Search Results (32,063)

This paper proposes a lightweight vital-sign detection framework based on Frequency-Modulated Continuous Wave (FMCW) radar. By integrating time-domain complex differencing and adaptive trend reconstruction, the proposed framework mitigates distortions in chest micro-motion signals caused by multipath reflections, radar cross-section (RCS) variations, high-frequency impulsive noise, and body-motion artifacts in practical monitoring scenarios. The framework first employs a Time-Domain Complex Differencing and Sliding Accumulated Energy (TDCD-SAE) algorithm to precisely lock onto the target range-bin, followed by phase-difference extraction utilizing Complex Conjugate Multiplication (CCM). To eliminate non-physiological interference, an Adaptive Threshold-Based Bidirectional Trend Reconstruction (AT-BTR) algorithm is introduced to restore the corrupted phase profile. The optimized phase signal is converted into chest-wall displacement, followed by body motion detection to realize the joint estimation of the respiration rate and the heart rate. The experimental results demonstrate that the proposed system achieves a high precision, yielding low absolute errors of 1.24/1.71 BPM (supine) and 1.39/2.24 BPM (lateral), thereby validating its efficacy and robustness across diverse sleeping postures in resource-constrained environments. Full article

Background: Thyroid carcinoma (THCA), especially papillary thyroid carcinoma (PTC), remains clinically challenging because recurrence and metastasis occur in a subset of patients. SMDT1 is an essential regulator of the mitochondrial calcium uniporter complex that may influence tumor progression, but its role in thyroid carcinoma is unclear. This study investigated the expression, clinical significance, and biological functions of SMDT1 in thyroid carcinoma. Methods: Public databases were used to analyze SMDT1 expression, diagnostic value, prognostic relevance, co-expression networks, functional enrichment, protein interactions, and immune infiltration. SMDT1 expression was validated in 50 paired PTC and adjacent non-tumorous tissues. In vitro SMDT1 overexpression was performed in PTC cell lines, followed by quantitative real-time polymerase chain reaction (qRT-PCR), Western blotting (WB), CCK-8, colony formation, wound healing, and transwell assays. Results:SMDT1 was significantly downregulated in thyroid carcinoma tissues, PTC tissues, and PTC cell lines. Low SMDT1 expression was associated with lymph node metastasis and shorter disease-free survival. Functional analyses linked SMDT1 with mitochondrial calcium transport, oxidative phosphorylation, apoptosis, cellular senescence, and immune infiltration, including CD8⁺ T cells and activated NK cells. SMDT1 overexpression significantly suppressed PTC cell proliferation, colony formation, migration, and invasion. Conclusions:SMDT1 may function as a tumor suppressor in thyroid carcinoma and has potential diagnostic and prognostic value. Its effects may involve mitochondrial calcium homeostasis, metabolic regulation, and immune microenvironment remodeling. Full article

Intravenous immunoglobulin (IVIG) resistance occurs in 10–20% of children with Kawasaki disease (KD) and is associated with a 3- to 5-fold higher risk of coronary artery lesions (CALs). Yet the mechanistic basis for why some patients progress from reversible inflammation to irreversible vascular damage after IVIG remains poorly understood. Most existing reviews have focused on risk prediction rather than the mechanistic chain linking resistance to CALs. Here, we synthesize current evidence across three interconnected pathways. First, autophagy dysfunction—particularly impaired mitophagy—sustains inflammation through cGAS-STING activation. Second, neutrophil extracellular traps (NETs) play a controversial role in KD vasculitis, with PAD2 and PAD4 possibly acting redundantly via the NLRP3 inflammasome. Third, endothelial-to-mesenchymal transition (EndMT), driven by the IL-1β/TNF axis and the USP7-TGFβ2/SMAD pathway, emerges as a core event in vascular remodeling. Building on these findings, we propose the “autophagy–inflammasome axis” as a candidate molecular switch that dictates whether inflammation resolves or persists. This hypothesis is actionable: it generates three explicit, testable predictions linking autophagic integrity to inflammatory outcomes and therapeutic response. Direct experimental validation in IVIG-resistant KD models and patient samples is now urgently needed. This review provides a systematic framework for understanding how IVIG resistance transitions to irreversible CALs. It also identifies candidate biomarkers (e.g., S100A12, mtDNA, and MCM8) and therapeutic targets (autophagy inducers, NLRP3 inhibitors, USP7 inhibitors, and anakinra) that could enable earlier intervention. Full article

