Mucosal Penetrative Polymeric Micelle Formulations for Insulin Delivery to the Respiratory Tract.

Purpose: Effective mucosal delivery of drugs continues to pose a significant challenge owing to the formidable barrier presented by the respiratory tract mucus, which efficiently traps and clears foreign particulates. The surface characteristics of micelles dictate their ability to penetrate the respiratory tract mucus. In this study, polymeric micelles loaded with insulin (INS) were modified using mucus-penetrative polymers. Methods: We prepared and compared polyethylene glycol (PEG)-coated micelles with micelles where cell-penetrating peptide (CPP) is conjugated to PEG. Systematic investigations of the physicochemical and aerosolization properties, performance, in vitro release, mucus and cell penetration, lung function, and pharmacokinetics/pharmacodynamics (PK/PD) of polymeric micelles were performed to evaluate their interaction with the respiratory tract. Results: The nano-micelles, with a particle size of <100 nm, exhibited a sustained-release profile. Interestingly, PEG-coated micelles exhibited higher diffusion and deeper penetration across the mucus layer. In addition, CPP-modified micelles showed enhanced in vitro cell penetration. Finally, in the PK/PD studies, the micellar solution demonstrated higher maximum concentration (Cmax) and AUC0-8h values than subcutaneously administered INS solution, along with a sustained blood glucose-lowering effect that lasted for more than 8 h. Conclusion: This study proposes the use of mucus-penetrating micelle formulations as prospective inhalation nano-carriers capable of efficiently transporting peptides to the respiratory tract.

Effectiveness and safety of acupuncture modalities for overweight and obesity treatment: a systematic review and network meta-analysis of RCTs.

Introduction: The effectiveness and safety of acupuncture in the treatment of obesity have not been assessed. This poses a challenge for clinicians who choose to use acupuncture in the treatment of obesity, as they are unable to prioritize this approach based on outcome variables. Methods: In May 2024, a literature search of five databases was conducted. Only randomized controlled trials evaluating body weight (BW), body mass index, waist circumference (WC), and adverse events in patients with a body mass index (BMI) of 25 or higher for various acupuncture modalities were included. The risk of bias was assessed using the Cochrane risk-of-bias tool for randomized trials, version 2. Pairwise meta-analysis (PMA) and Bayesian network meta-analysis (NMA) were performed using a random effects model for quantitative synthesis. Results: Fourteen studies (n = 868) were included. The included studies evaluated the following acupuncture modalities: electroacupuncture (EA) (N = 6), laser acupuncture (LA) (N = 2), auricular acupuncture (AA) (N = 5), and manual acupuncture (MA) (N = 3). The PMA found that adding EA to usual care (UC), compared to UC alone, reduced BW (MD = 2.46, 95% CI = 1.12 to 3.80, I 2 = 58%, REM, N = 3, n = 157). The NMA of BW showed the following effect sizes for UC alone versus each acupuncture modality combined with UC: LA (MD = 2.09, 95% CI = 0.04 to 3.86), EA (MD = 2.04, 95% CI = 0.88 to 3.50), AA (MD = 1.69, 95% CI = -0.11 to 3.58), and MA (MD = 1.02, 95% CI = -0.82 to 2.94). The probability of each modality being the optimal treatment was evaluated using the surface under the cumulative ranking curve. EA was the most efficacious for BW and BMI, while LA was the most efficacious for WC. Discussion: EA and LA can effectively complement clinical obesity management. The number of included studies was limited, and publication bias may have occurred, necessitating a cautious interpretation of the results. Furthermore, most studies lasted between six and 12 weeks. Future clinical studies of acupuncture for obesity should include longer follow-up periods. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=387788, identifier CRD42023387788.

Study of active food processing technology using computer vision and AI in coffee roasting.

In the modern food processing industry, which is more complex than in the past, it is important to utilize real-time computer vision for active food processing technology using artificial intelligence. An integrated solution of computer vision and Deep Learning (DL) technology provides quality control and optimization of food processing in complex environments with obstacles. In this study, Coffee Bean Classification Model (CBCM) made by Machine Learning (ML) showed excellent performance, accurately distinguishing coffee beans through the avoidance of obstacles and empty spaces inside a rotating roasting machine. CBCM achieved a maximum validation accuracy of 98.44% and a minimum validation loss of 5.40% after the fifth epoch. Using a test dataset of 137 samples, CBCM achieved an accuracy of 99.27% and a loss of 2.82%. The developed solution using the CBCM was able to quantify the color change of the coffee beans during roasting.

