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This editorial examines the nature of scientific discovery by studying the characteristics, methods, and tools that have historically defined a 'successful scientist'. It first considers the traditional hypothesis-driven model of science founded on existing knowledge, testable hypotheses, and experiments based on precise measurements. Through illustrative examples, it distinguishes three types of highly successful scientific contributors: those who formulated novel hypotheses and theories that proved groundbreaking, those who developed transformative measurement tools that enabled experimental testing, and those who managed to do both, even in multiple scientific fields. While success has historically depended on access to knowledge, exceptional creativity, perseverance, and experimental rigor, this editorial reminds that serendipity and timing have also played important roles. It also points to the limits of science when hypotheses remain untestable and theories unfalsifiable. It then reviews modern developments that challenge traditional approaches, such as genome-wide association studies (GWAS), where clear and specific hypotheses are no longer a prerequisite for important novel discoveries, and where discovery can emerge from data mining, rather than from pre-existing knowledge and creative hypothesis. The editorial then progresses onto the emergence of machine-led scientific discovery, using the example of the DeepMind team's development of AlphaFold - an artificial intelligence (AI) system that accurately predicted protein folding without relying on traditional hypotheses. AlphaFold learned directly from data and revolutionised the entire field of structural biology, bringing a new era of machine-inferred and AI-based science. The described frameworks are then used to analyse the development of an emerging field of science, 'ideometrics', based on the theory of the brain's 'sense of ideas', explaining how it could potentially contribute to understanding the purpose of consciousness as a complex trait in evolutionary terms. Being conscious provides an advantage by enabling the brain's 'sense of ideas' to reduce informational entropy of all possible future states of a conscious being to a narrower range of the outcomes that are perceived as more favourable for its survival. Perception of time and space in humans is, therefore, intertwined with their conscious 'sense of ideas', both while awake and dreaming, and further research in neuroscience will be required to elucidate these relationships. In conclusion, the editorial offers a comprehensive reflection on how the definition of a 'successful scientist' is being substantially reshaped in the 21st century.

This editorial explores the concept of the 'value of information' in the 21st century through five distinct domains: science, medicine, policy, media, and markets. It uses examples to show that not all information is of equal value. Valuable information shifts probabilities assigned to our hypotheses in the most meaningful ways. It significantly alters our knowledge and understanding of our context, helps us prioritise more rational ideas, and facilitates better decision-making. This paper also addresses the necessity of a conscious observer for the value of information to exist. It develops a framework for how the brain perceives, processes, and assigns value to new information. An example with childhood memories is used to explain the incremental and disruptive shifts in understanding that information of different value can cause. Further examples illustrate how well-designed research can amplify the value of information, how long-neglected information can be rediscovered and used to radically reshape policy, and how expert crowdsourcing can prioritise ideas and democratise decision-making. They also show how key pieces of the most valuable information can redirect policies, have large real-world impact, and save lives. The role of disinformation is also addressed, particularly its power to distort our shared understanding of the related context, mislead rational activities and prompt susceptible people to prioritise irrational ideas. Mainstream media and social media can enable rapid spread of disinformation, exploiting the greater intensity of the brain's response to an unexpected surprise over the expected truth. Stock market prices, meanwhile, reflect in real time how each new information changes the perceived value of a company. Valuable information seems to have some inherent traits: relevance, i.e. it influences an observer's perception of the related beliefs or hypotheses; credibility, i.e. it needs to be trusted by an observer; and leverage, i.e. it influences an observer's ideas and decisions decisively. Where all three criteria align, information assumes a 'high value coefficient' and serves in revising prioritised ideas. Designing activities to increase this coefficient is therefore of common interest to scientists, health professionals, policymakers, journalists, and investors. As artificial intelligence accelerates the volume and speed of data production, human and machine systems alike will focus not merely on data collection, but on discerning which missing information, if generated, could enable the greatest positive impact on the real world. Ultimately, future success in science, medicine, policy, media, and markets may not necessarily be linked to gathering the largest amount information, but rather being good at identifying, generating, and acting upon the information that is most valuable.

