Search Results (500)

In the present circumstances, it is exceedingly hard for people to monitor their blood sugar levels on a regular basis. Checking the blood glucose levels of diabetic individuals is often an essential part of managing diabetes. Now, this means repeatedly pricking your finger and bleeding. Non-invasive (NI) detection methods are anticipated to have several benefits, including the elimination of discomfort, avoidance of sharp items and biohazardous chemicals, the possibility of more frequent testing, and, therefore, better regulation of glucose levels. Infrared technology has become one of the most important technologies for the development of the NI self-monitoring of blood glucose (NI-SMBG). One good thing about this approach is that it does not need any chemicals and can employ fiber optic parts. So, only insulators come into direct contact with the skin. Also, the spectrometer may be made without any moving parts, which makes it strong. For this method to be effective, the spectral signature of glucose must be uniquely identifiable from all other chemical constituents in the human body, and this glucose-specific data must be obtained with a sufficiently high signal-to-noise ratio to facilitate reliable differentiation between glucose-dependent signals and those generated by other matrix components. In this paper, we have addressed the issue of infrared signature analysis for blood glucose, which has been done in the infrared region of the electromagnetic spectrum. Firstly, the analysis is carried out for the glucose molecule only. Later, looking at the presence of numerous other analyses in whole blood, tissues, skin, etc., for in vivo measurement of blood glucose, a set of wavelengths is identified on which in vivo measurements can be done with minimal interference from other body fluid analyses. Absorption of spectroscopic information collected on these wavelengths, along with a suitable calibration model, can be a step ahead for in vivo NI glucose measurement. The main innovative features of the present study are non-invasive glucose sensing, Patient-friendly and continuous monitoring opportunity, Progress towards wearable and real-time diagnostics, Clinical and Social Relevance and Contribution to Research. The paper investigates a non-invasive infrared-based methodology for glucose estimation, focusing on the spectral response characteristics of glucose in biological tissue. While the complexity of tissue spectroscopy involves potential interference from other biomolecules, the present work emphasizes the feasibility of glucose detection without invasive blood extraction, rather than conducting a dedicated interference-analysis study. Full article

Skeleton-based action recognition via graph convolutional networks (GCNs) has achieved remarkable progress, yet two persistent bottlenecks limit practical deployment: (1) systematic confusion among fine-grained actions that differ primarily in hand or finger movements, which the standard 25-joint skeleton cannot disambiguate; and (2) training instability under small batch sizes caused by BatchNorm (BN) running-statistics pollution, leading to catastrophic accuracy drops during training. In this paper, we propose BPA-STGCN (Body-Part-Aware STGCN), which addresses both challenges through an integrated framework of architectural and training innovations. First, a Partition Attention (PA) module adapting the Squeeze-and-Excitation concept to anatomically defined joint groups that decomposes the 25-joint skeleton into four anatomical partitions and learns sample-specific importance weights for each partition, enabling the model to focus on the most discriminative body region for each action. Then, the information losing global average pooling is replaced by a Temporal Pyramid Pooling (TPP) module adapting the temporal-segment and pyramid-pooling concepts to skeleton feature maps that captures multi-scale temporal dynamics through segmented pooling. Moreover, we design a stability-first training protocol comprising low-momentum BN, Mixup augmentation, gradient clipping, and extended warmup. The experiments are performed on the NTU RGB+D 60 and NTU RGB+D 120 dataset, and BPA-STGCN achieves 93.7% and 90.9% accuracy. Comprehensive ablation studies reveal that the architectural innovations and the stability protocol contribute complementarily, and that BPA-STGCN achieves the best accuracy–stability trade-off among all tested configurations. Full article

