Human Placenta Decellularized Extracellular Matrix Hydrogel Promotes the Generation of Human Spinal Cord Organoids with Dorsoventral Organization from Human Induced Pluripotent Stem Cells.

Spinal cord organoids are of significant value in the research of spinal cord-related diseases by simulating disease states, thereby facilitating the development of novel therapies. However, the complexity of spinal cord structure and physiological functions, along with the lack of human-derived inducing components, presents challenges in the in vitro construction of human spinal cord organoids. Here, we introduce a novel human decellularized placenta-derived extracellular matrix hydrogel (DPECMH) and, combined with a new induction protocol, successfully construct human spinal cord organoids. The human placenta-sourced decellularized extracellular matrix (dECM), verified through hematoxylin and eosin staining, DNA quantification, and immunofluorescence staining, retained essential ECM components such as elastin, fibronectin, type I collagen, laminin, and so forth. The temperature-sensitive hydrogel made from human placenta dECM demonstrated good biocompatibility and promoted the differentiation of human induced pluripotent stem cell (hiPSCs)-derived spinal cord organoids into neurons. It displayed enhanced expression of laminar markers in comparison to Matrigel and showed higher expression of laminar markers compared to Matrigel, accelerating the maturation process of spinal cord organoids and demonstrating its potential as an organoid culture substrate. DPECMH has the potential to replace Matrigel as the standard additive for human spinal cord organoids, thus advancing the development of spinal cord organoid culture protocols and their application in the in vitro modeling of spinal cord-related diseases.

Deep-learning-based real-time individualization for reduce-order haemodynamic model.

The reduced-order lumped parameter model (LPM) has great computational efficiency in real-time numerical simulations of haemodynamics but is limited by the accuracy of patient-specific computation. This study proposed a method to achieve the individual LPM modeling with high accuracy to improve the practical clinical applicability of LPM. Clinical data was collected from two medical centres comprising haemodynamic indicators from 323 individuals, including brachial artery pressure waveforms, cardiac output data, and internal carotid artery flow waveforms. The data were expanded to 5000 synthesised cases that all fell within the physiological range of each indicator. LPM of the human blood circulation system was established. A double-path neural network (DPNN) was designed to input the waveforms of each haemodynamic indicator and their key features and then output the individual parameters of the LPM, which was labelled using a conventional optimization algorithm. Clinically collected data from the other 100 cases were used as the test set to verify the accuracy of the individual LPM parameters predicted by DPNN. The results show that DPNN provided good convergence in the training process. In the test set, compared with clinical measurements, the mean differences between each haemodynamic indicator and the estimate calculated by the individual LPM based on the DPNN were about 10 %. Furthermore, DPNN prediction only takes 4 s for 100 cases. The DPNN proposed in this study permits real-time and accurate individualization of LPM's. When facing medical issues involving haemodynamics, it lays the foundation for patient-specific numerical simulation, which may be beneficial for potential clinical application.

Dynamical modulation of hypersynchronous seizure onset with transcranial magneto-acoustic stimulation in a hippocampal computational model.

Hypersynchronous (HYP) seizure onset is one of the frequently observed seizure-onset patterns in temporal lobe epileptic animals and patients, often accompanied by hippocampal sclerosis. However, the exact mechanisms and ion dynamics of the transition to HYP seizures remain unclear. Transcranial magneto-acoustic stimulation (TMAS) has recently been proposed as a novel non-invasive brain therapy method to modulate neurological disorders. Therefore, we propose a biophysical computational hippocampal network model to explore the evolution of HYP seizure caused by changes in crucial physiological parameters and design an effective TMAS strategy to modulate HYP seizure onset. We find that the cooperative effects of abnormal glial uptake strength of potassium and excessive bath potassium concentration could produce multiple discharge patterns and result in transitions from the normal state to the HYP seizure state and ultimately to the depolarization block state. Moreover, we find that the pyramidal neuron and the PV+ interneuron in HYP seizure-onset state exhibit saddle-node-on-invariant-circle/saddle homoclinic (SH) and saddle-node/SH at onset/offset bifurcation pairs, respectively. Furthermore, the response of neuronal activities to TMAS of different ultrasonic waveforms revealed that lower sine wave stimulation can increase the latency of HYP seizures and even completely suppress seizures. More importantly, we propose an ultrasonic parameter area that not only effectively regulates epileptic rhythms but also is within the safety limits of ultrasound neuromodulation therapy. Our results may offer a more comprehensive understanding of the mechanisms of HYP seizure and provide a theoretical basis for the application of TMAS in treating specific types of seizures.

