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Studies have suggested that pediatric patients with heart transplants (HT) due to congenital heart disease (CHD) perform differently on cardiopulmonary exercise testing compared to pediatric patients with HT due to cardiomyopathy (CM). However, it is not known if this relationship changes over time. The aim of this study was to examine the differences in cardiopulmonary exercise test (CPET) parameters over time between patients with HT due to CHD versus CM. A large single-institution CPET database was used for this study. We conducted a retrospective cohort study of 250 total CPETs from 93 unique patients, examining how patients with HT due to CHD (109 CPETs, 40 unique patients) differed in CPET performance from patients with HT due to CM (141 CPETs, 53 unique patients) from < 2 years post-HT, 2 to < 6 years post-HT, and ≥ 6 years post-HT. There were no differences between patients with HT due to CHD compared to CM in CPETs performed < 2 years post-HT. In CPETs performed 2 to < 6 years post-HT, the CM group had higher maximal HR and percentage of age-predicted maximal heart rate (APMHR) achieved. At ≥ 6 years post-HT, the CM group continued to have higher maximal HR and percentage of APMHR achieved, but also improved HR recovery at one minute. Initial indication for transplant may affect performance on CPETs post-transplant. Patients with HT due to CM have improved chronotropic measures compared to patients with HT due to CHD and these differences are more pronounced with increased time post-HT.
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The brain connectivity-based atlas is a promising tool for understanding neural communication pathways in the brain, gaining relevance in predicting personalized outcomes for various brain pathologies. This critical review examines the robustness of the brain connectivity-based atlas for predicting post-stroke outcomes. A comprehensive literature search was conducted from 2012 to May 2023 across PubMed, Scopus, EMBASE, EBSCOhost, and Medline databases. Twenty-one studies were screened, and through analysis of these studies, we identified 18 brain connectivity atlases employed by the studies for lesion analysis in their predictions. The brain atlases were assessed for study cohorts, connectivity measures, identified brain regions, atlas applications, and limitations. Based on the analysis of these studies, most atlases were based on diffusion tensor imaging and resting-state functional magnetic resonance imaging (MRI). Studies predicting post-stroke functional outcomes relied on the atlases for multivariate lesion analysis and region of interest identification, often employing atlases derived from young, healthy populations. Current brain connectivity-based atlases for stroke applications lack standardized methods to define and map brain connectivity across atlases and cover sensorimotor functional connectivity to a limited extent. In conclusion, this review highlights the need to develop more comprehensive, robust, and adaptable brain connectivity-based atlases specifically tailored to post-stroke populations.
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Introduction: Brain medical image segmentation is a critical task in medical image processing, playing a significant role in the prediction and diagnosis of diseases such as stroke, Alzheimer's disease, and brain tumors. However, substantial distribution discrepancies among datasets from different sources arise due to the large inter-site discrepancy among different scanners, imaging protocols, and populations. This leads to cross-domain problems in practical applications. In recent years, numerous studies have been conducted to address the cross-domain problem in brain image segmentation. Methods: This review adheres to the standards of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for data processing and analysis. We retrieved relevant papers from PubMed, Web of Science, and IEEE databases from January 2018 to December 2023, extracting information about the medical domain, imaging modalities, methods for addressing cross-domain issues, experimental designs, and datasets from the selected papers. Moreover, we compared the performance of methods in stroke lesion segmentation, white matter segmentation and brain tumor segmentation. Results: A total of 71 studies were included and analyzed in this review. The methods for tackling the cross-domain problem include Transfer Learning, Normalization, Unsupervised Learning, Transformer models, and Convolutional Neural Networks (CNNs). On the ATLAS dataset, domain-adaptive methods showed an overall improvement of ~3 percent in stroke lesion segmentation tasks compared to non-adaptive methods. However, given the diversity of datasets and experimental methodologies in current studies based on the methods for white matter segmentation tasks in MICCAI 2017 and those for brain tumor segmentation tasks in BraTS, it is challenging to intuitively compare the strengths and weaknesses of these methods. Conclusion: Although various techniques have been applied to address the cross-domain problem in brain image segmentation, there is currently a lack of unified dataset collections and experimental standards. For instance, many studies are still based on n-fold cross-validation, while methods directly based on cross-validation across sites or datasets are relatively scarce. Furthermore, due to the diverse types of medical images in the field of brain segmentation, it is not straightforward to make simple and intuitive comparisons of performance. These challenges need to be addressed in future research.