Field-Assisted Efficient Capturing and Analysis of Airborne Nanoparticulate Matter Using a Multifunctional Nanoporous Membrane.

As the potential adverse health and environmental effects of nanoscale pollutants have garnered significant attention, the demand for monitoring and capturing ultrafine particulate matter has been growing. With the rise in ultrafine dust emissions, this issue has become increasingly important. However, submicron particles require advanced strategies to be captured because of their limited inertial effect. For example, electrostatic air filters have been investigated for their improved performance in the fine particle regime. On the other hand, Raman spectroscopy was proposed as a promising analytical strategy for aerosol particles because it can be used to conveniently detect analytes in a label-free manner. Thus, the synergistic integration of these strategies can open new applications for addressing environment-related challenges. This study presents a multifunctional approach for achieving both air filtration and surface-enhanced Raman scattering (SERS) for analyte identification. We propose a nanoporous membrane composed of a thin gold layer, copper, and copper oxide to provide the desired functions. The structures are produced by performing scalable electrodeposition and subsequent electron-beam evaporation, attaining an excellent filtration efficiency of 95.9% with an applied voltage of 5 kV for 300 nm KCl particles and a pressure drop of 121 Pa. Raman intensity measurements confirm that the nanodendritic surface of the membrane intensifies the Raman signals and allows for the detection of 10 µL of nanoplastic particle dispersion with a concentration of 50 µg/mL. Rhodamine 6G aerosol stream with an approximate particle deposition rate of 0.040 × 106 mm-2·min-1 is also identified in a minimum detectable time of 50 s. The membrane is shown to be recyclable owing to its structural robustness in organic solvents. In addition, the fatigue resistance of the structure is evaluated through 22,000 iterative loading cycles at a pressure of 177 kPa. No performance degradation is observed after the fatigue loading.

Placement of 2 cages using navigation-guided biportal endoscopic transforaminal lumbar interbody fusion: illustrative case.

BACKGROUND: In recent developments, full endoscopic and unilateral biportal endoscopic (UBE) spine surgery have emerged to aid the transforaminal lumbar interbody fusion (TLIF) procedure. Yet, both approaches present a challenge due to limited space for cage insertion, potentially leading to complications such as cage subsidence or nonfusion in long-term assessments. Utilizing double cages may mitigate these concerns. This paper presents a unique case in which a patient successfully underwent computed tomography (CT) navigation-guided UBE-TLIF with 2 converging cages, highlighting the potential benefits and feasibility of this innovative approach. OBSERVATIONS: A 59-year-old female diagnosed with degenerative spondylolisthesis at the L4-5 level underwent a UBE-TLIF. The operation is detailed step by step and supported by illustrative figures and surgical videos. Postsurgery results revealed a significant improvement in the patient's condition, with the visual analog scale score decreasing from 7 to 3 on the first day, leading to a satisfaction rate of 90% at the last follow-up. LESSONS: Utilizing endoscopic visualization complemented by contrast medium has substantially elevated the quality of disc preparation. From their observations, the authors affirm that the integration of intraoperative CT navigation systems significantly augments safety and pinpoint accuracy in UBE-TLIF procedures. The strategy of employing 2 converging cages through a unilateral technique stands as a practical solution, potentially optimizing the fusion outcomes of UBE-TLIF surgery. https://thejns.org/doi/10.3171/CASE23512.

Multiwavelength Achromatic Deflector in the Visible Using a Single-Layer Freeform Metasurface.

Deflectors are essential for modulating beam direction in optical systems but often face form factor issues or chromatic aberration with conventional optical elements, such as prisms, mirrors, and diffractive/holographic optical elements. Despite recent efforts to address such issues using metasurfaces, their practicality remains limited due to operation wavelengths in the near-infrared or the fabrication difficulties inherent in the multilayer scheme. Here, we propose a novel single-layer metasurface achieving multiwavelength chromatic aberration-free deflection across the visible spectrum by employing the robust freeform design strategy to simplify the fabrication process. By properly selecting diffraction orders for red, green, and blue wavelengths to achieve identical wavelength-diffraction-order products, the metasurface deflects light at a consistent angle of 41.3° with a high efficiency. The coupled Bloch mode analysis explains the physical properties, and experimental fabrication and characterization confirm its effectiveness. This approach holds potential for various applications such as AR/VR, digital cameras, and high-quality optical systems.

