-care systems, economies, and societies are prepared to address the world's health needs, particularly in populations with higher rates of mortality than previously known. The estimates from this study provide a robust framework for GBD and a valuable foundation for policy development, implementation, and evaluation around the world. Gates Foundation.
Violence against women and against children are human rights violations with lasting harms to survivors and societies at large. Intimate partner violence (IPV) and sexual violence against children (SVAC) are two major forms of such abuse. Despite their wide-reaching effects on individual and community health, these risk factors have not been adequately prioritised as key drivers of global health burden. Comprehensive x§and reliable estimates of the comparative health burden of IPV and SVAC are urgently needed to inform investments in prevention and support for survivors at both national and global levels. We estimated the prevalence and attributable burden of IPV among females and SVAC among males and females for 204 countries and territories, by age and sex, from 1990 to 2023, as part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2023. We searched several global databases for data on self-reported exposure to IPV and SVAC and undertook a systematic review to identify the health outcomes associated with each of these risk factors. We modelled IPV and SVAC prevalence using spatiotemporal Gaussian process regression, applying data adjustments to account for measurement heterogeneity. We employed burden-of-proof methodology to estimate relative risks for outcomes associated with IPV and SVAC. These estimates informed the calculation of population attributable fractions, which were then used to quantify disability-adjusted life-years (DALYs) attributable to each risk factor. Globally, in 2023, we estimated that 608 million (95% uncertainty interval 518-724) females aged 15 years and older had ever been exposed to IPV, and 1·01 billion (0·764-1·48) individuals aged 15 years and older had experienced sexual violence during childhood. 18·5 million (8·74-30·0) DALYs were attributed to IPV among females and 32·2 million (16·4-52·5) DALYs were attributed to SVAC among males and females in 2023. IPV and SVAC were among the top contributors to the global disease burden in 2023, particularly among females aged 15-49 years, ranking as the fourth and fifth leading risk factors, respectively, for DALYs in this group. Among the eight health outcomes found to be associated with IPV, anxiety disorders and major depressive disorder were the leading causes of IPV-attributed DALYs, accounting for 5·43 million (-1·25 to 14·6) and 3·96 million (1·71 to 6·92) DALYs in 2023, respectively. SVAC was associated with 14 health outcomes, including mental health disorder, substance use disorder, and chronic and infectious disease outcomes. Self-harm and schizophrenia were the leading causes of SVAC-attributed burden, with SVAC accounting for 6·71 million (2·00 to 12·7) DALYs due to self-harm and 4·15 million (-1·92 to 13·1) DALYs due to schizophrenia in 2023. IPV and SVAC are substantial contributors to global health burden, and their health consequences span a variety of individual health outcomes. Importantly, mental health disorders account for the greatest share of disease burden among survivors. Investing in prevention of these avoidable risk factors has the potential to avert millions of DALYs and considerable premature mortality each year. Our findings represent strong evidence for global and national leaders to elevate IPV and SVAC among public health priorities. Sustained investments are needed to prevent IPV and SVAC and to implement interventions focused on supporting the complex social and health needs of survivors. Gates Foundation.
The rise in popularity of two-photon polymerization (TPP) as an additive manufacturing technique has impacted many areas of science and engineering, particularly those related to biomedical applications. Compared with other fabrication methods used for biomedical applications, TPP offers 3D, nanometer-scale fabrication dexterity (free-form). Moreover, the existence of turnkey commercial systems has increased accessibility. In this review, we discuss the diversity of biomedical applications that have benefited from the unique features of TPP. We also present the state of the art in approaches for patterning and reading 3D TPP-fabricated structures. The reading process influences the fidelity for both in situ and ex situ characterization methods. We also review efforts to leverage machine learning to facilitate process control for TPP. Finally, we conclude with a discussion of both the current challenges and exciting opportunities for biomedical applications that lie ahead for this intriguing and emerging technology.
Physics-informed machine learning (PIML) is emerging as a potentially transformative paradigm for modeling complex biomedical systems by integrating parameterized physical laws with data-driven methods. Here, we review three main classes of PIML frameworks: physics-informed neural networks (PINNs), neural ordinary differential equations (NODEs), and neural operators (NOs), highlighting their growing role in biomedical science and engineering. We begin with PINNs, which embed governing equations into deep learning models and have been successfully applied to biosolid and biofluid mechanics, mechanobiology, and medical imaging, among other areas. We then review NODEs, which offer continuous-time modeling, especially suited to dynamic physiological systems, pharmacokinetics, and cell signaling. Finally, we discuss deep NOs as powerful tools for learning mappings between function spaces, enabling efficient simulations across multiscale and spatially heterogeneous biological domains. Throughout, we emphasize applications where physical interpretability, data scarcity, or system complexity make conventional black-box learning insufficient. We conclude by identifying open challenges and future directions for advancing PIML in biomedical science and engineering, including issues of uncertainty quantification, generalization, and integration of PIML and large language models.
