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Background This study aimed to investigate the knowledge, attitudes, and practices (KAP) regarding strabismus among parents whose children have strabismus. Methods This cross-sectional study was conducted among parents of children with strabismus at the First Affiliated Hospital of Anhui Medical University between January and June 2024. A total of 459 parents were recruited using convenience sampling. Demographic information and KAP scores were collected and assessed using a self-designed questionnaire. Data were analyzed using group comparisons, and correlation analyses to assess distributions, differences between groups, and relationships among KAP scores. Results The study included 459 participants, of whom 247 (53.81%) were male. The mean knowledge, attitude, and practice scores were 11.80 ± 2.08 (possible range: 0–20), 37.73 ± 3.80 (possible range: 10–50), and 17.34 ± 2.94 (possible range: 5–25), respectively. The attitude scores were significantly influenced by the presence of hyperopia ( P  = 0.018), astigmatism ( P  = 0.032), surgical intervention ( P  = 0.048), and eye patch use ( P  = 0.024) in their children. In addition, practice scores were notably impacted by the parent’s education level ( P  = 0.015) and whether the child had myopia ( P  = 0.039). Pearson correlation analyses showed insignificant correlations between knowledge and attitudes ( r  = 0.071, P  = 0.131), attitudes and practices ( r  = 0.043, P  = 0.354), and knowledge and practices ( r = -0.091, P  = 0.052). Conclusions In our study, parents of children with strabismus exhibited low levels of knowledge and practices, despite high levels of attitudes towards managing the condition. To address these gaps, targeted educational interventions are essential, to enhance parental understanding of strabismus, including its causes, treatment options, and the importance of follow-up care. Clinical trial number not applicable.

Leaf Area Index (LAI) is a fundamental indicator of plant growth status in agronomy and environmental research. With the rapid development of drone technology, the estimation of crop LAI based on drone imagery and vegetation indices is becoming increasingly popular. However, there is still a lack of detailed research on the feasibility of using image texture to estimate LAI and the impact of combining texture indices with vegetation indices on LAI estimation accuracy. In this study, two key growth stages of winter wheat (i.e., the stages of green-up and jointing) were selected, and LAI was calculated using digital hemispherical photography. The feasibility of predicting winter wheat LAI was explored under three conditions: vegetation index, texture index, and a combination of vegetation index and texture index, at flight heights of 20 m and 40 m. Two feature selection methods (Lasso and recursive feature elimination) were combined with four machine learning regression models (multiple linear regression, random forest, support vector machine, and backpropagation neural network). The results showed that during the vegetative growth stage of winter wheat, the model combining texture information with vegetation indices performed better than the models using vegetation indices alone or texture information alone. Among them, the best prediction result based on vegetation index was RFECV-MLR at a flight height of 40 m (R2 = 0.8943, RMSE = 0.4139, RRMSE = 0.1304, RPD = 3.0763); the best prediction result based on texture index was RFECV-RF at a flight height of 40 m (R2 = 0.8894, RMSE = 0.4236, RRMSE = 0.1335, RPD = 3.0063); and the best prediction result combining texture and index was RFECV-RF at a flight height of 40 m (R2 = 0.9210, RMSE = 0.3579, RRMSE = 0.1128, RPD = 3.5575). The results of this study demonstrate that combining vegetation indices and texture from multispectral drone imagery can improve the accuracy of LAI estimation during the vegetative growth stage of winter wheat. In addition, selecting a flight height of 40 m can improve efficiency in large-scale agricultural field monitoring, as this study showed that drone data at flight heights of 20 m and 40 m did not significantly affect model accuracy.

