The Relationship Between the Anaerobic Speed Reserve and Acute Responses to High-Intensity Interval Training in Female Soccer Players.

ABSTRACT: Aspin, GL, Graham, M, Franklin, J, Hicks, KM, and Taylor, JM. The relationship between the anaerobic speed reserve and acute responses to high-intensity interval training in female soccer players. J Strength Cond Res XX(X): 000-000, 2024-The anaerobic speed reserve (ASR) is a popular method of profiling soccer players, often used to individualize training prescription. This study explored the reliability of ASR profiling, and the relationship between the ASR and acute physiological responses to high-intensity interval training (HIIT). Acute physiological responses to different HIIT types were also compared. Thirteen subelite female soccer players aged 20.2 ± 4.6 years completed 6 exercise sessions. In sessions 1-2, players completed a 40-m sprint to assess maximal sprint speed (MSS) and 1600-m time-trial to estimate maximal aerobic speed (MAS), which were used to calculate ASR and assess test-retest reliability. In sessions 3-6, players completed 4 HIIT sessions (repeated-sprint training, sprint interval training, long intervals, and short intervals HIIT). Intensities for long and short intervals HIIT were individualized according to MAS. Ratings of perceived exertion (RPE), heart rate (HR), and postsession blood lactates were recorded throughout. Relationships between the ASR and acute responses to HIIT, and between HIIT session comparisons in outcome measures were assessed. Anaerobic speed reserve (coefficient of variation ± 95% confidence limits; 3.1 ± 1.5%), MAS (1.8 ± 1.3%), and MSS (0.8 ± 0.6%) indicated acceptable reliability. Moderate correlations between ASR and RPE (r = 0.33), postsession blood lactate (r = 0.34), and HR (r = 0.37) were observed during long intervals HIIT. A strong correlation was observed between ASR and RPE during SIT (r = 0.50). Sprint interval training elicited higher RPE's and postsession blood lactate's than other HIIT sessions. Anaerobic speed reserve has good reliability and may influence acute physiological responses to HIIT in female soccer players.

Exploratory Study on COPD Phenotypes and their Progression: Integrating SPECT and qCT Imaging Analysis.

Background: The objective of this study is to understand chronic obstructive pulmonary disease (COPD) phenotypes and their progressions by quantifying heterogeneities of lung ventilation from the single photon emission computed tomography (SPECT) images and establishing associations with the quantitative computed tomography (qCT) imaging-based clusters and variables. Methods: Eight COPD patients completed a longitudinal study of three visits with intervals of about a year. CT scans of these subjects at residual volume, functional residual capacity, and total lung capacity were taken for all visits. The functional and structural qCT-based variables were derived, and the subjects were classified into the qCT-based clusters. In addition, the SPECT variables were derived to quantify the heterogeneity of lung ventilation. The correlations between the key qCT-based variables and SPECT-based variables were examined. Results: The SPECT-based coefficient of variation (CVTotal), a measure of ventilation heterogeneity, showed strong correlations (|r| ≥ 0.7) with the qCT-based functional small airway disease percentage (fSAD%Total) and emphysematous tissue percentage (Emph%Total) in the total lung on cross-sectional data. As for the two-year changes, the SPECT-based maximum tracer concentration (TCmax), a measure of hot spots, exhibited strong negative correlations with fSAD%Total, Emph%Total, average airway diameter in the left upper lobe, and airflow distribution in the middle and lower lobes. Conclusion: Small airway disease is highly associated with the heterogeneity of ventilation in COPD lungs. TCmax is a more sensitive functional biomarker for COPD progression than CVTotal. Besides fSAD%Total and Emph%Total, segmental airways narrowing and imbalanced ventilation between upper and lower lobes may contribute to the development of hot spots over time.

Enhancing predictive capabilities in data-driven dynamical modeling with automatic differentiation: Koopman and neural ODE approaches.

