Dynamic Assessment Research Articles

Magnetic resonance neurography in the diagnosis of neurological subtypes of thoracic outlet syndrome.

Neurological thoracic outlet syndrome (TOS) can be challenging to diagnose, particularly given its described subtypes of neurogenic TOS (NTOS) and disputed TOS (DTOS) that exhibit variable clinical presentations and etiologies. The diagnostic workup of TOS often includes magnetic resonance neurography (MRN) of the brachial plexus. Specific MRN imaging modifications for TOS evaluation are required to maximize spatial and contrast resolution to increase the conspicuity of nerve segments and their relationships to surrounding osseous structures. Dynamic assessment with arm positioning is used to evaluate outlet narrowing and compression of the plexus. Individual nerve segments are interrogated for their longitudinal and cross-sectional morphologies and signal characteristics. In patients with NTOS, MRN may reveal focal impingement of the C8/T1 nerve roots and/or lower trunk with accompanying abnormal T2-weighted signal hyperintensity. Predisposing anatomical entities include cervical ribs, rib synostoses, hypertrophic callous following clavicular fracture, remnant first thoracic rib from prior incomplete resection, and variable perineural scarring. In comparison, DTOS patients frequently demonstrate signal hyperintensity and enlargement of the mid plexus (trunk and division level), with narrowing of the costoclavicular interval. Following comprehensive diagnostic workup that frequently includes electrodiagnostic testing, patients are directed to different management pathways. Nonsurgical management is considered for all cases of DTOS; all patients with NTOS or DTOS who fail conservative treatment warrant referral for a surgical opinion. If surgery is pursued, MRN can be helpful in preoperative planning.

An open source in silico workflow to assist in the design of fusion proteins

Fusion proteins have the potential to become the new norm for targeted therapeutic treatments. Highly specific payload delivery can be achieved by combining custom targeting moieties, such as VHH domains, with active parts of proteins that have a particular activity not naturally targeted to the intended cells. Conversely, novel drug products may make use of the highly specific targeting properties of naturally occurring proteins and combine them with custom payloads. When designing such a product, there is rarely a known structure for the final construct which makes it difficult to assess molecular behaviour that may ultimately impact therapeutic outcome. Considering the time and cost of expressing a construct, optimising the purification procedure, obtaining sufficient quantities for biophysical characterisation, and performing structural studies in vitro, there is an enormous benefit to conduct in silico studies ahead of wet lab work.By following a repeatable, streamlined, and fast workflow of molecular dynamics assessment, it is possible to eliminate low-performing candidates from costly experimental work. There are, however, many aspects to consider when designing a novel fusion protein and it is crucial not to overlook some elements. In this work, we suggest a set of user-friendly, open-source methods which can be used to screen fusion protein candidates from the sequence alone. We used the light chain and translocation domain of botulinum toxin A (BoNT/A) fused with a selected VHH domain, termed here LC-HN-VHH, as a case study for a general approach to designing, modelling, and simulating fusion proteins. Its behaviour in silico correlated well with initial in vitro work, with SEC HPLC showing multiple protein states in solution and a dynamic protein shifting between these states over time without loss of material.

Global climate governance inequality unveiled through dynamic influence assessment

This study defines the concept of major and minor countries and establishes a multidimensional and dynamic assessment system to evaluate the influence of countries participating in global climate governance. We systematically calculate 45 indicators for 53 countries across 9 global climate negotiation interest groups over the seven years. Our findings indicate a significant difference in influence between major and minor countries. This gap is particularly significant regarding losses incurred from climate disasters, with a more than fourfold difference between the top 25% and the bottom 25% in the influence rankings. The influence-climate disaster Index Gini coefficient further indicates that climate governance is characterized by extreme inequality. We argue that this phenomenon arises because those with more decisive influence in global climate governance do not bear losses from climate disasters commensurate with their influence. Therefore, their willingness to participate in climate governance is lower than those who suffer greater consequences.

An assessment of potential interventions to reduce the totoaba illegal trade market.

