Association of Retinal Age Gap and Risk of Kidney Failure: A UK Biobank Study.

RATIONALE & OBJECTIVE: The incidence of kidney failure is known to increase with age. We have previously developed and validated the use of retinal age based on fundus images as a biomarker of aging. However, the association of retinal age with kidney failure is not clear. We investigated the association of retinal age gap (the difference between retinal age and chronological age) with future risk of kidney failure. STUDY DESIGN: Prospective cohort study. SETTING & PARTICIPANTS: 11,052 UK Biobank study participants without any reported disease for characterizing retinal age in a deep learning algorithm. 35,864 other participants with retinal images and no kidney failure were followed to assess the association between retinal age gap and the risk of kidney failure. EXPOSURE: Retinal age gap, defined as the difference between model-based retinal age and chronological age. OUTCOME: Incident kidney failure. ANALYTICAL APPROACH: A deep learning prediction model used to characterize retinal age based on retinal images and chronological age, and Cox proportional hazards regression models to investigate the association of retinal age gap with incident kidney failure. RESULTS: After a median follow-up period of 11 (IQR, 10.89-11.14) years, 115 (0.32%) participants were diagnosed with incident kidney failure. Each 1-year greater retinal age gap at baseline was independently associated with a 10% increase in the risk of incident kidney failure (HR, 1.10 [95% CI, 1.03-1.17]; P=0.003). Participants with retinal age gaps in the fourth (highest) quartile had a significantly higher risk of incident kidney failure compared with those in the first quartile (HR, 2.77 [95% CI, 1.29-5.93]; P=0.009). LIMITATIONS: Limited generalizability related to the composition of participants in the UK Biobank study. CONCLUSIONS: Retinal age gap was significantly associated with incident kidney failure and may be a promising noninvasive predictive biomarker for incident kidney failure.

Association of Retinal Age Gap With Arterial Stiffness and Incident Cardiovascular Disease.

BACKGROUND: Retinal parameters could reflect systemic vascular changes. With the advances of deep learning technology, we have recently developed an algorithm to predict retinal age based on fundus images, which could be a novel biomarker for aging and mortality. Therefore, we aim to investigate associations of retinal age gap with arterial stiffness index and incident cardiovascular disease (CVD). METHODS: A deep learning model was trained based on 19 200 fundus images of 11 052 participants without any medical history at baseline to predict the retinal age. Retinal age gap (retinal age predicted minus chronological age) was generated for the remaining 35 917 participants. Regression models were used to assess the association between retinal age gap and arterial stiffness index. Cox proportional hazards regression models and restricted cubic splines were used to explore the association between retinal age gap and incident CVD. RESULTS: We found each 1-year increase in retinal age gap was associated with increased arterial stiffness index (ß=0.002 [95% CI, 0.001-0.003]; P<0.001). After a median follow-up of 5.83 years (interquartile range: 5.73-5.97), 675 (2.00%) developed CVD. In the fully adjusted model, each 1-year increase in retinal age gap was associated with a 3% increase in the risk of incident CVD (hazard ratio=1.03 [95% CI, 1.01-1.06]; P=0.014). In the restricted cubic splines analysis, the risk of incident CVD increased significantly when retinal age gap reached 1.21 (hazard ratio=1.05 [95% CI, 1.00-1.10]; P-overall <0.0001; P-nonlinear=0.0681). CONCLUSIONS: We found that retinal age gap was significantly associated with arterial stiffness index and incident CVD events, supporting the potential of this novel biomarker in identifying individuals at high risk of future CVD events.

Retinal age gap as a predictive biomarker of stroke risk.