Digitally captured handwriting preserves pen trajectories and dynamic signals, but it also records hardware- and input-dependent properties that can confound forensic interpretation. This study revises a support vector machine (SVM) screening framework using 16,500 samples from 30 writers, 11 writing-content categories, and five acquisition conditions spanning three tablets and stylus or finger input. Twenty-four raw and derived time-series variables were summarized by maximum, minimum, mean, median, and standard deviation, yielding 120 features; the mode statistic was removed. Writing direction and angular velocity were recalculated with atan2-based vector formulas. Unavailable device/API channels were encoded as zero, and Z-score parameters were estimated only from training folds. Writer and content evaluations used rotating pooled-“other” categories as rejection-class proxies, whereas acquisition-condition classification remained closed-set. Every outer five-fold split contained an inner five-fold forward-selection loop; RBF-SVM hyperparameters were fixed a priori (C = 1.0, gamma = scale, balanced class weights, and random seed 42). Writer classification achieved 94.85% accuracy (descriptive 95% CI: 94.69–95.01%) and 86.44% pooled-other recall. Content classification achieved 96.64% accuracy (95% CI: 95.74–97.53%) and 98.74% pooled-other recall. Acquisition-condition classification achieved 99.99% accuracy (99.98–100.00%), with one error among 16,500 outer-test predictions. The acquisition result is interpreted primarily as evidence that channel availability and device-specific measurement scales are strongly encoded in the feature space. Because the folds were sample-level, the samples were collected contemporaneously, and pooled-other writers were represented during training, these results do not establish session-disjoint, cross-device writer, or strict open-set generalization. The proposed workflow should therefore be regarded as an experimental triage aid that supports, rather than replaces, examiner-led comparison. Full article

Flexible pressure sensors serve as core sensing components for wearable health monitoring systems, electronic skins for soft robots, and flexible human–machine interaction devices. Benefiting from the interfacial electric double-layer polarization effect, iontronic sensing delivers far higher pressure response sensitivity than conventional parallel-plate capacitive sensors, endowing it with distinctive advantages in the detection of weak physiological signals. Nevertheless, current dense ionic thin-film dielectric layers suffer from limited deformation space under compression and poor low-pressure sensing capability. Mainstream high-precision micropillar arrays are fabricated via photolithography, 3D printing, and metal etching molds, which require costly equipment and complicated fabrication procedures, making large-area mass production unfeasible. Random frosted concave-convex microstructures feature disordered dimensions, leading to severe device hysteresis and narrow linear ranges, which fail to achieve ultrahigh sensitivity alongside a wide pressure detection range simultaneously. To address the aforementioned multiple bottlenecks, this paper proposes a low-cost resin template replication process to fabricate TPU-based ionic thin-film dielectric layers with ordered micropillar array microstructures. Combined with inkjet-printed silver conductive PI flexible electrodes, an iontronic flexible pressure sensor with a sandwich layered structure is constructed. Multi-dimensional investigations including microscopic morphology characterization, electromechanical sensing performance calibration, and human wearable application tests are systematically implemented to thoroughly elucidate the synergistic enhancement mechanism of the arrayed micropillars. Test results demonstrate that the effective pressure detection range of the sensor spans 0–1038 kPa, accommodating ultra-low pressures such as pulse signals as well as medium-to-high-pressure loads including joint bending. The sensitivity reaches 23.27 kPa⁻¹ within the low-pressure range of 0–200 kPa and remains stable at 3.52 kPa⁻¹ in the high-pressure range of 200–1038 kPa, with piecewise linear fitting correlation coefficients of 0.93 and 0.96 respectively. Both the response time and recovery time of the device are 40 ms, and the hysteresis error throughout the loading-unloading cycle is merely 2.62%. After 20,000 consecutive cyclic loading-unloading tests, the peak capacitance output only decays by 5.1%, verifying outstanding mechanical fatigue resistance and electrical stability. Validations in multi-scenario applications prove that the sensor can accurately capture human physiological and motion signals including radial artery pulses, laryngeal deformation induced by multi-syllable vocalization, and multi-angle bending of fingers and elbow joints, suitable for home-based health monitoring, quantitative rehabilitation training, flexible tactile interaction and other scenarios. The entire fabrication process eliminates high-precision micro-nano processing equipment such as photolithography systems, plasma etchers and 3D printers; only general chemical raw materials and conventional laboratory instruments are adopted. The reusable templates enable low manufacturing costs and large-area coating forming, offering a novel low-cost technical solution for the engineering implementation and industrialization of high-performance iontronic flexible pressure sensors. Full article

