Impact of Microsurgery and Postoperative Radiotherapy on Neurological Function in Intramedullary Spinal Cord Gliomas.

OBJECTIVE: To assess the clinical efficacy of combined microsurgery and postoperative radiotherapy for the treatment of intramedullary spinal gliomas and its impact on neurological function. STUDY DESIGN: An observational study. Place and Duration of the Study: Department of Neurosurgery, Baoding No.1 Central Hospital, Hebei, China, between January 2020 and 2023. METHODOLOGY: Sixty patients diagnosed with spinal cord intramedullary gliomas were divided equally into an experimental and control group. The control group received microsurgical treatment, and the experimental group received microsurgical treatment combined with postoperative radiotherapy. The treatment effectiveness, neurological function, and follow-up results of the two groups were compared. RESULTS: After treatment, the clinical efficacy of the experimental group treatment was significantly better than that of the control group (p <0.05). The National Institutes of Health Stroke Scale (NIHSS) scores were significantly lower, and the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire-30 (EORTC QLQ-C30) scores were significantly higher in the experimental group than in the control group (p <0.05). The 1-3-year survival rate and median survival time of the experimental group were significantly higher than those of the control group (p <0.05). The incidence of complications was 3.33% in the experimental group and 6.67% in the control group, but the difference was not statistically significant (p >0.05). The postoperative recurrence rate was significantly lower in the experimental (0%) than in the control group (13.33%, p <0.05). CONCLUSION: Combined microsurgery and postoperative radiotherapy was found to be more effective than microsurgery alone. It was also more conducive to the recovery of neurological function and improved the patient's quality of life. KEY WORDS: Intramedullary spinal cord glioma, Microsurgery, Neurological function, Radiotherapy.

Individualized C1-2 intra-articular three-dimensional printed porous titanium alloy cage for craniovertebral deformity.

BACKGROUND: Congenital craniovertebral deformity, including basilar invagination (BI) and atlantoaxial instability (AAI), are often associated with three-dimensional (3D) deformity, such as C1-2 rotational deformity, craniocervical kyphosis, C1 lateral inclination, among other abnormalities. Effective management of these conditions requires the restoration of the 3D alignment to achieve optimal reduction. Recently, 3D printing technology has emerged as a valuable tool in spine surgery, offering the significant advantage of allowing surgeons to customize the prosthesis design. This innovation provides an ideal solution for precise 3D reduction in the treatment of craniovertebral deformities. OBJECTIVE: This study aims to describe our approach to individualized computer-simulated reduction and the design of C1-2 intra-articular 3D printed porous titanium alloy cages for the quantitative correction of craniovertebral junction deformities. METHODS: A retrospective analysis was conducted on patients with craniovertebral deformities treated at our institution using individualized 3D-printed porous titanium alloy cages. Preoperative CT data were used to construct models for 3D realignment simulations. Cage designs were tailored to the simulated joint morphology following computer-assisted realignment. Preoperative and postoperative parameters were statistically analyzed. RESULTS: Fourteen patients were included in the study, with a total of 28 3D-printed porous titanium alloy cages implanted. There were no cases of C2 nerve root resection or vertebral artery injury. All patients experienced symptom relief and stable implant fixation achieved in all cases. No implant-related complications were reported. CONCLUSION: The use of individualized computer-simulated reduction and the design of C1-2 intra-articular 3D printed porous titanium alloy cage facilitates precise 3D realignment in patients with craniovertebral deformities, demonstrating effectiveness in symptom relief and stability.

A screw algorithm for congenital C2-3 fusion with high-riding vertebral arteries: feasibilities and clinical outcomes of five different fixation techniques.