The expansive domain of Biomaterials Science represents a pivotal axis in the advancement of basic and translational research, bearing significant societal implications for human health worldwide. Within the methodological limitation (2004-2023), a comprehensive scientometric review enabled us to critically explore the growth of research output (70746 publications) and inventions (66434 patents) across various micro-topics, which include hydroxyapatite, hydrogels, protein adsorption, silk fibroin, mesenchymal stem cells, electrospinning, dental implants, Mg-alloys, chitosan, Ti-alloys, additive manufacturing, decellularisation and polyurethane. Intriguingly, the compound annual growth rate (CAGR) of research publications is three times that of patents in the case of Mg-alloys, while comparable CAGR values were recorded for additive manufacturing. Most importantly, this review identifies a notable discrepancy between the number of publications and patents in a specific micro-topic, e.g., naturally derived and decellularized tissue. An emphasis was placed on h-index/g-index metrics as well as comparing multi-country versus single-country publications of the most productive 20 countries, in our eforts to highlight the global influence, visibility and contributions of the top 20 WoS-indexed journals to advance scientific knowledge across disciplines. Despite several decades of research to develop new biomaterials, fewer than 15 biomaterials, either synthetic or naturally derived, have been approved by global regulatory agencies. This gap can be addressed through more invention-driven and industry-focused innovation programs within a strict regulatory framework, as analysed in this review using collaboration network maps and other quantitative metrics related to patents. The policy recommendations, together with the proposed roadmap, call for cohesive international efforts to bridge existing research gaps, foster innovation, and implement strategic policies to ensure the accelerated translation of labscale research to regulatory approved implantable biomaterials and medical devices.
Anterior-segment optical coherence tomography (AS-OCT) has broad clinical and research utility. The utility of quantitative data derived from AS-OCT is, however, dependent on the quality and consistency of the cumulative evidence base. To develop consensus-based nomenclature that supports standardized reporting of AS-OCT image acquisition and analyses as a foundation to improve research reproducibility. A multistage consensus exercise was undertaken, in 2024, with an expert panel of ophthalmologists, optometrists, and vision scientists, informed by a scoping review to identify the range of AS-OCT applications and terminologies in use. Panel members were selected to represent the breadth of clinical areas identified within the review or key stakeholder groups (ie, ocular image acquisition, imaging analysis, consensus methodologies). Nominal group technique was used to seek consensus on existing and newly proposed terminologies for normal anatomical structures identifiable within cross-sectional swept-source and spectral-domain AS-OCT images. An ophthalmic histopathologist then reviewed the output for concordance with histological terminology. Data were analyzed from July 2024 to January 2025. Expert consensus on nomenclature of ocular structures. Agreement on nomenclature of ocular structures, with consensus defined as at least 80% expert agreement. A multinational group of 14 experts (mean [SD] age, 46.1 [7.8] years; 10 male [71%]) participated in the consensus process. Scoping review findings resulted in the generation of 45 terms for the annotation of identifiable structures. After presentation of multiple image annotations representing these structures, consensus was reached for 31 terms across 7 images. There was consensus on the use of standardized descriptive terms from the Federative Committee on Anatomical Terminology (FCAT) alongside established eponymous terms. There was absence of consensus on annotations and visualization of structures within less optically accessible areas, such as the episcleral and scleral vasculature. Results of this survey study describe international, multidisciplinary consensus on terminology for ocular structures identifiable in AS-OCT images and present representative annotated images for reference use. Future expansion of nomenclature and annotation are anticipated, facilitated by advances in imaging technology. Follow-up consensus exercises should consider consensus-based terminology and annotations of pathological structural changes.