This editorial examines the factors contributing to the success of science, tracing its evolution from fundamental human curiosity to contemporary advancements propelled by technology, data, and artificial intelligence (AI). Beginning with the hypothesis-testing process, it highlights how imaginative individuals throughout history have offered explanations for the natural world, designed experiments, and amassed evidence to confirm or reject their ideas and theories, thus generating new knowledge and understanding of nature. Early humans formulated simple myths and legends as the first scientific hypotheses, partly to lessen their fear of the unknown. A more scientific turn appeared when rare explorer-scientists ventured beyond their ancestral homes, gathered empirical information using their limited senses, made choices based on observations, and sometimes relocated entire communities. Their efforts reflected the timeless elements of the scientific method: from generating a hypothesis to its experimental proof, broad validation and application of new knowledge. The paper then examines the characteristics of successful scientific disciplines. They attract many researchers who generate novel ideas and hypotheses, building an accelerating momentum of discovery. Further hallmarks of such fields are swift and fair peer validation and robust mechanisms for applying new knowledge to improve human well-being. By contrast, less successful fields will struggle with attracting talent, leading to slower progress, which could also be coupled with resistance to new ideas and obstacles to real-world translation of new knowledge. A central theme of the paper is the contribution of measurement and tools to science's success. Modern instruments, from microscopes and telescopes to satellites and statistical tools, have extended our perception of nature, revealing realms far smaller and far larger than human senses can access. The paper also addresses the revolution of 'hypothesis-free science', driven by computers and big data. Rather than framing a single hypothesis, modern researchers gather enormous datasets and use algorithms to test large numbers of possible hypotheses simultaneously and systematically, free of human bias introduced through existing knowledge. Finally, the paper explores how AI could advance science to unprecedented successes: not just by improving human senses like a microscope does, providing additional ones like the Large Hadron Collider does, or extending human memory and computational capacity like computers do, but also by expanding human reasoning itself. Unlike previous tools, AI can synthesise human knowledge and generate hypotheses, design studies, explore patterns and write papers, thus becoming both a 'philosopher 2.0' and a 'scientist 2.0'. Therefore, AI may transform science from a human-centred endeavour into collaborative effort that relies on hybrid intelligence. This unprecedented new frontier will require attention to questions of its explainability, bias, authorship, ethics, and accountability. In the future, science will remain successful by staying aligned with its fundamental mission: to improve the human condition through the expansion of knowledge and understanding of our world.

Retinal vein occlusion (RVO) is the second most common retinal vascular disorder that affected 16.4 million people worldwide in 2008. The last decade has seen new epidemiological data on RVO, enabling us to provide a contemporary estimation of RVO epidemiology. We searched PubMed, Medline, Embase, GLOBAL HEALTH, World Health Organization Global Health Library, China National Knowledge Infrastructure for studies that reported prevalence or incidence of RVO in the general population. The age- and sex-specific prevalence of RVO was estimated by a multilevel mixed-effects logistic regression, the incidence of RVO and potential risk factors for RVO were respectively pooled by a random-effects meta-analysis. The prevalence of any RVO, branch RVO (BRVO) and central RVO (CRVO) all increased with advanced age, but didn't differ significantly between sexes. In 2015, the global prevalence of any RVO, BRVO and CRVO in people aged 30-89 years was 0.77% (95% confidence interval CI = 0.55-1.08), 0.64% (95% CI = 0.47-0.87) and 0.13% (95% CI = 0.08-0.21), equivalent to an overall of 28.06 million, 23.38 million and 4.67 million affected people. For any RVO, the pooled five-year cumulative incidence was 0.86% (95% CI = 0.70-1.07) and the pooled ten-year cumulative incidence was 1.63% (95% CI = 1.38-1.92). Hypertension was the strongest risk factor for any RVO, with a meta- odds ratio (OR) of 2.82 (95% CI = 2.12-3.75). This study provides an updated summary of RVO epidemiology in the general population. More epidemiological studies worldwide are still needed to better understand the global disease burden of RVO.

We reflect on our experiences of using Generative Pre-trained Transformer ChatGPT, a chatbot launched by OpenAI in November 2022, to draft a research article. We aim to demonstrate how ChatGPT could help researchers to accelerate drafting their papers. We created a simulated data set of 100 000 health care workers with varying ages, Body Mass Index (BMI), and risk profiles. Simulation data allow analysts to test statistical analysis techniques, such as machine-learning based approaches, without compromising patient privacy. Infections were simulated with a randomized probability of hospitalisation. A subset of these fictitious people was vaccinated with a fictional vaccine that reduced this probability of hospitalisation after infection. We then used ChatGPT to help us decide how to handle the simulated data in order to determine vaccine effectiveness and draft a related research paper. AI-based language models in data analysis and scientific writing are an area of growing interest, and this exemplar analysis aims to contribute to the understanding of how ChatGPT can be used to facilitate these tasks.