OBJECTIVE: To propose a screw algorithm and investigate the anatomical feasibilities and clinical outcomes of five distinct fixation methods for C2-3 fused vertebra with high-ridding vertebral arteries (VA) (HRVA) when the C2 pedicle screw placement is unfeasible. METHODS: Thirty surgical patients with congenital C2-3 fusion, HRVA, and atlantoaxial dislocation (AAD) were included. We designed a algorithm for alternative screw implantation into C2-3 fused vertebrae, including C2 pedicle screw with in-out-in (passing VA groove) technique (in-out-in screw), subfacetal screw, translaminar screw, lateral mass screw, C3 pedicle screw. VA diameter and position, C2 and C3 pedicles, superior facets, fused lamina, and fused lateral mass dimensions were evaluated for screw implantation indication. Implant failure, reduction loss, implant placement accuracy were investigated by computed tomography. RESULTS: A total of 5 VAs were identified as distant VAs; a total of 2 VAs were categorized as occlusive VAs. Sufficient dimension of lateral mass and lamina provided the broadest indications for screw implantation, while the distant or occlusive VA provided the most limited indications for in-out-in screw. The indications of five alternative methods ranged from narrowest to widest as follows: in-out-in screw, C3 pedicle screw, subfacetal screw, translaminar screw, lateral mass screw. The translaminar screws and the lateral mass screws increased the probability of implant failure. All patients who received in-out-in screws, C3 pedicle screws, and subfacetal screws achieved fusion. The accuracy ranged from lowest to highest as follows: C3 pedicle screw, lateral mass screw, in-out-in screw, subfacetal screw, translaminar screw. No translaminar screws deviated. CONCLUSIONS: The algorithm proved to be a valuable tool for screw selection in cases of C2-3 fused vertebrae with HRVAs. The subfacetal screw, boasting broad indications, a high fusion rate, and exceptional accuracy, stood as the primary preferred alternative.

Improving Hand Gesture Recognition Robustness to Dynamic Posture Variations by Multimodal Deep Feature Fusion.

Surface electromyography (sEMG), a human-machine interface for gesture recognition, has shown promising potential for decoding motor intentions, but a variety of nonideal factors restrict its practical application in assistive robots. In this paper, we summarized the current mainstream gesture recognition strategies and proposed a gesture recognition method based on multimodal canonical correlation analysis feature fusion classification (MCAFC) for a nonideal condition that occurs in daily life, i.e., posture variations. The deep features of the sEMG and acceleration signals were first extracted via convolutional neural networks. A canonical correlation analysis was subsequently performed to associate the deep features of the two modalities. The transformed features were utilized as inputs to a linear discriminant analysis classifier to recognize the corresponding gestures. Both offline and real-time experiments were conducted on eight non-disabled subjects. The experimental results indicated that MCAFC achieved an average classification accuracy, average motion completion rate, and average motion completion time of 93.44%, 94.05%, and 1.38 s, respectively, with multiple dynamic postures, indicating significantly better performance than that of comparable methods. The results demonstrate the feasibility and superiority of the proposed multimodal signal feature fusion method for gesture recognition with posture variations, providing a new scheme for myoelectric control.

A lightweight method of integrated local load forecasting and control of edge computing in active distribution networks.

The strong resource constraints of edge-computing devices and the dynamic evolution of load characteristics put forward higher requirements for forecasting methods of active distribution networks. This paper proposes a lightweight adaptive ensemble learning method for local load forecasting and predictive control of active distribution networks based on edge computing in resource constrained scenarios. First, the adaptive sparse integration method is proposed to reduce the model scale. Then, the auto-encoder is introduced to downscale the model variables to further reduce computation time and storage overhead. An adaptive correction method is proposed to maintain the adaptability. Finally, a multi-timescale predictive control method for the edge side is established, which realizes the collaboration of local load forecasting and control. All cases can be deployed on an actual edge-computing device. Compared to other benchmark methods and the existing researches, the proposed method can minimize the model complexity without reducing the forecasting accuracy.

A Geometrically Flexible Three-Dimensional Nanocarbon.

The development of architecturally unique molecular nanocarbons by bottom-up organic synthesis is essential for accessing functional organic materials awaiting technological developments in fields such as energy, electronics, and biomedicine. Herein, we describe the design and synthesis of a triptycene-based three-dimensional (3D) nanocarbon, GFN-1, with geometrical flexibility on account of its three peripheral π-panels being capable of interconverting between two curved conformations. An effective through-space electronic communication among the three π-panels of GFN-1 has been observed in its monocationic radical form, which exhibits an extensively delocalized spin density over the entire 3D π-system as revealed by electron paramagnetic resonance and UV-vis-NIR spectroscopies. The flexible 3D molecular architecture of GFN-1, along with its densely packed superstructures in the presence of fullerenes, is revealed by microcrystal electron diffraction and single-crystal X-ray diffraction, which establish the coexistence of both propeller and tweezer conformations in the solid state. GFN-1 exhibits strong binding affinities for fullerenes, leading to host-guest complexes that display rapid photoinduced electron transfer within a picosecond. The outcomes of this research could pave the way for the utilization of shape and electronically complementary nanocarbons in the construction of functional coassemblies.

