Discovery of a highly potent NPAS3 heterodimer inhibitor by covalently modifying ARNT.

Neuronal PAS domain protein 3 (NPAS3), a basic helix-loop-helix PER-ARNT-SIM (bHLH-PAS) family member, is a pivotal transcription factor in neuronal regeneration, development, and related diseases, regulating the expression of downstream genes. Despite several modulators of certain bHLH-PAS family proteins being identified, the NPAS3-targeted compound has yet to be reported. Herein, we discovered a hit compound BI-78D3 that directly blocks the NPAS3-ARNT heterodimer formation by covalently binding to the aryl hydrocarbon receptor nuclear translocator (ARNT) subunit. Further optimization based on the hit scaffold yielded a highly potent Compound 6 with a biochemical EC50 value of 282 ± 61 nM and uncovered the 5-nitrothiazole-2-sulfydryl as a cysteine-targeting covalent warhead. Compound 6 effectively down-regulated NPAS3's transcriptional function by disrupting the interface of NPAS3-ARNT complexes at cellular level. In conclusion, our study identifies the 5-nitrothiazole-2-sulfydryl as a cysteine-modified warhead and provides a strategy that blocks the NPAS3-ARNT heterodimerization by covalently conjugating ARNT Cys336 residue. Compound 6 may serve as a promising chemical probe for exploring NPAS3-related physiological functions.

Design, synthesis and biological evaluation of a novel spiro oxazolidinedione as potent p300/CBP HAT inhibitor for the treatment of ovarian cancer.

Histone acetylation is one of the most essential parts of epigenetic modification, mediating a variety of complex biological functions. In these procedure, p300/CBP could catalyze the acetylation of lysine 27 on histone 3 (H3K27ac), and had been reported to mediate tumorigenesis and development in a variety of tumors by enhancing chromatin transcription activity. Ovarian cancer, as an extremely malignant tumor, has also been observed to undergo abnormal acetylation of histones. However, whether the treatment of ovarian cancer could be achieved by inhibiting the acetylation activity of p300/CBP on H3K27 has not been well investigated. In this article, we modified the structure of p300/CBP HAT domain inhibitor A-485 and obtained a highly active small molecule known as 13f, which has an IC50 value of 0.49 nM for inhibiting the in vitro enzyme activity of p300, as well as the anti-proliferation IC50 value on ovarian cancer cell line OVCAR-3 was 153 nM. In addition, 13f had strong acetylase family selectivity, good metabolic stability and promising in vivo anti-tumor activity in OVCAR-3 xenograft model. The discovery of 13f revealed a more active chemical entity of the HATs domain of p300/CBP and provided a novel idea for the application of epigenetic inhibitors in the treatment of ovarian cancer.

Lead discovery, chemical optimization, and biological evaluation studies of novel histone methyltransferase SET7 small-molecule inhibitors.

The post-translational modifications of histones, including histone methylation and demethylation, control the expression switch of multiple genes. SET domain-containing lysine methyltransferase 7 (SET7) is the only methyltransferase, which can specifically monomethylate lysine-4 of histone H3 (H3K4me1) and play critical roles in various diseases, including breast cancer, hepatitis C virus (HCV), atherosclerotic vascular disease, diabetes, prostate cancer, hepatocellular carcinoma, and obesity. However, several known SET7 inhibitors exhibit weak activity or poor selectivity. Therefore, the development of novel SET7 inhibitors is highly desirable and of great clinical value. In this study, we identified 2-79 as a new hit compound by structure-based virtual screening and further AlphaLISA-based biochemical evaluation. Via chemical optimization, the synthesized compound DC21 was confirmed as a potent SET7 inhibitor with an IC50 value of 15.93 µM. The interaction between DC21 and SET7 was also validated through SPR experiment. Especially, DC21 retarded proliferation of MCF7 cells with an IC50 value of 25.84 µM in cellular level. In addition, DC21 has good selectivity for several other epigenetic targets, such as SUV39H1, G9a, NSD1, DOT1L and MOF. DC21 can serve as a lead compound to develop more potential SET7 inhibitors and as a chemical probe for SET7 biological function studies.