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The parameter setting of functional electrical stimulation (FES) is important for active recovery training since it affects muscle health. Among the FES parameters, current amplitude is the most influential factor. To explore the FES effect on the maximum stimulation time, this study establishes a curve between FES current amplitude and the maximum stimulation time based on muscle fatigue. We collect 10 subjects' surface electromyography under dumbbell weightlifting training and analyze the muscle fatigue state by calculating the root mean square (RMS) of power. By analyzing signal RMS, the fatigue characteristic curves under different fatigue levels are obtained. According to the muscle response under FES, the relationship curve between the current amplitude and the maximum stimulation time is established and FES parameters' effect on the maximum stimulation time is obtained. The linear curve provides a reference for FES parameter setting, which can help to set stimulation time safely, thus preventing the muscles from entering an excessive fatigue state and becoming more active to muscle recovery training.
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BACKGROUND: Obesity and impaired exercise tolerance following heart transplantation increase the risk of post-transplant morbidity and mortality. The aim of this study was to evaluate the effect of body mass index on markers of exercise capacity in pediatric heart transplant recipients and compare this effect with a healthy pediatric cohort. METHODS: A retrospective analysis of cardiopulmonary exercise test data between 2004 and 2022 was performed. All patients exercised on a treadmill using the Bruce protocol. Inclusion criteria included patients aged 6-21 years, history of heart transplantation (transplant cohort) or no cardiac diagnosis (control cohort) at the time of testing, and a maximal effort test. Patients were further stratified within these two cohorts as underweight, normal, overweight, and obese based on body mass index groups. Two-way analyses of variance were performed with diagnosis and body mass index category as the independent variables. RESULTS: A total of 250 exercise tests following heart transplant and 1963 exercise tests of healthy patients were included. Heart transplant patients across all body mass index groups had higher resting heart rate and lower maximal heart rate, heart rate recovery at 1 min, exercise duration, and peak aerobic capacity (VO2peak). Heart transplant patients in the normal and overweight body mass index categories had higher VO2peak and exercise duration when compared to underweight and obese patients. CONCLUSION: Underweight status and obesity are strongly associated with lower VO2peak and exercise duration in heart transplant patients. Normal and overweight heart transplant patients had the best markers of exercise capacity.
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Índice de Massa Corporal , Teste de Esforço , Tolerância ao Exercício , Transplante de Coração , Humanos , Adolescente , Criança , Masculino , Feminino , Estudos Retrospectivos , Tolerância ao Exercício/fisiologia , Adulto Jovem , Estudos de Casos e Controles , Magreza , Frequência Cardíaca/fisiologiaRESUMO
The brain controls the nerve system, allowing complex emotional and cognitive activities. The microbiota-gut-brain axis is a bidirectional neural, hormonal, and immune signaling pathway that could link the gastrointestinal tract to the brain. Over the past few decades, gut microbiota has been demonstrated to be an essential component of the gastrointestinal tract that plays a crucial role in regulating most functions of various body organs. The effects of the microbiota on the brain occur through the production of neurotransmitters, hormones, and metabolites, regulation of host-produced metabolites, or through the synthesis of metabolites by the microbiota themselves. This affects the host's behavior, mood, attention state, and the brain's food reward system. Meanwhile, there is an intimate association between the gut microbiota and exercise. Exercise can change gut microbiota numerically and qualitatively, which may be partially responsible for the widespread benefits of regular physical activity on human health. Functional magnetic resonance imaging (fMRI) is a non-invasive method to show areas of brain activity enabling the delineation of specific brain regions involved in neurocognitive disorders. Through combining exercise tasks and fMRI techniques, researchers can observe the effects of exercise on higher brain functions. However, exercise's effects on brain health via gut microbiota have been little studied. This article reviews and highlights the connections between these three interactions, which will help us to further understand the positive effects of exercise on brain health and provide new strategies and approaches for the prevention and treatment of brain diseases.
