Natural silk spinning exemplifies a pathway-dependent material construction, in which soft protein precursors are transformed into fibers that combine high strength, toughness, and functionality under mild conditions. Replicating such a pathway-governed integration in artificial fibers, particularly while enabling functional conductivity and device compatibility, remains challenging. Here, we report a biomimetic, pathway-guided wet-spinning strategy to construct multicomponent silk-based fibers that integrate mechanical robustness with high ionic conductivity. By directly dissolving undegummed silkworm cocoons, we establish an intrinsically multicomponent spinning dope that undergoes mesoscale preorganization and traction-dominated solidification. This process converts preorganized structures into an oriented fibroin nanofibrillar scaffold while preserving a continuous, reconfigurable interfacial network enriched in sericin and ionic species. The resulting fibers exhibit a balanced combination of strength, stiffness, and toughness, together with ionic conductivity comparable to soft ionic conductors. Importantly, stable ionic transport is maintained under large deformation, enabling strain sensing and direct textile integration. Textiles with crossing-point structures further support dynamic electroluminescent display under complex geometries. This work highlights pathway reconstruction as an effective route for integrating structure, function, and device compatibility in multifunctional fibers.

Flexible touchpads are rapidly emerging as pivotal interfaces for wearable electronics, soft robotics, and embodied intelligence, enabling deformable, conformal, and multimodal human-machine interaction, yet their commercial adoption remains hindered by persistent challenges despite significant academic and technological progress. This review provides a quantitative, critical, and system-oriented analysis to identify key bottlenecks and propose actionable strategies for scalable deployment. It begins with examining dominant working mechanisms and benchmarking representative devices across critical performance metrics, including pressure range, sensitivity, resolution, response time, and mechanical durability. Then it traces a technology roadmap from material selection and routing architectures to fabrication methods, with applications spanning gesture recognition, health monitoring, and in-vehicle interaction, while emphasizing system-level considerations such as integration with flexible electronics and power efficiency. Key challenges are distilled, including material fatigue and interfacial delamination under prolonged deformation, signal drift and crosstalk in large-area arrays, packaging robustness and washability for daily use, and the absence of standardized testing protocols, alongside proposed solutions such as mechanically graded structures, durable interfacial bonding, optimized low-wire-count routing, and algorithm-hardware co-design. The review offers a foundational guide for researchers and engineers aiming to bridge the gap between flexible touchpad innovation and practical deployment.

An appropriate pressure, exerted by compression garments to targeted body areas, is pivotal to the effectiveness of compression therapy. However, the systematic investigation of pressure decline in compression garments over extended periods of wear remains lacking. This study introduces an innovative method for assessing pressure decline in compression garments during 8-hour wear, utilizing a smart bionic morphing leg mannequin. The measurement results revealed a non-uniform pattern of pressure decline across the compression stockings. The leg mannequin features the specialized sensor network design with 25 FBG pressure sensors and 4 FBG temperature sensors, along with a precise calibration procedure and a data analysis approach based on linear decoupling of pressure and temperature influences. During static wear for eight hours, the pressure exerted by commercial compression stockings exhibited a time-dependent decrease, with average reductions ranging from 9% to 24% at various measurement points. As-made stockings demonstrated even greater declines, with reductions spanning from 21% to 46%. These findings validate the use of the bionic leg mannequin for evaluating the spatial and temporal pressure distribution of compression stockings, establishing it as a potent tool for assessing stocking performance and facilitating the stocking design.

This paper presents a novel lightweight fabric strain sensor array specifically designed for comprehensive knee joint monitoring. The sensor system features a unique two-layer design incorporating eight strategically positioned sensing elements, enabling effective spatial mapping of strain distribution across the knee during movement. This configuration offers advantages in capturing complex multi-axis kinematics (flexion/extension, rotation) and localized tissue deformation when compared to simpler sensor layouts. To evaluate the system, ten subjects performed three distinct activities (seated leg raise, standing, walking), generating resistance data from the sensors. A hybrid deep learning model (CNN + BiLSTM + Attention) processed the data and significantly improved performance to 95%. This enhanced accuracy is attributed to the model’s ability to extract spatial-temporal features and leverage long-term dependencies within the time-series sensor data. Furthermore, channel attention analysis within the deep learning model identified sensors 2, 4, and 6 as major contributors to classification performance. The results demonstrate the feasibility of the proposed fabric sensor array for accurately recognizing fundamental knee movements. Despite limitations in the diversity of postures, this system holds significant promise for future applications in rehabilitation monitoring, sports science analytics, and personalized healthcare within the medical and athletic domains.