BACKGROUND: The aim of this study is to investigate the association of retinal age gap with the risk of incident stroke and its predictive value for incident stroke. METHODS: A total of 80,169 fundus images from 46,969 participants in the UK Biobank cohort met the image quality standard. A deep learning model was constructed based on 19,200 fundus images of 11,052 disease-free participants at baseline for age prediction. Retinal age gap (retinal age predicted based on the fundus image minus chronological age) was generated for the remaining 35,917 participants. Stroke events were determined by data linkage to hospital records on admissions and diagnoses, and national death registers, whichever occurred earliest. Cox proportional hazards regression models were used to estimate the effect of retinal age gap on risk of stroke. Logistic regression models were used to estimate the predictive value of retinal age and well-established risk factors in 10-year stroke risk. RESULTS: A total of 35,304 participants without history of stroke at baseline were included. During a median follow-up of 5.83 years, 282 (0.80%) participants had stroke events. In the fully adjusted model, each one-year increase in the retinal age gap was associated with a 4% increase in the risk of stroke (hazard ratio [HR] = 1.04, 95% confidence interval [CI]: 1.00-1.08, P = 0.029). Compared to participants with retinal age gap in the first quintile, participants with retinal age gap in the fifth quintile had significantly higher risks of stroke events (HR = 2.37, 95% CI: 1.37-4.10, P = 0.002). The predictive capability of retinal age alone was comparable to the well-established risk factor-based model (AUC=0.676 vs AUC=0.661, p=0.511). CONCLUSIONS: We found that retinal age gap was significantly associated with incident stroke, implying the potential of retinal age gap as a predictive biomarker of stroke risk.

Plasma metabolomic profiles of dementia: a prospective study of 110,655 participants in the UK Biobank.

BACKGROUND: Plasma metabolomic profile is disturbed in dementia patients, but previous studies have discordant conclusions. METHODS: Circulating metabolomic data of 110,655 people in the UK Biobank study were measured with nuclear magnetic resonance technique, and incident dementia records were obtained from national health registers. The associations between plasma metabolites and dementia were estimated using Cox proportional hazard models. The 10-fold cross-validation elastic net regression models selected metabolites that predicted incident dementia, and a 10-year prediction model for dementia was constructed by multivariable logistic regression. The predictive values of the conventional risk model, the metabolites model, and the combined model were discriminated by comparison of area under the receiver operating characteristic curves (AUCs). Net reclassification improvement (NRI) was used to estimate the change of reclassification ability when adding metabolites into the conventional prediction model. RESULTS: Amongst 110,655 participants, the mean (standard deviation) age was 56.5 (8.1) years, and 51 186 (46.3%) were male. A total of 1439 (13.0%) developed dementia during a median follow-up of 12.2 years (interquartile range: 11.5-12.9 years). A total of 38 metabolites, including lipids and lipoproteins, ketone bodies, glycolysis-related metabolites, and amino acids, were found to be significantly associated with incident dementia. Adding selected metabolites (n=24) to the conventional dementia risk prediction model significantly improved the prediction for incident dementia (AUC: 0.824 versus 0.817, p =0.042) and reclassification ability (NRI = 4.97%, P = 0.009) for identifying high risk groups. CONCLUSIONS: Our analysis identified various metabolomic biomarkers which were significantly associated with incident dementia. Metabolomic profiles also provided opportunities for dementia risk reclassification. These findings may help explain the biological mechanisms underlying dementia and improve dementia prediction.

Retinal age gap as a predictive biomarker of future risk of Parkinson's disease.

INTRODUCTION: retinal age derived from fundus images using deep learning has been verified as a novel biomarker of ageing. We aim to investigate the association between retinal age gap (retinal age-chronological age) and incident Parkinson's disease (PD). METHODS: a deep learning (DL) model trained on 19,200 fundus images of 11,052 chronic disease-free participants was used to predict retinal age. Retinal age gap was generated by the trained DL model for the remaining 35,834 participants free of PD at the baseline assessment. Cox proportional hazards regression models were utilised to investigate the association between retinal age gap and incident PD. Multivariable logistic model was applied for prediction of 5-year PD risk and area under the receiver operator characteristic curves (AUC) was used to estimate the predictive value. RESULTS: a total of 35,834 participants (56.7 ± 8.04 years, 55.7% female) free of PD at baseline were included in the present analysis. After adjustment of confounding factors, 1-year increase in retinal age gap was associated with a 10% increase in risk of PD (hazard ratio [HR] = 1.10, 95% confidence interval [CI]: 1.01-1.20, P = 0.023). Compared with the lowest quartile of the retinal age gap, the risk of PD was significantly increased in the third and fourth quartiles (HR = 2.66, 95% CI: 1.13-6.22, P = 0.024; HR = 4.86, 95% CI: 1.59-14.8, P = 0.005, respectively). The predictive value of retinal age and established risk factors for 5-year PD risk were comparable (AUC = 0.708 and 0.717, P = 0.821). CONCLUSION: retinal age gap demonstrated a potential for identifying individuals at a high risk of developing future PD.