In Fusion Deposition Modeling-based manufacturing, process parameters affect the mechanical and physical properties of the print. Considering these properties, accurately predicting print quality is essential. This is where machine learning (ML) models for three-dimensional (3D) print quality prediction come to the forefront. In this study, a dataset was prepared under strict operational measurement standards—utilizing the Interquartile Range (IQR) method for data sanitization—encompassing 10 material types, 2 printer types, and 4 printing parameters. Five hundred different sample combinations were prepared and printed in sets of three according to ISO 527-2 Type 4 standard dimensions. Tensile, hardness, and surface roughness tests were applied to the prepared samples. Using this validated dataset, a Hybrid Multi-Material Quality–Ensemble System–Stacking–Gradient Boosting Regressor (HMQ-ES-Stack-GBR) architecture is proposed as a diagnostic framework for multi-output quality prediction. Particularly in terms of quality outputs such as tensile strength, hardness, and surface roughness, while also providing a quantitative analysis of the effect of material type on print quality. Furthermore, a multi-objective optimization pipeline integrating three distinct meta-heuristic algorithms—Non-dominated Sorting Genetic Algorithm II (NSGA-II), Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO)—was coupled with the framework to systematically derive material-specific optimal processing parameter configurations. Furthermore, the study shows that open-system printers exhibit higher prediction errors than closed-system printers. Reflecting system-induced variability rather than full hardware independence. Although the study is limited to internal validation within the current experimental design and includes material imbalance across filament groups, the findings suggest that the proposed framework provides a promising diagnostic decision-support tool for pre-print quality estimation within the studied dataset. By accurately reflecting rather than physically overcoming manufacturing variability, it supports decision-making processes through pre-print quality estimation, thereby enabling proactive interventions that reduce raw material, time, and energy losses. Full article

The present study explores the effect of incorrect tire mounting on vehicle braking performance with a specific focus on its interaction with the Anti-Lock Braking System (ABS). Experimental testing was conducted utilizing a passenger vehicle under controlled conditions according to ISO 21994. Two tire configurations were studied: a complying axle-wise configuration and a non-compliant asymmetric arrangement with the same tires mounted longitudinally on each side of the vehicle. ABS was switched on and off throughout the braking testing. The dynamics of the vehicle and its brake parameters were recorded and analyzed using nonlinear statistical modelling. The obtained coefficients of determination of the suggested nonlinear model were in the range of 0.8265 to 0.9938 and showed a satisfactory agreement with the experimental data. Chow-type statistical testing revealed significant differences across all tire designs and ABS operational modes tested (p < 0.001). Ljung–Box test indicated the lateral acceleration signals were significantly temporally dependent. The results indicate that the incorrect asymmetric tire mounting has a negative influence on the braking dynamics and this effect is more significant when the ABS is turned off. While ABS mitigates these effects to some degree, we still observe statistically significant disparities in performance. The results give empirically validated proof of the need for proper tire installation for the preservation of braking performance and vehicle safety and contribute to a better understanding of tire–road interaction and braking dynamics under asymmetric tire configurations. Full article

