Rest State Research Articles

Modulation of neural networks and symptom correlated in fibromyalgia: A randomized double-blind multi-group explanatory clinical trial of home-based transcranial direct current stimulation.

Transcranial direct current stimulation (tDCS) might modulate neural activity and promote neural plasticity in patients with fibromyalgia (FM). This multi-group randomized clinical trial compared home-based active tDCS (HB-a-tDCS) on the left dorsolateral prefrontal cortex (l-DLPFC) or home-based sham tDCS (HB-s-tDCS), and HB-a-tDCS or HB-s-tDCS on the primary motor cortex (M1) in the connectivity analyses in eight regions of interest (ROIs) across eight resting-state electroencephalography (EEG) frequencies. We included 48 women with FM, aged 30 to 65, randomly assigned to 2:1:2:1 to receive 20 sessions during 20 minutes of HB-a-tDCS 2mA or HB-s-tDCS, over l-DLPFC or M1, respectively. EEG recordings were obtained before and after treatment with eyes open (EO) and eyes closed (EC). In the EC condition, comparing pre to post-treatment, the HB-a-tDCS on l-DLPFC decreased the lagged coherence connectivity in the delta frequency band between the right insula and left anterior cingulate cortex (ACC) (t = -3.542, p = .048). The l-DLPFC HB-a-tDCS compared to HB-s-tDCS decreased the lagged coherence connectivity in the delta frequency band between the right insula and left ACC (t = -4.000, p = .017). In the EO condition, the l-DLPFC HB-a-tDCS compared to M1 HB-s-tDCS increased the lagged coherence connectivity between the l-DLPFC and left ACC in the theta band (t = -4.059, p = .048). Regression analysis demonstrated that the HB-a-tDCS effect on the l-DLPFC was positively correlated with sleep quality. On the other hand, the HB-a-tDCS on l-DLPFC and HB-s-tDCS on M1 were positively correlated with pain catastrophizing. These results show that HB-a-tDCS affects the neural connectivity between parts of the brain that control pain's emotional and attentional aspects, which are most noticeable at lower EEG frequencies in a rest state. This effect on neural oscillations could serve as a neural marker associated with its efficacy in alleviating fibromyalgia symptoms. identifier [NCT03843203].

Assessment of coronary microvascular dysfunction by angiography-based index of microcirculatory resistance: an indirect meta-analysis.

There is a lack of consensus on diagnosing coronary microvascular dysfunction (CMD) using the angiography-based index of microcirculatory resistance (Angio-IMR) due to the absence of evidence. This study aims to explore the efficacy of Angio-IMR in diagnosing CMD. A systematic search was conducted in PubMed, Embase, Scopus, and Cochrane Library for studies primarily focusing on Angio-IMR diagnosing CMD, using IMR as the gold standard. The primary results were pooled sensitivity, specificity, and the area under curve (AUC). A total of 15 studies involving 2202 individuals and 2330 vessels were included in our study, Angio-IMR demonstrated high performance in predicting IMR with overall pooled sensitivity and specificity of 0.84 (95% confidence interval (CI): 0.81 to 0.87) and 0.87 (95%CI: 0.83 to 0.99), respectively, and AUC=0.91 (95%CI: 0.89 to 0.94). This indicates that Angio-IMR has good diagnostic characteristics. Subgroup analysis by indirect meta-analysis showed higher sensitivity in the rest state. However, there is no significant difference in sensitivity and specificity between the hyperemic and rest states when using the AccuIMR system. Furthermore, sensitivity and specificity were more pronounced in the group without coronary pressure monitoring compared to the group with monitoring. Angio-IMR is an alternative tool to identify CMD.