Association between dual sensory impairment and risk of mortality: a cohort study from the UK Biobank.

BACKGROUND: Dual sensory impairment is affecting over 10% of older adults worldwide. However, the long-term effect of dual sensory impairment (DSI) on the risk of mortality remains controversial. We aim to investigate the impact of single or/and dual sensory impairment on the risk of mortality in a large population-based sample of the adult in the UK with 14-years of follow-up. METHODS: This population-based prospective cohort study included participants aged 40 and over with complete records of visual and hearing functions from the UK Biobank study. Measurements of visual and hearing functions were performed at baseline examinations between 2006 and 2010, and data on mortality was obtained by 2021. Dual sensory impairment was defined as concurrent visual and hearing impairments. Cox proportional hazards regression models were employed to evaluate the impact of sensory impairment (dual sensory impairment, single visual or hearing impairment) on the hazard of mortality. RESULTS: Of the 113,563 participants included in this study, the mean age (standard deviation) was 56.8 (8.09) years, and 61,849 (54.5%) were female. At baseline measurements, there were 733 (0.65%) participants with dual sensory impairment, 2,973 (2.62%) participants with single visual impairment, and 13,560 (11.94%) with single hearing impairment. After a follow-up period of 14 years (mean duration of 11 years), 5,992 (5.28%) participants died from all causes. Compared with no sensory impairment, dual sensory impairment was significantly associated with an estimated 44% higher hazard of mortality (hazard ratio: 1.44 [95% confidence interval, 1.11-1.88], p = 0.007) after multiple adjustments. CONCLUSIONS: Individuals with dual sensory impairment were found to have an independently 44% higher hazard of mortality than those with neither sensory impairment. Timely intervention of sensory impairment and early prevention of its underlying causes should help to reduce the associated risk of mortality.

Unmet Supportive Care Needs and Associated Factors: a Cross-sectional Survey of Chinese Cancer Survivors.

To assess cancer survivors' unmet supportive care needs and associated factors. Limited evidence is available about the supportive care needs of Chinese cancer survivors to inform future service planning. A cross-sectional survey was conducted (STROBE cross-sectional checklist used for report). Standardized questionnaires were administered to 364 cancer survivors. Using the Supportive Care Framework as conceptual framework, factors concerning individuals' cognitive appraisals of their situation and social resources were examined to explore their association with unmet supportive care needs. The most common unmet supportive care needs included concern about the cancer coming back (51.5%), the need for up-to-date information (49.3%), collaborative management with the medical team (48.8%), and financial support (48.8%). Factors associated with greater strength of unmet supportive care needs included being female, having higher personal support, and higher self-efficacy pertaining to social relationship. Having lower support from family and friends was a contributing factor associated with greater strength of unmet needs in comprehensive cancer care and relationship, as was lower self-efficacy pertaining to uncertainty management associated with greater strength of unmet needs in quality of life, and lower self-efficacy pertaining to health professional interaction associated with greater strength of unmet needs in information. Chinese cancer survivors experience a number of unmet supportive needs. Female and rural cancer survivors, and those with lower social support level and self-efficacy are susceptible to having higher levels of unmet supportive care needs. Consistent information provision and peer support system establishment are two potentially beneficial approaches to meet cancer patients' long-term supportive care needs. Females and rural cancer survivors, those with less support from family and friends, and those with lower self-efficacy in interactions with health professionals and in managing uncertainty are especially at risk for unmet supportive care needs.

[Research Progress of Adaptive Intervention].

Adaptive intervention(AI)is a methodology which dynamically evaluates adaptive variables at decision points and timely adjusts and develops tailored strategies to meet individual needs.The study reviewed the origin and development and elaborated the core elements(including intervention outcomes,intervention options,decision points,tailoring variables,and decision rules)and the classification of AI.Based on the literature,the key points of the design and implementation of AI were prospected,which can provide evidence for the research and development of health behavior intervention.

The effects of Tai Chi on quality of life of cancer survivors: a systematic review and meta-analysis.