Decellularized Brain Extracellular Matrix Hydrogel Aids the Formation of Human Spinal-Cord Organoids Recapitulating the Complex Three-Dimensional Organization.

The intricate electrophysiological functions and anatomical structures of spinal cord tissue render the establishment of in vitro models for spinal cord-related diseases highly challenging. Currently, both in vivo and in vitro models for spinal cord-related diseases are still underdeveloped, complicating the exploration and development of effective therapeutic drugs or strategies. Organoids cultured from human induced pluripotent stem cells (hiPSCs) hold promise as suitable in vitro models for spinal cord-related diseases. However, the cultivation of spinal cord organoids predominantly relies on Matrigel, a matrix derived from murine sarcoma tissue. Tissue-specific extracellular matrices are key drivers of complex organ development, thus underscoring the urgent need to research safer and more physiologically relevant organoid culture materials. Herein, we have prepared a rat decellularized brain extracellular matrix hydrogel (DBECMH), which supports the formation of hiPSC-derived spinal cord organoids. Compared with Matrigel, organoids cultured in DBECMH exhibited higher expression levels of markers from multiple compartments of the natural spinal cord, facilitating the development and maturation of spinal cord organoid tissues. Our study suggests that DBECMH holds potential to replace Matrigel as the standard culture medium for human spinal cord organoids, thereby advancing the development of spinal cord organoid culture protocols and their application in in vitro modeling of spinal cord-related diseases.

The fourth species of Leptobrachella (Anura, Megophryidae) found at Shiwandashan National Nature Reserve, Guangxi, China.

A new species of the genus Leptobrachella, L.guinanensissp. nov., is described in this study based on morphological, molecular, and bioacoustic data. The species was discovered in the Shiwandashan National Nature Reserve in Shangsi County, Guangxi, China. Phylogenetically, L.guinanensissp. nov. is closely related to L.ventripunctata. However, there are distinct morphological differences between L.guinanensissp. nov. and L.ventripunctata, as well as three other sympatric species (L.shangsiensis, L.shiwandashanensis, and L.sungi). These differences include body size (SVL 30.5-32.5 mm in males; 38.7-41.8 mm in females in the new species vs 25.5-28.0 mm in males, 31.5-35.0 mm in females in L.ventripunctata), the absence of brown spots on the ventral surface (vs chest and belly creamy white with many scattered brown spots in L.ventripunctata), 1/3 toe webbing and wide toe lateral fringes (vs no toe webbing and no lateral fringes in L.ventripunctata), and distinct dermal ridges under toes (vs absent in L.ventripunctata). Furthermore, the dominant vocal frequencies of the new species range from 7.3 to 8.3 kHz, which is unique compared to other Leptobrachella species and represents the highest dominant frequencies ever recorded. The Shiwandashan National Nature Reserve is now home to four known sympatric species of Leptobrachella.

A real-time patient-specific treatment strategy for enhanced external counterpulsation.

Diastolic/systolic blood pressure ratio (D/S) ≥ 1.2 is the gold standard of enhanced external counterpulsation (EECP) treatment, but it does not show a clear clinical correspondence with the configuration of the EECP mode. As such, a single target results in different treatment effects in different individuals. The local haemodynamic effect (wall shear stress, WSS) of EECP on vascular endothelial cells is conducive to promote the growth of collateral circulation vessels and restore the blood supply distal to the stenosis lesion. Considering the haemodynamic effects of WSS on human arteries, this study developed a real-time patient-specific treatment strategy of EECP for patients with cardio-cerebrovascular diseases. Based on patient-specific haemodynamic data from 113 individuals, an optimization algorithm was developed to achieve the individualization of a 0D lumped-parameter model of the human circulatory system, thereby simulating the patient-specific global haemodynamic effects. 0D/3D coupled cardio-cerebrovascular models of two subjects were established to simulate the local WSS. We then established statistical models to evaluate clinically unmeasurable WSS based on measurable global haemodynamic indicators. With the aim of attaining appropriate area- and time-averaged WSS (ATAWSS, 4-7 Pa), as evaluated by global haemodynamic indicators, a closed-loop feedback tuning method was developed to provide patient-specific EECP treatment strategies. Results showed that for clinical data collected from 113 individuals, the individualized 0D model can accurately simulate patient-specific global haemodynamic effects (average error <5%). Based on two subjects, the statistical models can be used to evaluate local ATAWSS (error <6%) for coronary arteries and for cerebral arteries. An EECP mode planned by the patient-specific treatment strategy can promote an appropriate ATAWSS within a 16 s calculation time. The real-time patient-specific treatment strategy of EECP is expected to improve the long-term outcome for each patient and have potential clinical significance.