Rigid lid limit in shallow water over a flat bottom

AbstractWe perform the so‐called rigid lid limit on different shallow water models such as the abcd Bousssinesq systems or the Green–Naghdi equations. To do so, we consider an appropriate nondimensionalization of these models where two small parameters are involved: the shallowness parameter and a parameter which can be interpreted as a Froude number. When the parameter tends to zero, the surface deformation formally goes to the rest state, hence the name rigid lid limit. We carefully study this limit for different topologies. We also provide rates of convergence with respect to and careful attention is given to the dependence on the shallowness parameter .

Motor-Related EEG Analysis Using a Pole Tracking Approach.

This study introduces an alternative approach to electroencephalography (EEG) time-frequency analysis based on time-varying autoregressive (TV-AR) models in a cascade configuration to independently monitor key EEG spectral components. The method is evaluated for its neurophysiological interpretation and effectiveness in motor-related brain-computer interface (BCI) applications. Specifically, we assess the ability of the tracked EEG poles to discriminate between rest, movement execution (ME) and movement imagination (MI) in healthy subjects, as well as movement attempts (MA) in individuals with spinal cord injury (SCI). Our results show that pole tracking effectively captures broad changes in EEG dynamics, such as transitions between rest and movement-related states. It outperformed traditional EEG-based features, increasing detection accuracy for ME by an average of 4.1% (with individual improvements reaching as high as 15%) and MI by an average of 4.5% (up to 13.8%) compared to time-domain low-frequency EEG features. Similarly, compared to alpha/beta band power, the method improved ME detection by an average of 5.9% (up to 10.4%) and MI by an average of 4.3% (up to 10.2%), with results averaged across 15 healthy participants. In one participant with SCI, pole tracking improved MA detection by 12.9% over low-frequency EEG features and 4.8% over alpha/beta band power. However, its ability to distinguish finer movement details within specific movement types was limited. Additionally, the temporal evolution of the extracted pole tracking features revealed event-related desynchronization phenomena, typically observed during ME, MA and MI, as well as increases in frequency, which are of neurophysiological interest.

Effect of repulsive interaction and initial velocity on collective motion process.

Self-propelled collective motion is a highly complex phenomenon, necessitating advanced practical and theoretical tools for comprehension. The significance of studying collective motion becomes apparent in its diverse applications. For instance, addressing evacuation challenges in scenarios with multiple agents can be achieved through an examination of collective motion. Research indicates that the transition of individuals (such as birds, fish, etc.) from a state of rest to equilibrium constitutes a phase transition. Our interest of the issue is to delve into the nature of this transitional phase and elucidate the parameters that shape it. Hence, the primary aim of this paper is to grasp the kinetic phase transition by examining how initial velocity and repulsive interactions impact the dynamics of the system. To gain insight into the complex behavior of multi-agent systems, we apply an extended version of the classical Vicsek model. This extension includes an additional interaction zone, the repulsive zone, where particles repel each other at close range to avoid collisions. Our study uses numerical simulations to explore the system's behavior under various conditions. The focus of this study is the impact of initial velocity on the collective movement of particles. The importance of this research lies in comprehending how velocity affects the overall movement. The conclusion we can draw from these results is that the initial velocity affects both the noise and the density. The novelty of the work is the transition phase, yet it lacks universal characteristics because the critical noise depends on the initial velocity system and the repulsion radius zone. Notably, the repulsion radius and particle density play pivotal roles in achieving a phase transition from one equilibrium state to another aligned equilibrium state.