Monitoring muscle fatigue is a critical area of research in both the fields of rehabilitation medicine and sports science. Despite its importance, practical measurement remains challenging due to constraints in equipment size and cost. This study leverages a commercially available, wearable high-resolution goniometer to capture joint angles during single-degree-of-freedom curling movements. From these data, we can deduce the torque and power of the biceps in the upper arm using an elbow musculoskeletal model. We proposed nine fatigue indicators, all of which showed significant correlations with the Root Mean Square (RMS) and Median Frequency (MDF) indicators derived from Electromyography (EMG) signals. Spectral clustering was utilized for the identification and classification of fatigue. Subsequently, we employed a K-Nearest Neighbors (KNN) model to predict muscular fatigue, achieving an impressive overall accuracy of 95%, an effective recall rate of 95%, an F1-score of 95%, and an Area Under the Curve (AUC) of 99%. This research presents an innovative and comprehensive approach to the identification and prediction of muscle fatigue.

OBJECTIVE:To assess the value of whole exome sequencing (WES) for the diagnosis of early-onset genetic diseases among infants aged 0 to 6 month in Ningbo region. METHODS:268 infants presented at the Women and Children's Hospital Affiliated to Ningbo University from January 2022 to June 2024 undergoing WES-based genetic testing were enrolled. Peripheral blood samples were collected from the infants and their parents and subjected to WES. Pathogenic variants were identified by clinical manifestations. This study has been approved by the Medical Ethics Committee of the Hospital (Ethics No. EC2023-017). RESULTS:Among the 268 infants, 124 (46.3%) had phenotype-explaining genetic variants. For 42 family-based WES tests, 20 (47.62%) were abnormal, whilst in 226 single-person WES tests, 104 (46.02%) had abnormalities, with 76 (33.63%) verified by parental testing. In 96 fully family-verified cases, 31 were de novo, 40 were parent-inherited, 25 were single-parent-inherited. These included 35 inborn metabolic errors, 28 rare syndromes, 9 neurodevelopmental disorders, 4 musculoskeletal diseases, 5 congenital deafness, 2 mitochondrial diseases, 4 endocrine diseases, and 9 others. Among these, there were 7 pathogenic copy number variations (all deletions), 3 chromosomal abnormalities, and 85 single-nucleotide variations. One case of Beckwith-Wiedemann syndrome was detected by methylation MLPA. Among the single-nucleotide variants, 114 pathogenic/likely pathogenic variants were identified in 61 genes, with common ones including missense variants (64.04%), frameshifting variants (20.18%) and splicing variants (4.39%). CONCLUSION:WES can offer effective diagnosis for hereditary diseases with specific/non-specific manifestations. For early-age infants, higher detection rates may be attained for inborn metabolic errors, rare syndromes, neurodevelopmental disorders, congenital deafness, and musculoskeletal diseases. Compared with single-person WES, family-based WES can attain a higher diagnostic efficiency.

A dual-polarized Fabry-P & eacute;rot (FP) antenna with ultra-wideband radar cross section (RCS) reduction using 3D printing technology is proposed. The proposed antenna consists of a dual-polarized primary antenna and a partially reflective surface (PRS) loaded with reflective surface (RS) and 3D-printed stepped absorbing structure (3D-PSAS). The combination of the RS and the 3D-PSAS effectively reduces the RCS of the FP antenna and maintains the gain of the antenna. Meanwhile, the proposed antenna can be used in the construction of stealth systems. Both the simulated results and the measured results verify the reliability of the design. The FP antenna owns 10-dB RCS reduction bands cover 3.0 similar to 3.8 GHz and 6 similar to 15 GHz, with a peak RCS reduction of 27 dB at 12.5 GHz. In the radiation, it owns a 10-dB return-loss bandwidth of 4.64-5.64 GHz (19.4%) and 4.76-5.61 GHz (16.3%) respectively in X polarization and Y polarization modes, with a maximum realized gain of 12.4 dBi at 4.9 GHz.