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This study presents a comprehensive review of the correlation between tibial acceleration (TA), ground reaction forces (GRF), and tibial bone loading, emphasizing the critical role of wearable sensor technology in accurately measuring these biomechanical forces in the context of running. This systematic review and meta-analysis searched various electronic databases (PubMed, SPORTDiscus, Scopus, IEEE Xplore, and ScienceDirect) to identify relevant studies. It critically evaluates existing research on GRF and tibial acceleration (TA) as indicators of running-related injuries, revealing mixed findings. Intriguingly, recent empirical data indicate only a marginal link between GRF, TA, and tibial bone stress, thus challenging the conventional understanding in this field. The study also highlights the limitations of current biomechanical models and methodologies, proposing a paradigm shift towards more holistic and integrated approaches. The study underscores wearable sensors' potential, enhanced by machine learning, in transforming the monitoring, prevention, and rehabilitation of running-related injuries.
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Averaging is commonly used for data reduction/aggregation to analyse high-dimensional MRI data, but this often leads to information loss. To address this issue, we developed a novel technique that integrates diffusion tensor metrics along the whole volume of the fibre bundle using a 3D mesh-morphing technique coupled with principal component analysis for delineating case and control groups. Brain diffusion tensor MRI scans of high school rugby union players (n = 30, age 16-18) were acquired on a 3â T MRI before and after the sports season. A non-contact sport athlete cohort with matching demographics (n = 12) was also scanned. The utility of the new method in detecting differences in diffusion tensor metrics of the right corticospinal tract between contact and non-contact sport athletes was explored. The first step was to run automated tractography on each subject's native space. A template model of the right corticospinal tract was generated and morphed into each subject's native shape and space, matching individual geometry and diffusion metric distributions with minimal information loss. The common dimension of the 20 480 diffusion metrics allowed further data aggregation using principal component analysis to cluster the case and control groups as well as visualization of diffusion metric statistics (mean, ±2 SD). Our approach of analysing the whole volume of white matter tracts led to a clear delineation between the rugby and control cohort, which was not possible with the traditional averaging method. Moreover, our approach accounts for the individual subject's variations in diffusion tensor metrics to visualize group differences in quantitative MR data. This approach may benefit future prediction models based on other quantitative MRI methods.
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The relationship between the mechanical forces associated with bowel movement and colonic mucosal physiology is understudied. This is partly due to the limited availability of physiologically relevant fecal models that can exert these mechanical stimuli in in vitro colon models in a simple-to-implement manner. In this report, we created a mucus-coated fecal surrogate that was magnetically propelled to produce a controllable sweeping mechanical stimulation on primary intestinal epithelial cell monolayers. The mucus layer was derived from purified porcine stomach mucins, which were first modified with reactive vinyl sulfone (VS) groups followed by reaction with a thiol crosslinker (PEG-4SH) via a Michael addition click reaction. Formation of mucus hydrogel network was achieved at the optimal mixing ratio at 2.5 % w/v mucin-VS and 0.5 % w/v PEG-4SH. The artificial mucus layer possessed similar properties as the native mucus in terms of its storage modulus (66 Pa) and barrier function (resistance to penetration by 1-µm microbeads). This soft, but mechanically resilient mucus layer was covalently linked to a stiff fecal hydrogel surrogate (based on agarose and magnetic particles, with a storage modulus of 4600 Pa). The covalent bonding between the mucus and agarose ensured its stability in the subsequent fecal sliding movement when tested at travel distances as long as 203 m. The mucus layer served as a lubricant and protected epithelial cells from the moving fecal surrogate over a 1 h time without cell damage. To demonstrate its utility, this mucus-coated fecal surrogate was used to mechanically stimulate a fully differentiated, in vitro primary colon epithelium, and the physiological stimulated response of mucin-2 (MUC2), interleukin-8 (IL-8) and serotonin (5HT) secretion was quantified. Compared with a static control, mechanical stimulation caused a significant increase in MUC2 secretion into luminal compartment (6.4 × ), a small but significant increase in IL-8 secretion (2.5 × and 3.5 × , at both luminal and basal compartments, respectively), and no detectable alteration in 5HT secretion. This mucus-coated fecal surrogate is expected to be useful in in vitro colon organ-on-chips and microphysiological systems to facilitate the investigation of feces-induced mechanical stimulation on intestinal physiology and pathology.
