Clinical Comparison between HD-tDCS and tDCS for Improving Upper Limb Motor Function: A Randomized, Double-Blinded, Sham-Controlled Trial.

Background: Stroke is a common and frequently occurring disease among middle-aged and elderly people, with approximately 55%-75% of patients remaining with upper limb dysfunction. How to promote the recovery of motor function at an early stage is crucial to the life of the patient. Objectives: This study aimed to investigate whether high-definition transcranial direct current stimulation (HD-tDCS) of the primary motor cortex (M1) functional area in poststroke patients in the subacute phase is more effective in improving upper limb function than conventional tDCS. Methods: This randomized, sham-controlled clinical trial included 69 patients with subcortical stroke. They were randomly divided into the HD-tDCS, anodal tDCS (a-tDCS), and sham groups. Each group received 20 sessions of stimulation. The patients were assessed using the Action Research Arm Test, Fugl-Meyer score for upper extremities, Motor Function Assessment Scale, and modified Barthel index (MBI) pretreatment and posttreatment. Results: The intragroup comparison scores improved after 4 weeks of treatment. The HD-tDCS group showed a slightly greater, but nonsignificant improvement as compared to a-tDCS group in terms of mean change observed in function of trained items. The MBI score of the HD-tDCS group was maintained up to 8 weeks of follow-up and was higher than that in the a-tDCS group. Conclusion: Both HD-tDCS and a-tDCS can improve upper limb motor function and daily activities of poststroke patients in the subacute stage. This trial is registered with ChiCTR2000031314.

[Simulation model of tumor-treating fields].

Tumor-treating fields (TTFields) is a novel treatment modality for malignant solid tumors, often employing electric field simulations to analyze the distribution of electric fields on the tumor under different parameters of TTFields. Due to the present difficulties and high costs associated with reproducing or implementing the simulation model construction techniques, this study used readily available open-source software tools to construct a highly accurate, easily implementable finite element simulation model for TTFields. The accuracy of the model is at a level of 1 mm 3. Using this simulation model, the study carried out analyses of different factors, such as tissue electrical parameters and electrode configurations. The results show that factors influncing the distribution of the internal electric field of the tumor include changes in scalp and skull conductivity (with a maximum variation of 21.0% in the treatment field of the tumor), changes in tumor conductivity (with a maximum variation of 157.8% in the treatment field of the tumor), and different electrode positions and combinations (with a maximum variation of 74.2% in the treatment field of the tumor). In summary, the results of this study validate the feasibility and effectiveness of the proposed modeling method, which can provide an important reference for future simulation analyses of TTFields and clinical applications.

40 Hz light flickering promotes sleep through cortical adenosine signaling.

Flickering light stimulation has emerged as a promising non-invasive neuromodulation strategy to alleviate neuropsychiatric disorders. However, the lack of a neurochemical underpinning has hampered its therapeutic development. Here, we demonstrate that light flickering triggered an immediate and sustained increase (up to 3 h after flickering) in extracellular adenosine levels in the primary visual cortex (V1) and other brain regions, as a function of light frequency and intensity, with maximal effects observed at 40 Hz frequency and 4000 lux. We uncovered cortical (glutamatergic and GABAergic) neurons, rather than astrocytes, as the cellular source, the intracellular adenosine generation from AMPK-associated energy metabolism pathways (but not SAM-transmethylation or salvage purine pathways), and adenosine efflux mediated by equilibrative nucleoside transporter-2 (ENT2) as the molecular pathway responsible for extracellular adenosine generation. Importantly, 40 Hz (but not 20 and 80 Hz) light flickering for 30 min enhanced non-rapid eye movement (non-REM) and REM sleep for 2-3 h in mice. This somnogenic effect was abolished by ablation of V1 (but not superior colliculus) neurons and by genetic deletion of the gene encoding ENT2 (but not ENT1), but recaptured by chemogenetic inhibition of V1 neurons and by focal infusion of adenosine into V1 in a dose-dependent manner. Lastly, 40 Hz light flickering for 30 min also promoted sleep in children with insomnia by decreasing sleep onset latency, increasing total sleep time, and reducing waking after sleep onset. Collectively, our findings establish the ENT2-mediated adenosine signaling in V1 as the neurochemical basis for 40 Hz flickering-induced sleep and unravel a novel and non-invasive treatment for insomnia, a condition that affects 20% of the world population.