Exploring Brain Activity in Different Mental Cognitive Workloads

Objective: Understanding neural mechanisms underlying cognitive workload is crucial for advancing our knowledge of human cognition and mental processes. In this study, we utilized electroencephalography (EEG) analysis to investigate brain activity associated with varying mental cognitive workloads from a psychological perspective. Method: We employed a publicly accessible EEG dataset consisting of a cohort of 36 healthy volunteers (75% female), aged 18 to 26 years, while the participants were at rest or engaged in an arithmetic task to explore mental cognitive workload. After preprocessing to reduce noise and various artifacts and to obtain a clean signal for every subject, functional connectivity and complexity features were calculated from EEGs through the coherence and permutation entropy algorithms, respectively. Then, repeated measures analysis of variance (ANOVA) was conducted to assess the differences in complexity and connectivity measures across various brain regions between the rest and task states. Results: Brain sites showed significant within-subject effects, and the interaction between states and channels was significant for connectivity values (F = 3.68, P = 0.034). Post hoc comparisons indicated that FP1-F7, FP1-F8 and FP1-Fz connectivity were significantly lower during the task state compared to the rest state (P < 0.05). Moreover, F4-P3, F4-P4, FP1-O1, FP2-O2, F3-O1, F4-O1, F8-O1, C4-O1, F3-O2, F4-O2, F7-O2, F8-O2, Fz-O1, Fz-O2, Cz-O1 and Fz-P4 connectivity were significantly higher during the arithmetic task state (P < 0.05). Furthermore, brain sites showed significant within-subject effects and the interaction between states and channels was significant for entropy values (F = 3.50, P = 0.041). Post hoc comparisons indicated that the permutation entropy was significantly higher in the FP1, T3, T4, P4 and Pz channels during the arithmetic task compared to the rest state (P < 0.05). Conclusion: During arithmetic tasks, the increased connectivity in the frontoparietal and frontooccipital networks and heightened complexity in the prefrontal, temporal and parietal lobes reflect the collaborative engagement of brain areas specialized in numerical processing, attention, working memory, cognitive control, and visual-spatial cognition. These changes in connectivity and complexity facilitate the integration of multiple cognitive processes essential for effective arithmetic problem-solving.

Integrating Electroencephalography Source Localization and Residual Convolutional Neural Network for Advanced Stroke Rehabilitation.

Motor impairments caused by stroke significantly affect daily activities and reduce quality of life, highlighting the need for effective rehabilitation strategies. This study presents a novel approach to classifying motor tasks using EEG data from acute stroke patients, focusing on left-hand motor imagery, right-hand motor imagery, and rest states. By using advanced source localization techniques, such as Minimum Norm Estimation (MNE), dipole fitting, and beamforming, integrated with a customized Residual Convolutional Neural Network (ResNetCNN) architecture, we achieved superior spatial pattern recognition in EEG data. Our approach yielded classification accuracies of 91.03% with dipole fitting, 89.07% with MNE, and 87.17% with beamforming, markedly surpassing the 55.57% to 72.21% range of traditional sensor domain methods. These results highlight the efficacy of transitioning from sensor to source domain in capturing precise brain activity. The enhanced accuracy and reliability of our method hold significant potential for advancing brain-computer interfaces (BCIs) in neurorehabilitation. This study emphasizes the importance of using advanced EEG classification techniques to provide clinicians with precise tools for developing individualized therapy plans, potentially leading to substantial improvements in motor function recovery and overall patient outcomes. Future work will focus on integrating these techniques into practical BCI systems and assessing their long-term impact on stroke rehabilitation.

Assessment of the functional capabilities of the heart and the severity of the stress response in women 30-49 years old who are starting to engage in fitness

In this article, based on the results of scientific publications, the current state of the fitness industry development in Kazakhstan in terms of the volume of fitness services and the number of people involved is studied. The results of our own research are presented using modern technologies: in particular, using the method of cardioscanning of cardiovascular system indicators, assessing the fitness level and cardiostress index, as well as based on bioimpedance analysis, the body mass index of untrained women (30-49 years old) of the Royal Club Bukhar Zhyrau fitness club in Almaty in a state of relative rest was determined. The bicycle ergometry method was used to assess the response of hemodynamic indicators of the surveyed contingent to standard physical activity of subthreshold intensity. The obtained data were processed using the methods of mathematical statistics and correlation analysis, analyzed, summarized and formulated in the research results.