Computational discovery and biological evaluation of novel inhibitors targeting histone-lysine N-methyltransferase SET7.

Histone-lysine N-methyltransferase SET7 emerged as a potential target for multiple cancers. In a virtual screening program used to explore new and potent inhibitors of SET7, compound 16 was discovered as a top hit with an IC50 value of 6.02 µM. A further similarity search afforded a new compound 23, which exhibited better activity against SET7 with an IC50 value of 1.96 µM. Importantly, compound 23 selectively inhibited the proliferation of MV4-11 cells. Comprehensively, compound 23 can serve as a lead for further identification and development of more potent SET7 inhibitors.

Anterior High-Intensity Zone in Lumbar Discs: Prevalence and Association with Low Back Pain.

OBJECTIVES: To explore the features of high-intensity zone (HIZ) in anterior annulus fibrosus and assess the association of anterior HIZ with low back pain (LBP). DESIGN, SETTING, AND SUBJECTS: A retrospective study of 5,940 discs in 1,188 individuals was conducted. METHODS: Subjects' information and LBP symptoms confirmed by an orthopedic surgeon were acquired from the medical record. Magnetic resonance (MR) image reading and analysis were performed by two experienced blinded radiologists. RESULTS: Two hundred eighty individuals exhibited 355 anterior HIZs in 355 discs. The prevalence was 23.57%; 88.45% were located in the inferior part of the annulus fibrosus. It frequently occurred in the middle and upper segments of lumbar spine, especially at L3/4 (45.63%). Of the 355 anterior HIZs, only 79 (22.25%) were consecutive-slides HIZ. Round type (63.38%) was the most common shape of anterior HIZs. The highest prevalence was found in individuals aged 60-69 years. LBP was confirmed in 141 anterior-HIZ individuals. The incidence of LBP in anterior-HIZ individuals was significantly higher than in non-HIZ subjects (50.36% vs 35.24%, χ2 = 18.314, P < 0.001). CONCLUSIONS: Anterior HIZ is a lower-prevalence, age-related sign on lumbar MR images. The spatial distribution of anterior HIZ can be distinguished from posterior HIZ. The number of consecutive anterior HIZ slides might suggest fewer Dallas grade 4 anterior annular disruptions in this sample. Anterior HIZ was correlated with LBP.

Simultaneous EEG-fNIRS Data Classification Through Selective Channel Representation and Spectrogram Imaging.

The integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) can facilitate the advancement of brain-computer interfaces (BCIs). However, existing research in this domain has grappled with the challenge of the efficient selection of features, resulting in the underutilization of the temporal richness of EEG and the spatial specificity of fNIRS data.To effectively address this challenge, this study proposed a deep learning architecture called the multimodal DenseNet fusion (MDNF) model that was trained on two-dimensional (2D) EEG data images, leveraging advanced feature extraction techniques. The model transformed EEG data into 2D images using a short-time Fourier transform, applied transfer learning to extract discriminative features, and consequently integrated them with fNIRS-derived spectral entropy features. This approach aimed to bridge existing gaps in EEG-fNIRS-based BCI research by enhancing classification accuracy and versatility across various cognitive and motor imagery tasks.Experimental results on two public datasets demonstrated the superiority of our model over existing state-of-the-art methods.Thus, the high accuracy and precise feature utilization of the MDNF model demonstrates the potential in clinical applications for neurodiagnostics and rehabilitation, thereby paving the method for patient-specific therapeutic strategies.

Proprioception enhancement for robot assisted neural rehabilitation: a dynamic electrical stimulation based method and preliminary results from EEG analysis.

