Search Results (776)

Fat embolism is a critical medical emergency often resulting from long bone fractures or amputations, leading to acute respiratory distress syndrome (ARDS). The NOD-like receptor pyrin domain-containing 3 (NLRP3) inflammasome, a key regulator of innate immunity, is activated by reactive oxygen species and tissue damage, contributing to inflammatory responses. This study examines the role of NLRP3 in fat embolism-induced ARDS and evaluates the therapeutic potential of MCC950, a selective NLRP3 antagonist. Fat embolism was induced by fatty micelle injection into the tail vein of Sprague Dawley rats. Pulmonary injury was assessed through lung weight gain as an edema indicator, NLRP3 expression via Western blot, and IL-1β levels using ELISA. Histological damage and macrophage infiltration were evaluated with hematoxylin and eosin staining. Fat embolism significantly increased pulmonary NLRP3 expression, lipid peroxidation, IL-1β release, and macrophage infiltration within four hours, accompanied by severe pulmonary edema. NLRP3 was localized in type I alveolar cells, co-localizing with aquaporin 5. Administration of MCC950 significantly reduced inflammatory responses, lipid peroxidation, pulmonary edema, and histological damage, while attenuating MAPK cascade phosphorylation of ERK and Raf. These findings suggest that NLRP3 plays a critical role in fat embolism-induced acute respiratory distress syndrome, and its inhibition by MCC950 may offer a promising therapeutic approach. Full article

Flavonoids serve as crucial plant antioxidants in drought tolerance, yet their antioxidant regulatory mechanisms within mycorrhizal plants remain unclear. In this study, using a two-factor design, trifoliate orange (Poncirus trifoliata (L.) Raf.) seedlings in the four-to-five-leaf stage were either inoculated with Funneliformis mosseae or not, and subjected to well-watered (70–75% of field maximum water-holding capacity) or drought stress (50–55% field maximum water-holding capacity) conditions for 10 weeks. Plant growth performance, photosynthetic physiology, leaf flavonoid content and their antioxidant capacity, reactive oxygen species levels, and activities and gene expression of key flavonoid biosynthesis enzymes were analyzed. Although drought stress significantly reduced root colonization and soil hyphal length, inoculation with F. mosseae consistently enhanced the biomass of leaves, stems, and roots, as well as root surface area and diameter, irrespective of soil moisture. Despite drought suppressing photosynthesis in mycorrhizal plants, F. mosseae substantially improved photosynthetic capacity (measured via gas exchange) and optimized photochemical efficiency (assessed by chlorophyll fluorescence) while reducing non-photochemical quenching (heat dissipation). Inoculation with F. mosseae elevated the total flavonoid content in leaves by 46.67% (well-watered) and 14.04% (drought), accompanied by significantly enhanced activities of key synthases such as phenylalanine ammonia-lyase (PAL), chalcone synthase (CHS), chalcone isomerase (CHI), 4-coumarate:coA ligase (4CL), and cinnamate 4-hydroxylase (C4H), with increases ranging from 16.90 to 117.42% under drought. Quantitative real-time PCR revealed that both mycorrhization and drought upregulated the expression of PtPAL1, PtCHI, and Pt4CL genes, with soil moisture critically modulating mycorrhizal regulatory effects. In vitro assays showed that flavonoid extracts scavenged radicals at rates of 30.07–41.60% in hydroxyl radical (•OH), 71.89–78.06% in superoxide radical anion (O₂^•−), and 49.97–74.75% in 2,2-diphenyl-1-picrylhydrazyl (DPPH). Mycorrhizal symbiosis enhanced the antioxidant capacity of flavonoids, resulting in higher scavenging rates of •OH (19.07%), O₂^•− (5.00%), and DPPH (31.81%) under drought. Inoculated plants displayed reduced hydrogen peroxide (19.77%), O₂^•− (23.90%), and malondialdehyde (17.36%) levels. This study concludes that mycorrhizae promote the level of total flavonoids in trifoliate orange by accelerating the flavonoid biosynthesis pathway, hence reducing oxidative damage under drought. Full article

