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In the context of rapid urbanization in China, many farmers still live in areas far away from urban energy supply networks. To meet the multi-level energy demands of rural communities, this study proposes a combined heat, power, and electricity (CCHP) supply system that uses solar and biomass energy as inputs, tailored to the natural resources and climatic conditions of the northwestern region. A theoretical model of this system was established in Nanan Community, Wuwei City, and its dynamic performance throughout the year was simulated and analyzed using TRNSYS software. The system was also evaluated for its economic viability, energy efficiency, and environmental impact. The results show that compared with the original and traditional energy supply systems, the CCHP system achieves average primary energy saving rates of −9.87% and 41.52% during the heating season, annual cost savings of 50.35% and 64.19%, carbon dioxide emission reduction rates of 32.89% and 66.86%, and a dynamic investment payback period of 3.14 years. This study provides development ideas for constructing modern integrated energy systems in rural areas that are remote from urban energy supply networks and offers references for investors.

To examine the association of physical activities (PA) with diabetic retinopathy (DR) progression based on a 10-year follow-up of a large cohort of working-aged diabetic populations in Australia. Nine thousand and eighteen working-aged diabetic patients were enrolled from the baseline of the 45 and Up Study from New South Wales, Australia. Self-reported PA collected by questionnaire at baseline in 2006 was graded into low (<5 sessions/week), medium (≥5-14), and high (≥14) levels. Retinal photocoagulation (RPC) treatment during the follow-up period was used as a surrogate for DR progression and was tracked through the Medicare Benefits Schedule, which was available from 2004 to 2016. Cox regression was used to estimate the association between PA and RPC incidence. In the fully adjusted model, higher PA level was significantly associated with a lower risk of RPC incident (Cox-regression, p-value for trend = 0.002; medium vs. low, hazard ratio (HR) = 0.78, 95% Confidence Interval (CI): 0.61-0.98; high vs. low, HR = 0.61, 95%CI: 0.36-0.84. In addition, gender, body mass index, insulin treatment, family history of diabetes, history of cardiovascular disease were significant effect modifiers for the association between PA and RPC. Higher PA level was independently associated with a lower risk of DR progression among working-aged diabetic populations in this large cohort study.

For medical artificial intelligence (AI) training and validation, human expert labels are considered the gold standard that represents the correct answers or desired outputs for a given data set. These labels serve as a reference or benchmark against which the model's predictions are compared. This study aimed to assess the accuracy of a custom deep learning (DL) algorithm on classifying diabetic retinopathy (DR) and further demonstrate how label errors may contribute to this assessment in a nationwide DR-screening program. Fundus photographs from the Lifeline Express, a nationwide DR-screening program, were analyzed to identify the presence of referable DR using both (1) manual grading by National Health Service England-certificated graders and (2) a DL-based DR-screening algorithm with validated good lab performance. To assess the accuracy of labels, a random sample of images with disagreement between the DL algorithm and the labels was adjudicated by ophthalmologists who were masked to the previous grading results. The error rates of labels in this sample were then used to correct the number of negative and positive cases in the entire data set, serving as postcorrection labels. The DL algorithm's performance was evaluated against both pre- and postcorrection labels. The analysis included 736,083 images from 237,824 participants. The DL algorithm exhibited a gap between the real-world performance and the lab-reported performance in this nationwide data set, with a sensitivity increase of 12.5% (from 79.6% to 92.5%, P<.001) and a specificity increase of 6.9% (from 91.6% to 98.5%, P<.001). In the random sample, 63.6% (560/880) of negative images and 5.2% (140/2710) of positive images were misclassified in the precorrection human labels. High myopia was the primary reason for misclassifying non-DR images as referable DR images, while laser spots were predominantly responsible for misclassified referable cases. The estimated label error rate for the entire data set was 1.2%. The label correction was estimated to bring about a 12.5% enhancement in the estimated sensitivity of the DL algorithm (P<.001). Label errors based on human image grading, although in a small percentage, can significantly affect the performance evaluation of DL algorithms in real-world DR screening.