Objective.In recent years, the robot assisted (RA) rehabilitation training has been widely used to counteract defects of the manual one provided by physiotherapists. However, since the proprioception feedback provided by the robotic assistance or the manual methods is relatively weak for the paralyzed patients, their rehabilitation efficiency is still limited. In this study, a dynamic electrical stimulation (DES) based proprioception enhancement and the associated quantitative analysis methods have been proposed to overcome the limitation mentioned above.Approach.Firstly, the DES based proprioception enhancement method was proposed for the RA neural rehabilitation. In the method, the relationship between the surface electromyogram (sEMG) envelope of the specified muscle and the associated joint angles was constructed, and the electrical stimulation (ES) pulses for the certain joint angles were designed by consideration of the corresponding sEMG envelope, based on which the ES can be dynamically regulated during the rehabilitation training. Secondly, power spectral density, source estimation, and event-related desynchronization of electroencephalogram, were combinedly used to quantitatively analyze the proprioception from multiple perspectives, based on which more comprehensive and reliable analysis results can be obtained. Thirdly, four modes of rehabilitation training tasks, namely active, RA, DES-RA, and ES-only training, were designed for the comparison experiment and validation of the proposed DES based proprioception enhancement method.Main results.The results indicated that the activation of the sensorimotor cortex was significantly enhanced when the DES was added, and the cortex activation for the DES-RA training was similar to that for the active training. Meanwhile, relatively consistent results from the multiple perspectives were obtained, which validates the effectiveness and robustness of the proposed proprioception analysis method.Significance.The proposed methods have the potential to be applied in the practical rehabilitation training to improve the rehabilitation efficiency.

Novel Vibrating Foot Orthoses for Improving Tactile Sensation in Type 2 Diabetes With Peripheral Neuropathy.

Improving tactile sensation by vibrating insoles was recommended to prevent foot ulcers in diabetic peripheral neuropathy (DPN). Lack of an insole design for diabetics was a challenge. Clinical trials on applying vibrating insoles with noise and stochastic resonance (SR) stimulating tactile were also required. In this study, vibrating foot orthoses (VFO) with a total contact design based on orthotics were proposed to provide proper insoles for diabetes. This study aimed to determine if VFO were beneficial at enhancing tactile in DPN. VFO were developed in combination with individual's custom-made foot orthoses and stimulation signals-integrating random 0-100 Hz square wave pulse signals with pseudorandom white noise by a SR approach. Sixty patients with mild-to-severe DPN were randomized to conduct crossover experiments: using and without VFO for 60 minutes stimulation at 90% of individuals' vibration perception threshold (VPT) level. VPT values when using VFO at the 1st and 5th metatarsophalangeal joints of the left foot decreased by 9.35% ( [Formula: see text].001); 9.04% ( [Formula: see text].001), and of the right foot decreased by 7.63% ( [Formula: see text].001); 7.24% ( [Formula: see text].001), respectively. Without VFO, there was no significant difference. Subgroups of mild and moderate DPN tended to benefit greatly from utilizing VFO. VFO can improve tactile in DPN. VFO may contribute to restoring/prolonging tactile and protective sensations, also decreasing peripheral nervous system deterioration. VFO might be useful for neurorehabilitation, and help prevent foot ulcers and disabilities.

Development and Validation of a Self-Aligning Knee Exoskeleton With Hip Rotation Capability.

The self-aligning capability of an exoskeleton is important to ensure wearing comfort, and the delicate motion ability of the exoskeleton is essential for motion assistance. Designing a self-aligning exoskeleton that offers improved wearing comfort and enhanced motion-assistance functions remains a challenge. This paper proposes a novel spatial self-aligning mechanism for a knee exoskeleton to enable simultaneous assistance in the flexion and extension (FE) of the knee joint and the internal and external rotation (IER) of the hip joint. Additionally, considering the misalignment of the human-robot joint axes, a kinematic model of the knee exoskeleton is established and analyzed to demonstrate the kinematic compatibility of the exoskeleton. Furthermore, a global torque manipulability (GTM) index is proposed to evaluate the effects of dimensional parameters on the exoskeleton's performance, and then the knee exoskeleton is optimized according to the GTM index. Finally, experiments are conducted to validate the performance of the proposed exoskeleton. The experimental results show that during knee FE and hip IER, the proposed exoskeleton exhibits lower interaction forces and torques than existing exoskeletons.

