Search Results (2,383)

Multiple-mode immunoassays have the advantages of self-correction, self-validation, and high accuracy and reliability. In this work, we developed a strategy for the design of triple-mode immunoassays with the self-assemblies of three-in-one small molecules as signal reporters. Pyrroloquinoline quinone (PQQ), with a well-defined redox peak and excellent spectroscopic and fluorescent signals, was chosen as the signaling molecule. PQQ was coordinated with Cu²⁺ to form metal–organic nanoparticle as the signal label. Hexahistidine (His₆)-tagged recognition element (recombinant streptavidin) was attached to the Cu-PQQ surface through metal coordination interaction between the His₆ tag and the unsaturated metal site. The captured Cu-PQQ nanoparticle released a large number of PQQ molecules under an acidic condition, which could be simultaneously monitoring by electrochemical, UV-vis, and fluorescent techniques, thereby allowing for the development of triple-model immunoassays. The three methods were used to determine the concentration of carcinoembryonic antigen (CEA) with the detection limits of 0.01, 0.1, and 0.1 ng/mL, respectively. This strategy opens up a universal route for the preparation of multiple-model signal labels and the oriented immobilization of bioreceptors for molecular recognition. Full article

Autonomous vehicles (AVs) hold strong potential to redefine traffic operations, yet their impacts at varying penetration levels within mixed traffic remain insufficiently quantified. This study evaluates the influence of SAE Level 5 AVs on traffic performance at two typical urban signalized intersections using a hybrid microsimulation approach that integrates behavioral AV modeling and performance evaluation. The analysis covers two typical intersection layouts, one with two through lanes and another with three, tested under varying traffic volumes and left-turn shares. A total of 324 simulation scenarios were conducted with AV penetration ranging from 0% to 100% (in 20% increments) and left-turn proportions of 15%, 30%, and 45%. The results show that 100% AV penetration lowers the average delay by up to 40% in the two-lane intersection scenario and 32% in the three-lane scenario, relative to the 0% AV baseline. Even 20% AV penetration yields about half of the maximum improvement. The greatest benefits occur with aggressive AV driving profiles, balanced approach volumes, and small left-turn shares. These findings provide preliminary evidence of AVs’ potential to enhance intersection efficiency and support Sustainable Development Goals (SDGs) 11 and 13, offering insights to guide intersection design and AV deployment strategies for data-driven, sustainable urban mobility. Full article

The cholinergic system is one of the most ancient and widespread signaling systems in the body, implicated in a range of pathological conditions—from neurodegenerative disorders to cancer. Given its broad relevance, there is growing interest in characterizing this system across diverse cellular models to enable drug screening, mechanistic studies, and exploration of new therapeutic avenues. In this study, we investigated four cancer cell lines: one of neuroblastoma origin previously used in cholinergic signaling studies (SH-SY5Y), one non-small cell lung adenocarcinoma line (A549), and two small cell lung carcinoma lines (H69 and H82). We assessed the expression and localization of key components of the cholinergic system, along with the cellular capacity for acetylcholine (ACh) synthesis and release. Whole-cell flow cytometry following membrane permeabilization revealed that all cell lines expressed the ACh-synthesizing enzyme choline acetyltransferase (ChAT). HPLC-MS analysis confirmed that ChAT was functionally active, as all cell lines synthesized and released ACh into the conditioned media, suggesting the presence of autocrine and/or paracrine ACh signaling circuits, consistent with previous reports. The cell lines also demonstrated choline uptake, indicative of functional choline and/or organic cation transporters. Additionally, all lines expressed the ACh-degrading enzymes acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), as well as the alfa seven (α7) nicotinic and M1 muscarinic ACh receptor subtypes. Notably, flow cytometry of intact SH-SY5Y cells revealed two novel findings: (1) ChAT was localized to the extracellular membrane, a feature not observed in the lung cancer cell lines, and (2) BChE, rather than AChE, was the predominant membrane-bound ACh-degrading enzyme. These results were corroborated by both whole-cell and surface-confocal microscopy. In conclusion, our findings suggest that a functional cholinergic phenotype is a shared feature of several carcinoma cell lines, potentially serving as a survival checkpoint that could be therapeutically explored. The discovery of extracellular membrane-bound ChAT uniquely in neuroblastoma SH-SY5Y cells points to a novel form of in situ ACh signaling that warrants further investigation. Full article