Precise control of the local electronic structure and properties of electrocatalysts is important for enhancing the multifunctionality and durability of electrocatalysts and for correlating the structure/chemistry with the catalytic properties. Herein, we report electronically coupled metallic hybrids of NiFe layered double hydroxide nanosheet/Ti3C2 MXene quantum dots deposited on a nitrogen‐doped graphene surface (LDH/MQD/NG) for high‐performance flexible Zn–air batteries (ZABs). As verified from the Mott–Schottky and Nyquist plots, as well as spectroscopic, electrochemical, and computational analyses, the electronic and chemical coupling of LDH/MQD/NG modulates the local electronic and surface structure of the active LDH to provide metallic conductivity and abundant active sites, leading to significantly improved bifunctional activity and electrocatalytic kinetics. The rechargeable ZABs with LDH/MQD/NG hybrids are superior to the previous LDH‐based ZABs, demonstrating a high power density (113.8 mW cm−2) and excellent cycle stability (150 h at 5 mA cm−2). Moreover, the corresponding quasi solid‐state ZABs are completely flexible and practical, affording a high power density of 57.6 mW cm−2 even in the bent state, and in real‐life operation of tandem cells for powering various electronic devices. Flexible, rechargeable zinc–air batteries have been considered as an attractive power source to meet the ever‐increasing demands for flexible and wearable electronic devices owing to their low cost and high safety. Electronically coupled metallic hybrids of NiFe layered double hydroxide nanosheets/Ti3C2 MXene quantum dots deposited on a nitrogen‐doped graphene surface (LDH/MQD/NG) were prepared as an efficient bifunctional oxygen electrocatalyst. With precise control of the local electronic structure and the structure/chemistry properties, the resultant hybrids exhibit metallic conductivity and abundant active sites, affording significantly improved electrocatalytic ORR/OER activity, and delivering high power density and flexibility for application in flexible Zn–air batteries.

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.

Background Perioperative respiratory adverse events (PRAE) may cause critical illness in children undergoing anesthesia and surgery. So, this study aims to explore the diagnostic value of miR-483-5p in PRAE. Methods This study included 112 patients with PRAE and 93 patients without PRAE. MiR-483-5p expression was quantified utilizing RT-qPCR. The receiver operating characteristic (ROC) curve was utilized to assess the diagnostic value. The clinicopathological characteristics of PRAE and miR-483-5p level were analyzed by the Chi-squared test. Risk factors for PRAE were analyzed using multivariate logistic regression. The multicellular human alveolar model was established by treating it with lipopolysaccharide (LPS) at 48 h. Then, cell viability and inflammatory factors were detected in the PRAE model by cell counting kit-8 (CCK-8) assay and ELISA. Results MiR-483-5p was obviously increased and had high diagnostic value in PRAE. In PRAE, miR-483-5p was a risk factor and was closely related to clinical indicators. In the multicellular human alveolar model, overexpression of miR-483-5p reduced proliferation and elevated inflammatory factors levels, while inhibition of miR-483-5p increased proliferation and reduced inflammatory factors levels, which is consistent with the trend in the PRAE cell model. Conclusion MiR-483-5p might participate in PRAE in children by regulating proliferation and inflammatory factors, proving that it may be a biomarker in PRAE. In addition, miR-483-5p was a risk factor for PRAE. In the multicellular human alveolar model, LPS treatment significantly enhanced miR-483-5p expression.

To provide a self-adaptive deep learning (DL) method to automatically detect the eye laterality based on fundus images. A total of 18394 fundus images with real-world eye laterality labels were used for model development and internal validation. A separate dataset of 2000 fundus images with eye laterality labeled manually was used for external validation. A DL model was developed based on a fine-tuned Inception-V3 network with self-adaptive strategy. The area under receiver operator characteristic curve (AUC) with sensitivity and specificity and confusion matrix were applied to assess the model performance. The class activation map (CAM) was used for model visualization. In the external validation (N = 2000, 50% labeled as left eye), the AUC of the DL model for overall eye laterality detection was 0.995 (95% CI, 0.993-0.997) with an accuracy of 99.13%. Specifically for left eye detection, the sensitivity was 99.00% (95% CI, 98.11%-99.49%) and the specificity was 99.10% (95% CI, 98.23%-99.56%). Nineteen images were wrongly classified as compared to the human labels: 12 were due to human wrong labelling, while 7 were due to poor image quality. The CAM showed that the region of interest for eye laterality detection was mainly the optic disc and surrounding areas. We proposed a self-adaptive DL method with a high performance in detecting eye laterality based on fundus images. Results of our findings were based on real world labels and thus had practical significance in clinical settings.