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Colo , Fezes , Mucosa Intestinal , Muco , Muco/metabolismo , Animais , Colo/fisiologia , Mucosa Intestinal/metabolismo , Mucosa Intestinal/citologia , Fezes/química , Suínos , Hidrogéis/química , Resistência ao Cisalhamento , Sulfonas/química , Estresse Mecânico , Polietilenoglicóis/químicaRESUMO
Foot and ankle joint models are widely used in the biomechanics community for musculoskeletal and finite element analysis. However, personalizing a foot and ankle joint model is highly time-consuming in terms of medical image collection and data processing. This study aims to develop and evaluate a framework for constructing a comprehensive 3D foot model that integrates statistical shape modeling (SSM) with free-form deformation (FFD) of internal bones. The SSM component is derived from external foot surface scans (skin measurements) of 50 participants, utilizing principal component analysis (PCA) to capture the variance in foot shapes. The derived surface shapes from SSM then guide the FFD process to accurately reconstruct the internal bone structures. The workflow accuracy was established by comparing three model-generated foot models against corresponding skin and bone geometries manually segmented and not part of the original training set. We used the top ten principal components representing 85 % of the population variation to create the model. For prediction validation, the average Dice similarity coefficient, Hausdorff distance error, and root mean square error were 0.92 ± 0.01, 2.2 ± 0.19 mm, and 2.95 ± 0.23 mm for soft tissues, and 0.84 ± 0.03, 1.83 ± 0.1 mm, and 2.36 ± 0.12 mm for bones, respectively. This study presents an efficient approach for 3D personalized foot model reconstruction via SSM generation of the foot surface that informs bone reconstruction based on FFD. The proposed workflow is part of the open-source Musculoskeletal Atlas Project linked to OpenSim and makes it feasible to accurately generate foot models informed by population anatomy, and suitable for rigid body analysis and finite element simulation.
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Pé , Imageamento Tridimensional , Humanos , Pé/anatomia & histologia , Pé/fisiologia , Imageamento Tridimensional/métodos , Feminino , Masculino , Adulto , Análise de Componente Principal , Análise de Elementos Finitos , Articulação do Tornozelo/diagnóstico por imagem , Articulação do Tornozelo/fisiologia , Articulação do Tornozelo/anatomia & histologia , Modelos Anatômicos , Fenômenos Biomecânicos , Tornozelo/fisiologiaRESUMO
Background: Heart rate, as the four vital signs of human body, is a basic indicator to measure a person's health status. Traditional electrocardiography (ECG) measurement, which is routinely monitored, requires subjects to wear lead electrodes frequently, which undoubtedly places great restrictions on participants' activities during the normal test. At present, the boom of wearable devices has created hope for non-invasive, simple operation and low-cost daily heart rate monitoring, among them, Ballistocardiogram signal (BCG) is an effective heart rate measurement method, but in the actual acquisition process, the robustness of non-invasive vital sign collection is limited. Therefore, it is necessary to develop a method to improve the robustness of heart rate monitoring. Objective: Therefore, in view of the problem that the accuracy of untethered monitoring heart rate is not high, we propose a method aimed at detecting the heartbeat cycle based on BCG to accurately obtain the beat-to-beat heart rate in the sleep state. Methods: In this study, we implement an innovative J-wave detection algorithm based on BCG signals. By collecting BCG signals recorded by 28 healthy subjects in different sleeping positions, after preprocessing, the data feature set is formed according to the clustering of morphological features in the heartbeat interval. Finally, a J-wave recognition model is constructed based on bi-directional long short-term memory (BiLSTM), and then the number of J-waves in the input sequence is counted to realize real-time detection of heartbeat. The performance of the proposed heartbeat detection scheme is cross-verified, and the proposed method is compared with the previous wearable device algorithm. Results: The accuracy of J wave recognition in BCG signal is 99.67%, and the deviation rate of heart rate detection is only 0.27%, which has higher accuracy than previous wearable device algorithms. To assess consistency between method results and heart rates obtained by the ECG, seven subjects are compared using Bland-Altman plots, which show no significant difference between BCG and ECG results for heartbeat cycles. Conclusions: Compared with other studies, the proposed method is more accurate in J-wave recognition, which improves the accuracy and generalization ability of BCG-based continuous heartbeat cycle extraction, and provides preliminary support for wearable-based untethered daily monitoring.