Data-driven approximations of the Koopman operator are promising for predicting the time evolution of systems characterized by complex dynamics. Among these methods, the approach known as extended dynamic mode decomposition with dictionary learning (EDMD-DL) has garnered significant attention. Here, we present a modification of EDMD-DL that concurrently determines both the dictionary of observables and the corresponding approximation of the Koopman operator. This innovation leverages automatic differentiation to facilitate gradient descent computations through the pseudoinverse. We also address the performance of several alternative methodologies. We assess a "pure" Koopman approach, which involves the direct time-integration of a linear, high-dimensional system governing the dynamics within the space of observables. Additionally, we explore a modified approach where the system alternates between spaces of states and observables at each time step-this approach no longer satisfies the linearity of the true Koopman operator representation. For further comparisons, we also apply a state-space approach (neural ordinary differential equations). We consider systems encompassing two- and three-dimensional ordinary differential equation systems featuring steady, oscillatory, and chaotic attractors, as well as partial differential equations exhibiting increasingly complex and intricate behaviors. Our framework significantly outperforms EDMD-DL. Furthermore, the state-space approach offers superior performance compared to the "pure" Koopman approach where the entire time evolution occurs in the space of observables. When the temporal evolution of the Koopman approach alternates between states and observables at each time step, however, its predictions become comparable to those of the state-space approach.

Change in Management After Radionuclide Gastric Emptying Studies Showing Slow Emptying.

The radionuclide gastric emptying study is the gold standard for the diagnosis of gastroparesis. Methods: We performed a retrospective analysis of 510 patients to evaluate how often a diagnosis of slow gastric emptying determined by gastric emptying scintigraphy (GES) changes clinical management at our institution. Results: We found evidence of gastroparesis in 100 patients. A change in management was recommended for 62% within 1 mo of the GES. Conclusion: Our results illustrate the importance of performing GES on patients with clinically suspected gastroparesis.

The malignant transformation of endometriosis: Is there a left lateral predisposition of ovarian clear cell and endometrioid carcinomas?

INTRODUCTION: Endometriosis affects 10% of women of reproductive age. There is evidence for a left lateral predisposition of endometriotic lesions and a 1.9-fold greater risk of ovarian cancer in endometriosis. The aim of this study is to determine whether a left lateral predisposition of ovarian clear-cell carcinoma (CCC) and endometrioid carcinoma (EC) exists. MATERIALS AND METHODS: A retrospective cohort study of all EC and CCC patients in Northern Ireland between March-2011 and June-2018. ANOVA was used to analyse preoperative prediction of stage, chi-squared (χ2) was used to compare left- and right-sided masses. Survival was estimated using Kaplan-Meier and log-rank test. A p-value <0.05 was considered significant. RESULTS: 158 patients were identified (95 EC, 55 CCC, 8 mixed). Mean age was 57.65 years with 69% presenting at stage 1. The mean CA125 was 559 U/mL (p = 0.850) and mean abdominal mass size was 14.12 cm (p = 0.732). The most common presenting symptom was an abdominal mass (37%). Despite 67% of patients having endometriosis on final pathology, only 8.9% had a known history pre-operatively. 51% of tumours were located on the left (p = 0.036). For unilateral tumours this was significant for EC (P = 0.002) but not for CCC (P = 0.555). The 1-, 3- and 5-year overall survival for all types/stages was 85%, 78% and 71% respectively. CONCLUSION: While CCC and EC are associated with endometriosis, only EC exhibits a left lateral predisposition. There is no association between preoperative CA125 or abdominal mass size and stage of disease.

Practical Aspects of Nuclear Medicine Ventriculoperitoneal Shunt Evaluation.

The radionuclide ventriculoperitoneal shunt evaluation study is a simple test that involves injecting a small volume of radionuclide into the shunt reservoir and then observing its disappearance using dynamic γ-camera imaging. Although it seems simple, there are several potential pitfalls that can result in a misinterpreted or uninterpretable study. This paper is a detailed description of how to avoid the pitfalls and also how to interpret the results.

Marginated aberrant red blood cells induce pathologic vascular stress fluctuations in a computational model of hematologic disorders.