Magnetically Compatible Brain Electrode Arrays Based on Single-Walled Carbon Nanotubes for Long-Term Implantation.

Advancements in brain-machine interfaces and neurological treatments urgently require the development of improved brain electrodes applied for long-term implantation, where traditional and polymer options face challenges like size, tissue damage, and signal quality. Carbon nanotubes are emerging as a promising alternative, combining excellent electronic properties and biocompatibility, which ensure better neuron coupling and stable signal acquisition. In this study, a new flexible brain electrode array based on 99.99% purity of single-walled carbon nanotubes (SWCNTs) was developed, which has 30 um × 40 um size, about 5.1 kΩ impedance, and 14.01 dB signal-to-noise ratio (SNR). The long-term implantation experiment in vivo in mice shows the proposed brain electrode can maintain stable LFP signal acquisition over 12 weeks while still achieving an SNR of 3.52 dB. The histological analysis results show that SWCNT-based brain electrodes induced minimal tissue damage and showed significantly reduced glial cell responses compared to platinum wire electrodes. Long-term stability comes from SWCNT's biocompatibility and chemical inertness, the electrode's flexible and fine structure. Furthermore, the new brain electrode array can function effectively during 7-Tesla magnetic resonance imaging, enabling the collection of local field potential and even epileptic discharges during the magnetic scan. This study provides a comprehensive study of carbon nanotubes as invasive brain electrodes, providing a new path to address the challenge of long-term brain electrode implantation.

A Dual-Branch Framework With Prior Knowledge for Precise Segmentation of Lung Nodules in Challenging CT Scans.

Lung cancer is one of the deadliest cancers globally, and early diagnosis is crucial for patient survival. Pulmonary nodules are the main manifestation of early lung cancer, usually assessed using CT scans. Nowadays, computer-aided diagnostic systems are widely used to assist physicians in disease diagnosis. The accurate segmentation of pulmonary nodules is affected by internal heterogeneity and external data factors. In order to overcome the segmentation challenges of subtle, mixed, adhesion-type, benign, and uncertain categories of nodules, a new mixed manual feature network that enhances sensitivity and accuracy is proposed. This method integrates feature information through a dual-branch network framework and multi-dimensional fusion module. By training and validating with multiple data sources and different data qualities, our method demonstrates leading performance on the LUNA16, Multi-thickness Slice Image dataset, LIDC, and UniToChest, with Dice similarity coefficients reaching 86.89%, 75.72%, 84.12%, and 80.74% respectively, surpassing most current methods for pulmonary nodule segmentation. Our method further improved the accuracy, reliability, and stability of lung nodule segmentation tasks even on challenging CT scans.

An Operational Approach for Optimizing Transcranial Direct Current Stimulation.

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique that has been utilized for treating brain disorders and improving cognitive function. In order to achieve targeted tDCS, many optimization methods of montages and electric currents have been proposed. However, these methods have some limitations. Most of them were proposed for single-objective optimization (focality or intensity) and have no constrain with the number of electrodes (Most devices only have less than 8 electrodes currently). In this study, we proposed an operational optimization approach for well-targeted tDCS, which aims to optimize for two objectives of electric field (EF) intensity and focality with constraints on the number of electrodes. Compared with traditional tDCS in our cohort (10 subjects), our method significantly improves the EF focality. When compared to commonly used 4×1 high-definition tDCS (HD-tDCS), our method can achieve higher EF intensity in the target region with less than 8 electrodes. Our method can balance the two objectives of EF and shorten optimization time, which is convenient for practical application.

Research Progress on the Mechanism of Anti-Tumor Immune Response Induced by TTFields.