Facial expression recognition (FER) is a critical research direction in artificial intelligence, which is widely used in intelligent interaction, medical diagnosis, security monitoring, and other domains. These applications highlight its considerable practical value and social significance. Face expression recognition models often need to run efficiently on mobile devices or edge devices, so the research on lightweight face expression recognition is particularly important. However, feature extraction and classification methods of lightweight convolutional neural network expression recognition algorithms mostly used at present are not specifically and fully optimized for the characteristics of facial expression images, yet fail to make full use of the feature information in face expression images. To address the lack of facial expression recognition models that are both lightweight and effectively optimized for expression-specific feature extraction, this study proposes a novel network design tailored to the characteristics of facial expressions. In this paper, we refer to the backbone architecture of MobileNet V2 network, and redesign LightExNet, a lightweight convolutional neural network based on the fusion of deep and shallow layers, attention mechanism, and joint loss function, according to the characteristics of the facial expression features. In the network architecture of LightExNet, firstly, deep and shallow features are fused in order to fully extract the shallow features in the original image, reduce the loss of information, alleviate the problem of gradient disappearance when the number of convolutional layers increases, and achieve the effect of multi-scale feature fusion. The MobileNet V2 architecture has also been streamlined to seamlessly integrate deep and shallow networks. Secondly, by combining the own characteristics of face expression features, a new channel and spatial attention mechanism is proposed to obtain the feature information of different expression regions as much as possible for encoding. Thus improve the accuracy of expression recognition effectively. Finally, the improved center loss function is superimposed to further improve the accuracy of face expression classification results, and corresponding measures are taken to significantly reduce the computational volume of the joint loss function. In this paper, LightExNet is tested on the three mainstream face expression datasets: Fer2013, CK+ and RAF-DB, respectively, and the experimental results show that LightExNet has 3.27 M Parameters and 298.27 M Flops, and the accuracy on the three datasets is 69.17%, 97.37%, and 85.97%, respectively. The comprehensive performance of LightExNet is better than the current mainstream lightweight expression recognition algorithms such as MobileNet V2, IE-DBN, Self-Cure Net, Improved MobileViT, MFN, Ada-CM, Parallel CNN(Convolutional Neural Network), etc. Experimental results confirm that LightExNet effectively improves recognition accuracy and computational efficiency while reducing energy consumption and enhancing deployment flexibility. These advantages underscore its strong potential for real-world applications in lightweight facial expression recognition. Full article

Facial expression plays an important role in human–computer interaction and affective computing. However, existing expression recognition methods cannot effectively capture multi-scale structural details contained in facial expressions, leading to a decline in recognition accuracy. Inspired by the multi-scale processing mechanism of the biological visual system, this paper proposes a wavelet-guided global–local feature aggregation network (WGGLFA) for facial expression recognition (FER). Our WGGLFA network consists of three main modules: the scale-aware expansion (SAE) module, which combines dilated convolution and wavelet transform to capture multi-scale contextual features; the structured local feature aggregation (SLFA) module based on facial keypoints to extract structured local features; and the expression-guided region refinement (ExGR) module, which enhances features from high-response expression areas to improve the collaborative modeling between local details and key expression regions. All three modules utilize the spatial frequency locality of the wavelet transform to achieve high-/low-frequency feature separation, thereby enhancing fine-grained expression representation under frequency domain guidance. Experimental results show that our WGGLFA achieves accuracies of 90.32%, 91.24%, and 71.90% on the RAF-DB, FERPlus, and FED-RO datasets, respectively, demonstrating that our WGGLFA is effective and has more capability of robustness and generalization than state-of-the-art (SOTA) expression recognition methods. Full article