Objective To analyze the vulnerability factors of snakebite patients in China. Methods Multi-stage random sampling was used as the main sampling method and snowball sampling as the auxiliary sampling method. The knowledge, attitude and behavior of snakebite among Chinese residents were investigated. Non-parametric test was used to compare the percentage differences in residents’ knowledge, attitude and behavior of snakebite, and generalized linear regression analysis was used to analyze the influencing factors, and the vulnerability factors of snakebite patients were comprehensively analyzed. Results A total of 6338 subjects were included in this study, of which 68.4% were males, and 58.6% were farmers, workers and service personnel. The median total score of knowledge, attitude, and behavior was 26 (22,36). The patients who were improperly treated after injury were ligation proximal to the affected area (23.43%), squeezing (21.82%), and oral and suction wounds (8.74%). Did not go to hospital due to poverty (1351 cases) and did not receive antivenom (2068 cases). There were 21.32% and 32.63%, respectively. Among 4270 patients injected with antivenom 30.7% were vaccinated within 2 h. Among the patients who went to the hospital for treatment (4987), 75.0% arrived at the hospital within 6 h; Among the 4,761 patients who made emergency calls, 37.4% were treated within 0.5 h. Conclusions Snakebite patients in China have weak knowledge about snakebite, low awareness of medical treatment, lack of correct prevention and emergency treatment measures, dependence on folk remedies, poor housing and so on. In addition, there are low availability of antivenoms and unreasonable distribution of medical resources in some areas of China. Multisectoral and multidisciplinary cooperation should be developed to prevent and control snakebites in order to reduce the burden caused by snakebites.

PurposeTo investigate the burden of near vision loss (NVL) in China by year, age and gender from 1990 to 2019.MethodsWe used estimates from the Global Burden of Disease (GBD) 2019 study to report the prevalence and disability-adjusted life-years (DALYs) due to NVL in China. Estimates of crude counts and age-standardised rates per 100 000 population are accompanied by 95% uncertainty intervals (UIs). We summarised the age-specific and sex-specific patterns and trends regarding the burden of NVL in China, compared with seven neighbouring countries.ResultsFrom 1990 to 2019, the all-age number and rate for NVL prevalence and DALYs increased significantly in China (all p<0.001). The age-standardised rate decreased from 7538.1 (95% UI 6946.3 to 8075.1) to 7392.9 (95% UI 6855.8 to 7890.5) per 100 000 population for NVL prevalence (p=0.107), and from 74.9 (95% UI 69.6 to 79.9) to 73.8 (95% UI 70.6 to 80.1) per 100 000 population for DALYs (p=0.388). Women had higher NVL prevalence (t=170.1, p<0.001) and DALYs (t=192.5, p<0.001) than men. Higher disease burden of NVL was observed in the middle-aged and elderly population. The age-standardised prevalence and DALY rate attributable to NVL in China were lower than in India, North Korea, Pakistan (all p<0.001), but higher than Russia, South Korea, Singapore and Japan (all p<0.001).ConclusionsDespite a small decrease in age-standardised prevalence and DALYs due to NVL in China in the past two decades, the existing burden is still considerable and significantly higher compared with neighbouring developed countries. An approach that includes all stakeholders is needed to further reduce this burden.

AimsTo investigate the prevalence and incidence of presbyopia in an urban Chinese population.Methods1817 subjects aged ≥35 years were identified by random cluster sampling in Yuexiu District, Guangzhou, China, at baseline in 2008, and all were invited for the follow-up examination in 2014. Distance and near visual acuity (VA) tests, as well as non-cycloplegic automated refraction were performed at each examination as per standardised protocol. Participants with presenting near VA ≤20/40 were further tested with add power at a standard distance of 40 cm to obtain their best-corrected near VA. Functional presbyopia was defined as near VA under presenting distance refraction correction of <20/50 and could be improved by at least one line with add power.ResultsA total of 1191 (83.5% of the 2014 follow-up) participants were included in the current analysis with a mean (SD) age of 50.4 (9.7) years, and 52.9% were female. Prevalence of functional presbyopia at baseline was 25.2% (95% CI 21.5 to 28.9) and the 6-year incidence was 42.8% (95% CI 39.4 to 50.1). Older and more hyperopic subjects had both higher prevalence and incidence of presbyopia (P<0.001). Average presbyopic correction coverage (PCC) was 87.7% at baseline and was significantly lower in myopic participants (P=0.006).ConclusionsPrevalence of functional presbyopia in urban China is relatively lower along with a higher PCC compared with previous population-based rural cohorts. We identified a high presbyopia incidence, and further studies are needed to understand longitudinal presbyopia progression as well as the urban–rural gap in presbyopia to throw light on future strategic planning.