A Double-Hurdle Quantification Model for Freezing of Gait of Parkinson's Patients.

Freezing of gait (FOG) leads to an increased risk of falls and limited mobility in individuals with Parkinson's disease (PD). However, existing research ignores the fine-grained quantitative assessment of FOG severity. This paper provides a double-hurdle model that uses typical spatiotemporal gait features to quantify the FOG severity in patients with PD. Moreover, a novel multi-output random forest algorithm is used as one hurdle of the double-hurdle model, further enhancing the model's performance. We conduct six experiments on a public PD gait database. Results demonstrate that the designed random forest algorithm in the double-hurdle model-hyperparameter independence framework achieves outstanding performances with the highest correlation coefficient (CC) of 0.972 and the lowest root mean square error (RMSE) of 2.488. Furthermore, we study the effect of drug state on the gait patterns of PD patients with or without FOG. Results show that "OFF" state amplifies the visibility of FOG symptoms in PD patients. Therefore, this study holds significant implications for the management and treatment of PD.

An Adaptive Hybrid Brain-Computer Interface for Hand Function Rehabilitation of Stroke Patients.

Motor imagery (MI) based brain computer interface (BCI) has been extensively studied to improve motor recovery for stroke patients by inducing neuroplasticity. However, due to the lower spatial resolution and signal-to-noise ratio (SNR) of electroencephalograph (EEG), MI based BCI system that involves decoding hand movements within the same limb remains lower classification accuracy and poorer practicality. To overcome the limitations, an adaptive hybrid BCI system combining MI and steady-state visually evoked potential (SSVEP) is developed to improve decoding accuracy while enhancing neural engagement. On the one hand, the SSVEP evoked by visual stimuli based on action-state flickering coding approach significantly improves the recognition accuracy compared to the pure MI based BCI. On the other hand, to reduce the impact of SSVEP on MI due to the dual-task interference effect, the event-related desynchronization (ERD) based neural engagement is monitored and employed for feedback in real-time to ensure the effective execution of MI tasks. Eight healthy subjects and six post-stroke patients were recruited to verify the effectiveness of the system. The results showed that the four-class gesture recognition accuracies of healthy individuals and patients could be improved to 94.37 ± 4.77 % and 79.38 ± 6.26 %, respectively. Moreover, the designed hybrid BCI could maintain the same degree of neural engagement as observed when subjects solely performed MI tasks. These phenomena demonstrated the interactivity and clinical utility of the developed system for the rehabilitation of hand function in stroke patients.

Design, Optimization, and Modeling of a Hydraulic Soft Robot for Chronic Total Occlusions.

Chronic total occlusion (CTO) is one of the most severe and sophisticated vascular stenosis because of complete blockage, greater operation difficulty, and lower procedural success rate. This study proposes a hydraulic-driven soft robot imitating the earthworm's locomotion to assist doctors or operators in actively opening thrombi in coronary or peripheral artery vessels. Firstly, a three-actuator bionic soft robot is developed based on earthworms' physiological structure. The soft robot's locomotion gait inspired by the earthworm's mechanism is designed. Secondly, the influence of structure parameters on actuator deformation, stress, and strain is explored, which can help us determine the soft actuators' optimal structure parameters. Thirdly, the relationship between hydraulic pressure and actuator deformation is investigated by performing finite element analysis using the bidirectional fluid-structure interaction (FSI) method. The kinematic models of the soft actuators are established to provide a valuable reference for the soft actuators' motion control.