Deep Reinforcement Learning-Based Adaptive Scheduling for Wireless Time-Sensitive Networking.

Time-sensitive networking (TSN) technologies have garnered attention for supporting time-sensitive communication services, with recent interest extending to the wireless domain. However, adapting TSN to wireless areas faces challenges due to the competitive channel utilization in IEEE 802.11, necessitating exclusive channels for low-latency services. Additionally, traditional TSN scheduling algorithms may cause significant transmission delays due to dynamic wireless characteristics, which must be addressed. This paper proposes a wireless TSN model of IEEE 802.11 networks for the exclusive channel access and a novel time-sensitive traffic scheduler, named the wireless intelligent scheduler (WISE), based on deep reinforcement learning. We designed a deep reinforcement learning (DRL) framework to learn the repetitive transmission patterns of time-sensitive traffic and address potential latency issues from changing wireless conditions. Within this framework, we identified the most suitable DRL model, presenting the WISE algorithm with the best performance. Experimental results indicate that the proposed mechanisms meet up to 99.9% under the various wireless communication scenarios. In addition, they show that the processing delay is successfully limited within the specific time requirements and the scalability of TSN streams is guaranteed by the proposed mechanisms.

Effects of Edible Insect Powders as Meat Partial Substitute on Physicochemical Properties and Storage Stability of Pork Patties.

In this study, physicochemical and antioxidant properties, and storage stability (1, 3, and 7 days) of pork patties added with edible insect powders (EIP) of four species (Larvae of Tenenbrio molitor, Protaetia brevitarsis seulensis, Allomyrina dichotoma, and Gryllus bimaculatus) as meat partial substitutes were investigated. Twenty percent of each EIP was added to pork patties, and four treatments were prepared. On the other hand, two control groups were set, one with 0.1 g of ascorbic acid and the other without anything. Adding EIP decreased water content but increased protein, fat, carbohydrate, and ash contents. In addition, the use of EIP increased the water holding capacity and texture properties as well as decreased the cooking loss. However, the sensory evaluation and storage stability were negatively affected by the addition of EIP. The 2,2-diphenyl-1-picrylhydrazyl radical scavenging activity had a positive effect on storage stability. It is believed that the addition of EIP resulted in high antioxidants due to the presence of polyphenol compounds in EIP. These results indicate that EIP has great potential to be used as meat partial substitute to improve the quality improvement and antioxidant in pork patties. However, in order to improve storage stability and consumer preference, further research is needed to apply it to patties by reducing the amount of EIP or adding auxiliary ingredients.

Glycinamide Facilitates Nanocomplex Formation and Functions Synergistically with Bone Morphogenetic Protein 2 to Promote Osteoblast Differentiation In Vitro and Bone Regeneration in a Mouse Calvarial Defect Model.

BACKGROUND: This study aimed to identify glycine analogs conducive to the formation of cell-absorbable nanocomplexes, enhancing collagen synthesis and subsequent osteogenesis in combination with BMP2 for improved bone regeneration. METHODS: Glycine and its derivatives were assessed for their effects on osteogenic differentiation in MC3T3-E1 cells and human bone marrow mesenchymal stem cells (BMSCs) under osteogenic conditions or with BMP2. Osteogenic differentiation was assessed through alkaline phosphatase staining and real-time quantitative polymerase chain reaction (RT-qPCR). Nanocomplex formation was examined via scanning electron microscopy, circular dichroism, and ultraviolet-visible spectroscopy. In vivo osteogenic effects were validated using a mouse calvarial defect model, and bone regeneration was evaluated through micro-computed tomography and histomorphometric analysis. RESULTS: Glycine, glycine methyl ester, and glycinamide significantly enhanced collagen synthesis and ALP activity in conjunction with an osteogenic medium (OSM). GA emerged as the most effective inducer of osteoblast differentiation marker genes. Combining GA with BMP2 synergistically stimulated ALP activity and the expression of osteoblast markers in both cell lines. GA readily formed nanocomplexes, facilitating cellular uptake through strong electrostatic interactions. In an in vivo calvarial defect mouse model, the GA and BMP2 combination demonstrated enhanced bone volume, bone volume/tissue volume ratio, trabecular numbers, and mature bone formation compared to other combinations. CONCLUSION: GA and BMP2 synergistically promoted in vitro osteoblast differentiation and in vivo bone regeneration through nanocomplex formation. This combination holds therapeutic promise for individuals with bone defects, showcasing its potential for clinical intervention.