Balance recovery for lower limb exoskeleton in standing posture based on orbit energy analysis.

Introduction: The need for effective balance control in lower limb rehabilitation exoskeletons is critical for ensuring stability and safety during rehabilitation training. Current research into specialized balance recovery strategies is limited, highlighting a gap in biomechanics-inspired control methods. Methods: We introduce a new metric called "Orbit Energy" (OE), which assesses the balance state of the human-exoskeleton system based on the dynamics of the overall center of mass. Our control framework utilizes OE to choose appropriate balance recovery strategies, including torque controls at the ankle and hip joints. Results: The efficacy of our control algorithm was confirmed through Matlab Simulink simulations, which analyzed the recovery of balance under various disturbance forces and conditions. Further validation came from physical experiments with human subjects wearing the exoskeleton, where a significant reduction in muscle activation was observed during balance maintenance under external disturbances. Discussion: Our findings underscore the potential of biomechanics-inspired metrics like OE in enhancing exoskeleton functionality for rehabilitation purposes. The introduction of such metrics could lead to more targeted and effective balance recovery strategies, ultimately improving the safety and stability of exoskeleton use in rehabilitation settings.

MF-Net: multi-scale feature extraction-integration network for unsupervised deformable registration.

Deformable registration plays a fundamental and crucial role in scenarios such as surgical navigation and image-assisted analysis. While deformable registration methods based on unsupervised learning have shown remarkable success in predicting displacement fields with high accuracy, many existing registration networks are limited by the lack of multi-scale analysis, restricting comprehensive utilization of global and local features in the images. To address this limitation, we propose a novel registration network called multi-scale feature extraction-integration network (MF-Net). First, we propose a multiscale analysis strategy that enables the model to capture global and local semantic information in the image, thus facilitating accurate texture and detail registration. Additionally, we introduce grouped gated inception block (GI-Block) as the basic unit of the feature extractor, enabling the feature extractor to selectively extract quantitative features from images at various resolutions. Comparative experiments demonstrate the superior accuracy of our approach over existing methods.

3D-CAM: a novel context-aware feature extraction framework for neurological disease classification.

In clinical practice and research, the classification and diagnosis of neurological diseases such as Parkinson's Disease (PD) and Multiple System Atrophy (MSA) have long posed a significant challenge. Currently, deep learning, as a cutting-edge technology, has demonstrated immense potential in computer-aided diagnosis of PD and MSA. However, existing methods rely heavily on manually selecting key feature slices and segmenting regions of interest. This not only increases subjectivity and complexity in the classification process but also limits the model's comprehensive analysis of global data features. To address this issue, this paper proposes a novel 3D context-aware modeling framework, named 3D-CAM. It considers 3D contextual information based on an attention mechanism. The framework, utilizing a 2D slicing-based strategy, innovatively integrates a Contextual Information Module and a Location Filtering Module. The Contextual Information Module can be applied to feature maps at any layer, effectively combining features from adjacent slices and utilizing an attention mechanism to focus on crucial features. The Location Filtering Module, on the other hand, is employed in the post-processing phase to filter significant slice segments of classification features. By employing this method in the fully automated classification of PD and MSA, an accuracy of 85.71%, a recall rate of 86.36%, and a precision of 90.48% were achieved. These results not only demonstrates potential for clinical applications, but also provides a novel perspective for medical image diagnosis, thereby offering robust support for accurate diagnosis of neurological diseases.

Efficient fabrication of bioinspired soft, ridged-slippery surfaces with large-range anisotropic wettability for droplet manipulation.