Silver-plated yarn is one of the fundamental materials in smart textiles. However, the plating can be corroded seriously by human sweat during daily wear, degrading the electrical conductivity. To address this issue, this article designed a new set of experimental protocols to faithfully simulate sweat immersion in daily wear. Artificial sweat with pH 5.5 and 8.0 was applied to five types of yarns on a daily basis, and changes in the morphological, electrical and mechanical properties of the yarns before and after treatment were recorded. The results showed increasing resistance of the yarns after exposure to sweat at both pH values. After 10–20 test cycles, almost all samples lost their electrical conductivity, and their mechanical properties also degraded. Yarn service life in acidic sweat was longer than in alkaline sweat. Morphological characterization revealed that the coating on the yarn surface peeled off after treatment. The coating reacted electrochemically with Cl − in sweat, resulting in interruption of the conductive pathway, hence a sudden increase in resistance. Compared to acidic environment, Ag is more prone to undergo redox reaction in alkaline environment. Therefore, silver-plated yarn corrodes faster in alkaline sweat. Finally, the decrease in mechanical properties was due to the swelling behavior of nylon 6 in sweat.

BACKGROUND: Migraine pathophysiology involves epigenetic mechanisms, but key molecular regulators remain poorly defined. This study aimed to elucidate these mechanisms using a comprehensive multi-omics approach. METHODS: We established a nitroglycerin (NTG)-induced migraine-like mouse model. Sensory hypersensitivity was assessed via behavioral tests, and neuronal activation was evaluated by immunofluorescence staining for c-Fos in the trigeminal nucleus caudalis. Integrated transcriptomic profiling (RNA-seq) and epigenomic analysis (ATAC-seq) were performed, followed by differential expression and accessibility analyses, functional enrichment, multi-omics integration, protein-protein interaction network construction, and in silico drug prediction using tools like Enrichr. RESULTS: NTG treatment induced significant sensory hypersensitivity, with reduced mechanical thresholds and increased c-Fos expression. RNA-seq identified 140 significantly upregulated genes, while ATAC-seq revealed 282 significantly open differentially accessible chromatin regions (DACRs). Multi-omics integration pinpointed Grid2 and Reln as central effectors, enriched in synaptic pathways. Functional analyses highlighted roles in synaptic signaling, and network analysis identified key transcription factors (e.g., Etv1, Neurod1) as hubs. Drug prediction prioritized glutamate receptor antagonists as potential therapeutics. CONCLUSION: This study identifies Grid2 and Reln as plausible regulators in migraine pathophysiology, with implications for synaptic mechanisms. The findings provide novel insights into migraine etiology and highlight promising targets for therapeutic intervention.

Fabric-based strain sensors hold significant potential across various applications, including sports, healthcare, rehabilitation, etc. Nonetheless, their complex performance under large deformation and varying loading rates, arising from material viscoelasticity and textile structure intricacies, remains inadequately understood. The primary constraint in evaluating their performance lies in the absence of electromechanically coupled instrumentation. This paper endeavors to overcome the limitation by developing a synchronized measurement system, which integrates mechanically controlled loading, voltage divider circuits, and visual measurement technologies. This system enables synchronized acquisition of mechanical and electrical signals spanning from 0.01 mm/min to 6 m/s, by employing material testing machines for low-speed loading and split Hopkinson pressure bars for medium-to-high-speed loading, and combining electrical performance measurements with displacement and strain field analyses. Experiments revealed that sensor sensitivity increased linearly with the logarithm of loading rates, while deformation patterns evolved with loading speed, thereby offering valuable insights into design and calibration of fabric-based strain sensors under dynamic conditions.

Autonomous robots mainly rely on visual perception to perceive their surroundings, but some tasks cannot be completed or are challenging without a sense of touch. In the design of electronic skins for robots, flexibility, resolution, and rapid response are crucial. In this paper, a high-resolution, low-cost, and high-speed perceptual system is proposed. A sensor array with a spatial density of 455 sensor elements per cm(2) 2 was fabricated. The acquisition system can transmit data at an adequate speed of 5.33 frames per millisecond, in which an improved zero-position method was proposed to eliminate crosstalk. The spatial resolution of the system can reach 0.5 mm, which allows surface feature acquisition. This allows estimation of the grasp stability of the robot. Further, a neural network was designed to recognize the shapes of small objects. In small-object shape recognition, 2400 data from 8 small objects were collected and used for network learning and validation. The accuracy for the validation set was 92.2 %, indicating that the system performed well in distinguishing the sizes and shapes of objects. The system (including the sensor array) costs only $24.41 but exhibits satisfactory performance in key parameters.