Tumor treating fields (TTFields), a biophysical therapy technology that uses alternating electric fields to inhibit tumor proliferation, has been approved by the U.S. Food and Drug Administration (FDA) for the treatment of newly diagnosed or recurrent glioblastomas (GBM) and malignant pleural mesotheliomas (MPM). Clinical trials have confirmed that TTFields are effective in slowing the tumor growth and prolonging patient survival. In recent years, many researchers have found that TTFields can induce anti-tumor immune responses, and their main mechanisms include upregulating the infiltration ratio and function of immune cells, inducing the immunogenic cell death of tumor cells, modulating immune-related signaling pathways, and upregulating the expression of immune checkpoints. Treatment regimens combining TTFields with tumor immunotherapy are emerging as a promising therapeutic approach in clinical practice. Given the increasing number of recently published studies on this topic, we provide an updated review of the mechanisms and clinical implications of TTFields in inducing anti-tumor immune responses. This review not only has important reference value for an in-depth study of the anticancer mechanism of TTFields but also provides insights into the future clinical application of TTFields.

Modularity Facilitates Classification Performance of Spiking Neural Networks for Decoding Cortical Spike Trains.

After the introduction of recurrence, an important property of the biological brain, spiking neural networks (SNNs) have achieved unprecedented classification performance. But they still cannot outperform many artificial neural networks. Modularity is another crucial feature of the biological brain. It remains unclear if modularity can also improve the performance of SNNs. To investigate this idea, we proposed the modular SNN, and compared its performance with a uniform SNN without modularity by employing them to classify cortical spike trains. For the first time, a significant improvement was found in our modular SNN. Further, we probed into the factors influencing the performance of the modular SNN and found: (a). The modular SNN outperformed the uniform SNN more significantly when the number of neurons in the networks increased; (b). The performance of the modular SNNs increased as the number of modules dropped. These preliminary but novel findings suggest that modularity may help develop better artificial intelligence and brain-machine interfaces. Also, the modular SNN may serve as a model for the study of neuronal spike synchrony.

A 3D-Printed Dual Driving Forces Scaffold with Self-Promoted Cell Absorption for Spinal Cord Injury Repair.

Stem cells play critical roles in cell therapies and tissue engineering for nerve repair. However, achieving effective delivery of high cell density remains a challenge. Here, a novel cell delivery platform termed the hyper expansion scaffold (HES) is developed to enable high cell loading. HES facilitated self-promoted and efficient cell absorption via a dual driving force model. In vitro tests revealed that the HES rapidly expanded 80-fold in size upon absorbing 2.6 million human amniotic epithelial stem cells (hAESCs) within 2 min, representing over a 400% increase in loading capacity versus controls. This enhanced uptake benefited from macroscopic swelling forces as well as microscale capillary action. In spinal cord injury (SCI) rats, HES-hAESCs promoted functional recovery and axonal projection by reducing neuroinflammation and improving the neurotrophic microenvironment surrounding the lesions. In summary, the dual driving forces model provides a new rationale for engineering hydrogel scaffolds to facilitate self-promoted cell absorption. The HES platform demonstrates great potential as a powerful and efficient vehicle for delivering high densities of hAESCs to promote clinical treatment and repair of SCI.

A Fast and Effective Spike Sorting Method Based on Multi-Frequency Composite Waveform Shapes.

Accurate spike sorting to the appropriate neuron is crucial for neural activity analysis. To improve spike sorting performance, it is essential to fully leverage each processing step, including filtering, spike detection, feature extraction, and clustering. However, compared to the latter two steps that were widely studied and optimized, the filtering process was largely neglected. In this study, we proposed a fast and effective spike sorting method (MultiFq) based on multi-frequency composite waveform shapes acquired through an optimized filtering process. When combined with the classical PCA-Km spiking sorting algorithm, our proposed MultiFq significantly improved its sorting performance and achieved as high performance as the complex Wave-clus did in both the simulated and in vivo datasets. But, the combined method was about 10 times faster than Wave-clus (0.16 s vs. 2.06 s in simulated datasets; 0.46 s vs. 2.03 s in in vivo datasets). Furthermore, we demonstrated the compatibility of our MultiFq by combining it with other sorting algorithms, which consistently resulted in significant improvement in sorting accuracy with the maximum improvement at 35.04%. The above results demonstrated that our proposed method could significantly improve the sorting performance with low computation cost and good compatibility by leveraging the multi-frequency composite waveform shapes.