Geometry-Driven Fabrication of Mini-Tablets via 3D Printing: Correlating Release Kinetics with Polyhedral Shapes.

The aim of this study was to fabricate mini-tablets of polyhedrons containing theophylline using a fused deposition modeling (FDM) 3D printer, and to evaluate the correlation between release kinetics models and their geometric shapes. The filaments containing theophylline, hydroxypropyl cellulose (HPC), and EUDRAGIT RS PO (EU) could be obtained with a consistent thickness through pre-drying before hot melt extrusion (HME). Mini-tablets of polyhedrons ranging from tetrahedron to icosahedron were 3D-printed using the same formulation of the filament, ensuring equal volumes. The release kinetics models derived from dissolution tests of the polyhedrons, along with calculations for various physical parameters (edge, SA: surface area, SA/W: surface area/weight, SA/V: surface area/volume), revealed that the correlation between the Higuchi model and the SA/V was the highest (R2 = 0.995). It was confirmed that using 3D- printing for the development of personalized or pediatric drug products allows for the adjustment of drug dosage by modifying the size or shape of the drug while maintaining or controlling the same release profile.

Unraveling the Potential of Attentive Bi-LSTM for Accurate Obesity Prognosis: Advancing Public Health towards Sustainable Cities.

The global prevalence of obesity presents a pressing challenge to public health and healthcare systems, necessitating accurate prediction and understanding for effective prevention and management strategies. This article addresses the need for improved obesity prediction models by conducting a comprehensive analysis of existing machine learning (ML) and deep learning (DL) approaches. This study introduces a novel hybrid model, Attention-based Bi-LSTM (ABi-LSTM), which integrates attention mechanisms with bidirectional Long Short-Term Memory (Bi-LSTM) networks to enhance interpretability and performance in obesity prediction. Our study fills a crucial gap by bridging healthcare and urban planning domains, offering insights into data-driven approaches to promote healthier living within urban environments. The proposed ABi-LSTM model demonstrates exceptional performance, achieving a remarkable accuracy of 96.5% in predicting obesity levels. Comparative analysis showcases its superiority over conventional approaches, with superior precision, recall, and overall classification balance. This study highlights significant advancements in predictive accuracy and positions the ABi-LSTM model as a pioneering solution for accurate obesity prognosis. The implications extend beyond healthcare, offering a precise tool to address the global obesity epidemic and foster sustainable development in smart cities.

Freeform metasurface color router for deep submicron pixel image sensors.

Advances in imaging technologies have led to a high demand for ultracompact, high-resolution image sensors. However, color filter-based image sensors, now miniaturized to deep submicron pixel sizes, face challenges such as low signal-to-noise ratio due to fewer photons per pixel and inherent efficiency limitations from color filter arrays. Here, we demonstrate a freeform metasurface color router that achieves ultracompact pixel sizes while overcoming the efficiency limitations of conventional architectures by splitting and focusing visible light instead of filtering. This development is enabled by a fully differentiable topology optimization framework to maximize the use of the design space while ensuring fabrication feasibility and robustness to fabrication errors. The metasurface can distribute an average of 85% of incident visible light according to the Bayer pattern with a pixel size of 0.6 µm. The device and design methodology enable the compact, high-sensitivity, and high-resolution image sensors for various modern technologies and pave the way for the advanced photonic device design.

Additive-Driven Nanoscale Architecture of Solid Electrolyte Interphase Revealed by Cryogenic Transmission Electron Microscopy.