Lower extremity peripheral artery disease is the third leading cause of atherosclerotic cardiovascular morbidity, following coronary artery disease and stroke. This study provides the first comparison of the prevalence of peripheral artery disease between high-income countries (HIC) and low-income or middle-income countries (LMIC), establishes the primary risk factors for peripheral artery disease in these settings, and estimates the number of people living with peripheral artery disease regionally and globally. We did a systematic review of the literature on the prevalence of peripheral artery disease in which we searched for community-based studies since 1997 that defined peripheral artery disease as an ankle brachial index (ABI) lower than or equal to 0·90. We used epidemiological modelling to define age-specific and sex-specific prevalence rates in HIC and in LMIC and combined them with UN population numbers for 2000 and 2010 to estimate the global prevalence of peripheral artery disease. Within a subset of studies, we did meta-analyses of odds ratios (ORs) associated with 15 putative risk factors for peripheral artery disease to estimate their effect size in HIC and LMIC. We then used the risk factors to predict peripheral artery disease numbers in eight WHO regions (three HIC and five LMIC). 34 studies satisfied the inclusion criteria, 22 from HIC and 12 from LMIC, including 112 027 participants, of which 9347 had peripheral artery disease. Sex-specific prevalence rates increased with age and were broadly similar in HIC and LMIC and in men and women. The prevalence in HIC at age 45–49 years was 5·28% (95% CI 3·38–8·17%) in women and 5·41% (3·41–8·49%) in men, and at age 85–89 years, it was 18·38% (11·16–28·76%) in women and 18·83% (12·03–28·25%) in men. Prevalence in men was lower in LMIC than in HIC (2·89% [2·04–4·07%] at 45–49 years and 14·94% [9·58–22·56%] at 85–89 years). In LMIC, rates were higher in women than in men, especially at younger ages (6·31% [4·86–8·15%] of women aged 45–49 years). Smoking was an important risk factor in both HIC and LMIC, with meta-OR for current smoking of 2·72 (95% CI 2·39–3·09) in HIC and 1·42 (1·25–1·62) in LMIC, followed by diabetes (1·88 [1·66–2·14] vs 1·47 [1·29–1·68]), hypertension (1·55 [1·42–1·71] vs 1·36 [1·24–1·50]), and hypercholesterolaemia (1·19 [1·07–1·33] vs 1·14 [1·03–1·25]). Globally, 202 million people were living with peripheral artery disease in 2010, 69·7% of them in LMIC, including 54·8 million in southeast Asia and 45·9 million in the western Pacific Region. During the preceding decade the number of individuals with peripheral artery disease increased by 28·7% in LMIC and 13·1% in HIC. In the 21st century, peripheral artery disease has become a global problem. Governments, non-governmental organisations, and the private sector in LMIC need to address the social and economic consequences, and assess the best strategies for optimum treatment and prevention of this disease. Peripheral Arterial Disease Research Coalition (Europe).

INTRODUCTIONChild Health and Nutrition Research Initiative (CHNRI) started as an initiative of the Global Forum for Health Research in Geneva, Switzerland. Its aim was to develop a method that could assist priority setting in health research investments. The first version of the CHNRI method was published in 2007-2008. The aim of this paper was to summarize the history of the development of the CHNRI method and its key conceptual advances.METHODSThe guiding principle of the CHNRI method is to expose the potential of many competing health research ideas to reduce disease burden and inequities that exist in the population in a feasible and cost-effective way.RESULTSThe CHNRI method introduced three key conceptual advances that led to its increased popularity in comparison to other priority-setting methods and processes. First, it proposed a systematic approach to listing a large number of possible research ideas, using the \"4D\" framework (description, delivery, development and discovery research) and a well-defined \"depth\" of proposed research ideas (research instruments, avenues, options and questions). Second, it proposed a systematic approach for discriminating between many proposed research ideas based on a well-defined context and criteria. The five \"standard\" components of the context are the population of interest, the disease burden of interest, geographic limits, time scale and the preferred style of investing with respect to risk. The five \"standard\" criteria proposed for prioritization between research ideas are answerability, effectiveness, deliverability, maximum potential for disease burden reduction and the effect on equity. However, both the context and the criteria can be flexibly changed to meet the specific needs of each priority-setting exercise. Third, it facilitated consensus development through measuring collective optimism on each component of each research idea among a larger group of experts using a simple scoring system. This enabled the use of the knowledge of many experts in the field, \"visualising\" their collective opinion and presenting the list of many research ideas with their ranks, based on an intuitive score that ranges between 0 and 100.CONCLUSIONSTwo recent reviews showed that the CHNRI method, an approach essentially based on \"crowdsourcing\", has become the dominant approach to setting health research priorities in the global biomedical literature over the past decade. With more than 50 published examples of implementation to date, it is now widely used in many international organisations for collective decision-making on health research priorities. The applications have been helpful in promoting better balance between investments in fundamental research, translation research and implementation research.