In this narrative review, we address the ongoing challenges of lung cancer (LC) screening using chest low-dose computerized tomography (LDCT) and explore the contributions of artificial intelligence (AI), in overcoming them. We focus on evaluating the initial (baseline) LDCT examination, which provides a wealth of information relevant to the screening participant's health. This includes the detection of large-size prevalent LC and small-size malignant nodules that are typically diagnosed as LCs upon growth in subsequent annual LDCT scans. Additionally, the baseline LDCT examination provides valuable information about smoking-related comorbidities, including cardiovascular disease, chronic obstructive pulmonary disease, and interstitial lung disease (ILD), by identifying relevant markers. Notably, these comorbidities, despite the slow progression of their markers, collectively exceed LC as ultimate causes of death at follow-up in LC screening participants. Computer-assisted diagnosis tools currently improve the reproducibility of radiologic readings and reduce the false negative rate of LDCT. Deep learning (DL) tools that analyze the radiomic features of lung nodules are being developed to distinguish between benign and malignant nodules. Furthermore, AI tools can predict the risk of LC in the years following a baseline LDCT. AI tools that analyze baseline LDCT examinations can also compute the risk of cardiovascular disease or death, paving the way for personalized screening interventions. Additionally, DL tools are available for assessing osteoporosis and ILD, which helps refine the individual's current and future health profile. The primary obstacles to AI integration into the LDCT screening pathway are the generalizability of performance and the explainability.
This systematic review explores the intersectoral costs associated with pneumococcal diseases across different age groups and related impacts on societal costs. Four databases were searched (1990-2023) for partial economic evaluation studies of pneumococcal diseases from a societal perspective. The identified intersectoral costs were presented narratively and in tabular form. Studies were appraised on quality, and the PRISMA guidelines were followed. Twenty-four studies were included with wide variation in types of analysis. Intersectoral costs were captured in five sectors: patient/family, paid labor, opportunity costs, education, and public/social services. Paid labor was the main sector considered in the included studies, followed by patient/family. Cost bearers varied according to patients' age group. Mean annual costs per child varied from $182 to $82,217 healthcare costs, $240 to $846 patient/family costs and $654 to $25,721 productivity cost. In adults, mean annual costs of $168 to $43,676 healthcare costs, and $23 to $33,149 productivity costs were reported. Intersectoral costs associated with pneumococcal diseases vary widely and appear in several sectors. More cost-of-illness studies are needed with wider perspectives, particularly in developing countries, to understand true economic costs of pneumococcal diseases and support the endeavor of informing a decision on treatment/preventive strategies. PROSPERO (CRD42023417042).
Despite the advances in detection, diagnosis, and treatments, cancer remains a lethal disease, claiming the lives of more than 600,000 people in the United States alone in 2024. To accelerate the development of new therapeutic strategies with improved responses, significant efforts have been made to develop microfabricated in vitro models of tumor microenvironments (TMEs) that address the limitations of animal-based cancer models. These models incorporate several advanced tissue engineering techniques to better reflect the organ- and patient-specific TMEs. Additionally, microfabricated models integrated with next-generation single-cell omics technologies provide unprecedented insights into patient's cellular and molecular heterogeneity and complexity. This review provides an overview of the recent understanding of cancer development and outlines the key TME elements that can be captured in microfabricated models to enhance their physiological relevance. We highlight the recent advances in microfabricated cancer models that reflect the unique characteristics of their organs of origin or sites of dissemination.
Heart failure (HF) affects over 64 million individuals worldwide and is a major cause of hospitalization and mortality, particularly among older adults. In Europe, HF imposes a significant and growing economic burden. This systematic review aimed to evaluate the economic impact of HF diagnosis, treatment and management across European healthcare systems. A systematic literature search was conducted using PubMed, Cochrane Library and Econlit databases including the terms 'heart failure' AND 'costs' OR 'cost of illness' OR 'cost analysis' OR 'economic burden' OR 'cost effectiveness' OR 'primary care' OR 'secondary care'. Studies published between January 2000 and January 2024 were included. A total of 49 studies were included: 17 on resource use, 11 on costs, 15 on resource use and costs, 1 on costs and cost-effectiveness, and 5 on resource use, costs and cost-effectiveness. Hospitalizations and medication use were the most frequently reported resource parameters. Annual HF-related costs varied widely across countries, ranging from €613 to €22,647 per patient. Hospitalizations represented the primary cost driver, accounting for 15% to 92% of total HF costs. Cost-reduction strategies included multidisciplinary care, telemonitoring and pharmacologic interventions. Several disease management programmes reduced hospital admissions and emergency visits. Cost-effectiveness analyses supported the use of certain HF therapies, with incremental cost-effectiveness ratios ranging from €1490 to €9406 per QALY gained. F imposes a substantial economic burden in Europe, largely driven by hospitalizations. Cost-effective interventions such as remote monitoring and integrated care programmes can reduce this burden. Broader adoption of these strategies may improve outcomes and optimize resource allocation across healthcare systems.