Coumarin derivatives constitute a versatile small-molecule chemotype with broad anticancer potential. This narrative review synthesizes recent in vitro and in vivo evidence on coumarin-based scaffolds, emphasizing breast cancer and covering lung, prostate, and colorectal models. We summarize major mechanisms of action—including induction of apoptosis (caspase activation and BAX/BCL-2 balance), modulation of PI3K/Akt/mTOR signaling, inhibition of angiogenesis (VEGFR-2), interference with estrogen biosynthesis (aromatase/ER axis), chaperone targeting (Hsp90), and attenuation of multidrug resistance (efflux pumps/autophagy)—and highlight representative chemotypes (e.g., benzimidazole, triazole, furocoumarins, topoisomerase- and CDK-oriented hybrids). Where available, we contrast potency and selectivity across models (e.g., MCF-7 vs. MDA-MB-231; A549; PC-3; colon lines) and discuss structure–activity trends linking substituent patterns (heteroaryl linkers, judicious halogenation, polar handles) to pathway engagement. We also delineate translational gaps limiting clinical progress—selectivity versus non-malignant cells, incomplete pharmacokinetic and safety characterization, and limited validation beyond xenografts. Finally, we outline priorities for preclinical optimization: biology-aligned target selection with biomarkers, resistance-aware combinations (e.g., PI3K/mTOR ± autophagy modulation; MDR mitigation), and early integration of ADME/tox and PK/PD to confirm on-target exposure. Collectively, the evidence supports coumarins as adaptable, multi-target anticancer leads, particularly promising in hormone-dependent breast cancer while remaining relevant to other tumor types. Full article

Background: Aryl hydrocarbon receptor (AhR) is a transcription factor that is involved in the regulation of immunity. AhR inhibits T cell activation in tumors, which induces immune suppression in the blood and solid tumors. We identified effective small-molecule AhR antagonists for cancer immunotherapy. Methods: A new series of pyrazolopyrimidine derivatives was synthesized and evaluated for AhR antagonistic activity. Results: Compound 7k exhibited significant antagonistic activity against AhR in a transgenic zebrafish model. In addition, 7k exhibited good AhR antagonist activity, with a half-maximal inhibitory concentration (IC₅₀) of 13.72 nM. Compound 7k showed a good pharmacokinetic profile with an oral bioavailability of 71.0% and a reasonable half-life of 3.77 h. Compound 7k selectively exerted anti-proliferative effects on colorectal cancer cells without affecting normal cells, concurrently suppressing the expression of AhR-related genes and the PD-1/PD-L1 signaling pathway. Compound 7k exhibited potent antitumor activity in syngeneic colorectal cancer models. Importantly, the combination of anti-PD1 and compound 7k enhanced antitumor immunity by augmenting cytotoxic T lymphocyte (CTL)-mediated activity. Conclusions: Collectively, a new pyrazolopyrimidine derivative, 7k, shows promise as a potential therapeutic agent for treating colorectal cancer. Full article