During protein synthesis, ribosome stalling, collision, and aberrant elongation can lead to the accumulation of defective nascent polypeptides and compromise cellular homeostasis. To counteract such translational disturbances, eukaryotic cells have evolved a highly conserved translational quality control network, in which the ribosome-associated quality control (RQC) pathway plays a central role in the recognition and elimination of aberrant translation complexes. Zinc Finger Protein 598 (ZNF598), a key E3 ubiquitin ligase in mammalian cells, functions as an essential factor in the early recognition and signal transduction steps of the RQC pathway. Accumulating evidence indicates that ZNF598 senses aberrant translational states, and particularly in the context of ribosome collision, mediates site-specific ubiquitination of 40S ribosomal proteins, thereby promoting ribosome splitting, nascent chain clearance, and subsequent processing of defective mRNAs. Beyond its canonical role in RQC, ZNF598 has also been implicated in the translational repression of defective mRNAs, regulation of inflammatory signaling, antiviral responses, and control of toxic translation products associated with neurodegenerative disorders. In this review, we summarize the structural features, molecular mechanisms, regulatory networks, and physiological as well as pathological functions of ZNF598. We also discuss current controversies and future directions in the field, with the aim of providing a broader framework for understanding translational quality control and its therapeutic potential. Full article

Polyimide (PI) film is a widely used insulation material in high-frequency power equipment, yet its frequency–lifetime relation and terminal discharge features have rarely been resolved together over a complete ageing trajectory. This paper reports three contributions. First, a condition-specific frequency–lifetime relation for PI surface insulation is presented, in which the discharge inception voltage is frequency-invariant while the flashover voltage and the surface lifetime both fall with frequency. Second, a stage-resolved discharge morphology derived from phase-resolved patterns is proposed, in which a late-stage finger-like cluster serves as a candidate flashover precursor, complemented by a count-aware, time-normalised discharge activity proxy. Third, a space-charge interpretation linking the per-cycle charge injection budget growth at high frequency to the suppressed inter-cycle dissipation, jointly accounting for the lifetime relation and the non-dendritic damage morphology, is presented. The fitted lifetime relation provides a quantitative planning basis over the tested 10–40 kHz grid for insulation design of high-frequency power equipment, and the finger signature provides a candidate device-level morphological criterion for online monitoring. Full article

Background: Finger citron (Citrus medica L. var. sarcodactylis Swingle, CM) is an important edible and medicinal plant. Owing to its diverse pharmacological properties, its fruit has long served as a traditional Chinese medicine and has increasingly been developed for functional food applications. However, its non-fruit parts, including leaves, branches, and roots, remain underutilized. Methods: In this study, CM fruit (CMF), leaves (CML), roots (CMR), and branches (CMB) were selected to systematically compare their small-molecule metabolic profiles and antioxidant and anti-inflammatory activities. Untargeted metabolomics was conducted based on ultra-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS). Meanwhile, antioxidant capacity was evaluated using 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical scavenging assays, and anti-inflammatory activity was assessed using a lipopolysaccharide (LPS)-induced RAW 264.7 macrophage inflammation model. Results: Metabolomic analysis revealed 826 differential metabolites across the four CM parts (CMs), reflecting pronounced organ-specific metabolic features. According to Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, the variations in metabolite profiles across the four CMs were primarily linked to amino acid metabolism, phenylalanine metabolism, and unsaturated fatty acid metabolism. In the antioxidant assays, the DPPH radical scavenging activity at 2 mg/mL followed the order CML (78.37 ± 7.29%), CMR (73.30 ± 2.68%), CMB (40.71 ± 0.16%), and CMF (36.67 ± 1.67%), whereas ABTS radical scavenging activity followed the order CMR (51.36 ± 2.17%), CML (48.30 ± 1.64%), CMB (45.23 ± 1.72%), and CMF (18.48 ± 0.22%). In the LPS-stimulated RAW 264.7 macrophage model, at 0.4 mg/mL, inhibition of nitric oxide (NO) production followed the order CML (88.18 ± 4.02%), CMB (87.21 ± 3.02%), CMF (68.05 ± 2.54%), and CMR (67.21 ± 9.12%). Correlation analysis further suggested that features putatively annotated as minecoside, maltotetraose, and (+)-catechin may be associated with antioxidant or anti-inflammatory activities. Conclusions: The study revealed differences in metabolite composition and bioactivities among different CMs, providing an experimental basis for the development and utilization of non-fruit parts, especially leaves, and for further investigation of their bioactive constituents. Full article