In Li metal batteries (LMBs), which boast the highest theoretical capacity, the chemical structure of the solid electrolyte interphase (SEI) serves as the key component that governs the growth of reactive Li. Various types of additives have been developed for electrolyte optimization, representing one of the most effective strategies to enhance the SEI properties for stable Li plating. However, as advanced electrolyte systems become more chemically complicated, the use of additives is empirically optimized. Indeed, their role in SEI formation and the resulting cycle life of LMBs are not well-understood. In this study, we employed cryogenic transmission electron microscopy combined with Raman spectroscopy, theoretical studies including molecular dynamics (MD) simulations and density functional theory (DFT) calculations, and electrochemical measurements to explore the nanoscale architecture of SEI modified by the most representative additives, lithium nitrate (LiNO3) and vinylene carbonate (VC), applied in a localized high-concentration electrolyte. We found that LiNO3 and VC play distinct roles in forming the SEI, governing the solvation structure, and influencing the kinetics of electrochemical reduction. Their collaboration leads to the desired SEI, ensuring prolonged cycle performance for LMBs. Moreover, we propose mechanisms for different Li growth and cycling behaviors that are determined by the physicochemical properties of SEI, such as uniformity, elasticity, and ionic conductivity. Our findings provide critical insights into the appropriate use of additives, particularly regarding their chemical compatibility.

Donor body mass index over 30 is no barrier for pure laparoscopic donor right hepatectomy.

Backgrounds/Aims: Challenges arise when translating pure laparoscopic donor right hepatectomy (PLDRH) results from Asian to Western donors, due to differences in body mass index (BMI). This study compares the outcomes of PLDRH and conventional open donor right hepatectomy (CDRH) in donors with BMI over 30. Methods: Medical records of live liver donors (BMI > 30) undergoing right hepatectomy (2010-2021) were compared: 25 PLDRH cases vs. 19 CDRH cases. Donor and recipient demographics, operative details, and outcomes were analyzed. Results: PLDRH and CDRH had similar donor and recipient characteristics. PLDRH had longer liver removal and warm ischemic times, but a shorter post-liver removal duration than CDRH. Donor complication rates were comparable, with the highest complication being grade IIIa in PLDRH, necessitating needle aspiration for biloma on postoperative day 11. Fortunately, this donor fully recovered without additional treatment. No complications exceeding Clavien-Dindo grade IIIa occurred in either group. Recipient outcomes between the groups were similar. Conclusions: This study supports PLDRH as a viable option for donors with BMI over 30, challenging the notion that high BMI should deter considering PLDRH. The findings provide valuable insights into the safety and feasibility of PLDRH, encouraging further exploration of this technique in diverse donor populations.

Sensor-Based Indoor Fire Forecasting Using Transformer Encoder.

Indoor fires may cause casualties and property damage, so it is important to develop a system that predicts fires in advance. There have been studies to predict potential fires using sensor values, and they mostly exploited machine learning models or recurrent neural networks. In this paper, we propose a stack of Transformer encoders for fire prediction using multiple sensors. Our model takes the time-series values collected from the sensors as input, and predicts the potential fire based on the sequential patterns underlying the time-series data. We compared our model with traditional machine learning models and recurrent neural networks on two datasets. For a simple dataset, we found that the machine learning models are better than ours, whereas our model gave better performance for a complex dataset. This implies that our model has a greater potential for real-world applications that probably have complex patterns and scenarios.

Nonvolatile Reconfigurable Logic Device Based on Photoinduced Interfacial Charge Trapping in van der Waals Gap.

Due to the increasing complexity in miniaturization of electronic devices, reconfigurable field-effect transistors (RFETs) have emerged as a solution. Although the foundational concepts of RFETs have matured over two decades, ongoing breakthroughs are needed to address challenges such as improving the device performance as well as achieving balanced symmetry between n-type and p-type transport modes with long-term stability. Herein, we present a nonvolatile WSe2-based RFET that utilizes photoassisted interfacial charge trapping at the h-BN and SiO2 interface. Unlike typical RFETs with two gate electrodes, our RFETs achieved polarity control with a single operating gate activated exclusively under white-light exposure. The threshold voltage was tunable, ranging from 27.4 (-31.6 V) to 0.9 (+19.5 V), allowing selective activation of n-type (p-type) operation at VGS = 0 V. Additionally, our WSe2-based RFETs show superior repeatability and long-term stability. Leveraging these advantages, various reconfigurable logic circuits were successfully demonstrated, including complementary inverters and switch circuits as well as pull-up and pull-down circuits, highlighting the potential of WSe2 FETs for future advancements of integrated circuits.

Impact of Preformed Donor-specific Antibodies in Comparison to ABO Incompatibility in Living Donor Liver Transplantation: A Propensity Score-Matched Analysis.