The illegal trade in totoaba (Totoaba macdonaldi) is causing adverse social, ecological, and economic impacts. This illegal activity is accelerating the overexploitation of totoaba and pushing the critically endangered vaquita (Phocoena sinus) closer to extinction. Despite extensive efforts to recover vaquita populations, scant attention has been given to the totoaba trade as an independent issue. As a result, data on the totoaba trade are limited, which hampers robust analyses and development of effective interventions to reduce illegal harvesting. We used a previously developed framework specifically designed to examine dynamics of illegal markets and guide measures to mitigate illegal use of totoaba. This framework separates markets into 3 analytical levels: characterization of participating actors (e.g., fishers, intermediaries); examination of how actors interact within the market (e.g., organization of supply chains); and assessment of the overall market dynamics that result from these interactions (e.g., factors determining price and quantity). We reviewed existing literature (108 initial articles) and interviewed key market actors, academics, and nongovernmental organization experts (14) to obtain data for this framework. Our findings offer an overview of the totoaba illegal market operation, highlighting intervention points (e.g., customs agents) and areas where additional information is required to decrease information gaps (e.g., US local market). We describe the structure and complexity of this market, emphasizing the influential role of organized crime in shaping its dynamics (e.g., controlling prices paid to fishers and stockpiling). By providing a systematic and in-depth understanding of the market operation, we aimed to establish a benchmark for effective interventions and future research aimed at reducing uncertainties. Our results provide a crucial step toward addressing this critical issue and can help facilitate development of effective strategies to combat the illegal totoaba trade and promote biodiversity conservation more broadly.

Innovative Approaches in Therapeutic Clinical Nutrition: Enhancing Quality of Care through Technology

Background: This systematic review explores a diverse range of international and indigenous healthcare research studies, spanning topics from diabetes management to cardiovascular care. The synthesis aims to identify overarching themes, sub-themes, and emerging trends in healthcare practices, emphasizing the integration of digital technologies and advancements in nutrition therapy. Aim: The primary objective is to provide a comprehensive overview of healthcare literature, combining international and indigenous perspectives, to uncover commonalities and distinctions in approaches to patient care, treatment modalities, and research methodologies. Method: A systematic and thorough review of 20 selected studies, encompassing international and Saudi Arabian research, was conducted. The studies, published between 2020 and 2024, were analyzed for their key themes, sub-themes, and trends, creating a detailed understanding of the current landscape of healthcare practices. Results: The synthesis revealed recurring themes, including the integration of digital technologies in diabetes care, advancements in medical nutrition therapy for obesity management, dynamic risk assessment in atrial fibrillation, and the potential of genetically modified food for nutritional security in Saudi Arabia. Sub-themes elucidated nuanced aspects of these overarching themes, offering a holistic perspective on contemporary healthcare strategies. Conclusion: International and indigenous research studies collectively underscore the importance of collaborative and context-specific approaches to healthcare. While global insights contribute to a broader understanding, indigenous studies emphasize the necessity of tailoring interventions to local needs. The synthesis not only highlights current achievements in healthcare but also identifies areas for further exploration and improvement.

Dynamic industrial risks assessment for enhanced safety in the oil and gas industry: a methodological innovations and applications

In recent years, continued and accelerated growth in the oil and gas sector has significantly contributed to catastrophic accidents in various regions of Algeria, especially those with high population densities. Therefore, assessing the risks associated with these industries is crucial to prevent such disasters. This study introduces a methodology integrating several Quantitative Risk Assessment techniques, such as Failure Modes and Effects Analysis, Fault Tree Analysis, Event Tree Analysis, and the Bow-Tie method, coupled with simulation tools (ALOHA and FLUENT). This integration created a Dynamic Industrial Risk Assessment Methodology (DIRAM), a comprehensive approach to assessing industrial risk consequences in the oil and gas industries. The study demonstrates DIRAM's effectiveness in understanding the dynamics of catastrophic events. It provides a visual representation of CO2 plume and heat dispersion after a Boiling Liquid Expanding Vapor Explosion (BLEVE), emphasising the importance of spatial-temporal awareness in risk assessment and emergency response. DIRAM offers a valuable basis for preventing and managing potential risks and reinforcing safety measures at hydrocarbon plants.