Background and Clinical Significance: Dupuytren’s disease (DD) is characterized by abnormal myofibroblast proliferation and excessive collagen deposition, leading to the formation of pathological fibrous cords. It typically affects the palmar surface of the hand, where these contractile cords cause progressive flexion contractures of the metacarpophalangeal (MCP) and proximal interphalangeal (PIP) joints. Lesions involving the proximal interphalangeal (PIP) joint without significant flexion contracture may be misdiagnosed as soft-tissue tumors or inflammatory lesions based on imaging findings, including magnetic resonance imaging (MRI) and ultrasound; Case Presentation: We present a case of a soft-tissue mass located on the volar aspect of the proximal phalanx of the little finger, associated with a mild PIP joint contracture. The initial MRI findings suggested a giant-cell tumor of the tendon sheath; however, the diagnosis of Dupuytren’s disease was established only after histopathological examination; Conclusions: This case highlights the importance of considering DD in the differential diagnosis of peripheral soft-tissue lesions of the finger, particularly when presenting with only mild PIP joint contracture and atypical imaging features. Full article

A sign language interpretation system aims to translate sign gestures into spoken or written language in real time, enabling signers and non-signers to communicate in their familiar linguistic forms. However, vision-based approaches suffer from hand occlusion, lighting variability, and complex backgrounds, while Deep Neural Network (DNN)-based methods incur heavy computational costs that hinder real-time use on resource-constrained platforms. In this paper, we propose sign language gloves and a lightweight motion retrieval method for real-time sign language interpretation that runs on mobile devices and embedded systems. The sign language gloves integrate flex sensors, an inertial measurement unit (IMU), and pressure sensors to accurately capture gesture features, including finger bending angles, hand orientation, movement trajectories, and fingertip contacts with body parts, enabling recognition of touch-based gestures. For the motion retrieval method, we build a comprehensive gesture dataset with the gloves and perform feature analysis on each sign language gesture to avoid redundant information in the dataset. During interpretation, our system employs a feature-labeling mechanism to ensure gesture distinguishability and a gesture retrieval algorithm to evaluate movement continuity and similarity. This allows the system to identify corresponding feature labels and consolidate them into complete sign language vocabulary entries. The proposed motion retrieval method is characterized by low computational complexity and a well-defined data structure. This makes it suitable for integration into embedded systems, offering real-time performance and high portability for practical deployment. In our experiments, the proposed system achieved an average recognition accuracy of $92\%$ on a gesture dataset covering 300 sign language words. Full article