On the optimal control function diagrams in the problem of the movement of a platform with oscillators

We consider the problem of the time-optimal movement of a rigid body moving translationally along a horizontal straight line and carrying n-linear oscillators. The only control force is applied to the platform and is limited in magnitude, there is no friction. The system is transferred from a state of rest to a specified distance with vibration damping. The evolution of optimal control functions depending on the distance of movement is investigated. A general approach to constructing a visual diagram reflecting such evolution is proposed. To do this, a geometric interpretation of the necessary optimality conditions is used as properties of an auxiliary “control” curve in n-dimensional space. Numerical examples of constructing diagrams of optimal control functions for a platform with three oscillators are given.

ДИНАМІКА РОСЛИННОГО ПОКРИВУ НА БОТАНІЧНИХ ПРОБНИХ ПЛОЩАХ ПРИРОДНОГО ЗАПОВІДНИКА «МЕДОБОРИ»

The work highlights the issue of using the mapping method in studies on botanical test plots of the geobotanical profile in the “Medobory” nature reserve. It examines the dynamics of changes that have occurred in the last 10 years in the grass cover, undergrowth, and forest within the botanical test areas in the Krasnianske nature protection research department. Experimental sites were established in 1996–1997 by A. V. Onishchenko, and since then, geobotanical descriptions have been conducted annually by scientific employees of the reserve. Since 2011, the mapping method has been used for a more detailed study of the flora in the test areas, which helps clarify the floristic composition in each trial area. Taxation indicators of the forest are also measured. The dynamics of the main taxation indicators were analyzed in four botanical test areas (BA) in fresh hornbeam groves. The forests are indigenous, of seed origin, characterized by high productivity, growing according to the first credit rating, high quality, with significant reserves (316–443 m3/ha). Carpinus betulus (L.) Moench dominates in the forest, except for trial area BA-4 where Carpinus betulus (L.) Moench is equally joined by Fraxinus excelsior L. and Acer platanoides L. In all test areas, a drop in weakened and dead trees has occurred, which is explained by natural processes. On BA-4 and BA-11, the loss is so significant that there has been a thinning of the crown and natural renewal is beginning to form the lower tier. According to the results of the work and processed materials, it was established that in the spring synusias in the botanical test areas two species prevail: Anemone nemorosa L. and Isopyrum thalictroides L. In summer, Carex pilosa Scop. dominates in three test areas, while Asarum europaeum L. is dominant in relation to Stellaria holostea L., Mercurialis perennis L., Glechoma hirsuta Waldst et Kit., and Galium odoratum (L.) Scop. In the undergrowth, Euonymus verrucosa Scop., Euonymus europaea L., Swida sanguinea Opiz., and Sambucus nigra L. are present in small numbers, with coverage from 0.1 to 0.9. Acer platanoides L. dominates the undergrowth in all test plots. Ulmus glabra Huds., Tilia cordata Mill., and Carpinus betulus (L.) Moench are also recovering, along with Acer campestre L., Fraxinus excelsior L., Acer pseudoplatanus L., and Cerasus avium (L.) Moench. With a certain periodicity in the annual geobotanical descriptions on trials in the areas of BA-9 and BA-11, rare species of plants listed in the Red Book of Ukraine were found in single specimens. In the first area, Platanthera chlorantha (Cust.) Reichenb. was found in 2011–2014, and Listera ovata L. R. Br. in 2013–2015, 2018, and 2022; Neottia nidus-avis (L.) Rich. was found in 2015. In the second area, Epipactis helleborine (L.) Crantz was found in 2012 and Epipactis purpurata Smith in 2020–2022. This frequency of sightings of these species is probably connected with the cyclical development of orchids and their state of rest, as well as possibly weather conditions.