Examining the low-voltage fast seizure-onset and its response to optogenetic stimulation in a biophysical network model of the hippocampus.

Low-voltage fast (LVF) seizure-onset is one of the two frequently observed temporal lobe seizure-onset patterns. Depth electroencephalogram profile analysis illustrated that the peak amplitude of LVF onset was deep temporal areas, e.g., hippocampus. However, the specific dynamic transition mechanisms between normal hippocampal rhythmic activity and LVF seizure-onset remain unclear. Recently, the optogenetic approach to gain control over epileptic hyper-excitability both in vitro and in vivo has become a novel noninvasive modulation strategy. Here, we combined biophysical modeling to study LVF dynamics following changes in crucial physiological parameters, and investigated the potential optogenetic intervention mechanism for both excitatory and inhibitory control. In an Ammon's horn 3 (CA3) biophysical model with light-sensitive protein channelrhodopsin 2 (ChR2), we found that the cooperative effects of excessive extracellular potassium concentration of parvalbumin-positive (PV+) inhibitory interneurons and synaptic links could induce abundant types of discharges of the hippocampus, and lead to transitions from gamma oscillations to LVF seizure-onset. Simulations of optogenetic stimulation revealed that the LVF seizure-onset and morbid fast spiking could not be eliminated by targeting PV+ neurons, whereas the epileptic network was more sensitive to the excitatory control of principal neurons with strong optogenetic currents. We illustrate that in the epileptic hippocampal network, the trajectories of the normal and the seizure state are in close vicinity and optogenetic perturbations therefore may result in transitions. The network model system developed in this study represents a scientific instrument to disclose the underlying principles of LVF, to characterize the effects of optogenetic neuromodulation, and to guide future treatment for specific types of seizures.

A comprehensive approach to prediction of fractional flow reserve from deep-learning-augmented model.

The underuse of invasive fractional flow reserve (FFR) in clinical practice has motivated research towards non-invasive prediction of FFR. Although the non-invasive derivation of FFR (FFRCT) using computational fluid dynamics (CFD) principles has become a common practice, its clinical application has been limited due to the considerable time required for computation of resulting changes in haemodynamic conditions. An alternative to CFD technology is incorporating a neural network into the computational process to reduce the time necessary for running an effective model. In this study we propose a cascade of data-driven and physic-based neural networks (DP-NN) for predicting FFR (DL-FFRCT). The first network of cascade network DP-NN includes geometric features, and the second network includes physical features. We compare the differences between data-driven neural network (D-NN) and DP-NN for predicting FFR. The training and testing datasets were obtained by solving the three-dimensional incompressible Navier-Stokes equations. Coronary flow and geometric features were used as inputs to train D-NN. In DP-NN the training process involves first training a D-NN to output resting ΔP as one input feature to the DP-NN. Secondly, the physics-based microcirculatory resistance as another input feature to the DP-NN. Using clinically measured FFR as the "gold standard", we validated the computational accuracy of DL-FFRCT in 77 patients. Compared to D-NN, DP-NN improved the prediction of ΔP (R2 = 0.87 vs. R2 = 0.92). Statistical analysis demonstrated that the diagnostic accuracy of DL-FFRCT was not inferior to FFRCT (85.71 % vs. 88.3 %) and the computational time was reduced by a factor of approximately 3000 (4.26 s vs. 3.5 h). DP-NN represents a near real-time, interpretable, and highly accurate deep-learning network, which contributes to the development of high-performance computational methods for haemodynamics. We anticipate that DP-NN will enable near real-time prediction of DL-FFRCT in personalized narrow blood vessels and provide guidance for cardiovascular disease treatments.

