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The continuous development of Educational Data Mining (EDM) and Learning Analytics (LA) technologies has provided more effective technical support for accurate early warning and interventions for student academic performance. However, the existing body of research on EDM and LA needs more empirical studies that provide feedback interventions, and more attention should be paid to primary and secondary school students. This study proposed a data-driven precision teaching intervention mechanism combining EDM and LA technologies. The proposed mechanism aims to assist teachers in predicting students' academic performance and implementing corresponding interventions. This approach enables early warnings and reminders for students in crisis, and offers teaching assistance and support tailored to students at different levels. A quasi-experimental design was employed to examine the impact of the data-driven precision teaching intervention mechanism on secondary school students' learning outcomes. A total of 142 seventh-grade students participated in the intervention experiment, with an experimental group (50) receiving the data-driven precision teaching intervention, control group2 (48) receiving a group intervention stratified by teacher experience, and control group1 (44) receiving a traditional group intervention. Posttest data were collected after three rounds of intervention. Compared to the two control groups, students in the experimental group demonstrated superior academic achievement, intrinsic motivation, self-efficacy, and meta-cognitive awareness. These findings indicate that the data-driven precision teaching intervention approach positively impacted students' academic development, and effectively promoted their personalized learning. The findings provide pedagogical insights into the application of EDM in conjunction with LA prediction and actionable interventions. [ABSTRACT FROM AUTHOR]

Background: As global aging intensifies, the prevalence of ocular fundus diseases continues to rise. In China, the tense doctor-patient ratio poses numerous challenges for the early diagnosis and treatment of ocular fundus diseases. To reduce the high risk of missed or misdiagnosed cases, avoid irreversible visual impairment for patients, and ensure good visual prognosis for patients with ocular fundus diseases, it is particularly important to enhance the growth and diagnostic capabilities of junior doctors. This study aims to leverage the value of electronic medical record data to developing a diagnostic intelligent decision support platform. This platform aims to assist junior doctors in diagnosing ocular fundus diseases quickly and accurately, expedite their professional growth, and prevent delays in patient treatment. An empirical evaluation will assess the platform's effectiveness in enhancing doctors' diagnostic efficiency and accuracy. Methods: In this study, eight Chinese Named Entity Recognition (NER) models were compared, and the SoftLexicon-Glove-Word2vec model, achieving a high F1 score of 93.02%, was selected as the optimal recognition tool. This model was then used to extract key information from electronic medical records (EMRs) and generate feature variables based on diagnostic rule templates. Subsequently, an XGBoost algorithm was employed to construct an intelligent decision support platform for diagnosing ocular fundus diseases. The effectiveness of the platform in improving diagnostic efficiency and accuracy was evaluated through a controlled experiment comparing experienced and junior doctors. Results: The use of the diagnostic intelligent decision support platform resulted in significant improvements in both diagnostic efficiency and accuracy for both experienced and junior doctors (P < 0.05). Notably, the gap in diagnostic speed and precision between junior doctors and experienced doctors narrowed considerably when the platform was used. Although the platform also provided some benefits to experienced doctors, the improvement was less pronounced compared to junior doctors. Conclusion: The diagnostic intelligent decision support platform established in this study, based on the XGBoost algorithm and NER, effectively enhances the diagnostic efficiency and accuracy of junior doctors in ocular fundus diseases. This has significant implications for optimizing clinical diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to investigate the infection status of Omicron in the population and the association between COVID-19 vaccination and infection with Omicron. Methods: We conducted a cross-sectional study to openly recruit participants for a survey of SARS-CoV-2 infection by convenience sampling from 1 January to 15 January 2023 after a COVID-19 pandemic swept across China. Additionally, the binary logistic regression model was adopted to evaluate the association between COVID-19 vaccination and the infection outcomes or symptom severity, respectively. Meanwhile, the relations between the vaccination and duration of the symptoms were estimated via ordinal logistic analysis. Results: Of the 2007 participants, the prevalence of infection with Omicron was 82.9%. Compared with unvaccinated individuals, inactivated COVID-19 vaccination could increase the risk of Omicron infection (OR = 1.942, 95% CI: 1.093–3.448), and the receipt of at least one dose of non-inactivated COVID-19 vaccines was a protective factor against infection (OR = 0.428, 95% CI: 0.226–0.812). By contrast, no relations were observed in COVID-19 vaccination with the symptoms of infection and duration of symptoms (p > 0.05). Conclusions: This cross-sectional study concluded that inactivated COVID-19 vaccination might increase the risk of Omicron infection, which should be a concern during COVID-19 vaccination and the treatment of variant infections in the future, and the receipt of at least one dose of non-inactivated COVID-19 vaccine was a protective factor against infection. [ABSTRACT FROM AUTHOR]