Total Antioxidant Capacity, Salivary Catalase, and Superoxide Dismutase in Hashimoto's Thyroiditis Patients

Hashimoto's thyroiditis (HT) is one of the common causes of hypothyroidism. Although various factors are involved in its development, recently the role of oxidative stress in its pathogenesis has been known. The present study aimed to investigate the level of total antioxidant capacity (TAC), catalase (CAT), and salivary superoxide dismutase (SOD) in patients with HT compared with the control group. The present case-control design included patients aged 18-80 years suffering from HT referred to the endocrine clinic. Eligible patients were selected by the available sampling method. Complete unstimulated saliva was collected under a rest state in a comfortable room between 10:00 AM and 12:00 AM and a checklist was used to collect data. The chi-square, t-test, and Mann-Whitney U tests were used for data analysis using SPSS 22 software. The mean age of the participants was 36.55±9.37 years (range: 20-56). The two groups were the same in terms of age and gender (P>0.05). The findings indicated that the difference in the means CAT between the two groups was 22.63 which was strongly and statistically significant (P<0.001). In this study, the level of TAC and SOD in Hashimoto's thyroid patients was decreased and the level of CAT was increased. These initial findings show that oxidative stress can be associated with Hashimoto's thyroid disease or the possibility of developing this disease increase.

Schizophrenia diagnosis based on diverse epoch size resting-state EEG using machine learning.

Schizophrenia is a severe mental disorder that impairs a person's mental, social, and emotional faculties gradually. Detection in the early stages with an accurate diagnosis is crucial to remedying the patients. This study proposed a new method to classify schizophrenia disease in the rest state based on neurologic signals achieved from the brain by electroencephalography (EEG). The datasets used consisted of 28 subjects, 14 for each group, which are schizophrenia and healthy control. The data was collected from the scalps with 19 EEG channels using a 250 Hz frequency. Due to the brain signal variation, we have decomposed the EEG signals into five sub-bands using a band-pass filter, ensuring the best signal clarity and eliminating artifacts. This work was performed with several scenarios: First, traditional techniques were applied. Secondly, augmented data (additive white Gaussian noise and stretched signals) were utilized. Additionally, we assessed Minimum Redundancy Maximum Relevance (MRMR) as the features reduction method. All these data scenarios are applied with three different window sizes (epochs): 1, 2, and 5 s, utilizing six algorithms to extract features: Fast Fourier Transform (FFT), Approximate Entropy (ApEn), Log Energy entropy (LogEn), Shannon Entropy (ShnEn), and kurtosis. The L2-normalization method was applied to the derived features, positively affecting the results. In terms of classification, we applied four algorithms: K-nearest neighbor (KNN), support vector machine (SVM), quadratic discriminant analysis (QDA), and ensemble classifier (EC). From all the scenarios, our evaluation showed that SVM had remarkable results in all evaluation metrics with LogEn features utilizing a 1-s window size, impacting the diagnosis of Schizophrenia disease. This indicates that an accurate diagnosis of schizophrenia can be achieved through the right features and classification model selection. Finally, we contrasted our results to recently published works using the same and a different dataset, where our method showed a notable improvement.

EEG classification of resting state and arithmetic cognitive workload using functional connectivity of different frequency bands and machine learning techniques

ABSTRACT This study aimed to perform a comparative study of the functional connectivity of different frequency bands for the identification of resting and arithmetic cognitive workload EEG using machine learning techniques. Functional connectivity was calculated from preprocessed EEGs for both rest and task states in 5 EEG sub-bands: alpha (8–13 Hz), theta (4–8 Hz), delta (1–4 Hz), gamma (30–45 Hz), and beta (13–30 Hz). This was done through Weighted Phase Lag Index (WPLI). After that, PCA was applied to the calculated feature vectors to decrease the dimensionality of the feature space. Eventually, the normalized chosen features were used as input for different machine learning-based classification models, and the performance was assessed through the leave-one-subject cross-validation (LOSOCV) algorithm. Experimental results showed that the classification results on the basis of the connectivity features of delta, theta, alpha, beta, and gamma frequency bands were 90.27%, 77.78%, 62.50%, 62.50%, and 76.39%, respectively. The obtained results showed that used machine learning models and functional connectivity technique are successfully applied to detect mental workload from rest- and task-EEG. In summary, EEG functional connectivity in the delta frequency is a potent tool for comprehending the neural basis of mental workload and has significant applications in various fields.