The droplet lossless directional motion control on slippery surfaces holds immense promise for applications in microfluidic chips, hazardous substance detection, chemical dispensing, etc. However, a significant challenge in this domain lies in efficiently developing soft, slippery surfaces with large-range anisotropic wettability and compatibility for curved scenarios. This study addressed this challenge through a quick 3D printing-assisted method to produce soft, ridged-slippery surfaces (SRSSs) as the droplet manipulation platform. The SRSSs demonstrated substantial anisotropic rolling resistances, measuring 116.9 µN in the perpendicular direction and 7.7 µN in the parallel direction, exhibiting a ratio of 15.2. Combining several extents of anisotropic wettability on a soft substrate could realize diverse reagent manipulation functions. Furthermore, these SRSSs showcased high compatibility with various droplet constituents, impressive liquid impact resistance, self-repair capability, and mechanical durability and thermal durability, ensuring exceptional applicability. As proofs of concept, the SRSSs were successfully applied in droplet control and classification for heavy metal ion detection, mechanical arm-based droplet grab and release, and cross-species transport, showcasing their remarkable versatility, compatibility, and practicality in advanced droplet microfluidic chips and water harvesting applications.

Adaptive control for shape memory alloy actuated systems with applications to human-robot interaction.

Introduction: Shape memory alloy (SMA) actuators are attractive options for robotic applications due to their salient features. So far, achieving precise control of SMA actuators and applying them to human-robot interaction scenarios remains a challenge. Methods: This paper proposes a novel approach to deal with the control problem of a SMA actuator. Departing from conventional mechanism models, we attempt to describe this nonlinear plant using a gray-box model, in which only the input current and the output displacement are measured. The control scheme consists of the model parameters updating and the control law calculation. The adaptation algorithm is founded on the multi-innovation concept and incorporates a dead-zone weighted factor, aiming to concurrently reduce computational complexities and enhance robustness properties. The control law is based on a PI controller, the gains of which are designed by the pole assignment technique. Theoretical analysis proves that the closed-loop performance can be ensured under mild conditions. Results: The experiments are first conducted through the Beckhoff controller. The comparative results suggest that the proposed adaptive PI control strategy exhibits broad applicability, particularly under load variations. Subsequently, the SMA actuator is designed and incorporated into the hand rehabilitation robot. System position tracking experiments and passive rehabilitation training experiments for various gestures are then conducted. The experimental outcomes demonstrate that the hand rehabilitation robot, utilizing the SMA actuator, achieves higher position tracking accuracy and a more stable system under the adaptive control strategy proposed in this paper. Simultaneously, it successfully accommodates hand rehabilitation movements for multiple gestures. Discussion: The adaptive controller proposed in this paper takes into account both the computational complexity of the model and the accuracy of the control results, Experimental results not only demonstrate the practicality and reliability of the controller but also attest to its potential application in human-machine interaction within the field of neural rehabilitation.

Design and assessment of a reconfigurable behavioral assistive robot: a pilot study.

Introduction: For patients with functional motor disorders of the lower limbs due to brain damage or accidental injury, restoring the ability to stand and walk plays an important role in clinical rehabilitation. Lower limb exoskeleton robots generally require patients to convert themselves to a standing position for use, while being a wearable device with limited movement distance. Methods: This paper proposes a reconfigurable behavioral assistive robot that integrates the functions of an exoskeleton robot and an assistive standing wheelchair through a novel mechanism. The new mechanism is based on a four-bar linkage, and through simple and stable conformal transformations, the robot can switch between exoskeleton state, sit-to-stand support state, and wheelchair state. This enables the robot to achieve the functions of assisted walking, assisted standing up, supported standing and wheelchair mobility, respectively, thereby meeting the daily activity needs of sit-to-stand transitions and gait training. The configuration transformation module controls seamless switching between different configurations through an industrial computer. Experimental protocols have been developed for wearable testing of robotic prototypes not only for healthy subjects but also for simulated hemiplegic patients. Results: The experimental results indicate that the gait tracking effect during robot-assisted walking is satisfactory, and there are no sudden speed changes during the assisted standing up process, providing smooth support to the wearer. Meanwhile, the activation of the main force-generating muscles of the legs and the plantar pressure decreases significantly in healthy subjects and simulated hemiplegic patients wearing the robot for assisted walking and assisted standing-up compared to the situation when the robot is not worn. Discussion: These experimental findings demonstrate that the reconfigurable behavioral assistive robot prototype of this study is effective, reducing the muscular burden on the wearer during walking and standing up, and provide effective support for the subject's body. The experimental results objectively and comprehensively showcase the effectiveness and potential of the reconfigurable behavioral assistive robot in the realms of behavioral assistance and rehabilitation training.

