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Abstract Taste, a pivotal sense modality, plays a fundamental role in discerning flavors and evaluating the potential harm of food, thereby contributing to human survival, physical and mental health. Patients with tongue cancer may experience a loss of taste following extensive surgical resection with flap reconstruction. Here, we designed a gustatory interface that enables the non-invasive detection of tongue electrical activities for a comprehensive operative assessment. Moreover, it decodes gustatory information from the reconstructed tongue without taste buds. Our gustatory interface facilitates the recording and analysis of electrical activities on the tongue, yielding an electrical mapping across the entire tongue surface, which delineates the safe margin for surgical management and assesses flap viability for postoperative structure monitoring and prompt intervention. Furthermore, the gustatory interface helps patients discern tastes with an accuracy of 97.8%. Our invention offers a promising approach to clinical assessment and management and holds potential for improving the quality of life for individuals with tongue cancer.

Background The aim of this study was to compare differences in shot placement and accuracy between national and international level squash players. Squash is a technically demanding sport and understanding the biomechanical characteristics of athletes at different levels is important for developing effective training strategies. Methods The study used a three-dimensional motion analysis system, a high-speed video camera, and a professional tee for biomechanical testing. Participants included national and international level squash players. The kinematic characteristics of the shoulder, elbow, and wrist joints of the upper extremity for backhand strokes, as well as the accuracy of the strokes, were analyzed to compare the differences between the two groups of athletes in terms of stroke posture and accuracy. Results The kinematic analysis of the backward backhand stroke revealed that national squash players showed significant differences compared to international players in several key parameters. Specifically, national players had significantly greater trunk flexion (P = 0.018) and less shoulder medial rotation (P = 0.027). They also had lower racket velocity in the X-direction (P = 0.043). However, there were no significant differences in trunk lateral flexion (P = 0.487), trunk rotation (P = 0.293), shoulder extension/flexion (P = 0.396), elbow flexion/extension (P = 0.818), wrist flexion/extension (P = 0.177), wrist rotation (P = 0.476), racket pitch (P = 0.112), racket velocity in the Y-direction (P = 0.587), or racket velocity in the Z-direction (P = 0.327). Additionally, data for racket yaw, racket roll, and racket Vx were not provided with significant values, indicating that these parameters do not show significant differences. Conclusion International level players outperformed national level players in squash stroke accuracy and control. Key kinematic factors influencing accuracy include trunk forward flexion, shoulder abduction, shoulder internal rotation, and racket angles. The study recommends that coaches design training to enhance technical details and positional control to improve squash performance.

Abstract In implantable electrophysiological recording systems, the headstage typically comprises neural probes that interface with brain tissue and integrated circuit chips for signal processing. While advancements in MEMS and CMOS technology have significantly improved these components, their interconnection still relies on conventional printed circuit boards and sophisticated adapters. This conventional approach adds considerable weight and volume to the package, especially for high channel count systems. To address this issue, we developed a through-polymer via (TPV) method inspired by the through-silicon via (TSV) technique in advanced three-dimensional packaging. This innovation enables the vertical integration of flexible probes, amplifier chips, and PCBs, realizing a flexible, lightweight, and integrated device (FLID). The total weight of the FLIDis only 25% that of its conventional counterparts relying on adapters, which significantly increased the activity levels of animals wearing the FLIDs to nearly match the levels of control animals without implants. Furthermore, by incorporating a platinum-iridium alloy as the top layer material for electrical contact, the FLID realizes exceptional electrical performance, enabling in vivo measurements of both local field potentials and individual neuron action potentials. These findings showcase the potential of FLIDs in scaling up implantable neural recording systems and mark a significant advancement in the field of neurotechnology.