The influence of mental calculations on brain regions and heart rates

Performing mathematical calculations is a cognitive activity that can affect biological signals. This study aims to examine the changes in electroencephalogram (EEG) and electrocardiogram (ECG) signals of 36 healthy subjects during the performance of arithmetic tasks. To process EEG signals in different frequency bands, the energy and entropy of entropy (EoE) were extracted from the power spectrum and phase spectrum, respectively. Statistical analysis was conducted to determine meaningful features. These features were sent into support vector machine (SVM) and multi-layer perception (MLP) classifiers to assess their capability in separating math and rest classes. Results indicated the highest classification accuracy of 98.4% for classifying good counters in math and rest state using the MLP method. Based on the majority of features selected for each EEG channel, discriminative brain areas were identified. Analyzing EEG signals proved that math calculation may have multiple influences on various parts of the brain. By comparing good counters’ brain activities to those in a resting state, prominent changes were observed in the F4, C4, T4, T5, P3, and O2 areas. However, O1 and O2 channels showed significant changes in the brain of bad counters compared to the resting state. Considering ECG signals also demonstrated that during math calculation the number of heart rates per minute surpasses the rest state. These alterations can occur due to cognitive abilities or emotional processes which were observed to be prominent in subjects who performed more accurate calculations.

The impact of increased cardiac output induced by dobutamine on cerebral blood flow based on MRI 3D-pcASL technology

Objective: To investigate the effect of increased cardiac output induced by dobutamine on cerebral blood flow (CBF) in healthy volunteers using magnetic resonance 3D-pseudo-continuous arterial spin labeling technology. Methods: A prospective study was conducted on 48 healthy volunteers recruited by handy sampling from June 2021 to January 2022. Physiological parameters before (at rest state) and after (under stress state) dobutamine-induced increase in cardiac output were analyzed. Quantitative CBF maps were generated by using arterial spin labeling difference imaging and proton density weighted reference image processing, and CBF changes under the rest and stress states were compared. Multivariable logistic regression model was used to analyze factors associated with reduced CBF. Results: A total of 48 subjects were included, with an age [M (Q1, Q3)] of 25.0 (24.0, 28.0) years, including 43 men and 5 women. Compared with the rest state, the CBF in the anterior cerebral artery [(36.2±6.9) vs (34.5±6.5) ml·(100 g)-1·min-1, P=0.006] and the middle cerebral artery perfusion area [(35.8±6.5) vs (34.1±6.4) ml·(100 g)-1·min-1, P=0.006] decreased under the stress state, however there was no statistically significant change in CBF in the posterior cerebral artery and the vertebral-basilar artery perfusion area (all P>0.05). Logistic regression analysis showed that the decrease in CBF in the anterior cerebral artery and middle cerebral artery supply regions during the stress state were correlated with an increase in diastolic blood pressure [OR (95%CI): 0.887 (0.796-0.989) and 0.895 (0.805-0.994), both P<0.05]. Conclusions: Dobutamine-induced increase in cardiac output leads to a decrease in CBF in anterior cerebral circulation but has no effect on posterior circulation. The increase in diastolic blood pressure is associated with decreased CBF under the stress state. Changes in CBF should be considered in the context of increased cardiac output.

Comparison of Brain Function Between Medication-Naïve ADHD with and without Comorbidity in Chinese Children Using Resting-State fNIRS.