A Multi-Modal Classification Method for Early Diagnosis of Mild Cognitive Impairment and Alzheimer's Disease Using Three Paradigms With Various Task Difficulties.

Alzheimer's Disease (AD) accounts for the majority of dementia, and Mild Cognitive Impairment (MCI) is the early stage of AD. Early and accurate diagnosis of dementia plays a vital role in more targeted treatments and effectively halting disease progression. However, the clinical diagnosis of dementia requires various examinations, which are expensive and require a high level of expertise from the doctor. In this paper, we proposed a classification method based on multi-modal data including Electroencephalogram (EEG), eye tracking and behavioral data for early diagnosis of AD and MCI. Paradigms with various task difficulties were used to identify different severity of dementia: eye movement task and resting-state EEG tasks were used to detect AD, while eye movement task and delayed match-to-sample task were used to detect MCI. Besides, the effects of different features were compared and suitable EEG channels were selected for the detection. Furthermore, we proposed a data augmentation method to enlarge the dataset, designed an extra ERPNet feature extract layer to extract multi-modal features and used domain-adversarial neural network to improve the performance of MCI diagnosis. We achieved an average accuracy of 88.81% for MCI diagnosis and 100% for AD diagnosis. The results of this paper suggest that our classification method can provide a feasible and affordable way to diagnose dementia.

Mercury isotope evidence for the importance of recycled fluids in collisional ore systems.

The sources of fluids and metals in porphyry systems of continental-collision settings are poorly constrained. Mercury isotopes display unique mass-independent fractionation (expressed as Δ199Hg) and may provide important constraints on metal and volatile sources given that Hg is a highly volatile metal. Here, we report Hg isotope data on ore-forming porphyries, barren magmatic rocks, and mantle-derived mafic magmas from southern Tibet. The fertile porphyries and coeval mafic magmas display mainly positive Δ199Hg values (up to +0.25 per mil), while Δ199Hg values in barren magmatic rocks and mafic magmas are largely negative (-0.54 to 0.00 per mil). The positive Δ199Hg values observed here are consistent with seawater and marine sediments, suggesting that the ultimate source of fluids involved in the genesis of post-subduction porphyry copper deposits was the mantle lithosphere metasomatized by previous oceanic plate subduction. Our Hg isotope data provide an alternative view to current metallogenetic models on collisional porphyry systems that focus on melting of the lower continental crust.

EEG-Based Focus of Attention Tracking and Regulation During Dual-Task Training for Neural Rehabilitation of Stroke Patients.

Dual-task training under variable-priority instructions (DT-VP), during which subjects are required to vary their focus of attention (FOA) between two concurrent tasks, has shown a more significant improvement in neural rehabilitation than that under fixed-priority instructions. Failed FOA switching not only diminishes the recovery benefits, but also causes anxieties, which is detrimental to rehabilitation. Developing a strategy for tracking and regulating patients' FOA to achieve a better performance in task priority-following during DT-VP is thus imperative. In this study, fifteen stroke patients participated in DT-VP that comprised two tasks: a mathematical problem-solving task and a cycling task, during which their electroencephalograms were recorded simultaneously. The significantly differentiated power spectra of four brain regions extracted from single-task training were fed into a support vector machine to build a FOA tracking algorithm for patients' attention assessment during the DT-VP. Moreover, dual-task difficulty adaptation method was designed to regulate patients' FOA when their FOA and the high-priority task were not coincident. The comparison experimental results showed that the proposed method significantly improved patients' FOA distributed on the high-priority task (analysis of variance, 0.05). Meanwhile, the absolute power spectral densities of the motor cortex and the frontal region could also be improved during DT-VP under high motor and cognitive task priority instructions, respectively. These phenomena demonstrated the feasibility of the proposed method in helping stroke patients better implement FOA switching and maintenance.

A learnable EEG channel selection method for MI-BCI using efficient channel attention.

