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Background and aims Flavonoids are a class of secondary plant metabolites that have been shown to have multiple health benefits, including antioxidant and anti-inflammatory. This study was to explore the association between dietary flavonoid consumption and the prevalence of chronic respiratory diseases (CRDs) in adults. Methods and results The six main types of flavonoids, including isoflavones, anthocyanidins, flavan-3-ols, flavanones, flavones, and flavonols, were obtained from the National Health and Nutrition Examination Survey (NHANES) 2007–2010 and 2017–2018 by the two 24-h recall interviews. The prevalence of CRDs, including asthma, emphysema, and chronic bronchitis, was determined through a self-administered questionnaire. The analysis included 15,753 participants aged 18 years or older who had completed a diet history interview. After adjustment for potential confounders, the inverse link was found with total flavonoids, anthocyanidins, flavanones, and flavones, with an OR (95%CI) of 0.86 (0.75–0.98), 0.84 (0.72–0.97), 0.80(0.69–0.92), and 0.85(0.73–0.98) for the highest group compared to the lowest group. WQS regression revealed that the mixture of flavonoids was negatively linked with the prevalence of CRDs (OR = 0.88 [0.82–0.95], P  < 0.01), and the largest effect was mainly from flavanones (weight = 0.41). In addition, we found that flavonoid intake was negatively linked with inflammatory markers, and systemic inflammation significantly mediated the associations of flavonoids with CRDs, with a mediation rate of 12.64% for CRP ( P  < 0.01). Conclusion Higher flavonoid intake was related with a lower prevalence of CRDs in adults, and this relationship may be mediated through systemic inflammation.

Infrared video imaging is an cornerstone technology for environmental perception, particularly in drone-based remote sensing applications such as disaster assessment and infrastructure inspection. Conventional systems, however, rely on bulky optical architectures that limit deployment on lightweight aerial platforms. Computational imaging offers a promising alternative by integrating optical encoding with algorithmic reconstruction, enabling compact hardware while maintaining imaging performance comparable to sophisticated multi-lens systems. Nonetheless, achieving real-time video-rate computational image restoration on resource-constrained unmanned aerial vehicles (UAVs) remains a critical challenge. To address this, we propose Mobile Wavelet Restoration-Net (MWR-Net), a lightweight deep learning framework tailored for real-time infrared image restoration. Built on a MobileNetV4 backbone, MWR-Net leverages depthwise separable convolutions and an optimized downsampling scheme to minimize parameters and computational overhead. A novel wavelet-domain loss enhances high-frequency detail recovery, while the modulation transfer function (MTF) is adopted as an optics-aware evaluation metric. With only 666.37 K parameters and 6.17 G MACs, MWR-Net achieves a PSNR of 37.10 dB and an SSIM of 0.964 on a custom dataset, outperforming a pruned U-Net baseline. Deployed on an RK3588 chip, it runs at 42 FPS. These results demonstrate MWR-Net’s potential as an efficient and practical solution for UAV-based infrared sensing applications.

Hepatic alveolar echinococcosis (HAE) is a rare zoonotic parasitic disease that closely resembles malignant tumors in both behavior and appearance. It can cause infiltration of affected organs and chronic liver damage. In advanced stages, it may metastasize or invade surrounding organs, resembling liver cancer, and is clinically referred to as “parasitic cancer.” However, the prognosis of HAE with pulmonary metastasis is poor, and no reliable method currently exists to predict lung metastasis. This study aims to investigate the efficacy of a nomogram model, based on CT and MRI imaging features in conjunction with clinical indicators, for predicting pulmonary metastasis in HAE. A retrospective analysis was conducted using imaging and clinical data from 297 patients diagnosed with HAE. Univariate and multivariate logistic regression analyses identified independent factors associated with pulmonary metastasis, including lesion size, the presence of metastasis to other organs, cavitary lesions, and enhancement characteristics. The nomogram, developed using these variables, demonstrated strong predictive performance in both the training and validation cohorts. This model provides an effective tool for predicting the risk of pulmonary metastasis, offering early insights into disease progression and assisting clinicians in formulating personalized treatment and prognostic plans.

Platycodonis radix is a commonly used traditional Chinese medicine (TCM) material. Its bioactive compounds and medicinal value are closely related to its geographical origin. The internal components of Platycodonis radix from different origins are different due to the influence of environmental factors such as soil and climate. These differences can affect the medicinal value. Therefore, accurate identification of Platycodonis radix origin is crucial for drug safety and scientific research. Traditional methods of identification of TCM materials, such as morphological identification and physicochemical analysis, cannot meet the efficiency requirements. Although emerging technologies such as computer vision and spectroscopy can achieve rapid detection, their accuracy in identifying the origin of Platycodonis radix is limited when relying solely on RGB images or spectral features. To solve this problem, we aim to develop a rapid, non-destructive, and accurate method for origin identification of Platycodonis radix using hyperspectral imaging (HSI) combined with deep learning. We captured hyperspectral images of Platycodonis radix slices in 400–1000 nm range, and proposed a deep learning classification model based on these images. Our model uses one-dimensional (1D) convolution kernels to extract spectral features and two-dimensional (2D) convolution kernels to extract spatial features, fully utilizing the hyperspectral data. The average accuracy has reached 96.2%, significantly better than that of 49.0% based on RGB images and 81.8% based on spectral features in 400–1000 nm range. Furthermore, based on hyperspectral images, our model’s accuracy is 14.6%, 8.4%, and 9.6% higher than the variants of VGG, ResNet, and GoogLeNet, respectively. These results not only demonstrate the advantages of HSI in identifying the origin of Platycodonis radix, but also demonstrate the advantages of combining 1D convolution and 2D convolution in hyperspectral image classification.