Membranous nephropathy (MN) is a common pathological type of nephrotic syndrome. The characteristic of MN is the presence of immune complex deposits containing immunoglobulin G (IgG), called electron-dense deposits (EDD), which can be observed by transmission electron microscopy (TEM). Quantitative analysis of the morphology and location of EDD can provide an essential reference for diagnosing and staging MN. However, accurately identifying and quantifying EDD is challenging due to their different morphologies, sizes, and locations with varying amounts. This paper proposes a two-stage Deformable R-CNN detector that overcomes these challenges, which has two characteristics: 1) The detector employs InternImage as the feature extractor, which extracts different morphological features of EDD using the core operator deformable convolution v3 (DCNv3). 2) The detector utilizes the multi-scale deformable attention model (MSDAM) as the attention mechanism to detect EDD of different sizes and locations effectively. The proposed Deformable R-CNN was tested on the Electron-Dense Deposit Detection for Membranous Nephropathy (EDDD-MN) dataset and outperformed other popular detectors, including two-stage, one-stage, and transformer-based detectors in detection and quantification. It also exhibited excellent performance in TIDE error analysis. Thus, this method would enable precise detection and rapid quantification of EDD, thereby reducing the workload of pathologists and helping them gain a comprehensive understanding of MN.

Global biomass burning represents a significant source of carbon emissions, exerting a substantial influence on the global carbon cycle and climate change. As global carbon emissions become increasingly concerning, accurately quantifying the carbon emissions from biomass burning has emerged as a pivotal and challenging area of scientific research. This paper presents a comprehensive review of the primary monitoring techniques for carbon emissions from biomass burning, encompassing both bottom-up and top-down approaches. It examines the current status and limitations of these techniques in practice. The bottom-up method primarily employs terrestrial ecosystem models, emission inventory methods, and fire radiation power (FRP) techniques, which rely on the integration of fire activity data and emission factors to estimate carbon emissions. The top-down method employs atmospheric observation data and atmospheric chemical transport models to invert carbon emission fluxes. Both methods continue to face significant challenges, such as limited satellite resolution affecting data accuracy, uncertainties in emission factors in regions lacking ground validation, and difficulties in model optimization due to the complexity of atmospheric processes. In light of these considerations, this paper explores the prospective evolution of carbon emission monitoring technology for biomass burning, with a particular emphasis on the significance of high-precision estimation methodologies, technological advancements in satellite remote sensing, and the optimization of global emission inventories. This study aims to provide a forward-looking perspective on the evolution of carbon emission monitoring from biomass burning, offering a valuable reference point for related scientific research and policy formulation.

Abstract Advancements in microscale electrode technology have revolutionized the field of neuroscience and clinical applications by offering high temporal and spatial resolution of recording and stimulation. Flexible neural probes, with their mechanical compliance to brain tissue, have been shown to be superior to rigid devices in terms of stability and longevity in chronic recordings. Shuttle devices are commonly used to assist flexible probe implantation; however, the protective membrane of the brain still makes penetration difficult. Hidden damage to brain vessels during implantation is a significant risk. Inspired by the anatomy of the mosquito mouthparts, we present a biomimetic neuroprobe system that integrates high-sensitivity sensors with a high-fidelity multichannel flexible electrode array. This customizable system achieves distributed and minimally invasive implantation across brain regions. Most importantly, the system’s nonvisual monitoring capability provides an early warning detection for intracranial soft tissues, such as vessels, reducing the potential for injury during implantation. The neural probe system demonstrates exceptional sensitivity and adaptability to environmental stimuli, as well as outstanding performance in postoperative and chronic recordings. These findings suggest that our biomimetic neural-probe device offers promising potential for future applications in neuroscience and brain-machine interfaces. A mosquito mouthpart-like bionic neural probe consisting of a highly sensitive tactile sensor module, a flexible microelectrode array, and implanted modules that mimic the structure of mosquito mouthparts. The system enables distributed implantation of electrode arrays across multiple brain regions while making the implantation minimally invasive and avoiding additional dural removal. The tactile sensor array can monitor the implantation process to achieve early warning of vascular damage. The excellent postoperative short-term recording performance and long-term neural activity tracking ability demonstrate that the system is a promising tool in the field of brain-computer interfaces.