B-raf (rapidly accelerated fibrosarcoma) is a crucial player within the ERK/MAPK signaling pathway. In the CNS, B-raf has been implicated in neuronal differentiation, long-term memory, and major depression. Mice with forebrain neuron-specific B-raf knockout show behavioral deficits in spatial learning tasks and impaired hippocampal long-term potentiation (LTP). To elucidate the mechanism(s) underlying diminished synaptic plasticity in B-raf-deficient mice, we performed whole-cell recordings from CA1 pyramidal cells in hippocampal slices of control and B-raf mutant mice. We found that the NMDA/AMPA ratio of excitatory postsynaptic currents (EPSCs) at the Schaffer collateral—CA1 pyramidal cell synapses was significantly reduced in B-raf mutants, which would at least partially account for their impaired LTP. Interestingly, the reduced NMDA component of field postsynaptic potentials in mutant preparations was partially reinstated by blocking the apamin-sensitive small-conductance Ca²⁺-activated K⁺ (SK) channels, which have also been reported to modulate hippocampal LTP and learning tasks. To determine the impact of B-raf-dependent signaling on SK current, we isolated the apamin-sensitive tail current after a strong depolarizing event and found indeed a significantly bigger SK current in B-raf-deficient cells compared to controls, which is consistent with the reduced action potential firing and the stronger facilitating effect of apamin on CA1 somatic excitability in B-raf-mutant hippocampus. Our data suggest that B-raf signaling readjusts the delicate balance between NMDA receptors and SK channels to promote synaptic plasticity and facilitate hippocampal learning and memory. Full article

In recent years, low temperature has seriously threatened the citrus industry. Arbuscular mycorrhizal fungi (AMF) can enhance the absorption of nutrients and water and tolerance to abiotic stresses. In this study, pot experiments were conducted to study the effects of low-temperature stress on citrus (trifoliate orange, Poncirus trifoliata L. Raf.) with AMF (Diversispora epigaea D.e). The results showed that AMF inoculation significantly increased plant growth, chlorophyll fluorescence, and photosynthetic parameters. Compared with 25 °C, −5 °C significantly increased the relative conductance rate and the contents of malondialdehyde, hydrogen peroxide, soluble sugar soluble protein, and proline, and also enhanced the activities of catalase and superoxide dismutase, but dramatically reduced photosynthetic parameters. Compared with the non-AMF group, AMF significantly increased the maximum light quantum efficiency and steady-state light quantum efficiency at 25 °C (by 16.67% and 61.54%), and increased the same parameters by 71.43% and 140% at −5 °C. AMF also significantly increased the leaf net photosynthetic rate and transpiration rate at 25 °C (by 54.76% and 29.23%), and increased the same parameters by 72.97% and 26.67% at −5 °C. Compared with the non-AMF treatment, the AMF treatment significantly reduced malondialdehyde and hydrogen peroxide content at 25 °C (by 46.55% and 41.29%), and reduced them by 28.21% and 29.29% at −5 °C. In addition, AMF significantly increased the contents of soluble sugar, soluble protein, and proline at 25 °C (by 15.22%, 34.38%, and 11.38%), but these increased by only 9.64%, 0.47%, and 6.09% at −5 °C. Furthermore, AMF increased the activities of superoxide dismutase and catalase at 25 °C (by 13.33% and 13.72%), but these increased by only 5.51% and 13.46% at −5 °C. In conclusion, AMF can promote the growth of the aboveground and underground parts of trifoliate orange seedlings and enhance their resistance to low temperature via photosynthesis, osmoregulatory substances, and their antioxidant system. Full article

Chronic coronary syndrome (CCS) and atrial fibrillation (AF) are prevalent cardiovascular conditions that share numerous risk factors and pathophysiological mechanisms. While clinical scores commonly used in AF—such as CHA₂DS₂VA (which includes congestive heart failure, hypertension, age ≥ 75, diabetes, stroke/TIA, vascular disease, and age 65–74), HAS-BLED (which incorporates hypertension, abnormal renal/liver function, stroke, bleeding history, labile INR, elderly age, and drug/alcohol use), and C₂HEST (incorporating coronary artery disease, COPD, hypertension, elderly age ≥ 75, systolic heart failure, and thyroid disease)—are traditionally applied to rhythm or bleeding risk prediction, their value in estimating the angiographic severity of coronary artery disease (CAD) remains underexplored. We conducted a prospective, single-center study including 131 patients with suspected stable CAD referred for coronary angiography, stratified according to coronary angiographic findings into two groups: significant coronary stenosis (S-CCS) and non-significant coronary stenosis (N-CCS). At admission, AF-related scores (CHA₂DS₂, CHA₂DS₂VA, CHA₂DS₂VA-HSF, CHA₂DS₂VA-RAF, CHA₂DS₂VA-LAF, HAS-BLED, C₂HEST, and HATCH) were calculated. CAD severity was subsequently assessed using the SYNTAX and Gensini scores. Statistical comparisons and Pearson correlation analyses were performed to evaluate the association between clinical risk scores and angiographic findings. Patients in the S-CCS group had significantly higher scores in CHA₂DS₂VA (4.09 ± 1.656 vs. 3.20 ± 1.338, p = 0.002), HAS-BLED (1.98 ± 0.760 vs. 1.36 ± 0.835, p < 0.001), CHA₂DS₂VA-HSF (6.00 ± 1.854 vs. 5.26 ± 1.712, p = 0.021), and C₂HEST (3.49 ± 1.501 vs. 2.55 ± 1.279, p < 0.001). Multivariate logistic regression identified HAS-BLED and C₂HEST as independent predictors of significant coronary lesions. A threshold value of HAS-BLED ≥ 1.5 and C₂HEST ≥ 3.5 demonstrated moderate discriminative ability (AUC = 0.694 and 0.682, respectively), with acceptable sensitivity and specificity. These scores also demonstrated moderate to strong correlations with both Gensini and SYNTAX scores. AF-related clinical scores, especially HAS-BLED and C₂HEST, may serve as practical and accessible tools for early CAD risk stratification in patients with suspected CCS. Their application in clinical practice may serve as supplementary triage tools to help prioritize patients for further diagnostic evaluation, but they are not intended to replace standard imaging or testing. Full article