Gear defect detection directly affects the operational reliability of critical equipment in fields such as automotive and aerospace. Gear defect detection technology based on machine vision, leveraging the advantages of non-contact measurement, high efficiency, and cost-effectiveness, has become a key support for quality control in intelligent manufacturing. However, it still faces challenges including difficulties in semantic alignment of multimodal data, the imbalance between real-time detection requirements and computational resources, and poor model generalization in few-shot scenarios. This paper takes the paradigm evolution of gear defect detection technology as the main line, systematically reviews its development from traditional image processing to deep learning, and focuses on the innovative application of intelligent algorithms. A research framework of “technical bottleneck-breakthrough path-application verification” is constructed: for the problem of multimodal fusion, the cross-modal feature alignment mechanism based on Transformer network is deeply analyzed, clarifying its technical path of realizing joint embedding of visual and vibration signals by establishing global correlation mapping; for resource constraints, the performance of lightweight models such as MobileNet and ShuffleNet is quantitatively compared, verifying that these models reduce Parameters by 40–60% while maintaining the mean Average Precision essentially unchanged; for small-sample scenarios, few-shot generation models based on contrastive learning are systematically organized, confirming that their accuracy in the 10-shot scenario can reach 90% of that of fully supervised models, thus enhancing generalization ability. Future research can focus on the collaboration between few-shot generation and physical simulation, edge-cloud dynamic scheduling, defect evolution modeling driven by multiphysics fields, and standardization of explainable artificial intelligence. It aims to construct a gear detection system with autonomous perception capabilities, promoting the development of industrial quality inspection toward high-precision, high-robustness, and low-cost intelligence. Full article

Background/Objective: Oncogenic tyrosine kinases (TKs) such as ALK and SRC promote cancer progression, but their effects on metabolic enzymes are still not well understood. This study examines how TK signaling regulates inosine monophosphate dehydrogenase 2 (IMPDH2), a rate-limiting enzyme in purine biosynthesis, and assesses its potential as a therapeutic target. Methods: Phosphoproteomic screening and in vitro kinase assays were used to identify phosphorylation sites on IMPDH2. Lipid-binding assays explored the role of phosphatidylinositol 3-phosphate (PI3P) in IMPDH2 regulation. Structure-based virtual screening discovered small-molecule allosteric inhibitors, which were tested in lymphoma cell models, including ALK and BTK-inhibitor resistant lines. Results: Here, we identify Inosine monophosphate dehydrogenase-2 (IMPDH2), a rate-limiting enzyme in purine biosynthesis, as a novel substrate of ALK and SRC. We show that phosphorylation at the conserved Y233 residue within the allosteric domain enhances IMPDH2 activity, linking TK signaling to metabolic reprogramming in cancer cells. We further identify PI3P as a natural lipid inhibitor that binds IMPDH2 and suppresses its enzymatic function. Using structure-based virtual screening, we developed Comp-10, a first-in-class allosteric IMPDH inhibitor. Unlike classical active-site inhibitors such as mycophenolic acid (MPA), Comp-10 decreases IMPDH1/2 protein levels, blocks filament (rod/ring) formation, and inhibits the growth of ALK and BTK inhibitor-resistant lymphoma cells. Comp-10 acts post-transcriptionally and avoids compensatory IMPDH upregulation observed with MPA (rod/ring) formation, and inhibited growth in TKI-resistant lymphoma cells. Notably, Comp-10 avoided the compensatory IMPDH upregulation observed with MPA. Conclusion: These findings uncover a novel TK–IMPDH2 signaling axis and provide mechanistic and therapeutic insight into the allosteric regulation of IMPDH2. Comp-10 represents a promising therapeutic candidate for targeting metabolic vulnerabilities in tyrosine kinase driven cancers. Full article

Data augmentation is crucial for electric motor fault diagnosis and lifetime prediction. However, the diversity of operating conditions and the challenge of augmenting small datasets often limit existing models. To address this, we propose an enhanced TimeGAN framework that couples the original architecture with transformer modules to jointly exploit time- and frequency-domain information to improve the fidelity of synthetic motor signals. The method fuses raw waveforms, envelope features, and instantaneous phase-change cues to strengthen temporal representation learning. The generator further incorporates frequency-domain descriptors and adaptively balances time–frequency contributions through learnable weighting, thereby improving generative performance. In addition, a state-conditioning mechanism (via explicit state annotations) enables controlled synthesis across distinct operating states. Comprehensive evaluations—including PCA and t-SNE visualizations, distance metrics such as DTW and FID, and downstream classifier tests—demonstrate strong adaptability and robustness on both public and in-house datasets, substantially enhancing the quality of generated time series. Full article