This study investigates the pervaporation dehydration of a quaternary esterification mixture containing water, 2-ethylhexyl acrylate, 2-ethylhexanol, and propionic acid using a pilot-scale HybSi membrane (BTESE on Al₂O₃). A design of experiments was implemented to evaluate the influence of mixture composition on water content in the retentate, permeation flux, and water removal efficiency. Descriptive analysis revealed that the initial water content is the dominant factor governing both permeation flux and dehydration performance, whereas acid, alcohol, and ester have secondary but interactive effects. Reduced cubic polynomial models including linear, binary, and ternary interactions were developed, showing good agreement with experimental data. Ternary diagrams highlighted composition regions where molecular interactions significantly affect separation performance. Multi-response optimization based on desirability functions identified optimal operating conditions at high initial water content (1.146 wt.%), yielding a permeation flux of 0.144 kg·m⁻²·h⁻¹, a final water content close to the industrial target (0.2 wt.%), and a water removal efficiency of 86%. Experimental validation confirmed the reliability of the predictive model. The results provide insights into composition–performance relationships and demonstrate the suitability of BTESE membranes for low-water-content esterification systems. Full article

This paper investigates the influence of weak but structured high-frequency disturbances on the linear system identification of lateral vehicle dynamics using experimental measurement data. The analyzed dataset originates from previously conducted driver-in-the-loop experiments involving free-driving and slalom maneuvers. Frequency-domain analysis confirms that the dominant vehicle dynamics are concentrated below approximately 2–3 Hz, while a weak but persistent narrow-band disturbance around 10 Hz is consistently present in the steering signal. To investigate the influence of this disturbance on identification, different disturbance-handling strategies are compared, including notch filtering, low-pass filtering, ARX, IV-ARX, and ARMAX model structures. The comparison considers prediction performance, model complexity, identified dynamics, and robustness under different measurement conditions. The results show that increasing the deterministic model order is generally less effective than either targeted preprocessing or explicit noise modeling. When the disturbance is spectrally well separated from the relevant vehicle dynamics, notch filtering combined with a low-order ARX model provides the most effective solution. If preprocessing is not possible, ARMAX models achieve comparable performance by representing part of the disturbance through the noise model. IV-ARX models are used as a benchmark to verify that the main conclusions remain valid under possible closed-loop bias. Based on these findings, a practical engineering workflow is proposed for selecting appropriate disturbance-handling strategies according to the spectral characteristics of the measured signals. The proposed methodology provides guidance for robust control-oriented identification of lateral vehicle dynamics using realistic measurement data. Full article

Transformative therapeutic innovation should not begin with a molecule—or even a molecular target. It should begin with a clearly defined unmet clinical need. Here, we present a seven-step Translational Therapeutic Development Roadmap that systematically connects an unmet medical need to a developable drug candidate through the disciplined sequence of (i) defining the need, (ii) understanding disease and resistance biology, (iii) building a mechanistic hypothesis, (iv) defining a target product profile (TPP), (v) molecular design and experimental validation, (vi) developability and manufacturability assessment, and (vii) clinical translation. A central conclusion emerging from this review is that resistance biology should be viewed not merely as a cause of therapeutic failure, but as a primary design input for next-generation therapeutic innovation. Our analysis identifies continuous alignment among unmet clinical needs, resistance biology, mechanistic hypothesis, molecular design, developability, and clinical translation as the defining characteristic of successful therapeutic development. We use dual-payload antibody–drug conjugates (ADCs) as a contemporary and highly illustrative case study of this resistance-informed therapeutic development approach. Single-payload ADCs such as trastuzumab deruxtecan and sacituzumab govitecan have transformed treatment across multiple solid tumors, yet most patients ultimately relapse through antigen loss, defective intracellular trafficking, drug efflux, payload-target alterations, and tumor heterogeneity, creating an emerging post-ADC treatment gap. Dual-payload ADCs, which deliver two mechanistically distinct warheads from a single antibody, represent a form of molecular combination therapy designed to increase the barrier to resistance and address multiple escape pathways simultaneously, as well as provide a clinically relevant model for resistance-informed therapeutic design. Using dual-payload ADCs as a worked example, we demonstrate how resistance biology directly informs payload pairing, molecular architecture, conjugation strategy, experimental validation, and developability. Our analysis indicates that successful dual-payload ADC design depends not simply on combining two cytotoxic payloads, but on selecting complementary mechanisms with non-overlapping resistance liabilities while satisfying predefined target product profiles and manufacturability requirements. We further summarize resistance-guided payload pairing strategies, including topoisomerase I plus tubulin inhibitors, topoisomerase I plus DNA-damage-response inhibitors, cytotoxic plus immunomodulatory payloads, and cell-permeable plus non-permeable combinations; the conjugation chemistries that enable defined dual-payload products; the preclinical validation, pharmacological optimization, and developability hurdles that separate promising biology from viable therapeutics; and the rapidly expanding clinical landscape, including the first-in-human program KH815 and emerging bispecific dual-payload constructs. Finally, we demonstrate that the same translational roadmap extends beyond ADCs to radiopharmaceutical conjugates, multispecific antibodies, targeted protein degraders, and cell and gene therapies, indicating that it represents a general framework for therapeutic innovation rather than an ADC-specific strategy. Collectively, this review supports the concept that therapeutic innovation is most successful when unmet clinical needs, resistance biology, molecular design, developability, and clinical translation are considered as an integrated continuum rather than as independent stages of drug discovery. This Translational Therapeutic Development Roadmap provides an organizing framework for guiding the rational development of next-generation targeted therapeutics across diverse therapeutic modalities. Full article