Recent advancements in neural probe technology have become pivotal in both neuroscience research and the clinical management of neurological disorders. State-of-the-art developments have led to the advent of multichannel, high-density bidirectional neural interfaces that are adept at both recording and modulating neuronal activity within the central nervous system. Despite this progress, extant bidirectional probes designed for simultaneous recording and stimulation are beset with limitations, including elicitation of inflammatory responses and insufficient charge injection capacity. In this paper, we delineate the design and application of an innovative ultraflexible bidirectional neural probe engineered from polyimide. This probe is distinguished by its ability to facilitate high-resolution recordings and precise stimulation control in deep brain regions. Electrodes enhanced with a PEDOT:PSS/IrOx composite exhibit a substantial increase in charge storage capacity, escalating from 0.14 ± 0.01 mC/cm2 to an impressive 24.75 ± 0.18 mC/cm2. This augmentation significantly bolsters the electrodes’ charge transfer efficacy. In tandem, we observed a notable reduction in electrode impedance, from 3.47 ± 1.77 MΩ to a mere 41.88 ± 4.04 kΩ, while the phase angle exhibited a positive shift from −72.61 ± 1.84° to −34.17 ± 0.42°. To substantiate the electrodes’ functional prowess, we conducted in vivo experiments, where the probes were surgically implanted into the bilateral motor cortex of mice. These experiments involved the synchronous recording and meticulous analysis of neural signal fluctuations during stimulation and an assessment of the probes’ proficiency in modulating directional turning behaviors in the subjects. The empirical evidence corroborates that targeted stimulation within the bilateral motor cortex of mice can modulate the intensity of neural signals in the stimulated locale, enabling the directional control of the mice’s turning behavior to the contralateral side of the stimulation site.

Abstract The combination of optogenetics and electrophysiological recording enables high-precision bidirectional interactions between neural interfaces and neural circuits, which provides a promising approach for the study of progressive neurophysiological phenomena. Opto-electrophysiological neural probes with sufficient flexibility and biocompatibility are desirable to match the low mechanical stiffness of brain tissue for chronic reliable performance. However, lack of rigidity poses challenges for the accurate implantation of flexible neural probes with less invasiveness. Herein, we report a hybrid probe (Silk-Optrode) consisting of a silk protein optical fiber and multiple flexible microelectrode arrays. The Silk-Optrode can be accurately inserted into the brain and perform synchronized optogenetic stimulation and multichannel recording in freely behaving animals. Silk plays an important role due to its high transparency, excellent biocompatibility, and mechanical controllability. Through the hydration of the silk optical fiber, the Silk-Optrode probe enables itself to actively adapt to the environment after implantation and reduce its own mechanical stiffness to implant into the brain with high fidelity while maintaining mechanical compliance with the surrounding tissue. The probes with 128 recording channels can detect high-yield well-isolated single units while performing intracranial light stimulation with low optical losses, surpassing previous work of a similar type. Two months of post-surgery results suggested that as-reported Silk-Optrode probes exhibit better implant-neural interfaces with less immunoreactive glial responses and tissue lesions. A silk optical fiber-based Silk-Optrode probe consisting of a natural silk optical fiber and a flexible micro/nano electrode array is reported. The multifunctional soft probe can modify its own Young’s modulus through hydration to achieve accurate implantation into the brain. The low optical loss and single-unit recording abilities allow simultaneous optogenetic stimulation and multichannel readout, which expands the applications in the operation and parsing of neural circuits in behavioral animals.

Soft robotic gloves have attracted significant interest in hand rehabilitation in the past decade. However, current solutions are still heavy and lack finger-state monitoring and versatile treatment options. To address this, we present a lightweight soft robotic glove actuated by twisted string actuators (TSA) that provides whole-hand finger motion tracking. We have developed a virtual reality environment for hand rehabilitation training, allowing users to interact with various virtual objects. Fifteen small inertial measurement units are placed on the glove to predict finger joint angles and track whole-hand finger motion. We performed TSA experiments to identify design and control rules, by understanding how their response varies with input load and voltages. Grasping experiments were conducted to determine the grasping force and range of motion. Finally, we showcase an application of the rehabilitation glove in a Unity-based VR interface, which can actuate the operator’s fingers to grasp different virtual objects.