Non-invasive fractional flow reserve derived from reduced-order coronary model and machine learning prediction of stenosis flow resistance.

BACKGROUND AND OBJECTIVE: Recently, computational fluid dynamics enables the non-invasive calculation of fractional flow reserve (FFR) based on 3D coronary model, but it is time-consuming. Currently, machine learning technique has emerged as an efficient and reliable approach for prediction, which allows saving a lot of analysis time. This study aimed at developing a simplified FFR prediction model for rapid and accurate assessment of functional significance of stenosis. METHODS: A reduced-order lumped parameter model (LPM) of coronary system and cardiovascular system was constructed for rapidly simulating coronary flow, in which a machine learning model was embedded for accurately predicting stenosis flow resistance at a given flow from anatomical features of stenosis. Importantly, the LPM was personalized in both structures and parameters according to coronary geometries from computed tomography angiography and physiological measurements such as blood pressure and cardiac output for personalized simulations of coronary pressure and flow. Coronary lesions with invasive FFR ≤ 0.80 were defined as hemodynamically significant. RESULTS: A total of 91 patients (93 lesions) who underwent invasive FFR were involved in FFR derived from machine learning (FFRML) calculation. Of the 93 lesions, 27 lesions (29.0%) showed lesion-specific ischemia. The average time of FFRML simulation was about 10 min. On a per-vessel basis, the FFRML and FFR were significantly correlated (r = 0.86, p < 0.001). The diagnostic accuracy, sensitivity, specificity, positive predictive value and negative predictive value were 91.4%, 92.6%, 90.9%, 80.6% and 96.8%, respectively. The area under the receiver-operating characteristic curve of FFRML was 0.984. CONCLUSION: In this selected cohort of patients, the FFRML improves the computational efficiency and ensures the accuracy. The favorable performance of FFRML approach greatly facilitates its potential application in detecting hemodynamically significant coronary stenosis in future routine clinical practice.

microRNA-365 attenuated intervertebral disc degeneration through modulating nucleus pulposus cell apoptosis and extracellular matrix degradation by targeting EFNA3.

This present study is aimed to investigate the role of microRNA-365 (miR-365) in the development of intervertebral disc degeneration (IDD). Nucleus pulposus (NP) cells were transfected by miR-365 mimic and miR-365 inhibitor, respectively. Concomitantly, the transfection efficiency and the expression level of miRNA were detected by quantitative reverse transcription polymerase chain reaction (qRT-PCR). Meanwhile, NP cells apoptosis was measured through propidium iodide (PI)-AnnexinV-fluorescein isothiocyanate (FITC) apoptosis detection kit. Subsequently, immunofluorescence (IF) staining was performed to assess the expression of collagen II, aggrecan and matrix metalloproteinase 13 (MMP-13). In addition, bioinformatic prediction and Luciferase reporter assay were used to reveal the target gene of miR-365. Finally, we isolated the primary NP cells from rats and injected NP-miR-365 in rat IDD models. The results showed that overexpression of miR-365 could effectively inhibit NP cells apoptosis and MMP-13 expression and upregulate the expression of collagen II and aggrecan. Conversely, suppression of miR-365 enhanced NP cell apoptosis and elevated MMP-13 expression, but decreased the expression of collagen II and aggrecan. Moreover, the further data demonstrated that miR-365 mediated NP cell degradation through targeting ephrin-A3 (EFNA3). In addition, the cells apoptosis and catabolic markers were increased in NP cells when EFNA3 upregulated. More importantly, the vivo data supported that miR-365-NP cells injection ameliorated IDD in rats models. miR-365 could alleviate the development of IDD by regulating NP cell apoptosis and ECM degradation, which is likely mediated by targeting EFNA3. Therefore, miR-365 may be a promising therapeutic avenue for treatment IDD through EFNA3.

Electrospun PCL Nerve Conduit Filled with GelMA Gel for CNTF and IGF-1 Delivery in Promoting Sciatic Nerve Regeneration in Rat.