Red blood cell (RBC) disorders such as sickle cell disease affect billions worldwide. While much attention focuses on altered properties of aberrant RBCs and corresponding hemodynamic changes, RBC disorders are also associated with vascular dysfunction, whose origin remains unclear and which provoke severe consequences including stroke. Little research has explored whether biophysical alterations of RBCs affect vascular function. We use a detailed computational model of blood that enables characterization of cell distributions and vascular stresses in blood disorders and compare simulation results with experimental observations. Aberrant RBCs, with their smaller size and higher stiffness, concentrate near vessel walls (marginate) because of contrasts in physical properties relative to normal cells. In a curved channel exemplifying the geometric complexity of the microcirculation, these cells distribute heterogeneously, indicating the importance of geometry. Marginated cells generate large transient stress fluctuations on vessel walls, indicating a mechanism for the observed vascular inflammation.

Head and Neck Cancer Segmentation in FDG PET Images: Performance Comparison of Convolutional Neural Networks and Vision Transformers.

Convolutional neural networks (CNNs) have a proven track record in medical image segmentation. Recently, Vision Transformers were introduced and are gaining popularity for many computer vision applications, including object detection, classification, and segmentation. Machine learning algorithms such as CNNs or Transformers are subject to an inductive bias, which can have a significant impact on the performance of machine learning models. This is especially relevant for medical image segmentation applications where limited training data are available, and a model's inductive bias should help it to generalize well. In this work, we quantitatively assess the performance of two CNN-based networks (U-Net and U-Net-CBAM) and three popular Transformer-based segmentation network architectures (UNETR, TransBTS, and VT-UNet) in the context of HNC lesion segmentation in volumetric [F-18] fluorodeoxyglucose (FDG) PET scans. For performance assessment, 272 FDG PET-CT scans of a clinical trial (ACRIN 6685) were utilized, which includes a total of 650 lesions (primary: 272 and secondary: 378). The image data used are highly diverse and representative for clinical use. For performance analysis, several error metrics were utilized. The achieved Dice coefficient ranged from 0.833 to 0.809 with the best performance being achieved by CNN-based approaches. U-Net-CBAM, which utilizes spatial and channel attention, showed several advantages for smaller lesions compared to the standard U-Net. Furthermore, our results provide some insight regarding the image features relevant for this specific segmentation application. In addition, results highlight the need to utilize primary as well as secondary lesions to derive clinically relevant segmentation performance estimates avoiding biases.

PET/CT Imaging in Treatment Planning and Surveillance of Sinonasal Neoplasms.

Sinonasal cancers are uncommon malignancies with a generally unfavorable prognosis, often presenting at an advanced stage. Their high rate of recurrence supports close imaging surveillance and the utilization of functional imaging techniques. Whole-body 18F-FDG PET/CT has very high sensitivity for the diagnosis of sinonasal malignancies and can also be used as a "metabolic biopsy" in the characterization of some of the more common subgroups of these tumors, though due to overlap in uptake, histological confirmation is still needed. For certain tumor types, radiotracers, such as 11C-choline, and radiolabeled somatostatin analogs, including 68Ga-DOTATATE/DOTATOC, have proven useful in treatment planning and surveillance. Although serial scans for posttreatment surveillance allow the detection of subclinical lesions, the optimal schedule and efficacy in terms of survival are yet to be determined. Pitfalls of 18F-FDG, such as post-surgical and post-radiotherapy crusting and inflammation, may cause false-positive hypermetabolism in the absence of relapse.

The Future of Nuclear Medicine in the United States.

Nuclear medicine (NM) in the United States is experiencing a manpower shortage that is steadily getting worse. It largely derives from inadequate production of well-trained NM physicians. It is different in the rest of the world, where NM is an independent specialty and training is more rigorous. Three suggestions are offered to help reverse the situation: (1) stop radiologists with inadequate training from practicing NM; (2) strengthen NM training programs; and (3) inform medical students of career opportunities in NM. If we do nothing, the rest of the world will move forward, leaving us behind.