PURPOSES: To assess the effects of Tai Chi on quality of life (QOL) of cancer survivors. METHODS: The following databases were searched: PubMed, Cochrane CENTRAL, EBSCO (including MEDLINE, CINAHL, and other databases), ScienceDirect, CNKI, Wangfang Data, and CQVIP until April 25, 2018. Randomized controlled trials (RCTs) published in English or Chinese examining the effects of Tai Chi intervention for cancer survivors were included. The primary outcome was QOL; the secondary outcomes were limb function/muscular strength, immune function indicators, cancer-related fatigue (CRF), and sleep disturbance. Methodological quality was assessed using the Cochrane Risk of Bias tool. Results of RCTs were pooled with mean difference (MD) or standardized mean difference (SMD) with 95% confidence intervals (CI). Quality of evidence for each outcome was assessed with the GRADE system. RESULTS: Twenty-two RCTs were included in this review. Tai Chi improved the physical (SMD 0.34, 95%CI 0.09, 0.59) and mental health (SMD 0.60, 95%CI 0.12, 1.08) domains of quality of life. The intervention improved the limb/muscular function of breast cancer survivors (SMD 1.19, 95%CI 0.63, 1.75) and in mixed samples of cancer survivors reduced the levels of cortisol (MD - 0.09, 95%CI - 0.16, - 0.02), alleviated CRF (SMD - 0.37, 95%CI - 0.70, - 0.04), and promoted sleep (SMD - 0.37, 95%CI - 0.72, - 0.02). CONCLUSION: There is low-level evidence suggesting that Tai Chi improves physical and mental dimensions of QOL and sleep. There is moderate-level evidence suggesting Tai Chi reduces levels of cortisol and CRF and improves limb function. Additional studies with larger sample sizes and with higher-quality RCT designs comparing different regimens of Tai Chi are warranted.

Analysis of energy monitoring for a double-pulsed CO2 integrated path differential absorption lidar at 1.57 µm.

For double-pulsed 1.57 µm integrated path differential absorption lidar, the transmitted pulse energy measurement is an important factor that can influence the uncertainty of CO2 concentration measurement. An energy monitoring experiment was performed to determine how to improve the measurement precision of the transmitted pulse energy. Ground glass diffusers were used to reduce the speckle effect during energy monitoring. The roughness and rotational speed of the ground glass diffusers were considered and compared. The normalized energy ratios between on-line and off-line echo pulses and on-line and off-line energy monitoring pulses were analyzed, and the Allan deviation was used to evaluate the energy monitoring results. Averaging 148 shots, the standard deviation of the normalized energy ratio reached 0.0757%, whereas the correlation between the energy ratio of the on-line and off-line energy monitoring pulses and the energy ratio of the on-line and off-line echo pulses was higher than 90%.

Cognitive decline in type 2 diabetic db/db mice may be associated with brain region-specific metabolic disorders.

Type 2 diabetes has been associated with cognitive decline, but its metabolic mechanism remains unclear. In the present study, we attempted to investigate brain region-specific metabolic changes in db/db mice with cognitive decline and explore the potential metabolic mechanism linking type 2 diabetes and cognitive decline. We analyzed the metabolic changes in seven brain regions of two types of mice (wild-type mice and db/db mice with cognitive decline) using a 1H NMR-based metabolomic approach. Then, a mixed-model analysis was used to evaluate the effects of mice type, brain region, and their interaction on metabolic changes. Compared with the wild-type mice, the db/db mice with cognitive decline had significant increases in lactate, glutamine (Gln) and taurine as well as significant decreases in alanine, aspartate, choline, succinate, γ-Aminobutyric acid (GABA), glutamate (Glu), glycine, N-acetylaspartate, inosine monophosphate, adenosine monophosphate, adenosine diphosphate, and nicotinamide adenine dinucleotide. Brain region-specific metabolic differences were also observed between these two mouse types. In addition, we found significant interaction effects of mice type and brain region on creatine/phosphocreatine, lactate, aspartate, GABA, N-acetylaspartate and taurine. Based on metabolic pathway analysis, the present study suggests that cognitive decline in db/db mice might be linked to a series of brain region-specific metabolic changes, involving an increase in anaerobic glycolysis, a decrease in tricarboxylic acid (TCA) and Gln-Glu/GABA cycles as well as a disturbance in lactate-alanine shuttle and membrane metabolism.

NMR-based metabolomics reveals brain region-specific metabolic alterations in streptozotocin-induced diabetic rats with cognitive dysfunction.