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Our objectives were to quantify geographical disparities in otolaryngology care access with respect to American Indian (AI) populations and to identify gaps in care. Although increased incidence and mortality rates of ear, nose, and throat (ENT) conditions in AI populations are well documented, few studies address factors contributing to these differential outcomes. We conducted a cross-sectional study of US states with AI areas that either met the population threshold for the American Community Survey annual estimate or annual supplemental estimate. A 2-tailed t test was used to compare the geographic distribution of ENT providers practicing within AI areas against non-AI areas, showing a statistically significant difference (P < .001) in the concentration of providers (0.409 vs 2.233 providers per 100,000 patients). To our knowledge, this is the first study to explore geographic barriers contributing to AI disparities within otolaryngology.
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Cardiac dysfunction is associated with mortality in children with hypoplastic left heart syndrome (HLHS). We evaluated the ability of qualitative and quantitative RV functional parameters to predict outcomes in HLHS patients. In this retrospective, single-center study, echocardiograms from 3 timepoints (pre-stage 1 palliation, 4-8 weeks post-stage 1 palliation, and pre-Glenn) were analyzed in infants with HLHS. Patients were stratified into two groups based on outcome of transplant-free survival post-Glenn (survivors) versus mortality or transplantation prior to Fontan (non-survivors). Images were retrospectively reviewed to obtain RV global longitudinal strain (RVGLS), RV-free wall strain (RVFWS), fractional area change (FAC), tricuspid annular systolic plane excursion (TAPSE), tissue motion annular displacement of the tricuspid valve (TMAD-TV) and qualitative systolic function assessment during the predetermined timepoints. An equal variance t-test and chi-square were used to determine significant differences and ROC curve analysis was performed to derive optimal cutoff values to predict mortality/transplant. A total of 47 patients met inclusion criteria, of which, 21 patients met composite endpoint. There were no significant differences in any RV functional parameter during the pre- or post-stage 1 palliation timepoints. The absolute values of RVFWS, RVGLS, and TMAD-TV were significantly greater in survivors than non-survivors during the pre-Glenn timepoint. A pre-Glenn RVGLS > -15.6 (AUC 0.79), RVFWS > -18.6 (AUC 0.75), and TMAD-TV < 12.6% (AUC 0.82) were sensitive and specific for predicting death or need for transplantation prior to Fontan completion. RVGLS, RVFWS, and TMAD-TV may help identify higher-risk HLHS patients during the interstage period.
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Ecocardiografia , Técnica de Fontan , Síndrome do Coração Esquerdo Hipoplásico , Humanos , Síndrome do Coração Esquerdo Hipoplásico/cirurgia , Síndrome do Coração Esquerdo Hipoplásico/mortalidade , Síndrome do Coração Esquerdo Hipoplásico/fisiopatologia , Masculino , Estudos Retrospectivos , Feminino , Prognóstico , Lactente , Transplante de Coração , Função Ventricular Direita/fisiologia , Recém-Nascido , Ventrículos do Coração/fisiopatologia , Ventrículos do Coração/diagnóstico por imagem , Curva ROCRESUMO
OBJECTIVE: To evaluate the effect of neighborhood-level characteristics on cardiorespiratory fitness (CRF) via peak oxygen consumption (VO2peak) for healthy pediatric patients. STUDY DESIGN: The institutional cardiopulmonary exercise testing (CPET) database was analyzed retrospectively. All patients aged ≤ 18 years without a diagnosis of cardiac disease and with a maximal effort CPET were included. Patients were divided into three self-identified racial categories: White, Black, and Latinx. The Child Opportunity Index (COI) 2.0 was used to analyze social determinants of health. CRF was evaluated based on COI quintiles and race. Assessment of the effect of COI on racial disparities in CRF was performed using ANCOVA. RESULTS: A total of 1753 CPETs met inclusion criteria. The mean VO2peak was 42.1 ± 9.8 mL/kg/min. The VO2peak increased from 39.1 ± 9.6 mL/kg/min for patients in the very low opportunity cohort to 43.9 ± 9.4 mL/kg/min for patients in the very high opportunity cohort. White patients had higher percent predicted VO2peak compared with both Black and Latinx patients (P < .01 for both comparisons). The racial differences in CRF were no longer significant when adjusting for COI. CONCLUSION: In a large pediatric cohort, COI was associated with CRF. Racial disparities in CRF are reduced when accounting for modifiable risk factors.
