Epileptic seizure prediction based on EEG using pseudo-three-dimensional CNN.

Epileptic seizures are characterized by their sudden and unpredictable nature, posing significant risks to a patient's daily life. Accurate and reliable seizure prediction systems can provide alerts before a seizure occurs, as well as give the patient and caregivers provider enough time to take appropriate measure. This study presents an effective seizure prediction method based on deep learning that combine with handcrafted features. The handcrafted features were selected by Max-Relevance and Min-Redundancy (mRMR) to obtain the optimal set of features. To extract the epileptic features from the fused multidimensional structure, we designed a P3D-BiConvLstm3D model, which is a combination of pseudo-3D convolutional neural network (P3DCNN) and bidirectional convolutional long short-term memory 3D (BiConvLstm3D). We also converted EEG signals into a multidimensional structure that fused spatial, manual features, and temporal information. The multidimensional structure is then fed into a P3DCNN to extract spatial and manual features and feature-to-feature dependencies, followed by a BiConvLstm3D input to explore temporal dependencies while preserving the spatial features, and finally, a channel attention mechanism is implemented to emphasize the more representative information in the multichannel output. The proposed has an average accuracy of 98.13%, an average sensitivity of 98.03%, an average precision of 98.30% and an average specificity of 98.23% for the CHB-MIT scalp EEG database. A comparison of the proposed model with other baseline methods was done to confirm the better performance of features through time-space nonlinear feature fusion. The results show that the proposed P3DCNN-BiConvLstm3D-Attention3D method for epilepsy prediction by time-space nonlinear feature fusion is effective.

Morphological entropy encodes cellular migration strategies on multiple length scales.

Cell migration is crucial for numerous physiological and pathological processes. A cell adapts its morphology, including the overall and nuclear morphology, in response to various cues in complex microenvironments, such as topotaxis and chemotaxis during migration. Thus, the dynamics of cellular morphology can encode migration strategies, from which diverse migration mechanisms can be inferred. However, deciphering the mechanisms behind cell migration encoded in morphology dynamics remains a challenging problem. Here, we present a powerful universal metric, the Cell Morphological Entropy (CME), developed by combining parametric morphological analysis with Shannon entropy. The utility of CME, which accurately quantifies the complex cellular morphology at multiple length scales through the deviation from a perfectly circular shape, is illustrated using a variety of normal and tumor cell lines in different in vitro microenvironments. Our results show how geometric constraints affect the MDA-MB-231 cell nucleus, the emerging interactions of MCF-10A cells migrating on collagen gel, and the critical transition from proliferation to invasion in tumor spheroids. The analysis demonstrates that the CME-based approach provides an effective and physically interpretable tool to measure morphology in real-time across multiple length scales. It provides deeper insight into cell migration and contributes to the understanding of different behavioral modes and collective cell motility in more complex microenvironments.

An entropy-based approach for assessing the directional persistence of cell migration.

Cell migration, which is primarily characterized by directional persistence, is essential for the development of normal tissues and organs, as well as for numerous pathological processes. However, there is a lack of simple and efficient tools to analyze the systematic properties of persistence based on cellular trajectory data. Here, we present a novel approach, the entropy of angular distribution , which combines cellular turning dynamics and Shannon entropy to explore the statistical and time-varying properties of persistence that strongly correlate with cellular migration modes. Our results reveal the changes in the persistence of multiple cell lines that are tightly regulated by both intra- and extracellular cues, including Arpin protein, collagen gel/substrate, and physical constraints. Significantly, some previously unreported distinctive details of persistence have also been captured, helping to elucidate how directional persistence is distributed and evolves in different cell populations. The analysis suggests that the entropy of angular distribution-based approach provides a powerful metric for evaluating directional persistence and enables us to better understand the relationships between cellular behaviors and multiscale cues, which also provides some insights into the migration dynamics of cell populations, such as collective cell invasion.

Development and Evaluation of a Flexible PVDF-Based Balloon Sensor for Detecting Mechanical Forces at Key Esophageal Nodes in Esophageal Motility Disorders.