Abstract Progress toward intelligent human–robotic interactions requires monitoring sensors that are mechanically flexible, facile to implement, and able to harness recognition capability under harsh environments. Conventional sensing methods have been divided for human‐side collection or robot‐side feedback and are not designed with these criteria in mind. However, the iontronic polymer is an example of a general method that operates properly on both human skin (commonly known as skin electronics or iontronics) and the machine/robotic surface. Here, a unique iontronic composite (silk protein/glycerol/Ca(II) ion) and supportive molecular mechanism are developed to simultaneously achieve high conductivity (around 6 kΩ at 50 kHz), self‐healing (within minutes), strong stretchability (around 1000%), high strain sensitivity and transparency, and universal adhesiveness across a broad working temperature range (−40–120 °C). Those merits facilitate the development of iontronic sensing and the implementation of damage‐resilient robotic manipulation. Combined with a machine learning algorithm and specified data collection methods, the system is able to classify 1024 types of human and robot hand gestures under challenging scenarios and to offer excellent object recognition with an accuracy of 99.7%.

Lamins are intermediate filaments that play a crucial role in sensing mechanical strain in the nucleus of cells. β-catenin and megakaryoblastic leukemia-1 (MKL1) are critical signaling molecules that need to be translocated to the nucleus for their transcription in response to mechanical strain that induces osteogenesis. However, the exact molecular mechanism behind the translocation of these molecules has not been fully investigated. This study used 10% cyclic strain to induce osteogenesis in the murine osteoblast precursor cell line (MC3T3). The translocation of β-catenin and MKL1 was studied by performing knockdown and overexpression of lamin A/C (LMNA). Cyclic strain increased the expression of osteogenic markers such as alkaline phosphatase (ALP), runt-related transcription factor 2 (RUNX2), and enhanced ALP staining after seven days of incubation. Resultantly, MKL1 and β-catenin were translocated in the nucleus from the cytoplasm during the stress-induced osteogenic process. Knockdown of LMNA decreased the accumulation of MKL1 and β-catenin in the nucleus, whereas overexpression of LMNA increased the translocation of these molecules. In conclusion, our study indicates that both MKL1 and β-catenin molecules are dependent on the expression of LMNA during strain-induced osteogenesis.

Objectives: We aimed to determine the current incidence rate and risk factors for surgical site infection (SSI) after abdominal surgery in China and to further demonstrate the clinical features of patients with SSI. Background: Contemporary epidemiology and clinical features of SSI after abdominal surgery remain poorly characterized. Methods: A prospective multicenter cohort study was conducted from March 2021 to February 2022; the study included patients who underwent abdominal surgery at 42 hospitals in China. Multivariable logistic regression analysis was performed to identify risk factors for SSI. Latent class analysis (LCA) was used to explore the population characteristics of SSI. Results: In total, 23,982 patients were included in the study, of whom 1.8% developed SSI. There was a higher SSI incidence in open surgery (5.0%) than in laparoscopic or robotic surgeries (0.9%). Multivariable logistic regression indicated that the independent risk factors for SSI after abdominal surgery were older age, chronic liver disease, mechanical bowel preparation, oral antibiotic bowel preparation, colon or pancreas surgery, contaminated or dirty wounds, open surgery, and colostomy- /ileostomy. LCA revealed 4 subphenotypes in patients undergoing abdominal surgery. Types α and β were mild subclasses with a lower SSI incidence; whereas types γ and δ were the critical subgroups with a higher SSI incidence, but their clinical features were different. Conclusions: LCA identified 4 subphenotypes in patients who underwent abdominal surgery. Types γ and δ were critical subgroups with a higher SSI incidence. This phenotype classification can be used to predict SSI after abdominal surgery. [ABSTRACT FROM AUTHOR]