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Aptidão Cardiorrespiratória , Teste de Esforço , Consumo de Oxigênio , Adolescente , Criança , Feminino , Humanos , Masculino , Negro ou Afro-Americano/estatística & dados numéricos , Aptidão Cardiorrespiratória/fisiologia , Disparidades nos Níveis de Saúde , Hispânico ou Latino/estatística & dados numéricos , Consumo de Oxigênio/fisiologia , Características de Residência , Estudos Retrospectivos , Determinantes Sociais da Saúde , BrancosRESUMO
This study introduces a hybrid analytical super-resolution (SR) pipeline aimed at enhancing the resolution of medical magnetic resonance imaging (MRI) scans. The primary objective is to overcome the limitations of clinical MRI resolution without the need for additional expensive hardware. The proposed pipeline involves three key steps: pre-processing to re-slice and register the image stacks; SR reconstruction to combine information from three orthogonal image stacks to generate a high-resolution image stack; and post-processing using an artefact reduction convolutional neural network (ARCNN) to reduce the block artefacts introduced during SR reconstruction. The workflow was validated on a dataset of six knee MRIs obtained at high resolution using various sequences. Quantitative analysis of the method revealed promising results, showing an average mean error of 1.40 ± 2.22% in voxel intensities between the SR denoised images and the original high-resolution images. Qualitatively, the method improved out-of-plane resolution while preserving in-plane image quality. The hybrid SR pipeline also displayed robustness across different MRI sequences, demonstrating potential for clinical application in orthopaedics and beyond. Although computationally intensive, this method offers a viable alternative to costly hardware upgrades and holds promise for improving diagnostic accuracy and generating more anatomically accurate models of the human body.
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The ankle joint plays a crucial role in gait, facilitating the articulation of the lower limb, maintaining foot-ground contact, balancing the body, and transmitting the center of gravity. This study aimed to implement long short-term memory (LSTM) networks for predicting ankle joint angles, torques, and contact forces using inertial measurement unit (IMU) sensors. Twenty-five healthy participants were recruited. Two IMU sensors were attached to the foot dorsum and the vertical axis of the distal anteromedial tibia in the right lower limb to record acceleration and angular velocity during running. We proposed a LSTM-MLP (multilayer perceptron) model for training time-series data from IMU sensors and predicting ankle joint biomechanics. The model underwent validation and testing using a custom nested k-fold cross-validation process. The average values of the coefficient of determination (R2), mean absolute error (MAE), and mean squared error (MSE) for ankle dorsiflexion joint and moment, subtalar inversion joint and moment, and ankle joint contact forces were 0.89 ± 0.04, 0.75 ± 1.04, and 2.96 ± 4.96 for walking, and 0.87 ± 0.07, 0.88 ± 1.26, and 4.1 ± 7.17 for running, respectively. This study demonstrates that IMU sensors, combined with LSTM neural networks, are invaluable tools for evaluating ankle joint biomechanics in lower limb pathological diagnosis and rehabilitation, offering a cost-effective and versatile alternative to traditional experimental settings.