Malignant melanoma (MM) affects not only humans but also animals, with gray horses being particularly predisposed to acquiring the disease. Multiomics have greatly advanced the understanding of human MM. In contrasty little is known regarding the pathogenesis of gray-horse melanoma and the unique phenomenon of melanoma “dormancy” in some animals. To help close this gap in knowledge, melanoma tissue and intact skin collected from gray horses were subjected to transcriptome analysis using RNAseq. In the next step, cultured primary tumor cells and normal skin fibroblasts were established from gray horses, and their protein expression profiles were determined. The obtained data unambiguously identified gray-horse melanoma (ghM) as a malignant tumor, as reflected by the overrepresentation of pathways typically activated in human melanoma and other human cancers. These included the RAS/RAF/MAPK, the IRS/IGF1R, and the PI3K/AKT signaling networks. In addition, the obtained data suggest that the key molecules RAC1, RAS, and BRAF, which are frequently mutated in human melanoma, may also contain activating mutations in ghM, whilst PTEN may harbor loss-of-function mutations. This issue will be subject to downstream analyses determining the mutational status in ghM to further advance the understanding of this frequent disease in gray horses. Full article

Background/Objectives: RAF pathway aberrations are one of the hallmarks of lung cancer. Sorafenib is a multi-kinase inhibitor targeting the RAF pathway and is FDA-approved for several cancers, yet its efficacy in lung cancer is controversial. Previous clinical research showed that a ARAF p.S214C mutation exhibited exceptional responsiveness to sorafenib in lung adenocarcinoma. Methods: Considering this promising clinical potential, the oncogenic potential and sorafenib response of the ARAF p.S214C mutation were investigated using lung cancer models. ARAF p.S214C mutant, ARAF wild-type (WT), and EGFP control genes were ectopically expressed in lung adenocarcinoma cell lines retroviral transduction. In vitro and in vivo sorafenib sensitivity studies were performed, followed by transcriptomics and proteomics analyses. Results: Compared to the ARAF-WT and EGFP-engineered cells, the ARAF p.S214C-engineered cells activated Raf-MEK-ERK signaling and exhibited enhanced oncogenic potential in terms of in vitro cell proliferation, colony and spheroid formation, migration, and invasion abilities, as well as in vivo tumorigenicity. The ARAF p.S214C-engineered cells also displayed heightened sensitivity to sorafenib in vitro and in vivo. RNA sequencing and reverse-phase protein array analyses demonstrated elevated expression of genes and proteins associated with tumor aggressiveness in the ARAF p.S214C mutants, and its sorafenib sensitivity was likely moderated through inhibition of the cell cycle and DNA replication. The ERK and PI3K signaling pathways were also significantly deregulated in the ARAF p.S214C mutants regardless of sorafenib treatment. Conclusions: This study demonstrates the oncogenicity and sorafenib sensitivity of the ARAF p.S214C mutation in lung cancer cells, which may serve as a biomarker for predicting the sorafenib response in lung cancer patients. Importantly, investigating the gene–drug sensitivity pairs in clinically exceptional responders may guide and accelerate personalized cancer therapies based on specific tumor mutations. Full article