Neural tissue engineering is an essential strategy to repair long-segment peripheral nerve defects. Modification of the nerve conduit is an effective way to improve the local microenvironment of the injury site and facilitate nerve regeneration. However, the concurrent release of multiple growth cues that regulate the activity of Schwann cells and neurons remains a challenge. The present study involved the fabrication of a composite hydrogel, specifically methacrylate-anhydride gelatin-ciliary neurotrophic factor/insulin-like growth factor-1 (GelMA-CNTF/IGF-1), with the aim of providing a sustained release of CNTF and IGF-1. The GelMA-CNTF/IGF-1 hydrogels exhibited a swelling rate of 10.2% following a 24 h incubation in vitro. In vitro, GelMA hydrogels demonstrated a high degree of efficiency in the sustained release of CNTF and IGF-1 proteins, with a release rate of 85.9% for CNTF and 90.9% for IGF-1 shown at day 28. In addition, the GelMA-CNTF/IGF-1 composite hydrogel promoted the proliferation of Schwann cells and the production of nerve growth factor (NGF), connective tissue growth factor (CTGF), fibronectin, and laminin and also considerably promoted the axonal growth of neurons. Furthermore, GelMA-CNTF/IGF-1 hydrogels were loaded into PCL electrospun nerve conduits to repair 15 mm sciatic nerve defects in rats. In vivo studies indicated that PCL-GelMA-CNTF/IGF-1 could efficiently accelerate the regeneration of the rat sciatic nerve, promote the formation of the myelin sheath of new axons, promote the electrophysiological function of regenerated nerves, and eventually improve the recovery of motor function in rats. Overall, the PCL-GelMA-CNTF/IGF-1 scaffold presents an attractive new approach for generating an optimal therapeutic alternative for peripheral nerve restoration.

A simplified coronary model for diagnosis of ischemia-causing coronary stenosis.

BACKGROUND AND OBJECTIVE: The functional assessment of the severity of coronary stenosis from coronary computed tomography angiography (CCTA)-derived fractional flow reserve (FFR) has recently attracted interest. However, existing algorithms run at high computational cost. Therefore, this study proposes a fast calculation method of FFR for the diagnosis of ischemia-causing coronary stenosis. METHODS: We combined CCTA and machine learning to develop a simplified single-vessel coronary model for rapid calculation of FFR. First, a zero-dimensional model of single-vessel coronary was established based on CCTA, and microcirculation resistance was determined through the relationship between coronary pressure and flow. In addition, a coronary stenosis model based on machine learning was introduced to determine stenosis resistance. Computational FFR (cFFR) was then obtained by combining the zero-dimensional model and the stenosis model with inlet boundary conditions for resting (cFFRr) and hyperemic (cFFRh) aortic pressure, respectively. We retrospectively analyzed 75 patients who underwent clinically invasive FFR (iFFR), and verified the model accuracy by comparison of cFFR with iFFR. RESULTS: The average computing time of cFFR was less than 2 s. The correlations between cFFRr and cFFRh with iFFR were r = 0.89 (p < 0.001) and r = 0.90 (p < 0.001), respectively. Diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio for cFFRr and cFFRh were 90.7%, 95.0%, 89.1%, 76.0%, 98.0%, 8.7, 0.1 and 92.0%, 95.0%, 90.9%, 79.2%, 98.0%, 10.5, 0.1, respectively. CONCLUSIONS: The proposed model enables rapid prediction of cFFR and exhibits high diagnostic performance in selected patient cohorts. The model thus provides an accurate and time-efficient computational tool to detect ischemia-causing stenosis and assist with clinical decision-making.

Curcumin-activated Olfactory Ensheathing Cells Improve Functional Recovery After Spinal Cord Injury by Modulating Microglia Polarization Through APOE/TREM2/NF-κB Signaling Pathway.