Dorsal root entry zone fenestration for intramedullary ependymal cyst: illustrative case.

BACKGROUND: Intramedullary ependymal cysts are rare and difficult to distinguish from syringomyelia and neuroenteric cysts. Almost all cases in the literature have been case reports and have been performed with the traditional posterior median sulcus incision, which is difficult to identify accurately during spinal rotation. Approximately 40% of cases have transient neurological deterioration. The dorsal root entry zone has been proven to be an effective incision area in the treatment of intramedullary lesions, but so far, its utilization in intramedullary ependymal cysts has been rarely reported. OBSERVATIONS: This study is the first to report on six cases of intramedullary ependymal cysts treated with an 8-mm incision in the dorsal root entry zone to fully establish the communication between the cyst and the subarachnoid space. Imaging changes and neurological improvement were analyzed in all cases before and after surgery and were followed up for 49.7 months. LESSONS: The utilization of dorsal root entry zone fenestration in intramedullary ependymal cyst has demonstrated feasibility and effectiveness, ensuring the functional integrity of the posterior column.

[Multi-modal synergistic quantitative analysis and rehabilitation assessment of lower limbs for exoskeleton].

In response to the problem that the traditional lower limb rehabilitation scale assessment method is time-consuming and difficult to use in exoskeleton rehabilitation training, this paper proposes a quantitative assessment method for lower limb walking ability based on lower limb exoskeleton robot training with multimodal synergistic information fusion. The method significantly improves the efficiency and reliability of the rehabilitation assessment process by introducing quantitative synergistic indicators fusing electrophysiological and kinematic level information. First, electromyographic and kinematic data of the lower extremity were collected from subjects trained to walk wearing an exoskeleton. Then, based on muscle synergy theory, a synergistic quantification algorithm was used to construct synergistic index features of electromyography and kinematics. Finally, the electrophysiological and kinematic level information was fused to build a modal feature fusion model and output the lower limb motor function score. The experimental results showed that the correlation coefficients of the constructed synergistic features of electromyography and kinematics with the clinical scale were 0.799 and 0.825, respectively. The results of the fused synergistic features in the K-nearest neighbor (KNN) model yielded higher correlation coefficients ( r = 0.921, P < 0.01). This method can modify the rehabilitation training mode of the exoskeleton robot according to the assessment results, which provides a basis for the synchronized assessment-training mode of "human in the loop" and provides a potential method for remote rehabilitation training and assessment of the lower extremity.

Assessment of oculomotor function after prolonged computer use.

To analyze the specific effects of prolonged computer use on oculomotor function, we propose an oculomotor function evaluation system to analyze changes in oculomotor movement function by using an eye tracker to record eye movement data when performing gaze, smooth pursuit, and saccade under normal condition, after one hour and one and a half hours of continuous working at a computer. The captured eye movement data is pre-processed, and then data features are calculated and analyzed to understand the specific effects of continuously using the computer on the oculomotor function. The results show that the oculomotor function decreases as we gaze at the computer screen for longer periods, as evidenced by a decrease in the stability of the gaze function, a reduction in the gaze focus, a reduction in the speed of eye saccades, and a decrease in the smooth pursuit function. In short, the oculomotor function worsens after prolonged working at a computer. This paper presents the effects of continuously using the computer quantificationally for the first time. The proposed oculomotor function evaluation system could also be used to assess patients who have a disability in oculomotor function and specific individuals, e.g. pilots.

[Human muscle fatigue monitoring method and its application for exoskeleton interactive control].

Aiming at the human-computer interaction problem during the movement of the rehabilitation exoskeleton robot, this paper proposes an adaptive human-computer interaction control method based on real-time monitoring of human muscle state. Considering the efficiency of patient health monitoring and rehabilitation training, a new fatigue assessment algorithm was proposed. The method fully combined the human neuromuscular model, and used the relationship between the model parameter changes and the muscle state to achieve the classification of muscle fatigue state on the premise of ensuring the accuracy of the fatigue trend. In order to ensure the safety of human-computer interaction, a variable impedance control algorithm with this algorithm as the supervision link was proposed. On the basis of not adding redundant sensors, the evaluation algorithm was used as the perceptual decision-making link of the control system to monitor the muscle state in real time and carry out the robot control of fault-tolerant mechanism decision-making, so as to achieve the purpose of improving wearing comfort and improving the efficiency of rehabilitation training. Experiments show that the proposed human-computer interaction control method is effective and universal, and has broad application prospects.