Introduction: During electroencephalography (EEG)-based motor imagery-brain-computer interfaces (MI-BCIs) task, a large number of electrodes are commonly used, and consume much computational resources. Therefore, channel selection is crucial while ensuring classification accuracy. Methods: This paper proposes a channel selection method by integrating the efficient channel attention (ECA) module with a convolutional neural network (CNN). During model training process, the ECA module automatically assigns the channel weights by evaluating the relative importance for BCI classification accuracy of every channel. Then a ranking of EEG channel importance can be established so as to select an appropriate number of channels to form a channel subset from the ranking. In this paper, the ECA module is embedded into a commonly used network for MI, and comparative experiments are conducted on the BCI Competition IV dataset 2a. Results and discussion: The proposed method achieved an average accuracy of 75.76% with all 22 channels and 69.52% with eight channels in a four-class classification task, outperforming other state-of-the-art EEG channel selection methods. The result demonstrates that the proposed method provides an effective channel selection approach for EEG-based MI-BCI.

Practical Predefined-Time Output-Feedback Consensus Tracking Control for Multiagent Systems.

This article addresses the issue of output-feedback consensus control of multiagent systems under the directed topology and subject to bounded external disturbances. By employing a smooth time-varying function, a distributed practical predefined-time (PPT) observer is developed to estimate the reference trajectory for the entire team (i.e., the leader's state) and a practical preset-time extended-state observer is also proposed to estimate bounded disturbances and unmeasurable system states. Next, a novel continuous and nonsingular PPT consensus control law is designed on the basis of the observers. Furthermore, the designed control protocol can achieve PPT stability, that is, consensus tracking errors are enforced to a neighborhood around zero within a predetermined time, which can be specified a priori, independent of initial states of agents and/or any other design parameters. Finally, illustrative numerical examples, including a comparative one, are provided to demonstrate the performance of the present predefined-time control approach.

Transformer Based Cross-Subject Mental Workload Classification Using FNIRS for Real-World Application.

Mental state monitoring is a hot topic especially in neurorehabilitation, skill training, etc, for which the functional near-infrared spectroscopy (fNIRS) has been suggested to be used, and fewer detection channels and cross-subject performance are usually required for real-world application. To this goal, we propose a transformer-based method for cross-subject mental workload classification using fewer channels of fNIRS. Firstly, the input fNIRS signals in a window are divided into patches in the temporal order and transformed into embeddings, to which a classification token and learnable position embeddings are added. Then, a transformer encoder is used to learn the long-range dependencies among the embeddings, of which the output classification token is sent to a multilayer perceptron (MLP) head. Mental workload classification results can be represented by the outputs of the MLP head. Finally, comparison experiments were conducted on the open-access fNIRS2MW dataset. The results show that, the proposed method can outperform previous methods in cross-subject classification accuracy, and relatively efficient computation can be obtained.

Graph Reasoning Module for Alzheimer's Disease Diagnosis: A Plug-and-Play Method.

Recent advances in deep learning have led to increased adoption of convolutional neural networks (CNN) for structural magnetic resonance imaging (sMRI)-based Alzheimer's disease (AD) detection. AD results in widespread damage to neurons in different brain regions and destroys their connections. However, current CNN-based methods struggle to relate spatially distant information effectively. To solve this problem, we propose a graph reasoning module (GRM), which can be directly incorporated into CNN-based AD detection models to simulate the underlying relationship between different brain regions and boost AD diagnosis performance. Specifically, in GRM, an adaptive graph Transformer (AGT) block is designed to adaptively construct a graph representation based on the feature map given by CNN, a graph convolutional network (GCN) block is adopted to update the graph representation, and a feature map reconstruction (FMR) block is built to convert the learned graph representation to a feature map. Experimental results demonstrate that the insertion of the GRM in the existing AD classification model can increase its balanced accuracy by more than 4.3%. The GRM-embedded model achieves state-of-the-art performance compared with current deep learning-based AD diagnosis methods, with a balanced accuracy of 86.2%.