Transplantation of curcumin-activated olfactory ensheathing cells (aOECs) improved functional recovery in spinal cord injury (SCI) rats. Nevertheless, little is known considering the underlying mechanisms. At the present study, we investigated the promotion of regeneration and functional recovery after transplantation of aOECs into rats with SCI and the possible underlying molecular mechanisms. Primary OECs were prepared from the olfactory bulb of rats, followed by treatment with 1µM CCM at 7-10 days of culture, resulting in cell activation. Concomitantly, rat SCI model was developed to evaluate the effects of transplantation of aOECs in vivo. Subsequently, microglia were isolated, stimulated with 100 ng/mL lipopolysaccharide (LPS) for 24 h to polarize to M1 phenotype and treated by aOECs conditional medium (aOECs-CM) and OECs conditional medium (OECs-CM), respectively. Changes in the expression of pro-inflammatory and anti-inflammatory phenotypic markers expression were detected using western blotting and immunofluorescence staining, respectively. Finally, a series of molecular biological experiments including knock-down of triggering receptor expressed on myeloid cells 2 (TREM2) and analysis of the level of apolipoprotein E (APOE) expression were performed to investigate the underlying mechanism of involvement of CCM-activated OECs in modulating microglia polarization, leading to neural regeneration and function recovery. CCM-activated OECs effectively attenuated deleterious inflammation by regulating microglia polarization from the pro-inflammatory (M1) to anti-inflammatory (M2) phenotype in SCI rats and facilitated functional recovery after SCI. In addition, microglial polarization to M2 elicited by aOECs-CM in LPS-induced microglia was effectively reversed when TREM2 expression was downregulated. More importantly, the in vitro findings indicated that aOECs-CM potentiating LPS-induced microglial polarization to M2 was partially mediated by the TREM2/nuclear factor kappa beta (NF-κB) signaling pathway. Besides, the expression of APOE significantly increased in CCM-treated OECs. CCM-activated OECs could alleviate inflammation after SCI by switching microglial polarization from M1 to M2, which was likely mediated by the APOE/TREM2/NF-κB pathway, and thus ameliorated neurological function. Therefore, the present finding is of paramount significance to enrich the understanding of underlying molecular mechanism of aOECs-based therapy and provide a novel therapeutic approach for treatment of SCI.

Physics-informed neural networks (PINNs) for 4D hemodynamics prediction: An investigation of optimal framework based on vascular morphology.

Hemodynamic parameters are of great significance in the clinical diagnosis and treatment of cardiovascular diseases. However, noninvasive, real-time and accurate acquisition of hemodynamics remains a challenge for current invasive detection and simulation algorithms. Here, we integrate computational fluid dynamics with our customized analysis framework based on a multi-attribute point cloud dataset and physics-informed neural networks (PINNs)-aided deep learning modules. This combination is implemented by our workflow that generates flow field datasets within two types of patient personalized models - aorta with fine coronary branches and abdominal aorta. Deep learning modules with or without an antecedent hierarchical structure model the flow field development and complete the mapping from spatial and temporal dimensions to 4D hemodynamics. 88,000 cases on 4 randomized partitions in 16 controlled trials reveal the hemodynamic landscape of spatio-temporal anisotropy within two types of personalized models, which demonstrates the effectiveness of PINN in predicting the space-time behavior of flow fields and gives the optimal deep learning framework for different blood vessels in terms of balancing the training cost and accuracy dimensions. The proposed framework shows intentional performance in computational cost, accuracy and visualization compared to currently prevalent methods, and has the potential for generalization to model flow fields and corresponding clinical metrics within vessels at different locations. We expect our framework to push the 4D hemodynamic predictions to the real-time level, and in statistically significant fashion, applicable to morphologically variable vessels.

An Interpretable Data-Driven Medical Knowledge Discovery Pipeline Based on Artificial Intelligence.

Difficulty in knowledge validation is a significant hindrance to knowledge discovery via data mining, especially automatic validation without artificial participation. In the field of medical research, medical knowledge discovery from electronic medical records is a common medical data mining method, but it is difficult to validate the discovered medical knowledge without the participation of medical experts. In this article, we propose a data-driven medical knowledge discovery closed-loop pipeline based on interpretable machine learning and deep learning; the components of the pipeline include Data Generator, Medical Knowledge Mining, Medical Knowledge Evaluation, and Medical Knowledge Application. In addition to completing the discovery of medical knowledge, the pipeline can also automatically validate the knowledge. We apply our pipeline's discovered medical knowledge to a traditional prognostic predictive model of heart failure in a real-world study, demonstrating that the incorporation of medical knowledge can effectively improve the performance of the traditional model. We also construct a scale model based on the discovered medical knowledge and demonstrate that it achieves good performance. To guarantee its medical effectiveness, every process of our pipeline involves the participation of medical experts.