This study aims to assess the associations of serum and red blood cell (RBC) folate with cardiovascular and all-cause mortality in hypertensive adults. Data on serum and RBC folate from the 1999-2014 National Health and Nutrition Examination Survey were included. Through December 31, 2015, cardiovascular and all-cause mortality were identified from the National Death Index. Multiple Cox regression and restricted cubic spline analyses were used to determine the relationship between folate concentrations and outcomes. A total of 13,986 hypertensive adults were included in the analysis (mean age, 58.5 ± 16.1 years; 6898 [49.3%] men). At a median of 7.0 years of follow-up, 548 cardiovascular deaths and 2726 all-cause deaths were identified. After multivariable adjustment, the fourth quartile of serum folate was associated with cardiovascular (HR = 1.32 [1.02-1.70]) and all-cause (HR = 1.20 [1.07-1.35]) mortality compared to the second quartile, whereas the first quartile was only linked with increased all-cause (HR = 1.29 [1.15-1.46]) mortality. The inflection points for the non-linear associations of serum folate with cardiovascular and all-cause mortality were 12.3 ng/mL and 20.5 ng/mL, respectively. In addition, the highest quartile of RBC folate was associated with cardiovascular (HR = 1.68 [1.30-2.16]) and all-cause (HR = 1.30 [1.16-1.46]) mortality compared to the second quartile, but the lowest quartile was not associated with either outcome. The inflection points for the non-linear associations of RBC folate with cardiovascular and all-cause mortality were 819.7 and 760.1 ng/mL, respectively. The findings suggest non-linear associations between serum and RBC folate levels and the risk of cardiovascular and all-cause mortality in hypertensive adults.

The BBX gene family functions as a key transcription factor implicated in plant growth, development, and stress responses. However, research on this gene family in melon remains absent. In the present study, we identified 19 BBX family genes within the melon genome, distributed across chromosomes 1, 2, 3, 4, 5, 7, 8, 10, 11, and 12. Phylogenetic analysis categorized these genes into five distinct subfamilies, with notable similarities observed in gene structure and conserved motifs among members of the same subfamily. Synteny analysis revealed seven syntenic relationships among melon BBX genes, 17 between melon and Arabidopsis, and one between melon and rice. Reanalysis of transcriptome data indicated that certain BBX genes exhibit high expression levels across various tissues and developmental stages of fruits, while others display tissue specificity. Under both abiotic and biotic stress conditions, genes such as CmBBX3, CmBBX5, CmBBX2, CmBBX18, CmBBX15, and CmBBX11 demonstrated significant differential expression, highlighting their critical roles in melon growth and development. Additionally, RT-qPCR analysis was conducted to examine the expression levels of melon BBX genes at different time points under salt stress, further validating the transcriptome data. This study provides a theoretical foundation for future molecular breeding efforts in melon.

Bone marrow mesenchymal stem cells (BMMSCs) are ideal seed cells for tissue engineering and regenerative medicine. Many studies have shown that 5-azacytidine (5-aza) can induce BMMSCs to differentiate into cardiomyogenic cells, but some issues still remain to be resolved. In this study, we investigated the effects of angiotensin II (Ang II) on the proliferation and differentiation of BMMSCs induced by 5-aza in vitro. BMMSCs were isolated from the bone marrow of Sprague-Dawley rats by density gradient centrifugation. The third-passage cells were divided into four groups: the Ang II group (0. 1 μmol/l) (group A), the 5-aza group (10 μmol/l) (group B), the Ang II combined with 5-aza group (0.1 and 10 μmol/l) (group C), and the untreated group as control. After 24 h of induction, the medium was changed to the complete culture medium without any inductor, and the cells were cultured for 3 weeks. Morphological changes were observed under a phase contrast microscope. The effect of Ang II and 5-aza on BMMSC proliferation was evaluated by the methyl thiazolyl tetrazolium (MTT) assay. Cardiomyogenic cells were identified through immunofluorescence staining, and the induction ratio was examined by flow cytometry. The level of cardiac troponin I (cTnI) was examined by western blotting, and the ultrastructures of the induced cells were viewed with a transmission electron microscope. The MTT assay showed that the cell proliferation in group C outweighed that in either group A or group B, but no significant difference existed between group A and group B. The expression of specific proteins, namely, cTnI and sarcomeric α-actin in induced BMMSCs was verified as positive. Flow cytometry showed that the induction ratio in group C was higher than that in group A or group B. The protein levels of cTnI in groups A, B, and C were significantly higher than those in the control group. Transmission electron microscopy showed that the induced cells had myofilaments, z line-like substances, desmosomes, and gap junctions. Angiotensin II and 5-azacytidine can promote the proliferation and differentiation of BMMSCs into cardiomyocyte-like cells.