Diabetes mellitus (DM) can result in cognitive dysfunction, but its potential metabolic mechanisms remain unclear. In the present study, we analyzed the metabolite profiling in eight different brain regions of the normal rats and the streptozotocin (STZ)-induced diabetic rats accompanied by cognitive dysfunction using a 1H NMR-based metabolomic approach. A mixed linear model analysis was performed to assess the effects of DM, brain region and their interaction on metabolic changes. We found that different brain regions in rats displayed significant metabolic differences. In addition, the hippocampus was more susceptible to DM compared with other brain regions in rats. More interestingly, significant interaction effects of DM and brain region were observed on alanine, creatine/creatine-phosphate, lactate, succinate, aspartate, glutamate, glutamine, γ-aminobutyric acid, glycine, choline, N-acetylaspartate, myo-inositol and taurine. Based on metabolic pathway analysis, we speculate that cognitive dysfunction in the STZ-induced diabetic rats may be associated with brain region-specific metabolic alterations involving energy metabolism, neurotransmitters, membrane metabolism and osmoregulation.

Retinal age gap as a predictive biomarker for future risk of clinically significant diabetic retinopathy.

AIMS: Retinal age derived from fundus images has been verified as a novel ageing biomarker. We aim to explore the association between retinal age gap (retinal age minus chronological age) and incident diabetic retinopathy (DR). METHODS: Retinal age prediction was performed by a deep learning model, trained and validated based on 19,200 fundus images of 11,052 disease-free participants. Retinal age gaps were determined for 2311 patients with diabetes who had no history of diabetic retinopathy at baseline. DR events were ascertained by data linkage to hospital admissions. Cox proportional hazards regression models were performed to evaluate the association between retinal age gaps and incident DR. RESULTS: During the median follow-up period of 11.0 (interquartile range: 10.8-11.1) years, 183 of 2311 participants with diabetes developed incident DR. Each additional year of the retinal age gap was associated with a 7% increase in the risk of incident DR (hazard ratio [HR] = 1.07, 95% confidence interval [CI] 1.02-1.12, P = 0.004), after adjusting for confounding factors. Participants with retinal age gaps in the fourth quartile had a significantly higher DR risk compared to participants with retinal age gaps in the lowest quartile (HR = 2.88, 95% CI 1.61-5.15, P < 0.001). CONCLUSIONS: We found that higher retinal age gap was associated with an increased risk of incident DR. As an easy and non-invasive biomarker, the retinal age gap may serve as an informative tool to facilitate the individualized risk assessment and personalized screening protocol for DR.

Effect of dietary patterns on mild cognitive impairment and dementia: a machine learning bibliometric and visualization analysis.

Objective: As a spectrum of neurodegenerative conditions, dementia presents a significant challenge to worldwide health. Mild cognitive impairment (MCI) is recognized as the intermediate stage between normal cognitive functioning and dementia. Studies highlight the significant impact of dietary patterns on the management of MCI and dementia. Currently, comprehensive research on dietary patterns specific to MCI and dementia is limited, but bibliometric analysis offers a method to pinpoint essential research directions. Methods: On November 18, 2023, a search was conducted in the Web of Science Core Collection (WoSCC) for publications on diet and MCI/dementia. Tools such as Rstudio, CiteSpace, and VOSviewer were employed to create a knowledge atlas. This atlas analyzed collaborations, reference co-citations, keyword patterns, and emerging trends. Results: The search yielded 1,493 publications on diet and MCI/dementia, indicating a growing interest despite fluctuations. Contributions came from 70 countries/regions and 410 organizations across 456 journals. The USA and China led in publication numbers, with significant contributions from Columbia University and Harvard Medical School. Top authors include Scarmeas Nikolaos, Morris Martha Clare, and Samieri Cecilia. The Ketogenic, Mediterranean, and MIND diets emerged as key dietary patterns for cognitive decline prevention, highlighting the role of genetic factors, especially ApoE polymorphisms, in cognitive deterioration. Conclusion: This study provides core countries, institutions, and authors in the field, and points out the development directions in the field. Future research directions in dietary for MCI and dementia will focus on: (1) the potential effects of the KD in alleviating oxidative stress and modulating gut microbiota in neurodegenerative diseases; (2) how diet influences cognitive health through patterns of ApoE and protein expression; (3) investigating the interactions between gut microbiota and brain function, known as the "gut-brain axis."