This study used functional near-infrared spectroscopy (fNIRS) to investigate brain activation patterns in children with attention deficit hyperactivity disorder (ADHD) with and without additional comorbidities to identify disease-related biomarkers by the neuroimaging that will facilitate to make a diagnosis decision. In this study, 165 medication-naive children aged 7 to 15 years were recruited and categorized into four groups: ADHD, ADHD with learning disabilities (ADHD&LD), ADHD with oppositional defiant disorder (ADHD&ODD), and healthy controls. A multichannel fNIRS system was used to monitor hemodynamic changes at rest state in the prefrontal and temporal lobes of the brain. The amplitude of a low-frequency fluctuation (ALFF) matrix was calculated by summation and averaging of the square root of the signal power spectrum. One-way analysis of variance was used to identify statistical differences between channels. All ADHD children presented significantly higher ALFF values in different brain regions when compared with the healthy controls. Patients with ADHD&LD exhibited higher ALFF values in the medial prefrontal cortex (P Ch38 = .01, P Ch48 = .01), temporal cortex (P Ch22 = .04, P Ch41 = .002, P Ch51 = .001), and the left ventrolateral prefrontal cortex (P Ch39 = .0009, P Ch50 = .001), whereas ADHD&ODD children were not significantly different to those diagnosed with ADHD. ADHD with learning disabilities (LD) possessed a different pathogenesis from ADHD, manifested as lower functional brain activity in the medial prefrontal cortex, temporal cortex, and the left ventrolateral prefrontal cortex, while ADHD&ODD did not present significant changes compared with ADHD. ODD-related symptoms may be part of ADHD symptoms rather than being an independent disorder.

Evaluation of enhanced external counterpulsation for diabetic foot based on a patient-specific 0D-1D cardiovascular system model

Background and objectiveDiabetic foot (DF) complications often lead to severe vascular issues. This study investigated the effectiveness of enhanced external counterpulsation (EECP) and its derived innovative compression strategies in addressing poor perfusion in DF. Although developing non-invasive and efficient treatment methods for DF is critical, the hemodynamic alterations during EECP remain underexplored despite promising outcomes in microcirculation. This research sought to address this gap by developing a patient-specific 0D-1D model based on clinical ultrasound data to identify potentially superior compression strategies that could substantially enhance blood flow in patients with DF complications. MethodsData were gathered from 10 patients with DF utilizing ultrasound for blood flow rate and computed tomography angiography (CTA) to identify lower limb conditions. Clinical measurements during standard EECP, with varying cuff pressures, facilitated the creation of a patient-specific 0D-1D model through a two-step parameter estimation process. The accuracy of this model was verified via comparison with the clinical measurements. Four compression strategies were proposed and rigorously evaluated using this model: EECP-Simp-I (removing hip cuffs), EECP-Simp-II (further removing the cuffs around the lower leg), EECP-Impr-I (removing all cuffs around the affected side), and EECP-Impr-II (building a loop circulation from the healthy side to the affected side). ResultsThe predicted results under the rest and standard EECP states were generally closely aligned with clinical measurements. The patient-specific 0D-1D model demonstrated that EECP-Simp-I and EECP-Impr-I contributed similar enhancement to perfusion in the dorsal artery (DA) and were comparable to standard EECP, while EECP-Simp-II had the least effect and EECP-Impr-II displayed the most significant enhancement. Pressure at the aortic root (AO) remained consistent across strategies. ConclusionsEECP-Simp-I is recommended for patients with DF, emphasizing device simplification. However, EECP-Simp-II is discouraged as it significantly diminished blood perfusion in this study, except in cases of limb fragility. EECP-Impr-II showed superior enhancement of blood perfusion in DA to all other strategies but required a more complex EECP device. Despite increased AO pressure in all the proposed compression strategies, safety could be guaranteed as the pressue remained within a safe range.