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Articulação do Tornozelo , Marcha , Humanos , Fenômenos Biomecânicos , Caminhada , PéRESUMO
Silicon microring modulator plays a critical role in energy-efficient optical interconnect and optical computing owing to its ultra-compact footprint and capability for on-chip wavelength-division multiplexing. However, existing silicon microring modulators usually require more than 2 V of driving voltage (Vpp), which is limited by both material properties and device structures. Here, we present a metal-oxide-semiconductor capacitor microring modulator through heterogeneous integration between silicon photonics and titanium-doped indium oxide, which is a high-mobility transparent conductive oxide (TCO) with a strong plasma dispersion effect. The device is co-fabricated by Intel's photonics fab and our in-house TCO patterning processes, which exhibits a high modulation efficiency of 117 pm/V and consequently can be driven by a very low Vpp of 0.8 V. At a 11 GHz modulation bandwidth where the modulator is limited by the RC bandwidth, we obtained 25 Gb/s clear eye diagrams with energy efficiency of 53 fJ/bit.
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Stroke is a medical condition that affects around 15 million people annually. Patients and their families can face severe financial and emotional challenges as it can cause motor, speech, cognitive, and emotional impairments. Stroke lesion segmentation identifies the stroke lesion visually while providing useful anatomical information. Though different computer-aided software are available for manual segmentation, state-of-the-art deep learning makes the job much easier. This review paper explores the different deep-learning-based lesion segmentation models and the impact of different pre-processing techniques on their performance. It aims to provide a comprehensive overview of the state-of-the-art models and aims to guide future research and contribute to the development of more robust and effective stroke lesion segmentation models.
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OBJECTIVE: To develop reference values for cardiorespiratory fitness, as quantified by peak oxygen uptake (VO2peak) and treadmill time, in patients aged 6 through 18 years referred for cardiopulmonary exercise testing (CPET). STUDY DESIGN: We reviewed a clinical pediatric CPET database for fitness data in children aged 6-18 years with no underlying heart disease. CPET was obtained via the Bruce protocol utilizing objectively confirmed maximal effort via respiratory exchange ratio. Fitness data (VO2peak and treadmill test duration) were analyzed to determine age- and sex-specific reference values for this pediatric cohort. RESULTS: Data from 2025 pediatric CPETs (53.2% female) were included in the analyses. VO2peak increased with age in males, but not females. Treadmill test duration increased with age in both males and females. Fitness was generally higher in males when compared with females in the same age groups. CONCLUSIONS: Our study provides extensive reference values for both VO2peak and total treadmill test time via the Bruce protocol for a pediatric population without known cardiac disease. Furthermore, the inclusion of objectively confirmed maximal exercise effort increases confidence in these findings compared with prior studies in this area. Clinicians performing CPET in pediatric populations can utilize these reference values to characterize test results according to representative peer data.
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Aptidão Cardiorrespiratória , Cardiopatias , Masculino , Humanos , Feminino , Criança , Valores de Referência , Teste de Esforço/métodos , Exercício Físico , Consumo de OxigênioRESUMO
Humans learn from a lot of information sources to make decisions. Once this information is learned in the brain, spatio-temporal associations are made, connecting all these sources (variables) in space and time represented as brain connectivity. In reality, to make a decision, we usually have only part of the information, either as a limited number of variables, limited time to make the decision, or both. The brain functions as a spatio-temporal associative memory. Inspired by the ability of the human brain, a brain-inspired spatio-temporal associative memory was proposed earlier that utilized the NeuCube brain-inspired spiking neural network framework. Here we applied the STAM framework to develop STAM for neuroimaging data, on the cases of EEG and fMRI, resulting in STAM-EEG and STAM-fMRI. This paper showed that once a NeuCube STAM classification model was trained on a complete spatio-temporal EEG or fMRI data, it could be recalled using only part of the time series, or/and only part of the used variables. We evaluated both temporal and spatial association and generalization accuracy accordingly. This was a pilot study that opens the field for the development of classification systems on other neuroimaging data, such as longitudinal MRI data, trained on complete data but recalled on partial data. Future research includes STAM that will work on data, collected across different settings, in different labs and clinics, that may vary in terms of the variables and time of data collection, along with other parameters. The proposed STAM will be further investigated for early diagnosis and prognosis of brain conditions and for diagnostic/prognostic marker discovery.