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.

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.

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.

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.

Technological Requirements and Challenges in Wireless Body Area Networks for Health Monitoring: A Comprehensive Survey.

With the rapid growth in healthcare demand, an emergent, novel technology called wireless body area networks (WBANs) have become promising and have been widely used in the field of human health monitoring. A WBAN can collect human physical parameters through the medical sensors in or around the patient's body to realize real-time continuous remote monitoring. Compared to other wireless transmission technologies, a WBAN has more stringent technical requirements and challenges in terms of power efficiency, security and privacy, quality of service and other specifications. In this paper, we review the recent WBAN medical applications, existing requirements and challenges and their solutions. We conducted a comprehensive investigation of WBANs, from the sensor technology for the collection to the wireless transmission technology for the transmission process, such as frequency bands, channel models, medium access control (MAC) and networking protocols. Then we reviewed its unique safety and energy consumption issues. In particular, an application-specific integrated circuit (ASIC)-based WBAN scheme is presented to improve its security and privacy and achieve ultra-low energy consumption.

Atypical sulcal pattern in boys with attention-deficit/hyperactivity disorder.

Neurodevelopmental disorders, such as attention-deficit/hyperactivity disorder (ADHD), are often accompanied by disrupted cortical folding. We applied a quantitative sulcal pattern analysis technique using graph structures to study the atypical cortical folding at the lobar level in ADHD brains in this study. A total of 183 ADHD patients and 167 typical developmental controls matched according to age and gender were enrolled. We first constructed sulcal graphs at the brain lobar level and then investigated their similarity to the typical sulcal patterns. The within-group variability and interhemispheric similarity in sulcal patterns were also compared between the ADHD and TDC groups. The results showed that, compared with controls, the left frontal, right parietal, and temporal lobes displayed altered similarities to the typical sulcal patterns in patients with ADHD. Moreover, the sulcal patterns in ADHD seem to be more heterogeneous than those in controls. The results also identified the disruption of the typical asymmetric sulcal patterns in the frontal lobe between the ADHD and control groups. Taken together, our results revealed the atypical sulcal pattern in boys with ADHD and provide new insights into the neuroanatomical mechanisms of ADHD.

Research on Panoramic Stitching Algorithm of Lateral Cranial Sequence Images in Dental Multifunctional Cone Beam Computed Tomography.

In the design of dental multifunctional Cone Beam Computed Tomography, the linear scanning strategy not only saves equipment cost, but also avoids the demand for patients to be repositioned when acquiring lateral cranial sequence images. In order to obtain panoramic images, we propose a local normalized cross-correlation stitching algorithm based on Gaussian Mixture Model. Firstly, the Block-Matching and 3D filtering algorithm is used to remove quantum and impulse noises according to the characteristics of X-ray images; Then, the segmentation of the irrelevant region and the extraction of the region of interest are performed by Gaussian Mixture Model; The locally normalized cross-relation is used to complete the registration with the multi-resolution strategy based on wavelet transform and Particle Swarm Optimization algorithm; Finally, image fusion is achieved by the weighted smoothing fusion algorithm. The experimental results show that the panoramic image obtained by this method has significant performance in both subjective vision and objective quality evaluation and can be applied to preoperative diagnosis of clinical dental deformity and postoperative effect evaluation.

Research on Finger Vein Image Segmentation and Blood Sampling Point Location in Automatic Blood Collection.

In the fingertip blood automatic sampling process, when the blood sampling point in the fingertip venous area, it will greatly increase the amount of bleeding without being squeezed. In order to accurately locate the blood sampling point in the venous area, we propose a new finger vein image segmentation approach basing on Gabor transform and Gaussian mixed model (GMM). Firstly, Gabor filter parameter can be set adaptively according to the differential excitation of image and we use the local binary pattern (LBP) to fuse the same-scale and multi-orientation Gabor features of the image. Then, finger vein image segmentation is achieved by Gabor-GMM system and optimized by the max flow min cut method which is based on the relative entropy of the foreground and the background. Finally, the blood sampling point can be localized with corner detection. The experimental results show that the proposed approach has significant performance in segmenting finger vein images which the average accuracy of segmentation images reach 91.6%.

[Detection of white blood cells in microscopic leucorrhea images based on deep active learning].