Facial expression recognition (FER) plays a critical role in affective computing, enabling machines to interpret human emotions through facial cues. While recent deep learning models have achieved progress, many still fail under real-world conditions such as occlusion, lighting variation, and subtle expressions. In this work, we propose FERONet, a novel hyper-attentive multimodal transformer architecture tailored for robust and real-time FER. FERONet integrates a triple-attention mechanism (spatial, channel, and cross-patch), a hierarchical transformer with token merging for computational efficiency, and a temporal cross-attention decoder to model emotional dynamics in video sequences. The model fuses RGB, optical flow, and depth/landmark inputs, enhancing resilience to environmental variation. Experimental evaluations across five standard FER datasets—FER-2013, RAF-DB, CK+, BU-3DFE, and AFEW—show that FERONet achieves superior recognition accuracy (up to 97.3%) and real-time inference speeds (<16 ms per frame), outperforming prior state-of-the-art models. The results confirm the model’s suitability for deployment in applications such as intelligent tutoring, driver monitoring, and clinical emotion assessment. Full article

Facial emotion recognition (FER) is vital for improving human–machine interactions, serving as the foundation for AI systems that integrate cognitive and emotional intelligence. This helps bridge the gap between mechanical processes and human emotions, enhancing machine engagement with humans. Considering the constraints of low hardware specifications often encountered in real-world applications, this study leverages recent advances in deep learning to propose an enhanced model for FER. The model effectively utilizes texture information from faces through Gabor and Local Binary Pattern (LBP) feature extraction techniques. By integrating these features into a specially modified AlexNet architecture, our approach not only classifies facial emotions more accurately but also demonstrates significant improvements in performance and adaptability under various operational conditions. To validate the effectiveness of our proposed model, we conducted evaluations using the FER2013 and RAF-DB benchmark datasets, where it achieved impressive accuracies of 98.10% and 93.34% for the two datasets, with standard deviations of 1.63% and 3.62%, respectively. On the FER-2013 dataset, the model attained a precision of 98.2%, a recall of 97.9%, and an F1-score of 98.0%. Meanwhile, for the other dataset, it achieved a precision of 93.54%, a recall of 93.12%, and an F1-score of 93.34%. These results underscore the model’s robustness and its capability to deliver high-precision emotion recognition, making it an ideal solution for deployment in environments where hardware limitations are a critical concern. Full article

This paper addresses the central issue arising from the (SDAC) of facial expressions, namely, to balance the competing demands of good global features for detection, and fine features for good facial expression classifications by replacing the feature extraction part of the “neck” network in the feature pyramid network in the You Only Look Once X (YOLOX) framework with a novel architecture involving three attention mechanisms—batch, channel, and neighborhood—which respectively explores the three input dimensions—batch, channel, and spatial. Correlations across a batch of images in the individual path of the dual incoming paths are first extracted by a self attention mechanism in the batch dimension; these two paths are fused together to consolidate their information and then split again into two separate paths; the information along the channel dimension is extracted using a generalized form of channel attention, an adaptive graph channel attention, which provides each element of the incoming signal with a weight that is adapted to the incoming signal. The combination of these two paths, together with two skip connections from the input to the batch attention to the output of the adaptive channel attention, then passes into a residual network, with neighborhood attention to extract fine features in the spatial dimension. This novel dual path architecture has been shown experimentally to achieve a better balance between the competing demands in an SDAC problem than other competing approaches. Ablation studies enable the determination of the relative importance of these three attention mechanisms. Competitive results are obtained on two non-aligned face expression recognition datasets, RAF-DB and SFEW, when compared with other state-of-the-art methods. Full article