Multidrug-resistant Klebsiella pneumoniae is an increasing global threat, and the limited availability of new antibiotics highlights vaccination as a promising strategy for infection prevention. Although different vaccine candidates have been evaluated, none is currently approved, mainly due to capsular heterogeneity among strains. Protein-based vaccines may overcome this limitation by targeting conserved epitopes. In this study, we assessed the immunogenicity of five outer membrane proteins (ChiP, LamB, RafY, OmpW, PagP) previously selected by reverse vaccinology (RV), comparing them with the well-characterized antigens OmpA and OmpK36. Mice were immunized intramuscularly with three doses of purified recombinant proteins, and antibody responses were analyzed by ELISA. All antigens elicited high, booster-induced IgG levels, with PagP slightly being less immunogenic. Regarding IgG subclasses, IgG₁ predominated, followed by IgG_2b and IgG_2c. Cross-reactivity was evaluated against six K. pneumoniae strains representing different clonal groups, and recognition by sera from previously infected mice was also examined. OmpA, ChiP and LamB showed the broadest cross-reactivity, while OmpA and LamB were most strongly recognized after infection. Overall, OmpA, LamB, ChiP and RafY emerged as the most promising vaccine candidates, although further optimization, such as a multicomponent vaccine, may be required. This work also highlights the importance of experimentally validating RV-selected antigens, as computational predictions alone do not ensure immunogenicity or in vivo relevance. Full article

The transition process from synchronous condenser pump (SCP) mode to pumping mode determines the response rapidity of the startup procedure and operational stability of pump-turbines; however, the complex gas–liquid interaction and transient hydraulic characteristics during this process remain insufficiently understood. To address this, this study develops a numerical framework for the SCP-to-pumping transition process, incorporating the full-passage system, a multiscale mesh strategy for coupling mainstream and clearance flow regions, and a gas–liquid two-phase flow model based on the Volume of Fluid (VOF) method. The reliability of the numerical model is verified through comparison with model experiments, demonstrating good agreement between simulations and experimental data. Based on the validated model, the transient evolution of hydraulic forces, pressure pulsations, and internal flow structures is systematically analyzed. Axial force analysis reveals a significant internal equilibrium; the crown bears a maximum instantaneous fluctuation of approximately 2800 kN. Conversely, the radial force is primarily dominated by blade hydraulic thrust (1294 kN), showing distinct anisotropic behavior. The runner blade channels and the upper draft tube region are identified as critical areas with intense pressure fluctuations, with peak-to-peak pressure amplitudes reaching 45~48 m and 54 m head, respectively. Furthermore, reducing the duration of the exhaust process constitutes the main strategy for accelerating the transition and mitigating prolonged high-amplitude force and pressure fluctuations. The findings provide new insights into the transient hydraulic mechanisms of SCP-to-pumping transitions and offer guidance for optimizing transition control strategies in pumped-storage units. Full article