Background: Acute lung injury (ALI) is characterized by dysfunction of the alveolar epithelial membrane caused by acute inflammation and tissue injury. Qingwenzhike (QWZK) prescription has been demonstrated to be effective against respiratory viral infections in clinical practices, including coronavirus disease 2019 (COVID-19) infection. So far, the chemical compositions, protective effects on ALI, and possible anti-inflammatory mechanisms remain unknown. Methods: In this study, the compositions of QWZK were determined via the linear ion trap/electrostatic field orbital trap tandem high-resolution mass spectrometry (UHPLC-LTQ-Orbitrap MS). To test the protective effects of QWZK on ALI, an ALI model induced by lipopolysaccharide (LPS) in rats was used. The effects of QWZK on the LPS-induced ALI were evaluated by pathological changes and the number and classification of white blood cell (WBC) in bronchoalveolar lavage fluid (BALF). To investigate the possible underlying mechanisms, the contents of interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), monocyte chemoattractant protein (MCP-1), interleukin-1β (IL-1β), interleukin-18 (IL-18), and immunoregulatory-related factors interferon-γ (IFN-γ) were detected by ELISA. Furthermore, the expression of Toll-like receptor 4 (TLR4), p-IKKα/β, IKKα, IKKβ, p-IκBα, IκBα, p-NF-κB, nuclear factor-κB (NF-κB), NOD-like receptor family pyrin domain containing 3 (NLRP3), cleaved caspase-1, pro-caspase-1, apoptosis-associated speck-like protein containing CARD (ASC), and β-actin were tested by Western blot. Results: A total of 99 compounds were identified in QWZK, including 33 flavonoids, 23 phenolic acids, 3 alkaloids, 3 coumarins, 20 triterpenoids, 5 anthraquinones, and 12 others. ALI rats induced by LPS exhibited significant increase in neutrophile, significant decrease in lymphocyte, and evidently thicker alveolar wall than control animals. QWZK reversed the changes in WBC count and alveolar wall to normal level on the model of ALI induced by LPS. ELISA results revealed that QWZK significantly reduced the overexpression of proinflammatory factors IL-6, TNF-α, MCP-1, IL-1β, IL-18, and IFN-γ induced by LPS. Western blot results demonstrated that QWZK significantly downregulated the overexpression of TLR4, p-IKKα/β, p-IκBα, p-NF-κB, NLRP3, cleaved caspase-1, and ASC induced by LPS, which suggested that QWZK inhibited TLR4/NF-κB signaling pathway and NLRP3 inflammasomes. Conclusions: The chemical compositions of QWZK were first identified. It was demonstrated that QWZK showed protective effects on ALI induced by LPS. The possible underlying mechanisms of QWZK on ALI induced by LPS was via inhibiting TLR4/NF-kB signaling pathway and NLRP3 inflammasome activation. This work suggested that QWZK is a potential therapeutic candidate for the treatments of ALI and pulmonary inflammation.

Unmanned aerial vehicle (UAV) multispectral imagery has been applied in the remote sensing of wheat SPAD (Soil and Plant Analyzer Development) values. However, existing research has yet to consider the influence of different growth stages and UAV flight altitudes on the accuracy of SPAD estimation. This study aims to optimize UAV flight strategies and incorporate multiple feature selection techniques and machine learning algorithms to enhance the accuracy of the SPAD value estimation of different wheat varieties across growth stages. This study sets two flight altitudes (20 and 40 m). Multispectral images were collected for four winter wheat varieties during the green-up and jointing stages. Three feature selection methods (Pearson, recursive feature elimination (RFE), and correlation-based feature selection (CFS)) and four machine learning regression models (elastic net, random forest (RF), backpropagation neural network (BPNN), and extreme gradient boosting (XGBoost)) were combined to construct SPAD value estimation models for individual growth stages as well as across growth stages. The CFS-RF (40 m) model achieved satisfactory results (green-up stage: R2 = 0.7270, RPD = 2.0672, RMSE = 1.1835, RRMSE = 0.0259; jointing stage: R2 = 0.8092, RPD = 2.3698, RMSE = 2.3650, RRMSE = 0.0487). For cross-growth stage modeling, the optimal prediction results for SPAD values were achieved at a flight altitude of 40 m using the Pearson-XGBoost model (R2 = 0.8069, RPD = 2.3135, RMSE = 2.0911, RRMSE = 0.0442). These demonstrate that the flight altitude of UAVs significantly impacts the estimation accuracy, and the flight altitude of 40 m (with a spatial resolution of 2.12 cm) achieves better SPAD value estimation than that of 20 m (with a spatial resolution of 1.06 cm). This study also showed that the optimal combination of feature selection methods and machine learning algorithms can more accurately estimate winter wheat SPAD values. In addition, this study includes multiple winter wheat varieties, enhancing the generalizability of the research results and facilitating future real-time and rapid monitoring of winter wheat growth.