Objective This study aims to evaluate the effectiveness of a nomogram model constructed using Diffusion Kurtosis Imaging (DKI) and 3D Arterial Spin Labeling (3D-ASL) functional imaging techniques in distinguishing between cerebral alveolar echinococcosis (CAE) and brain metastases (BM). Methods Prospectively collected were 24 cases (86 lesions) of patients diagnosed with CAE and 16 cases (69 lesions) of patients diagnosed with BM at the affiliated hospital of Qinghai University from 2018 to 2023, confirmed either pathologically or through comprehensive diagnosis. Both patient groups underwent DKI and 3D-ASL scanning. DKI parameters (Kmean, Dmean, FA, ADC) and cerebral blood flow (CBF) were analyzed for the parenchymal area, edema area, and symmetrical normal brain tissue area in both groups. There were 155 lesions in total in the two groups of patients. We used SPSS to randomly select 70% as the training set (108 lesions) and the remaining 30% as the test set (47 lesions) and performed a difference analysis between the two groups. The independent factors distinguishing CAE from BM were identified using univariate and multivariate logistic regression analyses. Based on these factors, a diagnostic model was constructed and expressed as a nomogram. Result Univariate and multivariate logistic regression analyses identified nDmean1 and nCBF1 in the lesion parenchyma area, as well as nKmean2 and nDmean2 in the edema area, as independent factors for distinguishing CAE from BM. The model's performance, measured by the area under the ROC curve (AUC), had values of 0.942 and 0.989 for the training and test sets, respectively. Calibration curves demonstrated that the predicted probabilities were highly consistent with the actual values, and DCA confirmed the model's high clinical utility. Conclusion The nomogram model, which incorporates DKI and 3D-ASL functional imaging, effectively distinguishes CAE from BM. It offers an intuitive, accurate, and non-invasive method for differentiation, thus providing valuable guidance for subsequent clinical decisions.

C-terminal Src kinase (CSK) is the major inhibitory kinase for Src family kinases (SFKs) through the phosphorylation of their C-tail tyrosine sites, and it regulates various types of cellular activity in association with SFK function. As a cytoplasmic protein, CSK needs be recruited to the plasma membrane to regulate SFKs’ activity. The regulatory mechanism behind CSK activity and its subcellular localization remains largely unclear. In this work, we developed a genetically encoded biosensor based on fluorescence resonance energy transfer (FRET) to visualize the CSK activity in live cells. The biosensor, with an optimized substrate peptide, confirmed the crucial Arg107 site in the CSK SH2 domain and displayed sensitivity and specificity to CSK activity, while showing minor responses to co-transfected Src and Fyn. FRET measurements showed that CSK had a relatively mild level of kinase activity in comparison to Src and Fyn in rat airway smooth muscle cells. The biosensor tagged with different submembrane-targeting signals detected CSK activity at both non-lipid raft and lipid raft microregions, while it showed a higher FRET level at non-lipid ones. Co-transfected receptor-type protein tyrosine phosphatase alpha (PTPα) had an inhibitory effect on the CSK FRET response. The biosensor did not detect obvious changes in CSK activity between metastatic cancer cells and normal ones. In conclusion, a novel FRET biosensor was generated to monitor CSK activity and demonstrated CSK activity existing in both non-lipid and lipid raft membrane microregions, being more present at non-lipid ones.

Species discrimination of insects is an important aspect of ecology and biodiversity research. The traditional methods based on human visual experience and biochemical analysis cannot strike a balance between accuracy and timeliness. Morphological identification using computer vision and machine learning is expected to solve this problem, but image features have poor accuracy for very similar species and usually require complicated networks that are unfriendly to portable edge devices. In this work, we propose a fast and accurate species discrimination method of similar insects using hyperspectral features and lightweight machine learning algorithm. Feature regions selection, feature spectra selection and model quantification are used for the optimization of discriminating network. The experimental results of six similar butterfly species in the genus of Graphium show that, compared with morphological recognition with machine vision, our work achieves a higher accuracy of 92.36 ± 3.04% and a shorter inference time of 0.6 ms, with the tiny-size convolutional neural network deployed on a neural network chip. This study provides a rapid and high-accuracy species discrimination method for insects with high appearance similarity and paves the way for field discriminations using intelligent micro-spectrometer based on on-chip microstructure and artificial intelligence chip.