Cost-Effectiveness of the Second COVID-19 Booster Vaccination in the USA.

OBJECTIVE: To assess the cost effectiveness of the second COVID-19 booster vaccination with different age groups. METHODS: We developed a decision-analytic Susceptible-Exposed-Infected-Recovered (SEIR)-Markov model by five age groups (0-4 years, 5-11 years 12-17 years, 18-49 years, and 50+ years) and calibrated the model by actual mortality in each age group in the USA. We conducted five scenarios to evaluate the cost effectiveness of the second booster strategy and incremental benefits if the strategy would expand to 18-49 years and 12-17 years, from a health care system perspective. The analysis was reported according to the Consolidated Health Economic Evaluation Reporting Standards 2022 statement. RESULTS: Implementing the second booster strategy for those aged ≥ 50 years cost $823 million but reduced direct medical costs by $1166 million, corresponding to a benefit-cost ratio of 1.42. Moreover, the strategy also resulted in a gain of 2596 quality-adjusted life-years (QALYs) during the 180-day evaluation period, indicating it was dominant. Further, vaccinating individuals aged 18-49 years with the second booster would result in an additional gain of $1592 million and 8790 QALYs. Similarly, expanding the vaccination to individuals aged 12-17 years would result in an additional gain of $16 million and 403 QALYs. However, if social interaction between all age groups was severed, vaccination expansion to ages 18-49 and 12-17 years would no longer be dominant but cost effective with an incremental cost-effectiveness ratio (ICER) of $37,572 and $26,705/QALY gained, respectively. CONCLUSION: The second booster strategy was likely to be dominant in reducing the disease burden of the COVID-19 pandemic. Expanding the second booster strategy to ages 18-49 and 12-17 years would remain dominant due to their social contacts with the older age group.

Population impact and cost-effectiveness of artificial intelligence-based diabetic retinopathy screening in people living with diabetes in Australia: a cost effectiveness analysis.

Background: We aimed to evaluate the cost-effectiveness of an artificial intelligence-(AI) based diabetic retinopathy (DR) screening system in the primary care setting for both non-Indigenous and Indigenous people living with diabetes in Australia. Methods: We performed a cost-effectiveness analysis between January 01, 2022 and August 01, 2023. A decision-analytic Markov model was constructed to simulate DR progression in a population of 1,197,818 non-Indigenous and 65,160 Indigenous Australians living with diabetes aged ≥20 years over 40 years. From a healthcare provider's perspective, we compared current practice to three primary care AI-based screening scenarios-(A) substitution of current manual grading, (B) scaling up to patient acceptance level, and (C) achieving universal screening. Study results were presented as incremental cost-effectiveness ratio (ICER), benefit-cost ratio (BCR), and net monetary benefits (NMB). A Willingness-to-pay (WTP) threshold of AU$50,000 per quality-adjusted life year (QALY) and a discount rate of 3.5% were adopted in this study. Findings: With the status quo, the non-Indigenous diabetic population was projected to develop 96,269 blindness cases, resulting in AU$13,039.6 m spending on DR screening and treatment during 2020-2060. In comparison, all three intervention scenarios were effective and cost-saving. In particular, if a universal screening program was to be implemented (Scenario C), it would prevent 38,347 blindness cases, gain 172,090 QALYs and save AU$595.8 m, leading to a BCR of 3.96 and NMB of AU$9,200 m. Similar findings were also reported in the Indigenous population. With the status quo, 3,396 Indigenous individuals would develop blindness, which would cost the health system AU$796.0 m during 2020-2060. All three intervention scenarios were cost-saving for the Indigenous population. Notably, universal AI-based DR screening (Scenario C) would prevent 1,211 blindness cases and gain 9,800 QALYs in the Indigenous population, leading to a saving of AU$19.2 m with a BCR of 1.62 and NMB of AU$509 m. Interpretation: Our findings suggest that implementing AI-based DR screening in primary care is highly effective and cost-saving in both Indigenous and non-Indigenous populations. Funding: This project received grant funding from the Australian Government: the National Critical Research Infrastructure Initiative, Medical Research Future Fund (MRFAI00035) and the NHMRC Investigator Grant (APP1175405). The contents of the published material are solely the responsibility of the Administering Institution, a participating institution or individual authors and do not reflect the views of the NHMRC. This work was supported by the Global STEM Professorship Scheme (P0046113), the Fundamental Research Funds of the State Key Laboratory of Ophthalmology, Project of Investigation on Health Status of Employees in Financial Industry in Guangzhou, China (Z012014075). The Centre for Eye Research Australia receives Operational Infrastructure Support from the Victorian State Government. W.H. is supported by the Melbourne Research Scholarship established by the University of Melbourne. The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Economic evaluation for medical artificial intelligence: accuracy vs. cost-effectiveness in a diabetic retinopathy screening case.