BACKGROUND: Immunological factors play a pivotal role in the outcomes of solid organ transplantation. We aimed to elucidate the effects of donor-specific antibodies (DSAs) and ABO compatibility on living donor liver transplantation (LDLT) outcomes. METHODS: A retrospective analysis was conducted on 584 LDLT recipients from 2015 to 2020. The recipients were stratified into 3 groups: ABO-compatible recipients without DSAs (group 1), ABO-compatible recipients with DSAs (group 2), and ABO-incompatible recipients without DSAs (group 3). Propensity score matching was used for balanced comparisons. RESULTS: In the matched comparisons, group 2 exhibited a higher incidence of T cell-mediated rejection compared with group 1 (22.7% versus 4.5%, P  = 0.030). Despite this, the 5-y survival rates were similar between groups 1 and 2 (81.6% versus 95.5%, P  = 0.085). Group 3, in comparison with group 1, showed elevated rates of cytomegalovirus infection (23.2% versus 7.3%, P  = 0.008), T cell-mediated rejection (28.0% versus 7.3%, P  = 0.001), and antibody-mediated rejection (13.4% versus 0%, P  = 0.001). However, the survival rates were comparable between group 3 and group 1 (82.0% versus 86.5%, P  = 0.220, respectively). Comparisons between group 2 and group 3 did not reveal significant differences in postoperative outcomes or survival rates ( P  > 0.05). CONCLUSIONS: DSA positivity and ABO incompatibility contribute to distinct posttransplant complications in LDLT. The integrated consideration of both factors in pretransplant assessment may enhance risk stratification and inform tailored interventions. Further research is required to corroborate these findings and provide mechanistic insights.

Energy Storage Performance of Electrode Materials Derived from Manganese Metal-Organic Frameworks.

Metal-organic frameworks (MOFs) are porous materials assembled using metal and organic linkers, showing a high specific surface area and a tunable pore size. Large portions of metal open sites in MOFs can be exposed to electrolyte ions, meaning they have high potential to be used as electrode materials in energy storage devices such as supercapacitors. Also, they can be easily converted into porous metal oxides by heat treatment. In this study, we obtained high energy storage performance by preparing electrode materials through applying heat treatment to manganese MOFs (Mn-MOFs) under air. The chemical and structural properties of synthesized and thermally treated Mn-MOFs were measured by Fourier-transform infrared spectroscopy (FTIR), Raman spectroscopy, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and transmission electron microscopy (TEM). The surface area and porosity were investigated by nitrogen adsorption/desorption isotherms. The electrochemical properties were studied by cyclic voltammetry (CV) and galvanostatic charge-discharge (GCD) using a three-electrode cell. It was found that Mn-MOF electrodes that underwent heat treatment at 400 °C under air consisted of Mn2O3 with high specific surface area and porosity. They also showed a superior specific capacitance of 214.0 F g-1 and an energy density value of 29.7 Wh kg-1 (at 0.1 A g-1) compared to non-treated Mn-MOFs.

High-Throughput Manufacturing of Multimodal Epidermal Mechanosensors with Superior Detectability Enabled by a Continuous Microcracking Strategy.

Non-invasive human-machine interactions (HMIs) are expected to be promoted by epidermal tactile receptive devices that can accurately perceive human activities. In reality, however, the HMI efficiency is limited by the unsatisfactory perception capability of mechanosensors and the complicated techniques for device fabrication and integration. Herein, a paradigm is presented for high-throughput fabrication of multimodal epidermal mechanosensors based on a sequential "femtosecond laser patterning-elastomer infiltration-physical transfer" process. The resilient mechanosensor features a unique hybrid sensing layer of rigid cellular graphitic flakes (CGF)-soft elastomer. The continuous microcracking of CGF under strain enables a sharp reduction in conductive pathways, while the soft elastomer within the framework sustains mechanical robustness of the structure. As a result, the mechanosensor achieves an ultrahigh sensitivity in a broad strain range (GF of 371.4 in the first linear range of 0-50%, and maximum GF of 8922.6 in the range of 61-70%), a low detection limit (0.01%), and a fast response/recovery behavior (2.6/2.1 ms). The device also exhibits excellent sensing performances to multimodal mechanical stimuli, enabling high-fidelity monitoring of full-range human motions. As proof-of-concept demonstrations, multi-pixel mechanosensor arrays are constructed and implemented in a robot hand controlling system and a security system, providing a platform toward efficient HMIs.