Epileptic seizures detection and the analysis of optimal seizure prediction horizon based on frequency and phase analysis.

Changes in the frequency composition of the human electroencephalogram are associated with the transitions to epileptic seizures. Cross-frequency coupling (CFC) is a measure of neural oscillations in different frequency bands and brain areas, and specifically phase-amplitude coupling (PAC), a form of CFC, can be used to characterize these dynamic transitions. In this study, we propose a method for seizure detection and prediction based on frequency domain analysis and PAC combined with machine learning. We analyzed two databases, the Siena Scalp EEG database and the CHB-MIT database, and used the frequency features and modulation index (MI) for time-dependent quantification. The extracted features were fed to a random forest classifier for classification and prediction. The seizure prediction horizon (SPH) was also analyzed based on the highest-performing band to maximize the time for intervention and treatment while ensuring the accuracy of the prediction. Under comprehensive consideration, the results demonstrate that better performance could be achieved at an interval length of 5 min with an average accuracy of 85.71% and 95.87% for the Siena Scalp EEG database and the CHB-MIT database, respectively. As for the adult database, the combination of PAC analysis and classification can be of significant help for seizure detection and prediction. It suggests that the rarely used SPH also has a major impact on seizure detection and prediction and further explorations for the application of PAC are needed.

Treatment strategy of different enhanced external counterpulsation frequencies for coronary heart disease and cerebral ischemic stroke: A hemodynamic numerical simulation study.

BACKGROUND AND OBJECTIVES: Currently, enhanced external counterpulsation (EECP) devices mainly produce one counterpulsation per cardiac cycle. However, the effect of other frequencies of EECP on the hemodynamics of coronary and cerebral arteries is still unclear. It should be investigated whether one counterpulsation per cardiac cycle leads to the optimal therapeutic effect in patients with different clinical indications. Therefore, we measured the effects of different frequencies of EECP on the hemodynamics of coronary and cerebral arteries to determine the optimal counterpulsation frequency for the treatment of coronary heart disease and cerebral ischemic stroke. METHODS: We established 0D/3D geometric multi-scale hemodynamics model of coronary and cerebral arteries in two healthy individuals, and performed clinical trials of EECP to verify the accuracy of the multi-scale hemodynamics model. The pressure amplitude (35 kPa) and pressurization duration (0.6 s) were fixed. The global and local hemodynamics of coronary and cerebral arteries were studied by changing counterpulsation frequency. Three frequency modes, including one counterpulsation in one, two and three cardiac cycles, were applied. Global hemodynamic indicators included diastolic / systolic blood pressure (D/S), mean arterial pressure (MAP), coronary artery flow (CAF), and cerebral blood flow (CBF), whereas local hemodynamic effects included area-time-averaged wall shear stress (ATAWSS) and oscillatory shear index (OSI). The optimal counterpulsation frequency was verified by analyzing the hemodynamic effects of different frequency modes of counterpulsation cycles and full cycles. RESULTS: In the full cycle, CAF, CBF and ATAWSS of coronary and cerebral arteries were the highest when one counterpulsation per cardiac cycle was applied. However, in the counterpulsation cycle, the global and local hemodynamic indicators of coronary and cerebral artery reached the highest when one counterpulsation in one cardiac cycle or two cardiac cycles was applied. CONCLUSIONS: For clinical application, the results of global hemodynamic indicators in the full cycle have more clinical practical significance. Combined with the comprehensive analysis of local hemodynamic indicators, it can be concluded that for coronary heart disease and cerebral ischemic stroke, applying one counterpulsation per cardiac cycle may provide the optimal benefit.

The Influence of Aortic Valve Disease on Coronary Hemodynamics: A Computational Model-Based Study.