Deep neural network pulmonary nodule segmentation methods for CT images: Literature review and experimental comparisons.

Automatic and accurate segmentation of pulmonary nodules in CT images can help physicians perform more accurate quantitative analysis, diagnose diseases, and improve patient survival. In recent years, with the development of deep learning technology, pulmonary nodule segmentation methods based on deep neural networks have gradually replaced traditional segmentation methods. This paper reviews the recent pulmonary nodule segmentation algorithms based on deep neural networks. First, the heterogeneity of pulmonary nodules, the interpretability of segmentation results, and external environmental factors are discussed, and then the open-source 2D and 3D models in medical segmentation tasks in recent years are applied to the Lung Image Database Consortium and Image Database Resource Initiative (LIDC) and Lung Nodule Analysis 16 (Luna16) datasets for comparison, and the visual diagnostic features marked by radiologists are evaluated one by one. According to the analysis of the experimental data, the following conclusions are drawn: (1) In the pulmonary nodule segmentation task, the performance of the 2D segmentation models DSC is generally better than that of the 3D segmentation models. (2) 'Subtlety', 'Sphericity', 'Margin', 'Texture', and 'Size' have more influence on pulmonary nodule segmentation, while 'Lobulation', 'Spiculation', and 'Benign and Malignant' features have less influence on pulmonary nodule segmentation. (3) Higher accuracy in pulmonary nodule segmentation can be achieved based on better-quality CT images. (4) Good contextual information acquisition and attention mechanism design positively affect pulmonary nodule segmentation.

A Fast Magnetic Flux Density Measurement Method With Skip-Echo Acquired Turbo Spin Echo (SATE).

The measurements of magnetic flux density ( Bz) needed in magnetic resonance electrical impedance tomography (MREIT) and magnetic resonance current density imaging (MRCDI) techniques often utilize spin echo (SE)-based sequences for data acquisition. The low imaging speed of SE-based methods significantly hampers the clinical applications of MREIT and MRCDI. Here, we propose a new sequence for substantially accelerating the acquisition of Bz measurements. A skip-echo acquired turbo spin echo (SATE) imaging sequence based on the conventional turbo spin echo (TSE) technique was proposed by adding a skip-echo module in front of the TSE acquisition module. The skip-echo module consisted of a series of refocusing pulses without acquisition. In SATE, amplitude-modulated crusher gradients were used to remove the stimulated echo pathways, and the radiofrequency (RF) pulse shape was specially selected to preserve more signals. In efficiency evaluation experiments performed on a spherical gel phantom, we demonstrated that SATE had improved measurement efficiency compared to the conventional TSE sequence via skipping one echo before acquiring signals. The accuracy of the Bz measurements by SATE was validated against those by the multi-echo injection current nonlinear encoding (ME-ICNE) method, while SATE was able to accelerate the data acquisition up to 10-fold. Volumetric coverage of Bz maps obtained in the phantom, pork, and human calf illustrated that SATE can reliably measure volumetric Bz distributions within clinically acceptable time. The proposed SATE sequence provides a fast and effective approach for volumetric coverage of Bz measurements, greatly facilitating the clinical applications of MREIT and MRCDI techniques.

The influence of non-stationarity of spike signals on decoding performance in intracortical brain-computer interface: a simulation study.