Background The decrease of vitamin D plays a critical role in diabetes mellitus (DM)-induced oxidative stress and vascular endothelial injury. Therefore, we investigated the effect and mechanism of 25-hydroxyvitamin D3 (25 (OH) D3) on oxidative stress and ferroptosis induced by high glucose in human retinal microvascular endothelial cells (hRMVECs). And the objective of this paper was to propose a new strategy for the prevention and treatment of diabetic retinopathy (DR). Methods First, hRMVECs were transfected with mimics NC or miR-93. After that, cells were treated with 100 nM / 500 nM 25 (OH) D3 and then cultured in a high glucose (30 mM) environment. Subsequently, qRT-PCR was employed to detect the expression level of miR-93; CCK-8 for the proliferation of cells in each group; biochemical tests for the level of intracellular reactive oxygen species (ROS), malondialdehyde (MDA), reduced glutathione (GSH) and ferrous ion (Fe 2+ ); and Western blot for the expression of ferroptosis-related proteins glutathione peroxidase 4 (GPX4) and SLC7A11). Results Under a high glucose environment, 25 (OH) D3 at 100 nM/500 nM could significantly promote the proliferation of hRMVECs, remarkably decrease the level of intracellular ROS/MDA, and up-regulate the level of GSH. Besides, 25 (OH) D3 greatly reduced Fe 2+ level in the cells while increased protein level of GPX4 and SLC7A11. Subsequently, we found that high glucose induced miR-93 expression, while 25 (OH) D3 markedly decreased high glucose-induced miR-93 overexpression. Furthermore, overexpression of miR-93 inhibited the functions of 25 (OH) D3 by activating ROS (ROS and MDA were up-regulated while GSH was down-regulated) and inducing Fe 2+ (Fe 2+ level was up-regulated while GPX4 and SLC7A11 level was down-regulated) in cells. Conclusion 25 (OH) D3 may inhibit oxidative stress and ferroptosis in hRMVECs induced by high glucose via down-regulation of miR-93.

Circular RNAs (circRNAs) are endogenous non-coding RNAs implicated in atherosclerosis. The aim of the present study was to explore the function of circRNA-0044073 in atherosclerosis. Reverse transcription quantitative polymerase chain reaction assays were used to measure the expression levels of circRNA-0044073, microRNA (miRNA/miR)-107, janus kinase 1 (JAK1), signal transducer and activator of transcription 3 (STAT3), B-cell lymphoma 2 (Bcl-2) and v-myc avian myelocytomatosis viral oncogene homolog (c-myc) in in blood cells from patients with atherosclerosis. RNA pull-down and luciferase reporter assays were then used to determine the association between circRNA and miR expression, and miR and gene expression, respectively. Matrigel invasion assay and flow cytometry were used to analyze cell invasion and cell cycle. Western blot analysis and ELISA were used to evaluate the expression levels of proteins. It was identified that the expression of circRNA-0044073 was upregulated and the expression of miR-107 was downregulated in atherosclerotic blood cells. Overexpression of circRNA-0044073 promoted the proliferation of human vascular smooth muscle cells (HUVSMCs) and human vascular endothelial cells (HUVECs), while overexpression of miR-107 inhibited their proliferation. In addition, circRNA-0044073 suppressed the levels of miR-107 via a sponge mechanism. Lipopolysaccharide (LPS) affected the proliferation of HUVSMCs and HUVECs, and also resulted in changes in circRNA-0044073 expression levels. CircRNA-0044073 promoted the proliferation and invasion of HUVSMCs and HUVECs in spite of the opposite effect observed with LPS treatment. The JAK/STAT signaling pathway was activated in patients with atherosclerosis. CircRNA-0044073 favored the activation of the JAK/STAT signaling pathway and inflammation in HUVSMCs and HUVECs. These data indicate that circRNA-0044073 is upregulated in atherosclerosis and promotes the proliferation and invasion of cells by targeting miR-107 and activating the JAK/STAT signaling pathway, potentially offering a target for novel treatment strategies against atherosclerosis.

In China’s second-largest wheat-producing region, the mid-lower Yangtze River area, cold stress impacts winter wheat production during the pre-heading growth stage. Previous research focused on specific growth stages, lacking a comprehensive approach. This study utilizes Unmanned Aerial Vehicle (UAV) multispectral imagery to monitor Soil-Plant Analysis Development (SPAD) values throughout the pre-heading stage, assessing crop stress resilience. Vegetation Indices (VIs) and Texture Indices (TIs) are extracted from UAV imagery. Recursive Feature Elimination (RFE) is applied to VIs, TIs, and fused variables (VIs + TIs), and six machine learning algorithms are employed for SPAD value estimation. The fused VIs and TIs model, based on Long Short-Term Memory (LSTM), achieves the highest accuracy (R2 = 0.8576, RMSE = 2.9352, RRMSE = 0.0644, RPD = 2.6677), demonstrating robust generalization across wheat varieties and nitrogen management practices. This research aids in mitigating winter wheat frost risks and increasing yields.