Artificial intelligence (AI) models have shown great accuracy in health screening. However, for real-world implementation, high accuracy may not guarantee cost-effectiveness. Improving AI's sensitivity finds more high-risk patients but may raise medical costs while increasing specificity reduces unnecessary referrals but may weaken detection capability. To evaluate the trade-off between AI model performance and the long-running cost-effectiveness, we conducted a cost-effectiveness analysis in a nationwide diabetic retinopathy (DR) screening program in China, comprising 251,535 participants with diabetes over 30 years. We tested a validated AI model in 1100 different diagnostic performances (presented as sensitivity/specificity pairs) and modeled annual screening scenarios. The status quo was defined as the scenario with the most accurate AI performance. The incremental cost-effectiveness ratio (ICER) was calculated for other scenarios against the status quo as cost-effectiveness metrics. Compared to the status quo (sensitivity/specificity: 93.3%/87.7%), six scenarios were cost-saving and seven were cost-effective. To achieve cost-saving or cost-effective, the AI model should reach a minimum sensitivity of 88.2% and specificity of 80.4%. The most cost-effective AI model exhibited higher sensitivity (96.3%) and lower specificity (80.4%) than the status quo. In settings with higher DR prevalence and willingness-to-pay levels, the AI needed higher sensitivity for optimal cost-effectiveness. Urban regions and younger patient groups also required higher sensitivity in AI-based screening. In real-world DR screening, the most accurate AI model may not be the most cost-effective. Cost-effectiveness should be independently evaluated, which is most likely to be affected by the AI's sensitivity.

The effect of empowering leadership on miners' unsafe behavior: a cross-level chain mediation model.

Miners' unsafe behavior has always been a key issue in coal mine safety management. Numerous studies have shown that leadership is an important factor influencing miners' unsafe behavior. A cross-level structural model was constructed based on social cognitive theory, using psychological safety and safety climate as intermediary variables, to analyze the effect of empowering leadership on miners' unsafe behavior. Data were gathered from 272 coal miners on 59 working teams. Multilevel regression analysis and the Monte Carlo method were employed to analyze the influence of the empowering leadership on miners' unsafe behavior. The results showed that empowering leadership was negatively related to miners' unsafe behavior. Psychological safety and safety climate mediated the relationship between empowering leadership and miners' unsafe behavior, and also jointly mediated in the chain. These findings enrich existing research results on miners' unsafe behavior and provide a beneficial enlightenment to coal mine safety management.

Characterization of Microstructural Evolution in Heat-Affected Zone of Cu-Bearing Ultra-High-Strength Steel with Lamellar Microstructure.

The advanced lamellar microstructure significantly improves the toughness of Cu-bearing ultra-high strength steel by delamination toughening (yield strength: 1370 MPa, impact toughness at -40 °C: 60 J). The lamellar microstructure affects the microstructure evolution of heat-affected zone (HAZ), resulting in separate distributions of lath martensite and granular bainite in the complete austenitizing zone and the formation of cluster fresh martensite in the partial austenitizing zone. The grain refinement and decrease in dislocation density are predominant features, especially for the complete austenitizing zone, where the grain is refined to 4.33 µm, and dislocation density is decreased by 27%. With the degree of austenitizing increase, the dissolution of Cu-rich precipitates (CRPs) aggravates during welding. A small fraction of CRPs in the complete austenitizing zone implies the onset of reprecipitation of CRPs. The reason for softening in HAZ is attributed to a combined effect of granular bainite forming, dislocation density decreasing, and CRPs dissolving. After PWTH, large numbered reprecipitation of coherent CRPs occurs, contributing to the hardness recovery of HAZ. Meanwhile, due to the high density of dislocation of lamellar microstructure inherited by partial austenitizing zone, coarsening of coherent CRPs is easy to occur, and various incoherent structures are observed.