Introduction: Intracortical Brain-Computer Interfaces (iBCI) establish a new pathway to restore motor functions in individuals with paralysis by interfacing directly with the brain to translate movement intention into action. However, the development of iBCI applications is hindered by the non-stationarity of neural signals induced by the recording degradation and neuronal property variance. Many iBCI decoders were developed to overcome this non-stationarity, but its effect on decoding performance remains largely unknown, posing a critical challenge for the practical application of iBCI. Methods: To improve our understanding on the effect of non-stationarity, we conducted a 2D-cursor simulation study to examine the influence of various types of non-stationarities. Concentrating on spike signal changes in chronic intracortical recording, we used the following three metrics to simulate the non-stationarity: mean firing rate (MFR), number of isolated units (NIU), and neural preferred directions (PDs). MFR and NIU were decreased to simulate the recording degradation while PDs were changed to simulate the neuronal property variance. Performance evaluation based on simulation data was then conducted on three decoders and two different training schemes. Optimal Linear Estimation (OLE), Kalman Filter (KF), and Recurrent Neural Network (RNN) were implemented as decoders and trained using static and retrained schemes. Results: In our evaluation, RNN decoder and retrained scheme showed consistent better performance under small recording degradation. However, the serious signal degradation would cause significant performance to drop eventually. On the other hand, RNN performs significantly better than the other two decoders in decoding simulated non-stationary spike signals, and the retrained scheme maintains the decoders' high performance when changes are limited to PDs. Discussion: Our simulation work demonstrates the effects of neural signal non-stationarity on decoding performance and serves as a reference for selecting decoders and training schemes in chronic iBCI. Our result suggests that comparing to KF and OLE, RNN has better or equivalent performance using both training schemes. Performance of decoders under static scheme is influenced by recording degradation and neuronal property variation while decoders under retrained scheme are only influenced by the former one.

A 0.99-to-4.38 uJ/class Event-Driven Hybrid Neural Network Processor for Full-Spectrum Neural Signal Analyses.

Versatile and energy-efficient neural signal processors are in high demand in brain-machine interfaces and closed-loop neuromodulation applications. In this paper, we propose an energy-efficient processor for neural signal analyses. The proposed processor utilizes three key techniques to efficiently improve versatility and energy efficiency. 1) Hybrid neural network design: The processor supports artificial neural network (ANN)- and spiking neural network (SNN)-based neuromorphic processing where ANN is used to support the processing of ExG signals and SNN is used for handling neural spike signals. 2) Event-driven processing: The processor can perform always-on binary neural network (BNN)-based event detection with low-energy consumption, and it only switches to the high-accuracy convolutional neural network (CNN)-based recognition mode when events are detected. 3) Reconfigurable architecture: By exploiting the computational similarity of different neural networks, the processor supports critical BNN, CNN, and SNN operations with the same processing elements, achieving significant area reduction and energy efficiency improvement over those of a naive implementation. It achieves 90.05% accuracy and 4.38 uJ/class in a center-out reaching task with an SNN and 99.4% sensitivity, 98.6% specificity, and 1.93 uJ/class in an EEG-based seizure prediction task with dual neural network-based event-driven processing. Moreover, it achieves a classification accuracy of 99.92%, 99.38%, and 86.39% and energy consumption of 1.73, 0.99, and 1.31 uJ/class for EEG-based epileptic seizure detection, ECG-based arrhythmia detection, and EMG-based gesture recognition, respectively.

Clinical application of enhanced recovery after surgery concept in laparoscopic treatment of pediatric acute appendicitis.

BACKGROUND: This study assesses whether enhanced recovery after surgery (ERAS) is beneficial in treating acute appendicitis in pediatrics by laparoscopic techniques. METHOD: The children with acute appendicitis (n = 116) were divided into the ERAS group (n = 54) and the control group (n = 62). Then the preoperative data, intraoperative observation indexes, and postoperative data were analyzed. RESULTS: There was no significant difference in preoperative data and intraoperative observation indexes between the two groups. C-reactive protein (CRP) and white blood cell (WBC) in the ERAS group were significantly lower than those in the control group 3 days after the operation. Moreover, no significant difference in the visual analog score (VAS) between the two groups 3 days after the operation, but the other postoperative observation indexes in the ERAS group were significantly better than those in the control group. Nausea and vomiting in the ERAS group were significantly lower than those in the control group, with no significant difference in other complications between the two groups. CONCLUSION: ERAS could improve children's comfort, reduce some postoperative complications, reduce hospitalization expenses, and speed up recovery from acute appendicitis treated by laparoscopy. Therefore, it has clinical application value.

Multifunctional Evaluation Technology for Diagnosing Malfunctions of Regional Pelvic Floor Muscles Based on Stretchable Electrode Array Probe.