Breakthroughs in thermoelectric converters based on ionic conductive materials have opened up new possibilities in low-grade waste heat harvesting and wearable electronic applications. In order to increase energy output of thermoelectric devices, electrodes with higher specific capacitance are required to increase the energy density and power density of thermoelectric devices. In this work, Prussian blue graphite (PB-G) electrode sheets are prepared by electrodepositing Prussian blue onto graphite electrodes to obtain highly efficient flexible electrode materials for thermoelectric devices. The PB-G electrode was assembled with ion-conductive hydrogel to form a thermoelectric device. The specific capacitance of the PB-G electrode calculated by a galvanostatic charge-discharge was 1254.6 F·m−2, which was 710.3 F·m−2 higher than that of the graphite electrode (544.3 F·m−2). A significant enhancement was made in the instantaneous power density (∆T=30 K, 1145.45 mW·m−2) of the PB-G based thermoelectric devices by 14.71 times and a high output energy density foy 1.5 h (23.12 J·m−2) by 53 times than graphite electrode based thermoelectric devices. This work presents a new solution for increasing the output power of thermoelectric devices which could lead to a new vista for the application of low thermal energy acquisition in tunable ion thermoelectric (i-TE) devices.

Recently, there has been a lot of interest in using the wearable sensors for tracking the exercise progress because of the unbiased accuracy and precision they are provided throughout the continual monitoring. For those with physical impairments, the system’s non-intrusive, lightweight ways of the monitoring activity may ease their load and enhance the quality of their decision-making. As a different measuring unit measures the exercise activity levels recorded by the each wearable sensor, it is challenging to assess the monitoring system. Hence, this paper proposes a Hybridized Fuzzy Multi-Attribute for Exercise Monitoring System (HFMA-EMS) to address the uncertainty issues of the wearable sensors. The Triangular Fuzzy membership function is proposed to begin classifying the observed values. Pair-wise attribute comparison and evaluator weighting in a T-spherical uncertain linguistic set setting utilizing the Techniques for Ordering of Preferences by Similarities to Ideal Solutions (TOPSIS). In the suggested method, a utility function is used to assess the merits of a model in which attribute the weights are calculated, followed by an exercise in which the attributes are ordered employing the Measurements of the Alternative and Ranking Compromise Solutions model (MARCOS). The performance is performed to analyze the proposed method’s accuracy, precision, recall, f1-score, and correct and incorrect exercise assessment by an accelerometer, gyroscope, and magnetic field sensor unit. The application scenario of the HFMA-EMS can be used in the clinical applications, healthcare management, and sports injury detection.

Solid-state ionic thermoelectric generators have emerged as promising solutions for efficient harvesting of low-grade waste heat. However, the main challenge in achieving continuous power supply is the low efficiency of thermoelectric conversion. In this work, substantial achievements have been made in improving the thermoelectric conversion characteristics by introducing redox pairs on the electrode surfaces. This approach takes advantage of the synergistic effect of thermal diffusion and thermoelectric effects to maximize the conversion efficiency. To improve the thermoelectric storage and output power performance, Prussian blue was attached to a carbon woven fabric and used as an electrode. The incorporation of Prussian blue/carbon woven fabric electrodes results in an increase in current density output and an instantaneous power density of 3.7 mW/m 2 ·K 2 . Furthermore, under a temperature gradient of 10 K, the output energy density for 2 h is 194 J/m 2 , and the Carnot relative efficiency is as high as 0.12% at a hot side temperature ( T H ) of 30 °C and a cold side temperature ( T C ) of 20 °C. Our findings validate the efficacy of integrating thermal diffusion and redox reactions in ionic thermoelectric generators, paving the way for the progress of thermocharged devices and their potential commercial applications.

Textile-based sweat sensors display great potential to enhance wearable comfort and health monitoring; however, their widespread application is severely hindered by the intricate manufacturing process and electrochemical characteristics. To address this challenge, we combined both impregnation coating technology and conjugated electrospinning technology to develop an electro-assisted impregnation core-spinning technology (EAICST), which enables us to simply construct a sheath-core electrochemical sensing yarn (TPFV/CPP yarn) via coating PEDOT:PSS-coated carbon fibers (CPP) with polyurethane (TPU)/polyacrylonitrile (PAN)/poloxamer (F127)/valinomycin as shell. The TPFV/CPP yarn was sewn into the fabric and integrated with a sensor to achieve a detachable feature and efficiently monitor K+ levels in sweat. By introducing EAICST, a speed of 10 m/h can be realized in the continuous preparation of the TPFV/CPP yarn, while the interconnected pores in the yarn sheath enable it to quickly capture and diffuse sweat. Besides, the sensor exhibited excellent sensitivity (54.26 mV/decade), fast response (1.7 s), anti-interference, and long-term stability (5000 s or more). Especially, it also possesses favorable washability and wear resistance properties. Taken together, this study provides a crucial technical foundation for the development of advanced wearable devices designed for sweat analysis.