Temporal assessment of forest cover dynamics in response to forest fires and other environmental impacts using AI.

The rapid reduction of forests due to environmental impacts such as deforestation, global warming, natural disasters such as forest fires as well as various human activities is an escalating concern. The increasing frequency and severity of forest fires are causing significant harm to the ecosystem, economy, wildlife, and human safety. During dry and hot seasons, the likelihood of forest fires also increases. It is crucial to accurately monitor and analyze the large-scale changes in the forest cover to ensure sustainable forest management. Remote sensing technology helps to precisely study such changes in forest cover over a wide area over time. This research analyzes the impact of forest fires over time, identifies hotspots, and explores the environmental factors that affect forest cover change. Sentinel-2 imagery was utilized to study changes in Brunei Darussalam's forest cover area over five years from 2017 to 2022. An object-based approach, Simple Non-Iterative Clustering (SNIC), is employed to cluster the region using NDVI values and analyze the changes per cluster. The results indicate that the area of the clusters reduced where fire incidence occurred as well as the precipitation dropped. Between 2017 and 2022, the increased forest fires and decreased precipitation levels resulted in the change in cluster areas as follows: 66.11%, 69.46%, 68.32%, 73.88%, 77.27%, and 78.70%, respectively. Additionally, hotspots in response to forest fires each year were identified in the Belait district. This study will help forest managers assess the causes of forest cover loss and develop conservation and afforestation strategies.

Quantitative assessment and genomic profiling of Campylobacter dynamics in poultry processing: a case study in the United Arab Emirates integrated abattoir system.

In the United Arab Emirates, no previous research has investigated the dynamics of the foodborne pathogen Campylobacter in broiler abattoir processing. This study conducted in one of the largest poultry producers in the UAE, following each key slaughter stage-defeathering, evisceration, and final chilling-five broiler carcasses were collected from 10 slaughter batches over a year. Additionally, one caecum was obtained from 15 chickens in each slaughter batch to evaluate the flock colonization. In total, 300 samples (150 carcasses and 150 caeca) were collected and enumerated for Campylobacter using standard methods. Campylobacter was pervasive in caecal samples from all slaughter batches, with 86% of carcasses post-defeathering and evisceration stages and 94% post-chilling tested positive for Campylobacter. Campylobacter coli predominates in 55.2% of positive samples, followed by Campylobacter jejuni in 21%, with both species co-existing in 23.8% of the samples. Campylobacter counts in caecal contents ranged from 6.7 to 8.5 log10 CFU/g, decreasing post-defeathering and evisceration to 3.5 log10 CFU/g of neck skin and further to 3.2 log10 CFU/g of neck skin post-evisceration. After chilling, 70% of carcasses exceeded 3 log10 CFU/g of neck skin. Whole-genome sequencing (WGS) of 48 isolates unveiled diverse sequence types and clusters, with isolates sharing the same clusters (less than 20 single nucleotide polymorphisms) between different farms, different flocks within the same farm, as well as in consecutive slaughter batches, indicating cross-contamination. Multiple antimicrobial resistance genes and mutations in gyrA T86I (conferring fluoroquinolone resistance) and an RNA mutation (23S r.2075; conferring macrolide resistance) were widespread, with variations between C. coli and C. jejuni. WGS results revealed that selected virulence genes (pglG, pseD, pseI, flaA, flaB, cdtA, and cdtC) were significantly present in C. jejuni compared to C. coli isolates. This study offers the first insights into Campylobacter dynamics in poultry processing in the UAE. This work provides a base for future research to explore additional contributors to Campylobacter contamination in primary production. In conclusion, effective Campylobacter management demands a comprehensive approach addressing potential contamination sources at every production and processing stage, guided by continued microbiological surveillance and genomic analysis to safeguard public health and food safety.