Aortic valve disease (AVD) often coexists with coronary artery disease (CAD), but whether and how the two diseases are correlated remains poorly understood. In this study, a zero-three dimensional (0-3D) multi-scale modeling method was developed to integrate coronary artery hemodynamics, aortic valve dynamics, coronary flow autoregulation mechanism, and systemic hemodynamics into a unique model system, thereby yielding a mathematical tool for quantifying the influences of aortic valve stenosis (AS) and aortic valve regurgitation (AR) on hemodynamics in large coronary arteries. The model was applied to simulate blood flows in six patient-specific left anterior descending coronary arteries (LADs) under various aortic valve conditions (i.e., control (free of AVD), AS, and AR). Obtained results showed that the space-averaged oscillatory shear index (SA-OSI) was significantly higher under the AS condition but lower under the AR condition in comparison with the control condition. Relatively, the overall magnitude of wall shear stress was less affected by AVD. Further data analysis revealed that AS induced the increase in OSI in LADs mainly through its role in augmenting the low-frequency components of coronary flow waveform. These findings imply that AS might increase the risk or progression of CAD by deteriorating the hemodynamic environment in coronary arteries.

Multiphysics Interaction Analysis of the Therapeutic Effects of the Sigmoid Sinus Wall Reconstruction in Patients with Venous Pulsatile Tinnitus.

Sigmoid sinus wall dehiscence (SSWD) is an important etiology of venous pulsatile tinnitus (VPT) and is treated by sigmoid sinus wall reconstruction (SSWR). This study aimed to investigate the therapeutic effects of the different degrees of SSWR and the prognostic effect in patients with VPT. Personalized models of three patients with SSWD (control), 3/4SSWD, 1/2SSWD, 1/4SSWD, and 0SSWD were reconstructed. A multiphysics interaction approach was applied to elucidate the biomechanical and acoustic changes. Results revealed that after SSWR, the average pressure of venous vessel on the SSWD region reduced by 33.70 ± 12.53%, 35.86 ± 12.39%, and 39.70 ± 12.45% (mean ± SD) in three patients with 3/4SSWD, 1/2SSWD, and 1/4SSWD. The maximum displacement of the SSWR region reduced by 25.91 ± 30.20%, 37.20 ± 31.47%, 52.60 ± 34.66%, and 79.35 ± 18.13% (mean ± SD) in three patients with 3/4SSWD, 1/2SSWD, 1/4SSWD, and 0SSWD, with a magnitude approximately 10-3 times that of the venous vessel in the SSWD region. The sound pressure level at the tympanum reduced by 23.72 ± 1.91%, 31.03 ± 14.40%, 45.62 ± 19.11%, and 128.46 ± 15.46% (mean ± SD). The SSWR region was still loaded with high stress in comparison to the surrounding region. The SSWR region of the temporal bone effectively shielded the high wall pressure and blocked the transmission of venous vessel vibration to the inner ear. Patients with inadequate SSWR still had residual VPT symptoms despite the remission of VPT symptoms. Complete SSWR could completely solve VPT issues. High-stress distribution of the SSWR region may be the cause of the recurrence of VPT symptoms.

Cerebral multi-autoregulation model based enhanced external counterpulsation treatment planning for cerebral ischemic stroke.

Enhanced external counterpulsation (EECP) treatment for cerebral ischemic stroke patients with differing severity of stenosis, is subject to uncertainties due to the varying effects of the cerebral autoregulation mechanism on haemodynamics. The current study reports the development of a cerebral multi-autoregulation (MR) mathematical model, based on cerebral arteriole regulation of neurogenic, vascular smooth muscle reflex and shear stress mechanisms which takes into account the severity of stenosis. The model was evaluated by comparison to authentic clinical measurements of cerebral autoregulatory efficiency. Then it was applied to a 0D/3D geometric multi-scale haemodynamic model of a cerebral artery. Haemodynamic indicators were calculated under different pressurization durations of EECP to evaluate the efficacy for different stenosis lesions. Moderate stenosis of 50% to 60% produced excessive time-averaged wall shear stress in the distal area of the stenosis (>7 Pa) during prolonged pressurization and may result in damage to vascular endothelial cells. However, prolonged pressurization did not result in haemodynamic risk for severe stenosis of 70% to 80%, indicating that the duration of pressurization may be extended with increasing severity of stenosis. The current MR model accurately simulated cerebral blood flow and has relevance to the simulation of cerebral haemodynamics in a clinical setting.