This study investigates the fatigue bending strength, measured as the Mean of Fatigue Cycles (N), of cylindrical components produced by external wire arc additive manufacturing (WAAM) through a Taguchi L25 orthogonal array and linear regression analysis. Five welding parameters—welding current (Ampe), offset distance (mm), step length (mm), welding speed (mm/min), and specimen gauge diameter (mm)—were evaluated to maximize N using signal-to-noise (S/N) ratios. Nominal bending stresses (σ) ranged from 45 to 54 MPa, ANOVA on raw replicate data (25 runs, 3 replicates) confirmed specimen gauge diameter = 17 mm and weld current = 125 A as dominant, with F = 171.62 (p < 0.001, eta² = 0.62 [95% confidence intervals (CI) 0.55–0.68]) for specimen gauge diameter and F = 6.13 (p < 0.001, eta² = 0.13 [95% CI 0.08–0.18]) for weld current, accounting for ~75% of the variance. Optimal settings (offset distance = 3.0 mm, step length = 1000 mm, welding speed = 550 mm/min, specimen gauge diameter = 17 mm) achieved S/N = 111.35 dB, predicting N ≈ 350,000–380,000 cycles, a 22–33% improvement. Interactions between specimen gauge diameter and speed, and between weld current and offset distance, suggested enhanced strength at speed = 400–450 mm/min for specimen gauge diameter = 17 mm. Basquin’s law (b ≈ 0.72, R² = 0.992) confirmed weld current as key. The linear regression model (adjusted R² = 0.9506) had coefficients for specimen gauge diameter (+70,120 cycles/mm, p < 0.001) and weld current (+1088 cycles/Ampe, p = 0.02), but lower test R² = 0.7212 via cross-validation (60/20/20 split) indicates overfitting due to small dataset size (25 runs), suggesting larger datasets or nonlinear models (e.g., polynomial regression, RSM). Confirmation runs (N = 317,082, 95% CI [287,000–347,000]) validated the results within ~13% error. WAAM reaches 80–90% of traditional manufacturing (TM) fatigue performance, with a 10–20% gap due to the microstructure; recommendations include post-treatments and safety factors (~1.2). Full article

Space-based infrared (IR) detection, with wide coverage, all-time operation, and stealth, is crucial for aerial target surveillance. Under low signal-to-noise ratio (SNR) conditions, however, its small target size, limited features, and strong clutters often lead to missed detections and false alarms, reducing stability and real-time performance. To overcome these issues of energy-integration imaging in perceiving dim targets, this paper proposes a biomimetic vision-inspired Infrared Temporal Differential Detection (ITDD) method. The ITDD method generates sparse event streams by triggering pixel-level radiation variations and establishes an irradiance-based sensitivity model with optimized threshold voltage, spectral bands, and optical aperture parameters. IR sequences are converted into differential event streams with inherent noise, upon which a lightweight multi-modal fusion detection network is developed. Simulation experiments demonstrate that ITDD reduces data volume by three orders of magnitude and improves the SNR by 4.21 times. On the SITP-QLEF dataset, the network achieves a detection rate of 99.31%, and a false alarm rate of $1.97\times {10}^{-5}$, confirming its effectiveness and application potential under complex backgrounds. As the current findings are based on simulated data, future work will focus on building an ITDD demonstration system to validate the approach with real-world IR measurements. Full article