Prevailing methods for esophageal motility assessments, such as perfusion manometry and probe-based function imaging, frequently overlook the intricate stress fields acting on the liquid-filled balloons at the forefront of the probing device within the esophageal lumen. To bridge this knowledge gap, we innovatively devised an infusible flexible balloon catheter, equipped with a quartet of PVDF piezoelectric sensors. This design, working in concert with a bespoke local key-node analytical algorithm and a sensor array state analysis model, seeks to shed new light on the dynamic mechanical characteristics at pivotal esophageal locales. To further this endeavor, we pioneered a singular closed balloon system and a complementary signal acquisition and processing system that employs a homogeneously distributed PVDF piezoelectric sensor array for the real-time monitoring of dynamic mechanical nuances in the esophageal segment. An advanced analytical model was established to scrutinize the coupled physical fields under varying degrees of balloon inflation, thereby facilitating a thorough dynamic stress examination of local esophageal nodes. Our rigorous execution of static, dynamic, and simulated swallowing experiments robustly substantiated the viability of our design, the logical coherence of our esophageal key-point stress analytical algorithm, and the potential clinical utility of a flexible esophageal key-node stress detection balloon probe outfitted with a PVDF array. This study offers a fresh lens through which esophageal motility testing can be viewed and improved upon.

Bubble pulsation characteristics in multi-bubble systems affected by bubble size polydispersity and spatial structure.

This study seeks to explore the bubble pulsation characteristics in multi-bubble environment with a special focus on the influences of the size polydispersity and the two-dimensional structure of bubbles. Three representative configurations of three interacting bubbles are formed by setting the initial radii of cavitation bubbles and inter-bubble distances appropriately, then the pulsation characteristics of a small bubble are investigated and compared by the bifurcation analysis. The results illustrate that the bubble size polydispersity and two-dimensional structure would greatly affect the bubble pulsations (i.e., the amplitude and nonlinearity of pulsations). Furthermore, the effects of two-dimensional structure are strong at a small inter-bubble distance of the large and small bubbles while the bubble size polydispersity always significantly affects the bubble pulsations for all cases. Moreover, the influences of both bubble size polydispersity and two-dimensional structure can be enhanced as the acoustic pressure increases, which can also become stronger when the large bubble is located at the same side as the small bubble and the initial radius of large bubble increases. Additionally, the effects would also be increased when the tissue viscoelasticity varies within a certain range. The present findings shed new light on the dynamics of multiple polydisperse microbubbles in viscoelastic tissues, potentially contributing to an optimization of their applications with ultrasound excitation.

Numerical investigation on acoustic cavitation characteristics of an air-vapor bubble: Effect of equation of state for interior gases.

The cavitation dynamics of an air-vapor mixture bubble with ultrasonic excitation can be greatly affected by the equation of state (EOS) for the interior gases. To simulate the cavitation dynamics, the Gilmore-Akulichev equation was coupled with the Peng-Robinson (PR) EOS or the Van der Waals (vdW) EOS. In this study, the thermodynamic properties of air and water vapor predicted by the PR and vdW EOS were first compared, and the results showed that the PR EOS gives a more accurate estimation of the gases within the bubble due to the less deviation from the experimental values. Moreover, the acoustic cavitation characteristics predicted by the Gilmore-PR model were compared to the Gilmore-vdW model, including the bubble collapse strength, the temperature, pressure and number of water molecules within the bubble. The results indicated that a stronger bubble collapse was predicted by the Gilmore-PR model rather than the Gilmore-vdW model, with higher temperature and pressure, as well as more water molecules within the collapsing bubble. More importantly, it was found that the differences between both models increase at higher ultrasound amplitudes or lower ultrasound frequencies while decreasing as the initial bubble radius and the liquid parameters (e.g., surface tension, viscosity and temperature of the surrounding liquid) increase. This study might offer important insights into the effects of the EOS for interior gases on the cavitation bubble dynamics and the resultant acoustic cavitation-associated effects, contributing to further optimization of its applications in sonochemistry and biomedicine.

Epileptic prediction using spatiotemporal information combined with optimal features strategy on EEG.