Spin-Frustrated Trisradical Trication of PrismCage.

Organic trisradicals featuring threefold symmetry have attracted significant interest because of their unique magnetic properties associated with spin frustration. Herein, we describe the synthesis and characterization of a triangular prism-shaped organic cage for which we have coined the name PrismCage6+ and its trisradical trication─TR3(•+). PrismCage6+ is composed of three 4,4'-bipyridinium dications and two 1,3,5-phenylene units bridged by six methylene groups. In the solid state, PrismCage6+ adopts a highly twisted conformation with close to C3 symmetry as a result of encapsulating one PF6- anion as a guest. PrismCage6+ undergoes stepwise reduction to its mono-, di-, and trisradical cations in MeCN on account of strong electronic communication between its 4,4'-bipyridinium units. TR3(•+), which is obtained by the reduction of PrismCage6+ employing CoCp2, adopts a triangular prism-shaped conformation with close to C2v symmetry in the solid state. Temperature-dependent continuous-wave and nutation-frequency-selective electron paramagnetic resonance spectra of TR3(•+) in frozen N,N-dimethylformamide indicate its doublet ground state. The doublet-quartet energy gap of TR3(•+) is estimated to be -0.08 kcal mol-1, and the critical temperature of spin-state conversion is found to be ca. 50 K, suggesting that it displays pronounced spin frustration at the molecular level. To the best of our knowledge, this example is the first organic radical cage to exhibit spin frustration. The trisradical trication of PrismCage6+ opens up new possibilities for fundamental investigations and potential applications in the fields of both organic cages and spin chemistry.

The impact of carbon quota allocation and low-carbon technology innovation on carbon market effectiveness: a system dynamics analysis.

As the problems of "valuing compliance over trading" and quota over-allocation seriously affect the effectiveness of China's national carbon emission trading (CET) market, the quota auction mechanism will be introduced timely to solve these problems. Since implementing the quota auction means reduced free quotas, regulated enterprises are more motivated to pursue low-carbon technology innovation (L-CTI). On these grounds, by establishing a system dynamics model of the national CET market and designing seven scenarios for simulation analysis, this paper investigates the impact of quota auction and L-CTI on the emission reduction effectiveness and cost effectiveness of the national CET market. The results indicate that for the national CET market, introducing quota auction is conducive to decreasing the CET price and improving its liquidity and emission reduction effectiveness, which is one of the quota allocation mechanisms to improve the CET market effectiveness at present. However, the quota auction will increase the abatement cost and reduce the cost effectiveness. Therefore, to improve the institutional performance of China's CET system, it is necessary to conduct L-CTI to alleviate the increasing abatement cost caused by quota auction, and thus improve the emission reduction effectiveness and cost effectiveness of the national CET market.

Exciplex Emission and Förster Resonance Energy Transfer in Polycyclic Aromatic Hydrocarbon-Based Bischromophoric Cyclophanes and Homo[2]catenanes.

Energy transfer and exciplex emission are not only crucial photophysical processes in many living organisms but also important for the development of smart photonic materials. We report, herein, the rationally designed synthesis and characterization of two highly charged bischromophoric homo[2]catenanes and one cyclophane incorporating a combination of polycyclic aromatic hydrocarbons, i.e., anthracene, pyrene, and perylene, which are intrinsically capable of supporting energy transfer and exciplex formation. The possible coconformations of the homo[2]catenanes, on account of their dynamic behavior, have been probed by Density Functional Theory calculations. The unique photophysical properties of these exotic molecules have been explored by steady-state and time-resolved absorption and fluorescence spectroscopies. The tetracationic pyrene-perylene cyclophane system exhibits emission emanating from a highly efficient Förster resonance energy transfer (FRET) mechanism which occurs in 48 ps, while the octacationic homo[2]catenane displays a weak exciplex photoluminescence following extremely fast (<0.3 ps) exciplex formation. The in-depth fundamental understanding of these photophysical processes involved in the fluorescence of bischromophoric cyclophanes and homo[2]catenanes paves the way for their use in future bioapplications and photonic devices.