Background: Multimorbidity, body pain, sleep disturbance, and depression are major clinical and public health challenges. This paper aimed to examine the associations of multimorbidity with body pain, sleep duration, and depression; and whether the associations varied by socioeconomic status. Methods: Data was derived from four waves of the nationally representative China Health and Retirement Longitudinal Study (CHARLS), including participants aged 45 years and older in 2011. 12 physical non-communicable diseases and 1 mental chronic disease were used to measure multimorbidity. Educational attainment and annual per-capita household consumption expenditure were employed as proxies for socioeconomic status. Results: Of the 16,931 participants aged 45 + years old, the proportion of people with multimorbidity was 37.87% at baseline. The number of multimorbidity increased with older age and higher socioeconomic status. Multimorbidity was associated with more body pain (incidence rate ratio (IRR) = 1.53, 95% CI = 1.45–1.61), and decreased sleep duration (β = -0.26, 95% CI = -0.36–-0.15). Furthermore, multimorbidity was associated with increased depression risks (odds ratio (OR) = 1.54, 95% CI = 1.44–1.64, adjusted for sociodemographic variables), with the mediating effects of the number of body pain and sleep duration. The associations between multimorbidity and depression persisted among different socioeconomic groups. Conclusions: Multimorbidity was associated with increased body pain, decreased sleep duration, and further led to increased depression risks. It is necessary to pay attention to the multimorbidity of middle-aged and older adults, relieve their body pain, guarantee sufficient sleep, so as to reduce depression risks. [ABSTRACT FROM AUTHOR]

Background: Oral health is a major global public health problem, but its risk factors have not been fully identified. The limited evidence suggests that AL may affect oral health conditions, but most of these studies focus only on middle‐aged western populations. Objectives: To examine whether allostatic load is associated with oral health conditions later in life among middle‐aged and older adults in China and there there is a correlation in both middle‐aged and older people. Materials and methods: Data were collected from the China Health and Retirement Longitudinal Study 2011–2018 (N = 10 890) and were analysed using logistic regressions for the overall sample and subsamples by age. Results: Results showed that higher inflammation load significantly increased the odds of edentulism (OR = 1.358, 95% CI = 1.020–1.809, p <.05). Higher metabolic load significantly increased the odds of denture use (OR = 1.375, 95% CI = 1.154–1.640, p < 0.001) and difficulty in chewing solid foods (OR = 1.100, 95% CI = 1.035–1.169, p <.01). These associations were manifested in older adults over 60 years of age, while in the middle‐aged subsample, the associations were not significant. Conclusion: The findings suggested that higher allostatic load was associated with poorer oral health conditions later in life. It is critical to lower allostatic load and improve oral health conditions, especially for older adults over 60 years of age. Prospective studies and intervention trials help to better understand whether allostatic load is causally linked to oral health. [ABSTRACT FROM AUTHOR]

Background: Cholesterol gallstone (CG) disease is a worldwide common disease characterized by cholesterol supersaturation in gallbladder bile. Ganoderma lucidum polysaccharide (GLP) has been shown to possess various beneficial effects against metabolic disorders. However, the role and underlying mechanism of GLP in CG formation are still unknown. This study aimed to determine the role of GLP in ameliorating lithogenic diet (LD)-induced CG formation. Methods: Mice were fed either a normal chow diet, a LD, or LD supplemented with GLP. Real-time quantitative polymerase chain reaction (RT-qPCR) and western blotting were used to detect the expression of genes involved in cholesterol and bile acid (BA) metabolism. The BA concentrations in the ileum were quantified by liquid chromatography-tandem mass spectrometry (LC–MS/MS). The microbiota in cecal contents were characterized using 16S ribosomal RNA (16S rRNA) gene sequencing. Results: GLP effectively alleviated CG formation induced by LD. Specifically, GLP reduced the total cholesterol (TC) levels, increased the total BA levels, and decreased the cholesterol saturation index (CSI) in gallbladder bile. The protective effect of GLP was attributed to the inhibition of farnesoid X receptor (FXR) signaling, increased hepatic BA synthesis and decreased hepatic cholesterol synthesis and secretion. GLP also altered the BA composition in the ileum, reducing FXR-agonistic BAs and increasing FXR-antagonistic BAs, which may contribute to the inhibition of intestinal FXR signaling. Additionally, GLP improved dysbiosis of the intestinal flora and reduced the serum levels of hydrogen sulfide (H2S), a bacterial metabolite that can induce hepatic FXR, thereby inhibiting hepatic FXR signaling. Moreover, the protective effect of GLP against CG formation could be reversed by both the global and gut-restricted FXR agonists. Conclusions: Taken together, GLP ameliorates CG formation by regulating cholesterol and BA metabolism in an FXR-dependent manner. Our study demonstrates that GLP may be a potential strategy for the prevention against CG disease. [ABSTRACT FROM AUTHOR]