Background: Latent myofascial trigger points (MTrPs) are clinically relevant because they lower local pressure pain thresholds (PPTs), can perturb motor control, and may sustain shoulder symptoms even when overt pain is absent. However, even if previous studies assessed stiffness and mechanosensitivity differences between MTrPs and asymptomatic regions, objective patient-level cut-offs and diagnostic-accuracy metrics to distinguish latent MTrPs from adjacent asymptomatic tissue are lacking. Objective: To quantify the diagnostic accuracy of pressure algometry (PPT) and shear-wave elastography (SWE) for distinguishing latent MTrPs from adjacent asymptomatic tissue. Methods: A single-center cross-sectional study was conducted including 76 volunteers with ≥1 latent infraspinatus MTrP (assessed by following the current Delphi consensus criteria). The most sensitive latent MTrP and a control site 2 cm cranial was measured on the dominant side infraspinatus muscle in each participant. PPT and SWE were acquired with a standardized protocol (long-axis imaging, anisotropy control, minimal probe pressure; three captures per site; 1 cm rectangular ROI; operator blinded to site type). ROC analyses estimated areas under the curve (AUCs), Youden-optimal cut-offs, sensitivity, specificity, and likelihood ratios (LR+/−). Results: Latent MTrPs showed lower PPTs than controls (p < 0.001) and higher stiffness (shear modulus: p = 0.009; shear-wave speed: p = 0.022). PPT yielded AUC = 0.704 with an optimal cut-off of 47.5 N (sensitivity 0.75; specificity 0.592; LR+ 1.84; LR− 0.42), outperforming SWE metrics (shear modulus AUC 0.611; cut-off 23.6 kPa; sensitivity 0.632; specificity 0.605; LR+ 1.60; LR− 0.61; shear-wave speed AUC 0.601; cut-off 2.55 m/s; sensitivity 0.592; specificity 0.632; LR+ 1.61; LR− 0.65). Conclusions: In the infraspinatus, PPT provides moderate discrimination between latent MTrPs and adjacent asymptomatic tissue, whereas resting SWE—despite small mean differences—exhibited lower accuracy. These findings support mechanosensitivity as a primary measurable signal and position SWE as an adjunct. External validation across devices and operators, and multivariable models integrating sensory, imaging, and clinical features, are warranted. Full article

With the rise of embodied agents and indoor service robots, object detection has become a critical component supporting semantic mapping, path planning, and human–robot interaction. However, indoor scenes often face challenges such as severe occlusion, large-scale variations, small and densely packed objects, and complex textures, making existing methods struggle in terms of both robustness and accuracy. This paper proposes MDF-YOLO, a multi-domain fusion detection framework based on Hölder regularity guidance. In the backbone, neck, and feature recovery stages, the framework introduces the CrossGrid Memory Block, Hölder-Based Regularity Guidance–Hierarchical Context Aggregation module, and Frequency-Guided Residual Block, achieving complementary feature modeling across the state space, spatial domain, and frequency domain. In particular, the HG-HCA module uses the Hölder regularity map as a guiding signal to balance the dynamic equilibrium between the macro and micro paths, thus achieving adaptive coordination between global consistency and local discriminability. Experimental results show that MDF-YOLO significantly outperforms mainstream detectors in metrics such as mAP@0.5, mAP@0.75, and mAP@0.5:0.95, achieving values of 0.7158, 0.6117, and 0.5814, respectively, while maintaining near real-time inference efficiency in terms of FPS and latency. Ablation studies further validate the independent and synergistic contributions of CGMB, HG-HCA, and FGRB in improving small-object detection, occlusion handling, and cross-scale robustness. This study demonstrates the potential of Hölder regularity and multi-domain fusion modeling in object detection, offering new insights for efficient visual modeling in complex indoor environments. Full article

A smart device unlocking scheme based on ultrasonic gesture recognition is proposed, allowing users to unlock their devices by customizing the unlock code through gesture movements. This method utilizes ultrasound to detect multiple consecutive gestures, identifying micro-features within these gestures for authentication. To enhance recognition accuracy, an unsupervised segmentation algorithm is employed to accurately segment the gesture feature region and extract the time-frequency domain data of the gestures. Additionally, two-stage data enhancement techniques are applied to generate user-specific data based on a small sample size. Finally, the user-specific model is deployed to mobile devices via transfer learning for on-device, real-time inference. Experimental validation on a commercial smartphone (Redmi K50) demonstrates that the entire authentication pipeline, from signal acquisition to decision, processes 8 types of gestures in a sequence in sequence in approximately 1.2 s, with the core model inference taking less than 50 milliseconds. This ensures that the raw biometric data (ultrasonic echoes) and the recognition results never leave the user’s device during authentication, thereby safeguarding privacy. It is important to note that while model training is performed offline on a server to leverage greater computational resources for personalization, the deployed system operates fully in real time on the edge device. Experimental results demonstrate that our system achieves accurate and robust identity verification, with an average five-fold cross-validation accuracy rate of up to $93.56\%$, and it shows robustness across different environments. Full article