Objective: Epilepsy is the second most common brain neurological disease after stroke, which has the characteristics of sudden and recurrence. Seizure prediction is seriously important for improving the quality of patients' lives. Methods: From the perspective of multiple dimensions including time-frequency, entropy and brain network, this paper proposed a novel approach by constructing the optimal spatiotemporal feature set to predict seizures. Based on strong independence and large information capabilities, the two-dimensional feature screening algorithm is performed to eliminate unnecessary redundant features. In order to verify the effectiveness of the optimal feature set, support vector machine (SVM) was used to classify the preictal and interictal states on both the Kaggle intracranial EEG and CHB-MIT scalp EEG dataset. Results: This model achieved an average accuracy of 98.01%, AUC of 0.96, F-Score of 98.3% and FPR of 0.0383/h on the Kaggle dataset; On the CHB-MIT dataset, the average accuracy, AUC, F-score and FPR were 95.93%, 0.92, 94.97% and 0.0473/h, respectively. Further ablation experiments have confirmed that the temporal and spatial features fusion has better performance than the individual temporal or spatial features. Conclusion: Compared to the state-of-the-art methods, our approach outperforms most of these existing techniques. The results show that our approach can effectively extract the spatiotemporal information of epileptic EEG signals to predict epileptic seizures with high performance.

Effects of medium viscoelasticity on bubble collapse strength of interacting polydisperse bubbles.

Due to its physical and/or chemical effects, acoustic cavitation plays a crucial role in various emerging applications ranging from advanced materials to biomedicine. The cavitation bubbles usually undergo oscillatory dynamics and violent collapse within a viscoelastic medium, which are closely related to the cavitation-associated effects. However, the role of medium viscoelasticity on the cavitation dynamics has received little attention, especially for the bubble collapse strength during multi-bubble cavitation with the complex interactions between size polydisperse bubbles. In this study, modified Gilmore equations accounting for inter-bubble interactions were coupled with the Zener viscoelastic model to simulate the dynamics of multi-bubble cavitation in viscoelastic media. Results showed that the cavitation dynamics (e.g., acoustic resonant response, nonlinear oscillation behavior and bubble collapse strength) of differently-sized bubbles depend differently on the medium viscoelasticity and each bubble is affected by its neighboring bubbles to a different degree. More specifically, increasing medium viscosity drastically dampens the bubble dynamics and weakens the bubble collapse strength, while medium elasticity mainly affects the bubble resonance at which the bubble collapse strength is maximum. Differently-sized bubbles can achieve resonances and even subharmonic resonances at high driving acoustic pressures as the elasticity changes to certain values, and the resonance frequency of each bubble increases with the elasticity increasing. For the interactions between the size polydisperse bubbles, it indicated that the largest bubble generally has a dominant effect on the dynamics of smaller ones while in turn it is almost unaffected, exhibiting a pattern of destructive and constructive interactions. This study provides a valuable insight into the acoustic cavitation dynamics of multiple interacting polydisperse bubbles in viscoelastic media, which may offer a potential of controlling the medium viscoelasticity to appropriately manipulate the dynamics of multi-bubble cavitation for achieving proper cavitation effects according to the desired application.

Bone age assessment based on deep neural networks with annotation-free cascaded critical bone region extraction.

Bone age assessment (BAA) from hand radiographs is crucial for diagnosing endocrinology disorders in adolescents and supplying therapeutic investigation. In practice, due to the conventional clinical assessment being a subjective estimation, the accuracy of BAA relies highly on the pediatrician's professionalism and experience. Recently, many deep learning methods have been proposed for the automatic estimation of bone age and had good results. However, these methods do not exploit sufficient discriminative information or require additional manual annotations of critical bone regions that are important biological identifiers in skeletal maturity, which may restrict the clinical application of these approaches. In this research, we propose a novel two-stage deep learning method for BAA without any manual region annotation, which consists of a cascaded critical bone region extraction network and a gender-assisted bone age estimation network. First, the cascaded critical bone region extraction network automatically and sequentially locates two discriminative bone regions via the visual heat maps. Second, in order to obtain an accurate BAA, the extracted critical bone regions are fed into the gender-assisted bone age estimation network. The results showed that the proposed method achieved a mean absolute error (MAE) of 5.45 months on the public dataset Radiological Society of North America (RSNA) and 3.34 months on our private dataset.