Computational design of custom-fit PAP masks

Positive airway pressure (PAP) therapy refers to sleep disordered breathing treatment that uses a stream of compressed air to support the airway during sleep. Even though the use of PAP therapy has been shown to be effective in improving the symptoms and quality of life, many patients are intolerant of the treatment due to poor mask fit. In this paper, our goal is to develop a computational approach for designing custom-fit PAP masks such that they can achieve better mask fit performance in terms of mask leakage and comfort. Our key observation is that a custom-fit PAP mask should fit a patient’s face in its deformed state instead of in its rest state since the PAP mask cushion undergoes notable deformation before reaching an equilibrium state during PAP therapy. To this end, we compute the equilibrium state of a mask cushion using the finite element method, and quantitatively measure the leakage and comfort of the mask cushion in this state. We further optimize the mask cushion geometry to minimize the two measures while ensuring that the cushion can be easily fabricated with molding. We demonstrate the effectiveness of our computational approach on a variety of face models and different types of PAP masks. Experimental results on real subjects show that our designed custom-fit PAP masks are able to achieve better mask fit performance than a generic PAP mask and custom-fit PAP masks designed by a state-of-the-art approach.

Application value of real-time shear wave elastography in the diagnosis and efficacy evaluation of venous thrombosis.

Deep vein thrombosis (DVT) of the lower limbs is a venous reflux disorder caused by abnormal coagulation of blood components, primarily characterised by swelling and pain in the lower limbs. Key risk factors include prolonged immobility due to bed rest, pregnancy, postpartum or postoperative states, traumas, malignant tumours and long-term contraceptive use. To investigate the application of real-time shear wave elastography (SWE) in diagnosing lower-limb deep vein thrombosis (DVT). A total of 91 patients with DVT were selected and divided into three groups: acute phase (n= 29), subacute phase (n= 30) and chronic phase (n= 32). The Young's modulus of the patients was measured using real-time SWE. The diagnostic efficacy of Young's modulus was evaluated by ROC curves. The hardness differences in Young's modulus across different parts of the same thrombus (head, body and tail) were measured using SWE. Before treatment, significant differences were observed in Young's modulus among patients with DVT (P< 0.001). Following anticoagulant therapy, catheter-directed thrombolysis and systemic thrombolysis, significant differences were noted in Young's modulus among patients at the same stage but receiving different treatments (acute phase: P= 0.003; subacute phase: P= 0.014; chronic phase: P= 0.004). Catheter-directed thrombolysis had greater efficacy than anticoagulant therapy. The area under the curve for SWE in staging patients was 0.917, with a sensitivity of 92.36% and specificity of 93.81%. Significant differences in Young's modulus were found between the thrombus head and thrombus body and tail but not between the thrombus body and thrombus tail. Measurement of Young's modulus using SWE can serve as an auxiliary means of evaluating staging, predicting pulmonary embolism and selecting treatment in patients with DVT.

Dynamical Tests of a Deep Learning Weather Prediction Model

Abstract Global deep learning weather prediction models have recently been shown to produce forecasts that rival those from physics-based models run at operational centers. It is unclear whether these models have encoded atmospheric dynamics or simply pattern matching that produces the smallest forecast error. Answering this question is crucial to establishing the utility of these models as tools for basic science. Here, we subject one such model, Pangu-Weather, to a set of four classical dynamical experiments that do not resemble the model training data. Localized perturbations to the model output and the initial conditions are added to steady time-averaged conditions, to assess the propagation speed and structural evolution of signals away from the local source. Perturbing the model physics by adding a steady tropical heat source results in a classical Matsuno–Gill response near the heating and planetary waves that radiate into the extratropics. A localized disturbance on the winter-averaged North Pacific jet stream produces realistic extratropical cyclones and fronts, including the spontaneous emergence of polar lows. Perturbing the 500-hPa height field alone yields adjustment from a state of rest to one of wind–pressure balance over ∼6 h. Localized subtropical low pressure systems produce Atlantic hurricanes, provided the initial amplitude exceeds about 4 hPa, and setting the initial humidity to zero eliminates hurricane development. We conclude that the model encodes realistic physics in all experiments and suggest that it can be used as a tool for rapidly testing a wide range of hypotheses.