In recent years, the delay in sowing has become a major obstacle to high wheat yield in Jiangsu Province, one of the major wheat producing areas in China; hence, it is necessary to screen wheat varieties are resilient for late sowing. This study aimed to provide an effective, fast, and non-destructive monitoring method of soil plant analysis development (SPAD) values, which can represent leaf chlorophyll contents, for late-sown winter wheat variety screening. This study acquired multispectral images using an unmanned aerial vehicle (UAV) at the overwintering stage of winter wheat growth, and further processed these images to extract reflectance of five single spectral bands and calculated 26 spectral vegetation indices. Based on these 31 variables, this study combined three variable selection methods (i.e., recursive feature elimination (RFE), random forest (RF), and Pearson correlation coefficient (r)) with four machine learning algorithms (i.e., random forest regression (RFR), linear kernel-based support vector regression (SVR), radial basis function (RBF) kernel-based SVR, and sigmoid kernel-based SVR), resulted in seven SVR models (i.e., RFE-SVR_linear, RF-SVR_linear, RF-SVR_RBF, RF-SVR_sigmoid, r-SVR_linear, r-SVR_RBF, and r-SVR_sigmoid) and three RFR models (i.e., RFE-RFR, RF-RFR, and r-RFR). The performances of the 10 machine learning models were evaluated and compared with each other according to the achieved coefficient of determination (R2), residual prediction deviation (RPD), root mean square error (RMSE), and relative RMSE (RRMSE) in SPAD estimation. Of the 10 models, the best one was the RF-SVR_sigmoid model, which was the combination of the RF variable selection method and the sigmoid kernel-based SVR algorithm. It achieved high accuracy in estimating SPAD values of the wheat canopy (R2 = 0.754, RPD = 2.017, RMSE = 1.716 and RRMSE = 4.504%). The newly developed UAV- and machine learning-based model provided a promising and real time method to monitor chlorophyll contents at the overwintering stage, which can benefit late-sown winter wheat variety screening.

Objective This study employed a bidirectional two-sample Mendelian randomization approach to investigate the causal links between Alzheimer’s disease, Lewy body dementia, vascular dementia, and various retinal diseases. Methods Summary data from large-scale genome-wide association studies of European ancestry were used to select genetic variants as instrumental variables. Causal estimates were derived using the inverse variance–weighted method, complemented by Mendelian randomization–Egger, weighted median, and weighted mode analyses to ensure robustness. Results Genetically predicted Alzheimer’s disease was associated with a reduced risk of disorders of the choroid and retina (odds ratio = 0.93, 95% confidence interval: 0.88–0.98), retinal detachments and breaks (odds ratio = 0.90, 95% confidence interval: 0.84–0.97), and retinal detachment with retinal break (odds ratio = 0.84, 95% confidence interval: 0.74–0.95). Lewy body dementia was negatively associated with age-related macular degeneration (odds ratio = 0.88, 95% confidence interval: 0.79–0.98), disorders of the choroid and retina (odds ratio = 0.96, 95% confidence interval: 0.93–0.99), and degeneration of the macula (odds ratio = 0.93, 95% confidence interval: 0.88–0.98). Vascular dementia showed negative associations with age-related macular degeneration (odds ratio = 0.93, 95% confidence interval: 0.87–0.99) and degeneration of the macula (odds ratio = 0.96, 95% confidence interval: 0.93–0.99). Conversely, reverse Mendelian randomization indicated that genetic liability to macular degeneration and choroidal/retinal disorders was causally associated with cognitive performance and a reduced risk of Alzheimer’s disease. Conclusions Findings support inverse causal relationships in which specific dementias may reduce retinal disease risk and vice versa, suggesting complex shared biological mechanisms.

Salicylic acid (SA) can induce plant resistance to biotic and abiotic stresses through cross talk with other signaling molecules, whereas the interaction between hydrogen peroxide (H O ) and abscisic acid (ABA) in response to SA signal is far from clear. Here, we focused on the roles and interactions of H O and ABA in SA-induced freezing tolerance in wheat plants. Exogenous SA pretreatment significantly induced freezing tolerance of wheat via maintaining relatively higher dark-adapted maximum photosystem II quantum yield, electron transport rates, less cell membrane damage. Exogenous SA induced the accumulation of endogenous H O and ABA. Endogenous H O accumulation in the apoplast was triggered by both cell wall peroxidase and membrane-linked NADPH oxidase. The pharmacological study indicated that pretreatment with dimethylthiourea (H O scavenger) completely abolished SA-induced freezing tolerance and ABA synthesis, while pretreatment with fluridone (ABA biosynthesis inhibitor) reduced H O accumulation by inhibiting NADPH oxidase encoding genes expression and partially counteracted SA-induced freezing tolerance. These findings demonstrate that endogenous H O and ABA signaling may form a positive feedback loop to mediate SA-induced freezing tolerance in wheat.