Aberrant intra- and internetwork functional connectivity patterns of the anterior and posterior hippocampal networks in schizophrenia.

AIM: Schizophrenia is associated with abnormal hippocampal structure and function. Available evidence suggests that the anterior and posterior hippocampus are differentially affected by schizophrenia pathology. This study was designed to provide new insight into the anterior and posterior hippocampus in schizophrenia from the perspective of functional connectivity. METHODS: Based on resting-state functional magnetic resonance imaging data of 71 schizophrenia patients and 74 normal controls, we utilized a data-driven approach to functionally segment the hippocampus into anterior and posterior segments and then investigated the functional connectivity patterns within and between the two hippocampal networks at the network, edge, and nodal levels. RESULTS: We found that schizophrenia patients showed hyperconnectivity of both the anterior and posterior hippocampal networks. We also observed that the network alterations appear somewhat greater in the anterior hippocampal network than the posterior network, the left side than the right, and the intranetwork connectivity than the internetwork connectivity. CONCLUSION: The results reveal convergent and divergent intranetwork and internetwork connectivity patterns of the anterior and posterior hippocampus in schizophrenia, providing novel and important insights into the mechanisms of hippocampal pathology in schizophrenia.

The intracortical myelin content of impulsive choices: results from T1- and T2-weighted MRI myelin mapping.

Delay discounting (DD) refers to a phenomenon that humans tend to choose small-sooner over large-later rewards during intertemporal choices. Steep discounting of delayed outcome is related to a variety of maladaptive behaviors and is considered as a transdiagnostic process across psychiatric disorders. Previous studies have investigated the association between brain structure (e.g. gray matter volume) and DD; however, it is unclear whether the intracortical myelin (ICM) influences DD. Here, based on a sample of 951 healthy young adults drawn from the Human Connectome Project, we examined the relationship between ICM, which was measured by the contrast of T1w and T2w images, and DD and further tested whether the identified associations were mediated by the regional homogeneity (ReHo) of brain spontaneous activity. Vertex-wise regression analyses revealed that steeper DD was significantly associated with lower ICM in the left temporoparietal junction (TPJ) and right middle-posterior cingulate cortex. Region-of-interest analysis revealed that the ReHo values in the left TPJ partially mediated the association of its myelin content with DD. Our findings provide the first evidence that cortical myelination is linked with individual differences in decision impulsivity and suggest that the myelin content affects cognitive performances partially through altered local brain synchrony.

Resonance behaviors of encapsulated microbubbles oscillating nonlinearly with ultrasonic excitation.

The resonance behaviors of a few lipid-coated microbubbles acoustically activated in viscoelastic media were comprehensively examined via radius response analysis. The size polydispersity and random spatial distribution of the interacting microbubbles, the rheological properties of the lipid shell and the viscoelasticity of the surrounding medium were considered simultaneously. The obtained radius response curves present a successive occurrence of linear resonances, nonlinear harmonic and sub-harmonic resonances with the acoustic pressure increasing. The microbubble resonance is radius-, pressure- and frequency-dependent. Specifically, the maximum bubble expansion ratio at the main resonance peak increases but the resonant radius decreases as the ultrasound pressure increases, while both of them decrease with the ultrasound frequency increasing. Moreover, compared to an isolated microbubble case, it is found that large microbubbles in close proximity prominently suppress the resonant oscillations while slightly increase the resonant radii for both harmonic and subharmonic resonances, even leading to the disappearance of the subharmonic resonance with the influences increasing to a certain degree. In addition, the results also suggest that both the encapsulating shell and surrounding medium can substantially dampen the harmonic and subharmonic resonances while increase the resonant radii, which seem to be affected by the medium viscoelasticity to a greater degree rather than the shell properties. This work offers valuable insights into the resonance behaviors of microbubbles oscillating in viscoelastic biological media, greatly contributing to further optimizing their biomedical applications.