To evaluate the robustness of radiomics features among photon-counting detector CT (PCD-CT) and dual-energy CT (DECT) systems. A texture phantom consisting of twenty-eight materials was scanned with one PCD-CT and four DECT systems (dual-source, rapid kV-switching, dual-layer, and sequential scanning) at three dose levels twice. Thirty sets of virtual monochromatic images at 70 keV were reconstructed. Regions of interest were delineated for each material with a rigid registration. Ninety-three radiomics were extracted per PyRadiomics. The test-retest repeatability between repeated scans was assessed by Bland-Altman analysis. The intra-system reproducibility between dose levels, and inter-system reproducibility within the same dose level, were evaluated by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). Inter-system variability among five scanners was assessed by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). The test-retest repeatability analysis presented that 97.1% of features were repeatable between scan-rescans. The mean ± standard deviation ICC and CCC were 0.945 ± 0.079 and 0.945 ± 0.079 for intra-system reproducibility, respectively, and 86.0% and 85.7% of features were with ICC > 0.90 and CCC > 0.90, respectively, between different dose levels. The mean ± standard deviation ICC and CCC were 0.157 ± 0.174 and 0.157 ± 0.174 for inter-system reproducibility, respectively, and none of the features were with ICC > 0.90 or CCC > 0.90 within the same dose level. The inter-system variability suggested that 6.5% and 12.8% of features were with CV < 10% and QCD < 10%, respectively, among five CT systems. The radiomics features were non-reproducible with significant variability in values among different CT techniques. Radiomics features are non-reproducible with significant variability in values among photon-counting detector CT and dual-energy CT systems, necessitating careful attention to improve the cross-system generalizability of radiomic features before implementation of radiomics analysis in clinical routine. CT radiomics stability should be guaranteed before the implementation in the clinical routine. Radiomics robustness was on a low level among photon-counting detectors and dual-energy CT techniques. Limited inter-system robustness of radiomic features may impact the generalizability of models.
To assess the impact of acquisition and reconstruction factors on the robustness of radiomics within photon-counting detector CT (PCD-CT). A phantom with twenty-eight texture materials was scanned with different acquisition and reconstruction factors including reposition, scan mode (standard vs high-pitch), tube voltage (120 kVp vs 140 kVp), slice thickness (1.0 mm vs 0.4 mm), radiation dose level (0.5 mGy, 1.0 mGy, 3.0 mGy, 5.0 mGy, vs 10.0 mGy), quantum iterative reconstruction level (0/4, 2/4, vs 4/4), and reconstruction kernel (Qr40, Qr44, vs Qr48). Thirteen sets of virtual monochromatic images at 70-keV were reconstructed. The regions of interest were drawn with rigid registrations. Ninety-three radiomics features were extracted from each material. The reproducibility of radiomics features was evaluated using the intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). The variability of radiomics features was assessed by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). The percentage of features with ICC > 0.90 and CCC > 0.90 were high when repositioned (88.2% and 88.2%) and tube voltage was changed (87.1% and 87.1%), but none of the features with ICC > 0.90 and CCC > 0.90 when high-pitch scan and different slice thickness were used. The percentage of features with CV < 10% and QCD < 10% were high when repositioned (47.3% and 68.8%) and tube voltage was changed (64.2% and 71.0%), but that with CV < 10% and QCD < 10% were low between standard and high-pitch scans (16.1% and 26.9%) and slice thickness (19.4% and 29.0%). The PCD-CT radiomics was robust to tube voltage, radiation dose, reconstruction strength level, and kernel, but brittle to high-pitch scan and slice thickness. Question The stability of radiomics features against acquisition and reconstruction factors within PCD-CT should be fully determined before academic research and clinical application. Findings The radiomics features are robust against tube voltage, radiation dose, reconstruction strength level, and kernel within PCD-CT but brittle to high-pitch scan and slice thickness. Clinical relevance The high-pitch scan and slice thickness that influence voxel size should be set with careful attention within PCD-CT, to allow a higher robustness of radiomics features before the implementation of radiomics analysis in clinical routine.