Quantile Connectedness Across Socially Responsible Equity Markets of The BRICT Nations

This paper investigates the shock linkages between the socially responsible equity indices of Brazil, Russia, India, China, and Türkiye, by using the quantile connectedness approach that is used by Chatziantoniou et al. (2021), to assess the median-based and tail connectivity, we will analyse daily time series data from April 4, 2018, to March 31, 2023. The outcomes of the static and dynamic analyses can be summarized as follows: for static quantile connectedness, Russia and India are net transmitters of shock at the tails, while China is a net receiver. China and Türkiye are net receivers, whereas Brazil, India, and Russia are net transmitters at the median quantile. Considering the dynamic quantile connectedness assessment, the findings indicate that the magnitude of connectedness significantly increases positive and negative shock connectedness. This suggests that during periods of extreme market volatility, socially responsible equity indices in BRICT nations experience more pronounced shock propagation. This suggests that socially responsible investments are susceptible to contagion and, as a result, provide restricted portfolio diversification advantages during periods of extreme market volatility. The analysis also indicates that there was a substantial rise in the overall dynamic connection during the COVID-19 pandemic and the Russia-Ukraine war.

Optimizing the patellofemoral compartment in total knee arthroplasty: Is it time for dynamic assessment?

Despite improvements in implant design, surgical techniques and assistive technologies for total knee arthroplasty (TKA), anterior knee pain (AKP) remains frequently reported, even by satisfied patients. This persistent problem calls for better understanding and management of the patellofemoral or anterior compartment during surgery, just as the techniques and strategies deployed to optimize the flexion and extension spaces through personalized alignment, bone cuts and ligament balancing. Assistive technologies such as navigation and robotics provide new tools to manage this 'third space' through precise pre-operative planning and dynamic intra-operative assessment. Such endeavors must start with clear definitions of the 'third space', how it should be measured, what constitutes its 'safe zone', and how it affects outcomes. There are yet no established methods to evaluate the patellofemoral compartment, and no clear thresholds to define over- or under-stuffing. Static assessment using lateral radiographs provides a limited understanding and depends considerably on flexion angle, while dynamic evaluation at multiple flexion angles or using intra-operative computer or robotic-assistance enables a broader perspective and solutions to manage patellar tracking and anterior offset. Future studies should investigate the impact of variations in anterior offset in TKA, define its safe zone, and understand the effects of of thresholds for over- or under-stuffing. Experimental methods such as in-vivo motion analysis and force sensors could elucidate the influence of anterior offset on flexion and extension biomechanics.

Assessment of neurofluid dynamics in relation to clinical improvement after tap-test: pilot study.

Idiopathic Normal pressure hydrocephalus (iNPH) is an under-diagnosed in elderly patients but none of the diagnostic tests are currently sufficiently sensitive or specific. The objective of this study was to analyze the dynamics of neurofluids by PC-MRI in relation to clinical evolution as measured using the iNPH grading scale after tap-test. We prospectively included patients with suspected iNPH. All these patients underwent PCMRI to assess craniospinal hemohydrodynamics with analysis of the stroke volume of the cephalospinal fluid (CSF) within the Sylvius' aqueduct, within the high cervical subarachnoid spaces and the arteriovenous stroke volume. By this means, we calculated a compliance index. Morphological analysis was carried out using the DESH score. The infusion test was measuring the resistance to CSF flow. We analysed all these parameters according to the clinical improvement of the patients. 23 patients were included. Compliance index assessed by PC-MRI was significantly higher in the group of patients with improvement > 10% (p = 0.015). Our study highlights the importance of investigating arteriovenous and CSF interactions in iNPH. This involves understanding the physiological and pathophysiological mechanisms related to the circulation of neurofluids. The analysis of the interactions of these neurofluids allows for a comprehensive understanding of the system.