Abstract A novel 2‐D beam steering technology using a Fabry–Pérot antenna with a liquid‐based reconfigurable metasurface is presented. The antenna employs a reconfigurable partially reflecting surface to regulate phase distribution and adopts a microstrip antenna as feed to realise 2‐D beam steering. The antenna beam can be tilted in four different directions by injecting liquid metal into the specific area of the microfluidic channels embedded in the metasurface. Moreover, the antenna has a simple and compact structure with a low profile. A prototype is manufactured, and good agreement between simulated and experimental results verifies the correctness of the design. The measured results of the manufactured antenna prototype demonstrate that the main beam tilts to maximum values of ±15° and ±28° in the yoz and xoz planes, respectively, between 9.5 and 9.7 GHz.

Nowadays, the need for protective devices at man–machine interfaces is increasing in the fields of traffic, sports, construction, and military, etc. Dynamic pressure sensing technology with wide measuring range, high sensitivity, softness, and fast response is crucial for evaluation and optimization of the personal protective equipment under impact scenarios. However, current sensors hardly possess all the aforesaid required characteristics. For the first time, this article reports the evaluation and application of an innovative soft pressure sensor with modulus of 2 MPa, maximum pressure of 8 MPa, and over 500‐Hz frequency. A theoretical model, taking strain rate into consideration, is established to characterize the dynamic sensing behavior. A sensing network in the form of smart clothing is developed and used in a sled crash test, which is a standard approach to evaluate the safety of automobiles in collisions. The pressure distribution over the dummy's surface during the crash is acquired in real‐time, and compared with numerical simulations. This study is important to the study of occupant injury and crashworthiness design for vehicles, and it will benefit the automotive industry. With the built‐in sensing network, the smart clothing has promising applications in the pressure mapping of 3D flexible man–machine interface under impact scenarios.

A Fabry–Perot antenna with dual-polarization and ultra-wideband radar cross section (RCS) reduction is proposed using stepped absorbing structure. The antenna consists of a dual-polarization feed and a partially reflective surface (PRS) loaded with reflective structure and 3D printed absorbing structure. The combination of these two structures can effectively reinforced the width of the antenna band and reduce the RCS of the antenna. The simulated result show that the antenna owns a 10-dB return-loss bandwidth of 4.7~5.7 GHz (19.2%) and a maximum realized gain of 7.42 dBi. Meanwhile, the simulated RCS reduction band covers 3~18 GHz (142%). At peak operation at 15Ghz, it is reduced to 40dB.

OBJECTIVE:To assess the value of genetic screening by high-throughput sequencing (HTS) for the early diagnosis of neonatal diseases.METHODS:A total of 2 060 neonates born at Ningbo Women and Children's Hospital from March to September 2021 were selected as the study subjects. All neonates had undergone conventional tandem mass spectrometry metabolite analysis and fluorescent immunoassay analysis. HTS was carried out to detect the definite pathogenic variant sites with high-frequency of 135 disease-related genes. Candidate variants were verified by Sanger sequencing or multiplex ligation-dependent probe amplification (MLPA).RESULTS:Among the 2 060 newborns, 31 were diagnosed with genetic diseases, 557 were found to be carriers, and 1 472 were negative. Among the 31 neonates, 5 had G6PD, 19 had hereditary non-syndromic deafness due to variants of GJB2, GJB3 and MT-RNR1 genes, 2 had PAH gene variants, 1 had GAA gene variants, 1 had SMN1 gene variants, 2 had MTTL1 gene variants, and 1 had GH1 gene variants. Clinically, 1 child had Spinal muscular atrophy (SMA), 1 had Glycogen storage disease II, 2 had congenital deafness, and 5 had G6PD deficiency. One mother was diagnosed with SMA. No patient was detected by conventional tandem mass spectrometry. Conventional fluorescence immunoassay had revealed 5 cases of G6PD deficiency (all positive by genetic screening) and 2 cases of hypothyroidism (identified as carriers). The most common variants identified in this region have involved DUOX2 (3.93%), ATP7B (2.48%), SLC26A4 (2.38%), GJB2 (2.33%), PAH (2.09%) and SLC22A5 genes (2.09%).CONCLUSION:Neonatal genetic screening has a wide range of detection and high detection rate, which can significantly improve the efficacy of newborn screening when combined with conventional screening and facilitate secondary prevention for the affected children, diagnosis of family members and genetic counseling for the carriers.