The pelvic floor dysfunction (PFD) has become a serious public health problem. Accurate diagnosis of regional pelvic floor muscle (PFM) malfunctions is vitally important for the prevention and treatment of PFD. However, there is a lack of reliable diagnostic devices to evaluate and diagnose regional PFM abnormality. In this work, we developed a multifunctional evaluation technology (MET) based on a novel airbag-type stretchable electrode array probe (ASEA) for the diagnosis of malfunctions of regional PFM. The inflatable ASEA has specifically distributed 32 electrodes along the muscles, and is able to adapt to different human bodies for tight contact with the muscles. These allow synchronous collection of high-quality multi-channel surface electromyography (MC-sEMG) signals, and then are used to diagnose regional PFM malfunctions and evaluate inter-regional correlation. Clinical trial was conducted on 15 postpartum stress urinary incontinence (PSUI) patients and 15 matched asymptomatic women. Results showed that SUI patients responded slowly to the command and have symptoms of muscle strength degeneration. The results were consistent with the relevant clinical manifestations, and proved the reliability of MET for multifunctional PFM evaluation. Furthermore, the MET can diagnose malfunctions of regional PFM, which is inaccessible with existing technology. The results also showed that the dysfunction of PSUI patients is mainly located in iliococcygeus, pubococcygeus, and urethral sphincter regions, and there is a weak correlation between these specific regions and nearby regions. In conclusion, MET provides a point-of-care diagnostic method for abnormal function of regional PFM, which has a potential for the targeted point-to-point electrical stimulation treatment and PFD pathology research.

Multimodal Electrocorticogram Active Electrode Array Based on Zinc Oxide-Thin Film Transistors.

Active electrocorticogram (ECoG) electrodes can amplify weak electrophysiological signals and improve anti-interference ability; however, traditional active electrodes are opaque and cannot realize photoelectric collaborative observation. In this study, an active and fully transparent ECoG array based on zinc oxide thin-film transistors (ZnO TFTs) is developed as a local neural signal amplifier for electrophysiological monitoring. The transparency of the proposed ECoG array is up to 85%, which is superior to that of the previously reported active electrode arrays. Various electrical characterizations have demonstrated its ability to record electrophysiological signals with a higher signal-to-noise ratio of 19.9 dB compared to the Au grid (13.2 dB). The high transparency of the ZnO-TFT electrode array allows the concurrent collection of high-quality electrophysiological signals (32.2 dB) under direct optical stimulation of the optogenetic mice brain. The ECoG array can also work under 7-Tesla magnetic resonance imaging to record local brain signals without affecting brain tissue imaging. As the most transparent active ECoG array to date, it provides a powerful multimodal tool for brain observation, including recording brain activity under synchronized optical modulation and 7-Tesla magnetic resonance imaging.

In Vivo Measurements of Transcranial Electrical Stimulation in Lesioned Human Brain: A Case Report.

Transcranial electrical stimulation (tES) has been utilized widely in populations with brain lesions, such as stroke patients. The tES-generated electric field (EF) within the brain is considered as one of the most important factors for physiological effects. However, it is still unclear how brain lesions may influence EF distribution induced by tES. In this case study, we reported in vivo measurements of EF in one epilepsy participant with brain lesions during different tES montages. With the in vivo EF data measured by implanted stereo-electroencephalography (sEEG) electrodes, the simulation model was investigated and validated. Our results demonstrate that the prediction ability of the current simulation model may be degraded in the lesioned human brain.

Chinese expert consensus on prevention and intervention for the elderly with malnutrition (2022).

Malnutrition is a state of altered body composition and body cell mass due to inadequate intake or utilization of energy or nutrients, leading to physical and mental dysfunction and impaired clinical outcomes. As one of the most common geriatric syndromes, malnutrition in the elderly is a significant risk factor for poor clinical outcomes, causing a massive burden on medical resources and society. The risk factors for malnutrition in the elderly are diverse and include demographics, chronic diseases, and psychosocial factors. Presently, recommendations for the prevention and intervention of malnutrition in the elderly are not clear or consistent in China. This consensus is based on the latest global evidence and multiregional clinical experience in China, which aims to standardize the prevention and intervention of malnutrition in the elderly in China and improve the efficacy of clinical practice and the prognosis of elderly patients.