Treatment-resistant depression (TRD) remains a major clinical challenge, with a substantial proportion of patients with major depressive disorder (MDD) failing to respond to conventional antidepressant therapies. Increasing evidence suggests that dysregulation of immune signaling contributes to the pathophysiology of TRD. While proinflammatory cytokines such as IL-6 and TNF-α have been extensively studied, less is known about the role of chemokines such as interleukin-8 (IL-8). This review aims to synthesize current knowledge on the biological functions of IL-8, its involvement in neuroimmune mechanisms, and its potential as a biomarker and therapeutic target in treatment-resistant depression. Clinical and preclinical studies evaluating IL-8 levels in MDD and TRD patients were discussed with a focus on treatment response, neuroinflammatory pathways, and predictive modeling. Methodological factors affecting IL-8 measurement and interpretation were critically assessed. Even though clinical studies indicate that IL-8 levels may predict treatment response to antidepressants, including ketamine, findings are inconsistent, partly due to methodological variability, small sample sizes, and confounding factors such as comorbid medical conditions. Therefore, future longitudinal and multimodal studies are warranted to validate its utility in psychiatric precision medicine. Full article

Bone drilling is a widely encountered process in orthopedic surgeries and keyhole neuro surgeries. We are developing a sensor-integrated smart end-effector for drilling for robotic surgical applications. In manual surgeries, surgeons assess tool wear based on experience and force perception. In this work, we propose a machine-learning (ML)-based tool condition monitoring system based on multi-sensor data to preempt excessive tool wear during drilling in robotic surgery. Real-time data is acquired from the six-component force sensor of a collaborative arm along with the data from the temperature and multi-axis vibration sensor mounted on the bone specimen being drilled upon. Raw data from the sensors may have noises and outliers. Signal processing in the time- and frequency-domain are used for denoising as well as to obtain additional features to be derived from the raw sensory data. This paper addresses the challenging problem of identification of the most suitable ML algorithm and the most suitable features to be used as inputs to the algorithm. While dozens of features and innumerable machine learning and deep learning models are available, this paper addresses the problem of selecting the most relevant features, the most relevant AI models, and the optimal hyperparameters to be used in the AI model to provide accurate prediction on the tool condition. A unique framework is proposed for classifying tool wear that combines machine learning-based modeling with multi-sensor data. From the raw sensory data that contains only a handful of features, a number of additional features are derived using frequency-domain techniques and statistical measures. Using feature engineering, we arrived at a total of 60 features from time-domain, frequency-domain, and interaction-based metrics. Such additional features help in improving its predictive capabilities but make the training and prediction complicated and time-consuming. Using a sequence of techniques such as variance thresholding, correlation filtering, ANOVA F-test, and SHAP analysis, the number of features was reduced from 60 to the 4 features that will be most effective in real-time tool condition prediction. In contrast to previous studies that only examine a small number of machine learning models, our approach systematically evaluates a wide range of machine learning and deep learning architectures. The performances of 47 classical ML models and 6 deep learning (DL) architectures were analyzed using the set of the four features identified as most suitable. The Extra Trees Classifier (an ML model) and the one-dimensional Convolutional Neural Network (1D CNN) exhibited the best prediction accuracy among the models studied. Using real-time data, these models monitored the drilling tool condition in real-time to classify the tool wear into three categories of slight, moderate, and severe. Full article