Tumors display genomic and phenotypic heterogeneity, which holds prognostic significance and may influence therapy response. Radiographic imaging modalities, such as computed tomography, magnetic resonance imaging, nuclear medicine techniques, and ultrasonography, are routinely used to generate parametric maps to identify, measure, and map tumor heterogeneity from different perspectives encompassing anatomy, physiology, and metabolism. This review underscores the potential of artificial intelligence (AI)-based habitat imaging analysis, referred to as Radiomics++, in decoding intratumor heterogeneity compared to conventional radiomics. We highlight the general workflow, underlying principles, detailed methodology, and clinical applications of habitat imaging analysis to guide researchers. Validation advancements are then reviewed to verify the reliability of generated habitats by correlating radiologic phenotypes with biologic underpinnings. Furthermore, we address key challenges and opportunities in clinical translation, including data heterogeneity, model performance, and interpretability. Finally, integrating AI-defined habitats with multi-omics is anticipated to deepen our understanding of tumor evolution and advance precision medicine.
We review how sensorimotor control is dictated by interacting neural populations, optimal feedback mechanisms, and the biomechanics of bodies. First, we outline the distributed anatomical loops that shuttle sensorimotor signals between cortex, subcortical regions, and spinal cord. We then summarize evidence that neural population activity occupies low-dimensional, dynam-ically evolving manifolds during planning and execution of movements. Next, we summarize literature explaining motor behavior through the lens of optimal control theory, which clarifies the role of internal models and feedback during motor control. Finally, recent studies on embodied sensorimotor control address gaps within each framework by aiming to elucidate neural population activity through the explicit control of musculoskeletal dynamics. We close by discussing open problems and opportunities: multitasking and cognitively rich behavior, multiregional circuit models, and the level of anatomical detail needed in body and network models. Together, this review and recent advances point toward reaching an integrative account of the neural control of movement.
Digital twins-virtual representations dynamically linked to physical systems-have the potential to transform biomedical engineering by enabling real-time prediction, optimization, and personalization in health and disease. In biofluids, digital twins offer a framework for integrating physics-based models with data from clinical imaging, sensors, and physiological measurements to support diagnostics, therapeutic planning, and device design. This article reviews modeling approaches used in the construction of digital twins for biofluid applications. We survey high-fidelity numerical methods alongside emerging machine learning techniques, highlighting their respective strengths and limitations. Key requirements for digital twins are discussed, emphasizing the bidirectional interaction between physical and virtual assets and the importance of selecting modeling strategies tailored to specific biomedical contexts. While notable progress has been made over the past decade, significant challenges remain, particularly in integrating multiphysics models with data-driven methods and in establishing standardized protocols for data acquisition, interoperability, and sharing.
The continuous, real-time measurement of specific molecules in situ in the body promises to revolutionize the precision of drug dosing, the reach of physiological research, and the accuracy and timeliness of clinical diagnostics. Motivated by these prospects, here we critically review the set of molecular sensing technologies that (a) have been demonstrated to support seconds-resolved measurements in mammalian subjects and (b) appear translatable to the clinic. The relevant technologies employ four readout modalities: (a) direct or enzymatic electrochemical detection, (b) indirect electrochemical detection, (c) optical strategies, and (d) photoacoustic sensing. For each, we analyze the engineering requirements to support real-time, in vivo operation-which include reversibility, reagentless interrogation, and selectivity-and the remaining barriers to clinical adoption, including sensitivity and long-term in vivo stability and biocompatibility. Collectively, the advances surveyed here suggest that seconds-resolved molecular monitoring in humans is within reach and will herald a new era of precision diagnostics and closed-loop therapeutics.
Regulation of the brain's neuroimmune system is central to development, normal function, and disease. Neuronal communication to microglia, the primary immune cells of the brain, is well known to involve purinergic signaling mediated via ATP secretion and the cytokine fractalkine. Recent evidence shows that neurons release multiple cytokines beyond fractalkine, yet these are less studied and poorly understood. In contrast to ATP, cytokines are a class of signaling molecule that are much larger, with longer signaling and farther diffusion. We posit that neuron-expressed cytokines are an essential mechanism of neuron-microglia communication that arises as part of both normal learning and memory and in response to tissue pathology. Thus, neurons are underappreciated immunomodulatory cells that express diverse immunomodulatory signals. While neuronally sourced cytokines have been understudied, new technical advances make this a timely topic. The goal of this review is to define what is known about the cytokines expressed from neurons, how they are regulated, and the effects of these cytokines on microglia. We delineate key knowledge gaps and needs for new tools to define and analyze neuronal roles in immunomodulation. Given that cytokines are central regulators of microglial function, a broad new body of work is required to illuminate functional links between these neuronally expressed cytokines and sustained and transient microglial function.