Fibroblast Activation Protein Inhibitor-Based Radionuclide Therapies: Current Status and Future Directions.

Metastatic malignancies have limited management strategies and variable treatment responses. Cancer cells develop beside and depend on the complex tumor microenvironment. Cancer-associated fibroblasts, with their complex interaction with tumor and immune cells, are involved in various steps of tumorigenesis, such as growth, invasion, metastasis, and treatment resistance. Prooncogenic cancer-associated fibroblasts emerged as attractive therapeutic targets. However, clinical trials have achieved suboptimal success. Fibroblast activation protein (FAP) inhibitor-based molecular imaging has shown encouraging results in cancer diagnosis, making them innovative targets for FAP inhibitor-based radionuclide therapies. This review summarizes the results of preclinical and clinical FAP-based radionuclide therapies. We will describe advances and FAP molecule modification in this novel therapy, as well as its dosimetry, safety profile, and efficacy. This summary may guide future research directions and optimize clinical decision-making in this emerging field.

Marginated aberrant red blood cells induce pathologic vascular stress fluctuations in a computational model of hematologic disorders.

Red blood cell (RBC) disorders affect billions worldwide. While alterations in the physical properties of aberrant RBCs and associated hemodynamic changes are readily observed, in conditions such as sickle cell disease and iron deficiency, RBC disorders can also be associated with vascular dysfunction. The mechanisms of vasculopathy in those diseases remain unclear and scant research has explored whether biophysical alterations of RBCs can directly affect vascular function. Here we hypothesize that the purely physical interactions between aberrant RBCs and endothelial cells, due to the margination of stiff aberrant RBCs, play a key role in this phenomenon for a range of disorders. This hypothesis is tested by direct simulations of a cellular scale computational model of blood flow in sickle cell disease, iron deficiency anemia, COVID-19, and spherocytosis. We characterize cell distributions for normal and aberrant RBC mixtures in straight and curved tubes, the latter to address issues of geometric complexity that arise in the microcirculation. In all cases aberrant RBCs strongly localize near the vessel walls (margination) due to contrasts in cell size, shape, and deformability from the normal cells. In the curved channel, the distribution of marginated cells is very heterogeneous, indicating a key role for vascular geometry. Finally, we characterize the shear stresses on the vessel walls; consistent with our hypothesis, the marginated aberrant cells generate large transient stress fluctuations due to the high velocity gradients induced by their near-wall motions. The anomalous stress fluctuations experienced by endothelial cells may be responsible for the observed vascular inflammation.

Deep learning delay coordinate dynamics for chaotic attractors from partial observable data.

A common problem in time-series analysis is to predict dynamics with only scalar or partial observations of the underlying dynamical system. For data on a smooth compact manifold, Takens' theorem proves a time-delayed embedding of the partial state is diffeomorphic to the attractor, although for chaotic and highly nonlinear systems, learning these delay coordinate mappings is challenging. We utilize deep artificial neural networks (ANNs) to learn discrete time maps and continuous time flows of the partial state. Given training data for the full state, we also learn a reconstruction map. Thus, predictions of a time series can be made from the current state and several previous observations with embedding parameters determined from time-series analysis. The state space for time evolution is of comparable dimension to reduced order manifold models. These are advantages over recurrent neural network models, which require a high-dimensional internal state or additional memory terms and hyperparameters. We demonstrate the capacity of deep ANNs to predict chaotic behavior from a scalar observation on a manifold of dimension three via the Lorenz system. We also consider multivariate observations on the Kuramoto-Sivashinsky equation, where the observation dimension required for accurately reproducing dynamics increases with the manifold dimension via the spatial extent of the system.

Pathologic mechanobiological interactions between red blood cells and endothelial cells directly induce vasculopathy in iron deficiency anemia.