Background/Objectives: Self-injurious behavior (SIB) is one of the most disabling manifestations of Lesch–Nyhan disease (LND), frequently involving the oral cavity and severely affecting patients’ quality of life and caregiver burden. Evidence regarding the impact of dental interventions on patient- and caregiver-reported outcomes remains limited. This study investigated the characteristics of SIB in patients with LND and explored the perceived effectiveness, tolerability, and psychosocial impact of intraoral devices and dental extractions. Methods: A questionnaire-based observational study was conducted among patients with LND. A purpose-built 32-item questionnaire, developed by dentists with expertise in Special Care Dentistry at Mauriziano Umberto I Hospital (Turin, Italy), was administered to patients attending the Pediatric Dentistry and Orthodontics Unit at IRCCS Istituto Giannina Gaslini (Genoa, Italy). The questionnaire assessed demographic and clinical characteristics, SIB features, dental management strategies, treatment-related complications, and patient- and caregiver-reported outcomes. Results: Twenty-four questionnaires were analyzed. SIB was reported in 21 patients (87.5%). Fingers (80%) and lips (70%) were the most frequently affected sites, with multiple anatomical sites involved in 80% of patients. Intraoral devices were used in 14 patients (66.7%), whereas dental extractions were performed in 4 (19%). Among patients treated with intraoral devices, 78.6% reported increased reassurance and protection. Device-related complications occurred in three patients and were mainly periodontal. All patients and caregivers reported relief following dental extraction, although SIB persisted in 50% of cases. Conclusions: SIB is highly prevalent among individuals with LND and commonly affects multiple anatomical sites. Intraoral devices are the most frequently adopted treatment strategy and are generally well tolerated, whereas dental extractions were associated with substantial perceived relief but do not necessarily eliminate SIB. Patient- and caregiver-reported outcomes should be considered when evaluating treatment effectiveness. Full article

To address low seeding accuracy and poor seed-fertilization coordination caused by wheel slip and vibration in undulating terrains, a model 2BJD-4 precision corn planter featuring an independent electric-drive transmission was developed. The planter integrates furrow opening, fertilization, single-seed precision metering, soil covering, and compaction into a coordinated one-pass operation. Key mechanical assemblies include a servo-motor-driven finger-clamp seed meter, a parallel four-bar terrain-following mechanism, and an external fluted-roller fertilization meter. To capture complex non-linear soil-tool interactions, a predictive surrogate model was established using Support Vector Regression (SVR) and coupled with the Dung Beetle Optimizer (DBO) for global parameter optimization. Comprehensive field trials validated that the SVR-DBO framework outperformed traditional Response Surface Methodology, securing an optimal qualified spacing index of 92.8% and a planting depth qualification rate of 93.0% under experimental conditions. These findings demonstrate the technical feasibility of the proposed design in maintaining seed spacing and depth uniformity under tested topographies, offering a practical reference for the development of precision planters in hilly and plain regions. Full article

This work presents the design and preliminary experimental validation of deformable sensors based on time-domain reflectometry (TDR) for rehabilitation-oriented interaction monitoring. Three architectures were investigated: a planar multilayer sensor and two coaxial configurations based on foam and engineered TPU–Hilbert structures. Controlled indentation tests were performed at different positions and deformation levels, extracting two TDR-derived features: the minimum reflection coefficient ${\rho }_{min}$, related to deformation intensity, and the perturbation time $t_{\mathrm{pert}}$, related to contact localization. Preliminary calibration curves and two-dimensional maps were used to analyze the coupled dependence of the response on position and indentation depth. Application-oriented manual tests confirmed the different suitability of the three geometries for localized finger pressing, distributed two-hand grasping, and controlled single-hand squeezing. Overall, the results support TDR-based deformable sensors as low-complexity and geometry-adaptable tools for spatially resolved monitoring of motor rehabilitation interactions. Full article

Background: Rheumatoid arthritis (RA) is a slow progressive autoimmune disease. RA disproportionately affects women due to hormonal and immune variations. During pregnancy, hormonal and immune system changes vary drastically and may lead to RA. Traditional diagnostic techniques are blood biomarkers and clinical assessments. The above methods fail to detect RA at earlier stage due to subclinical inflammation and pregnancy-related physiological changes. Thermal imaging, as a non-invasive and radiation-free approach, can reveal temperature asymmetries across inflamed joints, offering a safer diagnostic pathway for pregnant women. Objective: In this paper, non-invasive RA detection is performed using finger, leg and hand thermal images. A pregnancy-aware rheumatoid arthritis (PARA) diagnostic framework is proposed. The PARA framework uses hybrid deep learning algorithms to classify RA inflammation states, such as normal, moderate and high. Methods: Using the PARA framework, thermal images were obtained from pregnant women. A total of 28 major bone joints were captured across four physiological states, including normal and before pregnancy. The thermal images were obtained from normal women, pregnant women, and women after pregnancy using a smartphone -based high-resolution USB thermal camera. Preprocessing was performed using bilateral, Non-Local Means (NLM), and guided filters to enhance thermal images for clarity. The guided filter preserves the edges and suppresses noise. Our proposed Denoising Convolutional Neural Network (DnCNN) algorithm was applied to preprocessed images to extract inflammation-sensitive thermal features. Finally, Multiple Linear Regression (MLR) was employed to predict the inflammation scale using the statistical values from the DnCNN-processed images. Results: The regression analysis revealed a strong correlation between thermal gradients and inflammatory severity across elbow, hand, and knee joints; i.e., the K-fold accuracy was 93.84 ± 0.71. The Modified Clinical Discord Activity Index (MCDAI) categorizes inflammation as low, moderate, and high, and these values were used in the PARA framework for inflammation level prediction supporting early clinical decision-making. Conclusion: The proposed PARA framework has high diagnostic potential to classify RA stages in pregnant women through a non-invasive and pregnancy-specific assessment. The PARA framework reduces dependency on laboratory tests and supports timely therapeutic interventions. Full article