China's diets are undergoing a profound transformation, and carbon storage loss generated by dietary structure change is increasingly serious, accompanied by concerns about sustainable diets and food supply–demand conflicts. This paper designed a dual 'material–environmental' framework based on the dietary structure classification, around which we conducted the heterogeneity analysis of food supply–demand patterns and land carbon loss, and then tracked hotspot diets of trade-generated carbon loss through the multi-regional input–output (MRIO) model. The research found that 31 provinces could be classified into five clusters with obvious spatial agglomeration patterns, most of which still had a high proportion of staches, but also showed an evolutionary trend towards diversified compositions. Driven by dietary structure, the national food supply–demand match under the material accessibility dimension showed barely balanced overall, while in terms of environmental effects, ecosystems lost significant amounts of carbon stocks, with losses of 331 and 950 Tg 1 1 Tg refers to teragram, a weight unit, 1 Tg = 106 t = 1012 g of vegetation carbon and soil organic carbon, respectively, which together accounted for 14 percent of the total carbon stocks in the vegetation and topsoil carbon pools. The clusters to which Henan, Jiangsu, Zhejiang and Guangdong belong were tracked as hot dietary clusters exhibiting hotspots in inter-regional trade, contributing to carbon loss in external provinces through trade linkages. Heilongjiang and Inner Mongolia were at a low level of supply–demand balance but played the role of a carbon storage loser, while Guangdong, Zhejiang, and Jiangsu not only possessed higher balance levels but also pulled more external carbon loss. China's food system should optimise internal cropping structures and monitor external land use conversion. It is equally important to track food consumption side rights and responsibilities. • The "Material-Environmental" framework to diagnose dietary sustainability was designed. • Provinces could be classified into five clusters with obvious spatial agglomeration patterns. • Land use changes driven by dietary transition have resulted in significant carbon losses of 1281 Tg. • Specific clusters were hotspot clusters that pulled carbon loss from external regions during inter-regional trade. • Diet sustainability enhancement based on land use environmental effects deserves attention. [ABSTRACT FROM AUTHOR]

China implemented the Targeted Poverty Alleviation (TPA) policy in 2015 to fight against poverty. In order to assess the health performance of the TPA policy, this study aims to evaluate the impact of the TPA policy on healthcare utilization among older adults who normally have higher vulnerability to poverty and diseases. Drawing on data from four waves of the China Health and Retirement Longitudinal Study (CHARLS), we investigated the impact of the TPA policy on older individuals' outpatient and inpatient utilization using the difference-in-differences (DID) approach. In total, 5285 older respondents were incorporated into a final sample. The results indicated that the implementation of the TPA policy had a significantly positive impact on increasing inpatient care utilization for poor older adults. However, its impact on outpatient service utilization was not significant. To ensure that the increased level of inpatient care utilization was not caused by deteriorating health status, we further analyzed the impact of the TPA policy on poor older adults' health outcomes. Results indicated that the TPA policy improved self-rated health and reduced the number of ADL limitations among older adults in registered poor households. The positive impact of the TPA policy on inpatient care utilization was found to be most beneficial for older adults in poor households who were female, coupled, and aged 70 years and above. The TPA policy in China improved healthcare access for economically disadvantaged older adults and contributed to the enhancement of their health outcomes. This evidence may have broad implications for other low- and middle-income countries aiming to reduce poverty and achieve health equity. • This study assessed the TPA policy's impact on healthcare utilization, focusing on older adults for the first time. • The TPA policy increased inpatient care utilization for older adults in poor households. • The TPA policy improved health outcomes for poor older adults. • Policy effect on inpatient care utilization is stronger for femle, coupled, or senior older adults. [ABSTRACT FROM AUTHOR]