Stress and the brain: Emotional support mediates the association between myelination in the right supramarginal gyrus and perceived chronic stress.

Perceived stress, which refers to people's evaluation of a stressful event and their ability to cope with it, has emerged as a stable predictor for physical and mental health outcomes. Increasing evidence has suggested the buffering effect of social support on perceived stress. Although previous studies have investigated the brain structural features (e.g., gray matter volume) associated with perceived stress, less is known about the association between perceived chronic stress and intra-cortical myelin (ICM), which is an important microstructure of brain and is essential for healthy brain functions, and the role of social support in this association. Using a sample of 1076 healthy young adults drawn from the Human Connectome Project, we quantified the ICMby the contrast of T1w and T2w images and examined its association with perceived chronic stress during the last month and social support. Behavioral results showed that perceived chronic stress was negatively associated with both emotional support and instrumental support. Vertex-wise multiple regression analyses revealed that higher level of perceived chronic stress was significantly associated with lower ICM content of a cluster in the right supramarginal gyrus (rSMG). Interestingly, the emotional support, but not the instrumental support, significantly mediated the association of perceived chronic stress with ICM in the rSMG. Overall, the present study provides novel evidence for the cortical myelination of perceived chronic stress in humans and highlights the essential role of the rSMG in perceived chronic stress and emotional support.

BSA-Cu3(PO4)2 hybrid nanoflower-an efficient and low-cost nanoenzyme for decolorization of organic pollutants.

The Fenton reaction is one of the most effective methods for treating organic wastewater, which is extremely harmful to humans but difficult to treat. However, finding simple, low-cost, and efficient catalysts for the Fenton reaction remains a challenge. In this study, a BSA-Cu3(PO4)2 hybrid nanoflower (NF) was synthesized to investigate its peroxidase-like activity for the treatment of organic wastewater. Its morphology, composition, and crystallization had been fully studied and the results confirmed that the NFs were successfully prepared. Subsequently, the origin of the peroxidase-like activity of the NFs was further analyzed, with the results suggesting two reasons: (i) the transformation between Cu(I) and Cu(II) and (ii) nano-effects. Additionally, Congo red was selected as the organic pollutant to simulate the decolorization of wastewater. After 3 h, the decolorization efficiency reached 96%. Furthermore, the NFs exhibited good storage performance, maintaining approximately 90% relative activity after storage for 30 days. In summary, the NFs have great application prospects in the treatment of organic wastewater.

Mechanism Research of Platelet Core Marker Prediction and Molecular Recognition in Cardiovascular Events.

BACKGROUND: Thrombosis triggered by platelet activation plays a vital role in the pathogenesis of cardiovascular and cerebrovascular diseases. OBJECTIVE: This study aims to find platelet combined biomarkers for cardiovascular diseases and investigate the possibility of Concanavalin A (ConA) acting on platelets as a new pharmacological target. METHODS: High-throughput Technology and bioinformatics analysis were combined and groups of microarray chip gene expression profiles for acute myocardial infarction (AMI) and sickle cell disease (SCD) were obtained using GEO database screening. R language limma package was used to obtain differentially expressed genes (DEGs). GO, KEGG, and other databases were utilized to perform the enrichment analysis of DEGs' functions, pathways, etc. PPI network was constructed using STRING database and Cytoscape software, and MCC algorithm was used to obtain the 200 core genes of the two groups of DEGs. Core targets were confirmed by constructing an intersection area screening. A type of molecular probe, ConA, was molecularly docked with the above core targets on the Zdock, HEX, and 3D-DOCK servers. RESULTS: We found six core markers, CD34, SOCS2, ABL1, MTOR, VEGFA, and SMURF1, which were simultaneously related to both diseases, and the docking effect showed that VEGFA is the best-performing. CONCLUSION: VEGFA is most likely to reduce its expression by binding to ConA, which could affect the downstream regulation of the PI3K/Akt signaling pathway during platelet activation. Some other core targets also have the opportunity to interact with ConA to affect platelet-activated thrombosis and trigger changes in cardiovascular events.