The Radiological Surveillance Network of the Autonomous Community of the Basque Country continuously monitors environmental dose rate levels to detect potential radiological anomalies. However, during rainfall events, increases in dose rate that occasionally exceed predefined alarm thresholds have been observed, despite the absence of radiological incidents. This study presents a predictive model to estimate the increase in dose rate caused by precipitation events at the Bilbao monitoring station. The model accounts for the atmospheric scavenging of radon progeny by raindrops and their subsequent deposition on horizontal surfaces surrounding the detector. Atmospheric transport, radioactive decay, washout processes, and rainwater accumulation dynamics are incorporated into the formulation. The model is implemented numerically using a finite-difference scheme and validated against experimental measurements from the monitoring station. The results (correlation coefficient, BIAS, RMSE) show good agreement between calculated and measured dose rate increases during precipitation episodes, indicating that the observed variations are mainly attributable to the deposition of short-lived radon progeny. Full article

Accurate prediction of enzyme turnover numbers ($k_{cat}$) is essential for applications in systems biology, metabolic engineering, and drug discovery, yet remains challenging due to the limited availability and uneven distribution of experimental data. Here, we present AUKAT, an integrated framework that combines conditional generative modeling with deep neural prediction to improve $k_{cat}$ estimation. A conditional variational autoencoder generates synthetic training instances in embedding space, followed by a selection pipeline that retains samples with strong agreement across independent evaluators, thereby ensuring data reliability. A hybrid convolutional neural network and transformer-based architecture is then used to predict $k_{cat}$ from substrate, enzyme functional, and species embeddings. Incorporating synthetic data improved predictive performance for both random forest and neural network models in five-fold cross-validation, with larger gains observed for the neural network architecture. Benchmarking against DLKcat demonstrated comparable predictive accuracy on the standard test set, while evaluation on stricter unseen subsets indicated improved generalization for low-similarity substrates and enzymes. Feature importance analysis further showed that AUKAT leverages substrate, enzyme functional, and species information in a more balanced manner rather than relying predominantly on a single feature source. In addition, AUKAT-human, a specialized model trained using a pre-training and fine-tuning strategy, achieved improved prediction accuracy for human enzyme kinetics. Overall, AUKAT provides a scalable approach for enzyme kinetics prediction and offers a practical solution to data scarcity in biochemical modeling. Full article

The rapid emergence of carbapenem-resistant Acinetobacter baumannii has become a major health concern, primarily driven by the dissemination of class D β-lactamases, particularly OXA-23, which compromise the efficacy of last-line β-lactam antibiotics. Drug repurposing combined with structure-based computational approaches provides a promising strategy for accelerating the discovery of novel therapeutic candidates against multidrug-resistant pathogens. This study aimed to identify FDA-approved non-steroidal anti-inflammatory drugs (NSAIDS) with potential inhibitory activity against OXA-23 β-lactamase by using a comprehensive computational drug discovery workflow. Twenty-six FDA-approved NSAIDs were evaluated using an integrated computational pipeline comprising network pharmacology, KEGG pathway analysis, molecular docking, molecular dynamics simulations and ADMET profiling. KEGG pathway analysis confirmed the central role of OXA-23 in β-lactam resistance, while network pharmacology prioritized nine candidates NSAIDS for subsequent structure-based investigation. Molecular docking was performed using the crystal structure of OXA-23 β-lactamase (PDB ID: 4K0X), followed by molecular dynamics simulations to assess the stability of the protein–ligand complexes. Among the prioritized compounds, sulfasalazine demonstrated the most favorable predicted binding affinity (−8.3 kcal/mol), forming stable interactions with key catalytic residues, including SER126, VAL128, and LEU166 and exhibiting a more favorable docking profile than the reference drug imipenem (−5.7 kcal/mol). Molecular dynamics simulations supported the structural stability of the sulfasalazine OXA-23 complex throughout the simulation period. Furthermore, ADMET analysis indicated favorable pharmacokinetic characteristics including good oral bioavailability, high gastrointestinal absorption, low central nervous system penetration, and an acceptable predicted safety profile. This integrated computational study identifies sulfasalazine as a promising repurposing candidate for targeting OXA-23 β-lactamase in carbapenem-resistant A. baumannii. The findings demonstrate the utility of combining network pharmacology with molecular modeling to prioritize candidate therapeutics and provide a computational framework for accelerating antimicrobial drug discovery. Experimental validation is warranted to confirm the inhibitory activity and therapeutic potential of sulfasalazine against multidrug-resistant A. baumannii. Full article