Recent work has shown that exposing Deep Reinforcement Learning (DRL) agents to network impairments during training allows them to perform better when deployed in realistic networks than ones trained under idealized conditions. We investigate systematically whether and what kind of information about the state of the network is beneficial to add to the agent’s observations. For this, we train PPO and SAC agents under a fixed impairment profile while varying the augmented observation space across eight feature groups on two common control tasks with different impairment sensitivity often seen in DRL research (CheetahRun and FingerTurnHard implemented in MuJoCo). Our results show that per-observation information about whether the packet was dropped or delayed recovers most of the performance of the trained algorithm, while adding statistics about latency, jitter, and packet loss provides little benefit or even destabilizes the training for PPO. SAC seems to be largely insensitive to the additional metadata. A window-size ablation shows that latency and jitter statistics provide no benefit across any tested window size, while the effects of the loss statistics are strongly window dependent. These results argue against a universal network metadata observation augmentation vector and in favor of algorithm- and task-specific feature selection. Full article

The zinc finger protein 36 (ZFP36) family, including ZFP36/tristetraprolin (TTP), ZFP36 CCCH-type-like 1 (ZFP36L1), and ZFP36 CCCH-type-like 2 (ZFP36L2), consists of conserved CCCH-type tandem zinc-finger RNA-binding proteins. These proteins recognize AU-rich elements (AREs) in target mRNAs and promote deadenylation, decay, and translational repression. In this review, we use the term post-transcriptional immune checkpoint in a restricted conceptual sense: ZFP36 family proteins are intracellular, RNA-level negative regulators that tune the magnitude, duration, and resolution of immune effector programs, rather than classical receptor-ligand immune checkpoints such as programmed cell death protein 1 (PD-1)/ programmed death-ligand 1 (PD-L1) or cytotoxic T-lymphocyte-associated protein 4 (CTLA-4). We summarize structural features, ARE-recognition mechanisms, mRNA decay pathways, translational repression mechanisms, and post-translational regulation of the ZFP36 family, while explicitly distinguishing mechanisms established for ZFP36 from those inferred for ZFP36L1 and ZFP36L2. We then review cell-type-specific roles in innate and adaptive immunity, including myeloid inflammatory responses, barrier tissue inflammation, innate lymphoid cell function, T cell activation and effector differentiation, regulatory T cell stability, B cell development, and antiviral immunity. In cancer, ZFP36 family members show context-dependent functions that should be separated into tumor-cell-intrinsic effects and immune-microenvironment-dependent effects. They suppress tumor progression by destabilizing pro-inflammatory, angiogenic, metabolic, and epithelial–mesenchymal transition (EMT)-associated transcripts, yet may also restrict antitumor immune responses or promote immune evasion in selected tumor contexts. Finally, we discuss autoimmune and inflammatory diseases, allergic disorders, transplant immunity, neuroimmune relevance, and therapeutic strategies, emphasizing the current evidentiary limits, preclinical status, and safety concerns of ZFP36 family modulation. Full article