Little is known about the association of ambient ozone with ovarian reserve. Based on a retrospective cohort study of 6008 women who attended a fertility center in Hubei, China, during 2018–2021, we estimated ozone exposure levels by calculating averages during the development of follicles (2-month [W1], 4-month [W2], 6-month [W3]) and 1-year before measurement (W4) according to Tracking Air Pollution in China database. We used multivariate logistic regression and linear regression models to investigate association of ozone exposure with anti-müllerian hormone (AMH), the preferred indicator of ovarian reserve. Each 10 μg/m3 increases in ozone were associated with 2.34% (0.68%, 3.97%), 2.08% (0.10%, 4.01%), 4.20% (1.67%, 6.67%), and 8.91% (5.79%, 11.93%) decreased AMH levels during W1-W4; AMH levels decreased by 15.85%, 11.90%, 16.92% in the fourth quartile during W1, W3, and W4 when comparing the extreme quartile, with significant exposure-response relationships during W4 (P < 0.05). Ozone exposure during W1 was positively associated with low AMH. Additionally, we detected significant effect modification by age, body mass index, and temperature in ozone-associated decreased AMH levels. Our findings highlight the potential adverse impact of ozone pollution on female ovarian reserve, especially during the secondary to small antral follicle stage and 1-year before measurement. [Display omitted] • Ambient ozone exposure was associated with diminished ovarian reserve. • Secondary to small antral follicle stage might be the susceptible exposure window. • Even below the current AQS, AMH levels decreased with increasing ozone exposure. • Age, BMI, and ambient temperature might modify the observed associations. [ABSTRACT FROM AUTHOR]

• A new Regional Geo-Statistics (RGS) Method is proposed to better extract remote sensing features for yield estimation. • Adding the surface reflectance bands to the vegetation index as features can improve the accuracy of yield estimation. • The multi-source data can realize the best yield estimation results. • LightGBM is capable of yield prediction on remote sensing data. • Quantitative analysis of the contribution of multi-source data. Timely and accurate estimation of maize yield is crucial for ensuring food security. This study integrated multi-source data (satellite, meteorological, and soil data) on Google Earth Engine (GEE) and used machine learning techniques to estimate summer maize yield across 469 counties in the Huang-Huai-Hai Plain of China from 2010 to 2020. A novel method for extracting features from remote sensing images was proposed called the Regional Geo-Statistics (RGS) Method. We compared its performance with the traditional county-level averages method and explained the impact and contributions of incorporating multi-source data on yield estimation models. Firstly, Enhanced Vegetation Index (EVI) and Near-Infrared Vegetation Reflectance (NIRv) were transformed into regional geostatistical vectors and county-level averages using GEE. Then, yield was estimated using LightGBM, RF and LASSO. The results highlighted the superiority of the RGS method, with LightGBM yielding the best model (R2 = 0.55, RMSE = 852.92 kg/ha, NRMSE = 13.66 %). Further improvements were achieved by gradually adding meteorological and soil variables, with R2 improvements of 0.03 and 0.20, respectively. The Comprehensive Factor Model (CFM), utilizing all data as features, achieved the best results (R2 = 0.76, RMSE = 629.33 kg/ha, NRMSE = 10.08 %). The interpretability analysis based on CFM model underscored the significance of soil-related variables, which contributed significantly (45.38 %) alongside remote sensing variables, emphasizing the crucial role of soil variables in maize yield estimation. This study presents a versatile and reliable method for integrating multi-source data for maize yield estimation, supporting agricultural management and food security. [ABSTRACT FROM AUTHOR]

• N-deficient citrus leaves and roots kept high abilities to detoxify ROS and MG. • N-deficiency (ND) affected ROS and MG metabolism more in roots than in leaves. • ND enhanced n distribution in roots and R/S, and delayed root senescence. • ND reduced MDA levels and electrolyte leakage in citrus leaves and roots. Nitrogen-deficiency (ND) usually occurs in some citrus orchard soils in China. The roles of reactive oxygen species (ROS) and methylglyoxal (MG) generation and their detoxification systems in ND tolerance of horticultural woody plants still need to be revealed. For the first time, we examined the effects of ND on ROS and MG generation and their detoxification systems in leaves and roots of Citrus sinensis seedlings. The objectives are to test the hypotheses that N-deficient leaves and roots can keep high abilities to scavenge ROS and MG, thereby protecting them from oxidative damage, and that ND-induced alterations of ROS and MG formation and their detoxification systems in leaves and roots are different. ND augmented superoxide anion production rate and MG concentrations, but it decreased malondialdehyde (MDA) concentrations and electrolyte leakage in leaves and roots. ND increased the activities of most enzymes involved in ROS (ascorbate-glutathione cycle-related enzymes, antioxidant enzymes, and sulfur metabolism-related enzymes) and MG (glyoxalases) detoxification expressed on a protein basis with a few exceptions, and the concentrations of ascorbate, phytochelatins, and total non-protein thiols in leaves and roots. These results suggested that nitrogen-deficient leaves and roots could keep high abilities to detoxify ROS and MG, and protect them from oxidative damage. Generally viewed, ND affected the production and removal of ROS and MG more in roots than in leaves. [ABSTRACT FROM AUTHOR]