To address the limitations of existing studies on harmonic drive (HD) systems, such as the lack of systematic exploration of the influence of flexspline (FS) structural parameters on service lifetime and insufficient parameter optimization strategies, this study establishes a comprehensive research framework integrating finite element (FE) simulation, response surface optimization, and experimental validation. Three key structural parameters of FS, including cup length (CL), cup wall thickness (CT), and tooth width (TW), which were selected as design variables to investigate their effects on the displacement, von Mises stress, and fatigue lifetime of the HD system. A reliable FE model of the HD system was constructed, with mesh independence verified and experimental validation conducted using a servo motor-driven test platform. The results showed that the relative errors between simulated and measured displacement (4.7%) and stress (4.0%) were within the acceptable engineering range (<10%), confirming the model’s reliability. Systematic analysis revealed that the FS lifetime is jointly determined by global stress distribution and structural rigidity, rather than local tooth root stress alone. Increasing cup length reduces tooth root stress but increases cup wall bending stress, which ultimately dominates the fatigue failure process. Based on Box–Behnken Design (BBD) response surface analysis, the optimal parameter combination was determined as CL = 48 mm, TW = 6 mm, and CT = 0.64 mm, achieving the dual optimization of low system stress (77.7 MPa) and long FS lifetime (2950 h). Relative to the baseline control group (CL = 48 mm, CT = 0.46 mm, TW = 6 mm), the optimized configuration reduces system stress by 10.1% and extends fatigue lifetime by 1.7%. This study clarifies the multi-factor coupling mechanism of structural parameters regulating HD system performance, providing a robust theoretical basis and engineering reference for the structural design and performance improvement of HD systems. Full article

Post-traumatic stress disorder (PTSD) is a severe condition triggered by traumatic exposure, characterized by symptoms like trauma re-experiencing, avoidance, mood alterations, hypervigilance, and sleep disturbances. While its exact mechanisms remain uncertain, PTSD involves dysregulation across neurobiological systems underlying fear conditioning, threat appraisal, executive control, and stress response. Although research highlights the sympathetic–adreno–medullary (SAM) system and the hypothalamic–pituitary–adrenal (HPA) axis, findings on stress-related mediators remain inconsistent regarding their precise contributions over time. To address this, we propose a hypothetical model viewing PTSD as a multistable system shifting from physiological to pathological steady states. We assume that intense, repeated emotional stress triggers spike activation in the amygdala, driving an amygdala–locus coeruleus loop into a high-activation state via reciprocal excitation, mediated by corticotropin-releasing hormone (CRH) and noradrenaline. This sequentially alters amygdala–hippocampus and prefrontal cortex loops, reinforcing fear expression and impairing extinction. This model is consistent with key features of PTSD, including its higher prevalence among females, increased glucocorticoid receptor sensitivity, the frequently observed hypocortisolism, and the partial efficacy of serotonin and norepinephrine reuptake inhibitor (SNRI) and CRH receptor antagonists. While requiring experimental validation, this framework connects molecular, circuit, and behavioral data to help identify novel interventions for restoring adaptive stress-response dynamics. Full article