Episodic future thinking predicts differences in delay discounting: The mediating role of hippocampal structure.

A growing body of evidence suggests that engagement in episodic future thinking (EFT) could reduce delay discounting rates. However, little is known about whether individual differences in the ability of EFT are associated with differences in delay discounting in young adults. In the present study, this association was tested in healthy college students (n = 106, 19.98 ± 1.56 years), and the neural basis underlying this association was also examined using voxel-based morphometry (VBM) method. Behavioral analysis indicated that individual differences in EFT ability can significantly negatively predict discounting rates. VBM analysis first revealed that the EFT score positively correlated with gray matter volume (GMV) of a cluster in hippocampus, while negatively correlated with GMV of a cluster in rostral anterior cingulate cortex. We also found the GMV of a cluster in the mPFC was positively correlated with delay discounting. ROI analysis further revealed that individual differences in delay discounting could be reliably predicted by the GMV in the hippocampus and mPFC. The final mediation analysis showed that the GMV of the hippocampus plays a significant mediating role in the association between EFT and delay discounting, and the indirect effect of the hippocampal GMV accounts for 33.2% of the total effect. Our results suggest that individuals' EFT ability may be an important determinant of differences in delay discounting, and highlight the hippocampal structure as a neural biomarker for explaining the association between EFT ability and delay discounting.

Epileptic seizure prediction in intracranial EEG using critical nucleus based on phase transition.

BACKGROUND AND OBJECTIVE: Epilepsy is the second most prevalent neurological disorder of brain activity, affecting about seventy million people, or nearly 1% of the world population. Epileptic seizures prediction is extremely important for improving the epileptic patients' life. This paper proposed a novel method to predict seizures by detecting the critical transition of brain activities with intracranial EEG (iEEG) signals. METHODS: This article used three key measures of fluctuation, correlation and percolation to quantify pre-ictal states of epilepsy. Based on these measures, a ritical nucleus of iEEG signals was constructed and a composite index was introduced to detect the likelihood of impending seizures. In addition, we analyzed the dynamical mechanism of seizures at the tipping point from the perspective of spatial diffusion and temporal fluctuation. RESULTS: The empirical results supported that the seizures are self-initiated via a critical transition in pre-ictal state and showed that the proposed model can achieve a good prediction performance. The average accuracy, sensitivity, specificity and false-positive rate (FPR) attain 87.96%, 82.93%, 89.33% and 0.11/h respectively. The results also suggest that the temporal and spatial factors have strong synergistic effect on triggering seizures. For those seizures consistent with critical transition, the predictive performance was greatly improved with sensitivity up to 96.88%. CONCLUSIONS: This article proposed a critical nucleus model combined with spatial and temporal features of iEEG signals capable of seizure prediction. The proposed model brings insight from phase transition into epileptic iEEG signals analysis and quantifies the transition of the state to predict epileptic seizures with high accuracy.

Temporal and spatial dynamic propagation of electroencephalogram by combining power spectral and synchronization in childhood absence epilepsy.