Diabetic retinopathy (DR), the primary retinal vascular complication of diabetes mellitus (DM), is a leading cause of vision impairment and blindness in working-age population globally. Despite mounting concerns about the emergence of DM as a major public health problem in the largest developing country, China, much remains to be understood about the epidemiology of DR. We aimed to investigate the prevalence of and risk factors for DR, and estimate the burden of DR in China in 2010. China National Knowledge Infrastructure (CNKI), Wanfang, Chinese Biomedicine Literature Database (CBM-SinoMed), PubMed, Embase and Medline were searched for studies that reported the prevalence of and risk factors for DR in Chinese population between 1990 and 2017. A random-effects meta-analysis model was adopted to pool the overall prevalence of DR. Variations in the prevalence of DR in different age groups, DM duration groups and settings were assessed by subgroup meta-analysis and meta-regression. Odds ratios (ORs) of major risk factors were pooled using random-effects meta-analysis. The number of people with DR in 2010 was estimated by multiplying the age-specific prevalence of DR in people with DM with the corresponding number of people with DM in China. Finally, the national number of people with DR was distributed into six geographic regions using a risk factor-based model. A total of 31 studies provided information on the prevalence of DR and 21 explored potential risk factors for DR. The pooled prevalence of any DR, nonproliferative DR (NPDR) and proliferative DR (PDR) was 1.14% (95% CI = 0.80-1.52), 0.90% (95% CI = 0.56-1.31) and 0.07% (95% CI = 0.02-0.14) in general population; In people with DM, the pooled prevalence rates were 18.45% (95% CI = 14.77-22.43), 15.06% (95% CI = 11.59-18.88) and 0.99% (95% CI = 0.40-1.80) for any DR, NPDR and PDR, respectively. The prevalence of any DR in DM patients peaked between 60 and 69 years of age, and increased steeply with the duration of DM. DM patients residing in rural China were at a higher risk to have DR than those in urban areas. In addition, insulin treatment, elevated FBG level and higher HbA1c concentration were confirmed to be associated with a higher prevalence of DR in people with DM, with meta-ORs of 1.99 (95% CI = 1.34-2.95), 1.33 (95% CI = 1.12-1.59) and 1.15 (95% CI = 1.09-1.20) respectively. In 2010, a total of 13.16 million (95% CI = 8.95-18.00) Chinese aged 45 years and above were living with DR, among whom the most were in South Central China and the least were in Northwest China. DR has become a serious public health problem in China. Optimal screening of and interventions on DR should be implemented. Improved epidemiological studies on DR are still required.

University administrators and mental health clinicians have raised concerns about depression and anxiety among Ph.D. students, yet no study has systematically synthesized the available evidence in this area. After searching the literature for studies reporting on depression, anxiety, and/or suicidal ideation among Ph.D. students, we included 32 articles. Among 16 studies reporting the prevalence of clinically significant symptoms of depression across 23,469 Ph.D. students, the pooled estimate of the proportion of students with depression was 0.24 (95% confidence interval [CI], 0.18–0.31; I 2  = 98.75%). In a meta-analysis of the nine studies reporting the prevalence of clinically significant symptoms of anxiety across 15,626 students, the estimated proportion of students with anxiety was 0.17 (95% CI, 0.12–0.23; I 2  = 98.05%). We conclude that depression and anxiety are highly prevalent among Ph.D. students. Data limitations precluded our ability to obtain a pooled estimate of suicidal ideation prevalence. Programs that systematically monitor and promote the mental health of Ph.D. students are urgently needed.