Facial expression recognition (FER) is a core technology that enables computers to understand and react to human emotions. In particular, the use of face alignment algorithms as a preprocessing step in image-based FER is important for accurately normalizing face images in terms of scale, rotation, and translation to improve FER accuracy. Recently, FER studies have been actively leveraging feature maps computed by face alignment networks to enhance FER performance. However, previous studies were limited in their ability to effectively apply information from specific facial regions that are important for FER, as they either only used facial landmarks during the preprocessing step or relied solely on the feature maps from the face alignment networks. In this paper, we propose the use of Keypoint Features extracted from feature maps at the coordinates of facial landmarks. To effectively utilize Keypoint Features, we further propose a Keypoint Feature regularization method using landmark perturbation for robustness, and an attention mechanism that emphasizes all Keypoint Features using representative Keypoint Features derived from a nasal base landmark, which carries information for the whole face, to improve performance. We performed experiments on the AffectNet, RAF-DB, and FERPlus datasets using a simply designed network to validate the effectiveness of the proposed method. As a result, the proposed method achieved a performance of 68.17% on AffectNet-7, 64.87% on AffectNet-8, 93.16% on RAF-DB, and 91.44% on FERPlus. Furthermore, the network pretrained on AffectNet-8 had improved performances of 94.04% on RAF-DB and 91.66% on FERPlus. These results demonstrate that the proposed Keypoint Features can achieve comparable results to those of the existing methods, highlighting their potential for enhancing FER performance through the effective utilization of key facial region features. Full article

Background/Objectives: Melanoma is one of the most aggressive forms of skin cancer and is frequently associated with the B-Raf⁶⁰⁰E mutation, which constitutively activates the MAPK signaling pathway. Although selective inhibitors such as Vemurafenib offer clinical benefits, their long-term efficacy is often hindered by resistance mechanisms and adverse effects. In this study, twelve phytochemicals from Brazilian green propolis were evaluated for their potential as selective B-Raf⁶⁰⁰E inhibitors using a computational approach. Methods: Physicochemical, ADME, and electronic properties were assessed, followed by molecular docking using the B-Raf⁶⁰⁰E crystal structure (PDB ID: 3OG7). Redocking validation and 500 ns molecular dynamics simulations were performed to investigate the stability of the ligand-protein complexes, and free energy calculations were then computed. Results: Among the tested compounds, Artepillin C exhibited the strongest binding affinity (−8.17 kcal/mol) in docking and maintained stable interactions with key catalytic residues throughout the simulation, also presenting free energy of binding ΔG of −20.77 kcal/mol. HOMO-LUMO and electrostatic potential analyses further supported its reactivity and selectivity. Notably, Artepillin C remained bound within the ATP-binding site, mimicking several critical interactions observed with Vemurafenib. Results: Among the tested compounds, Artepillin C exhibited the strongest binding affinity (−8.17 kcal/mol) and maintained stable interactions with key catalytic residues throughout the simulation. HOMO-LUMO and electrostatic potential analyses further supported its reactivity and selectivity. Notably, Artepillin C remained bound within the ATP-binding site, mimicking several critical interactions observed with Vemurafenib. Conclusions: These findings indicate that Artepillin C is a promising natural compound for further development as a selective B-Raf⁶⁰⁰E inhibitor and suggest its potential utility in melanoma treatment strategies. This study reinforces the value of natural products as scaffolds for targeted drug design and supports continued experimental validation. Full article

This study presents the first comprehensive theoretical investigation of the antioxidant mechanisms of trans-isoferulic acid against hydroperoxyl (HOO^•) radicals in aqueous solution, using the DFT/M062X/6-311+G(d,p)/PCM method. Thermodynamic and kinetic parameters, including reaction energy barriers and bimolecular rate constants, were determined for the three major pathways: hydrogen transfer (HT), radical adduct formation (RAF), and single electron transfer (SET). The results indicate that, at physiological pH, the RAF mechanism is both more exergonic and approximately eight-times faster than HT. At a higher pH, where the phenolate anion dominates, antioxidant activity is enhanced by an additional fast, diffusion-limited SET pathway. Isoferulic acid was also found to effectively chelate Fe²⁺ ions at pH 7.4 and above, forming stable complexes that could inhibit Fenton-type hydroxyl radical generation. Moreover, its strong UV absorption suggests a role in limiting photo-induced free radical formation. These findings not only clarify the antioxidant behavior of isoferulic acid but also provide novel theoretical insights applicable to related phenolic compounds. The compound’s multi-target antioxidant profile highlights its potential as a photoprotective agent in sunscreen formulations. Full article