Objective: During the transition from normal to seizure and then to termination, electroencephalography (EEG) signals have complex changes in time-frequency-spatial characteristics. The quantitative analysis of EEG characteristics and the exploration of their dynamic propagation in this paper would help to provide new biomarkers for distinguishing between pre-ictal and inter-ictal states and to better understand the seizure mechanisms. Methods: Thirty-three children with absence epilepsy were investigated with EEG signals. Power spectral and synchronization were combined to provide the time-frequency-spatial characteristics of EEG and analyze the spatial distribution and propagation of EEG in the brain with topographic maps. To understand the mechanism of spatial-temporal evolution, we compared inter-ictal, pre-ictal, and ictal states in EEG power spectral and synchronization network and its rhythms in each frequency band. Results: Power, frequency, and spatial synchronization are all enhanced during the absence seizures to jointly dominate the epilepsy process. We confirmed that a rapid diffusion at the onset accompanied by the frontal region predominance exists. The EEG power rapidly bursts in 2-4 Hz through the whole brain within a few seconds after the onset. This spatiotemporal evolution is associated with spatial diffusion and brain regions interaction, with a similar pattern, increasing first and then decreasing, in both the diffusion of the EEG power and the connectivity of the brain network during the childhood absence epilepsy (CAE) seizures. Compared with the inter-ictal group, we observed increases in power of delta and theta rhythms in the pre-ictal group (P < 0.05). Meanwhile, the synchronization of delta rhythm decreased while that of alpha rhythm enhanced. Conclusion: The initiation and propagation of CAE seizures are related to the abnormal discharge diffusion and the synchronization network. During the seizures, brain activity is completely changed with the main component delta rhythm. Furthermore, this article demonstrated for the first time that alpha inhibition, which is consistent with the brain's feedback regulation mechanism, is caused by the enhancement of the network connection. Temporal and spatial evolution of EEG is of great significance for the transmission mechanism, clinical diagnosis and automatic detection of absence epilepsy seizures.

A 3D reconstruction based on an unsupervised domain adaptive for binocular endoscopy.

In minimally invasive surgery, endoscopic image quality plays a crucial role in surgery. Aiming at the lack of a real parallax in binocular endoscopic images, this article proposes an unsupervised adaptive neural network. The network combines adaptive smoke removal, depth estimation of binocular endoscopic images, and the 3D display of high-quality endoscopic images. We simulated the smoke generated during surgery by artificially adding fog. The training images of U-Net fused by Laplacian pyramid are introduced to improve the network's ability to extract intermediate features. We introduce Convolutional Block Attention Module to obtain the optimal parameters of each layer of the network. We utilized the disparity transformation relationship between left- and right-eye images to combine the left-eye images with disparity in HS-Resnet to obtain virtual right-eye images as labels for self-supervised training. This method extracts and fuses the parallax images at different scale levels of the decoder, making the generated parallax images more complete and smoother. A large number of experimental research results show that the scheme can remove the smoke generated during the operation, effectively reconstruct the 3D image of the tissue structure of the binocular endoscope, and at the same time, preserve the contour, edge, detail, and texture of the blood vessels in the medical image. Compared with the existing similar schemes, various indicators have been greatly improved. It has good clinical application prospects.

Lens culinaris agglutinin inhibits human hepatoma cell migration via mannose and fucose-mediated ERK1/2 and JNK1/2/3 signalling pathway.

BACKGROUND: Hepatocellular carcinoma (HCC) is the main types of primary liver cancer, which shows some abnormal glycosylation, such as the increase of fucose. Lens culinaris agglutinin (LCA), a natural plant lectin that can bind to mannose and fucose, has been reported to be antiproliferative to may tumors. However, the effect of LCA on the vitality and migration ability of human hepatoma cells is not demonstrated. Therefore, the aim of this study is to investigate the effects of LCA on vitality and migration in human hepatoma cells and its potential mechanisms. METHODS AND RESULTS: LCA had no significant effect on viability of human hepatoma cells (HCCLM3, MHCC97L and HepG2) and hepatocytes (L02) by CCK-8 kit, but it could inhibit human hepatoma cells migration significantly without affecting hepatocytes by Transwell method. Sugar inhibition assay was used to verify the possible binding site between LCA and human hepatoma cells. The result showed that Mannose- and fucose- related sites were associated with LCA inhibiting human hepatoma cells migration. Moreover, LCA could affect HCCLM3 migration by activating ERK1/2 and JNK1/2/3 signalling pathways. LCA did not affect MMP-2 and MMP-9 of HCCLM3 through gelatinase zymography. However, the results of immunofluorescence standing showed that LCA could reduce the F-actin formation in HCCLM3 via ERK1/2 and JNK1/2/3 signalling pathways. CONCLUSIONS: LCA might inhibit human hepatoma cell migration by reducing the F-actin formation via the mannose and fucose-mediated ERK1/2 and JNK1/2/3 signalling pathway. This result will deepen people's understanding on plant lectin as a drug in tumor glycobiology.