The correlation between cardiovascular disease and iron deficiency anemia (IDA) is well documented but poorly understood. Using a multi-disciplinary approach, we explore the hypothesis that the biophysical alterations of red blood cells (RBCs) in IDA, such as variable degrees of microcytosis and decreased deformability may directly induce endothelial dysfunction via mechanobiological mechanisms. Using a combination of atomic force microscopy and microfluidics, we observed that subpopulations of IDA RBCs (idRBCs) are significantly stiffer and smaller than both healthy RBCs and the remaining idRBC population. Furthermore, computational simulations demonstrated that the smaller and stiffer idRBC subpopulations marginate toward the vessel wall causing aberrant shear stresses. This leads to increased vascular inflammation as confirmed with perfusion of idRBCs into our "endothelialized" microfluidic systems. Overall, our multifaceted approach demonstrates that the altered biophysical properties of idRBCs directly lead to vasculopathy, suggesting that the IDA and cardiovascular disease association extends beyond correlation and into causation.

Data-driven reduced-order modeling of spatiotemporal chaos with neural ordinary differential equations.

Dissipative partial differential equations that exhibit chaotic dynamics tend to evolve to attractors that exist on finite-dimensional manifolds. We present a data-driven reduced-order modeling method that capitalizes on this fact by finding a coordinate representation for this manifold and then a system of ordinary differential equations (ODEs) describing the dynamics in this coordinate system. The manifold coordinates are discovered using an undercomplete autoencoder-a neural network (NN) that reduces and then expands dimension. Then, the ODE, in these coordinates, is determined by a NN using the neural ODE framework. Both of these steps only require snapshots of data to learn a model, and the data can be widely and/or unevenly spaced. Time-derivative information is not needed. We apply this framework to the Kuramoto-Sivashinsky equation for domain sizes that exhibit chaotic dynamics with again estimated manifold dimensions ranging from 8 to 28. With this system, we find that dimension reduction improves performance relative to predictions in the ambient space, where artifacts arise. Then, with the low-dimensional model, we vary the training data spacing and find excellent short- and long-time statistical recreation of the true dynamics for widely spaced data (spacing of ∼ 0.7 Lyapunov times). We end by comparing performance with various degrees of dimension reduction and find a "sweet spot" in terms of performance vs dimension.

Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties.

Polymer electrolytes are promising candidates for the next generation lithium-ion battery technology. Large scale screening of polymer electrolytes is hindered by the significant cost of molecular dynamics (MD) simulation in amorphous systems: the amorphous structure of polymers requires multiple, repeated sampling to reduce noise and the slow relaxation requires long simulation time for convergence. Here, we accelerate the screening with a multi-task graph neural network that learns from a large amount of noisy, unconverged, short MD data and a small number of converged, long MD data. We achieve accurate predictions of 4 different converged properties and screen a space of 6247 polymers that is orders of magnitude larger than previous computational studies. Further, we extract several design principles for polymer electrolytes and provide an open dataset for the community. Our approach could be applicable to a broad class of material discovery problems that involve the simulation of complex, amorphous materials.

A socio-ecological examination of the primary school playground: Primary school pupil and staff perceived barriers and facilitators to a physically active playground during break and lunch-times.

Using Brofenbrenner's socio-ecological model as a conceptual framework, the objective of this study was to determine playground users (primary school staff and pupils) perceptions of the barriers and facilitators to a physically active school playground at an intra-personal (individual), inter-personal (social), environmental and policy level. Results from a series of qualitative interactions with children (n = 65) from years five and six (9 to 11 years old), and structured interviews with adult teachers (n = 11) revealed key differences in the child and adult perceptions of the playground and the purpose of break-times. A number of inter-related environmental boundaries and school policies were identified as restrictive to children's explorations and activity levels during 'free play' periods, which centred on resource availability, accessibility and health and safety. Further, traditional playground hierarchies act to promote and prevent physical activity engagement for different groups (e.g. gender and age). Finally, differences between the adult and child perception of the primary school playground were observed. Playground physical activity, during break-times appears to be affected by a number of variables at each level of the socio-ecological model. This study provides an opportunity for primary schools to reflect on primary school playground strategies and practices that are implemented at each level of the socio-ecological model to encourage a more effective use of the playground during school break-times.