The number of white blood cells in the leucorrhea microscopic image can indicate the severity of vaginal inflammation. At present, the detection of white blood cells in leucorrhea mainly relies on manual microscopy by medical experts, which is time-consuming, expensive and error-prone. In recent years, some studies have proposed to implement intelligent detection of leucorrhea white blood cells based on deep learning technology. However, such methods usually require manual labeling of a large number of samples as training sets, and the labeling cost is high. Therefore, this study proposes the use of deep active learning algorithms to achieve intelligent detection of white blood cells in leucorrhea microscopic images. In the active learning framework, a small number of labeled samples were firstly used as the basic training set, and a faster region convolutional neural network (Faster R-CNN) training detection model was performed. Then the most valuable samples were automatically selected for manual annotation, and the training set and the corresponding detection model were iteratively updated, which made the performance of the model continue to increase. The experimental results show that the deep active learning technology can obtain higher detection accuracy under less manual labeling samples, and the average precision of white blood cell detection could reach 90.6%, which meets the requirements of clinical routine examination.

[Research on Kalman interpolation prediction model based on micro-region PM2.5 concentration].

In recent years, the pollution problem of particulate matter, especially PM2.5, is becoming more and more serious, which has attracted many people's attention from all over the world. In this paper, a Kalman prediction model combined with cubic spline interpolation is proposed, which is applied to predict the concentration of PM2.5 in the micro-regional environment of campus, and to realize interpolation simulation diagram of concentration of PM2.5 and simulate the spatial distribution of PM2.5. The experiment data are based on the environmental information monitoring system which has been set up by our laboratory. And the predicted and actual values of PM2.5 concentration data have been checked by the way of Wilcoxon signed-rank test. We find that the value of bilateral progressive significance probability was 0.527, which is much greater than the significant level α = 0.05. The mean absolute error (MEA) of Kalman prediction model was 1.8 µg/m 3, the average relative error (MER) was 6%, and the correlation coefficient R was 0.87. Thus, the Kalman prediction model has a better effect on the prediction of concentration of PM2.5 than those of the back propagation (BP) prediction and support vector machine (SVM) prediction. In addition, with the combination of Kalman prediction model and the spline interpolation method, the spatial distribution and local pollution characteristics of PM2.5 can be simulated.

[Study on Electrocardiogram Signal De-noising Methods Based on Ensemble Empirical Mode Decomposition Decomposed by White Noise].

Ensemble empirical mode decomposition(EEMD)is an effective method for non-stationary signal analysis,such as electrocardiogram(ECG)signals.However,the precision and correctness of EEMD are affected by the two parameters,ratio of the added noise and ensemble number.The values of two parameters are set relying on experience and lacking of adaptability for uncertain signals.In order to solve these problems,we proposed a method based on white noise decomposed by EEMD in the present study shown in this paper.Empirical mode decomposition(EMD)was applied to decompose the signal to different intrinsic mode functions(IMFs)in the de-noising process.The white noise IMFs were selected to constitute high frequency part based on the character that the product of the energy density of white noise and its average period tended to be a constant.Then the two parameters of EEMD were adaptively obtained according to the criterion which was used to avoid modal aliasing.Experimental results showed that the method was an effective one for ECG signal de-noising.

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.

Three autism subtypes based on single-subject gray matter network revealed by semi-supervised machine learning.

Autism spectrum disorder (ASD) is a heterogeneous, early-onset neurodevelopmental condition characterized by persistent impairments in social interaction and communication. This study aims to delineate ASD subtypes based on individual gray matter brain networks and provide new insights from a graph theory perspective. In this study, we extracted and normalized single-subject gray matter networks and calculated each network's topological properties. The heterogeneity through discriminative analysis (HYDRA) method was utilized to subtype all patients based on network properties. Next, we explored the differences among ASD subtypes in terms of network properties and clinical measures. Our investigation identified three distinct ASD subtypes. In the case-control study, these subtypes exhibited significant differences, particularly in the precentral gyrus, lingual gyrus, and middle frontal gyrus. In the case analysis, significant differences in global and nodal properties were observed between any two subtypes. Clinically, subtype 1 showed lower VIQ and PIQ compared to subtype 3, but exhibited higher scores in ADOS-Communication and ADOS-Total compared to subtype 2. The results highlight the distinct brain network properties and behaviors among different subtypes of male patients with ASD, providing valuable insights into the neural mechanisms underlying ASD heterogeneity.

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.

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.

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.

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.

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.

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.