Ecotoxicological assessment of sanitary sewer overflows and rainfall dynamics offers insights into conditions for potential adverse ecological outcomes

Sewer overflows are an environmental concern due to their potential to introduce contaminants that can adversely affect downstream aquatic ecosystems. As these overflows can occur during rainfall events, the influence of rainwater ingress from inflow and infiltration on raw untreated wastewater (influent) within the sewer is a critical factor influencing the dilution and toxicity of the contaminants. The Vineyard sewer carrier in the greater city of Sydney, Australia, was selected for an ecotoxicological investigation of a sanitary (separate from stormwater) sewerage system and a wet-weather overflow (WWO). Three influent samples were collected representing dry-weather (DW), intermediate wet-weather (IWW) and wet-weather (WW). In addition, a receiving water sample was also collected downstream in Vineyard Creek (WW-DS) coinciding with a WWO.We employed direct toxicity assessment (DTA) and toxicity identification evaluation (TIE) approaches to gain comprehensive insights into the nature and magnitude of the impact on influent from rainwater ingress into the sewer. Three standard ecotoxicological model species, a microalga, Chlorella vulgaris, the water flea, Ceriodaphnia dubia and the midge larva, Chironomus tepperi were used for both acute and chronic tests. The study revealed variable toxicity responses, with the sample of influent collected in wet-weather displaying lower toxicity compared to the dry-weather sample of influent. Ammonia, and metals, were identified in dry weather as contributors to the observed toxicity, however, this risk was alleviated through rainwater ingress in wet-weather with further dilution within the receiving water. Based on toxicity data, dilutions of influent to minimise effects on C. vulgaris and C. dubia ranged from 1 in 12 in DW to 1 in 2.8 in WW, and further diminished in the receiving water to 1 in 1.8. The successful application of ecotoxicological approaches enabled the assessment of cumulative effects of contaminants in influent, offering valuable insights into the sanitary sewer system under rainwater ingress.

Multi-modal, Label-free, Optical Mapping of Cellular Metabolic Function and Oxidative Stress in 3D Engineered Brain Tissue Models.

Brain metabolism is essential for the function of organisms. While established imaging methods provide valuable insights into brain metabolic function, they lack the resolution to capture important metabolic interactions and heterogeneity at the cellular level. Label-free, two-photon excited fluorescence imaging addresses this issue by enabling dynamic metabolic assessments at the single-cell level without manipulations. In this study, we demonstrate the impact of spectral imaging on the development of rigorous intensity and lifetime label-free imaging protocols to assess dynamically over time metabolic function in 3D engineered brain tissue models comprising human induced neural stem cells, astrocytes, and microglia. Specifically, we rely on multi-wavelength spectral imaging to identify the excitation/emission profiles of key cellular fluorophores within human brain cells, including NAD(P)H, LipDH, FAD, and lipofuscin. These enable development of methods to mitigate lipofuscin's overlap with NAD(P)H and flavin autofluorescence to extract reliable optical metabolic function metrics from images acquired at two excitation wavelengths over two emission bands. We present fluorescence intensity and lifetime metrics reporting on redox state, mitochondrial fragmentation, and NAD(P)H binding status in neuronal monoculture and triculture systems, to highlight the functional impact of metabolic interactions between different cell types. Our findings reveal significant metabolic differences between neurons and glial cells, shedding light on metabolic pathway utilization, including the glutathione pathway, OXPHOS, glycolysis, and fatty acid oxidation. Collectively, our studies establish a label-free, non-destructive approach to assess the metabolic function and interactions among different brain cell types relying on endogenous fluorescence and illustrate the complementary nature of information that is gained by combining intensity and lifetime-based images. Such methods can improve understanding of physiological brain function and dysfunction that occurs at the onset of cancers, traumatic injuries and neurodegenerative diseases.

Dynamic Land Degradation Assessment: Integrating Machine Learning with Landsat 8 OLI/TIRS for Enhanced Spectral, Terrain, and Land Cover Indices

Dynamic Land Degradation Assessment: Integrating Machine Learning with Landsat 8 OLI/TIRS for Enhanced Spectral, Terrain, and Land Cover Indices

Spatio-temporal assessment of land use and land cover dynamics in Urmia lake basin of Iran: A bi-directional approach using optical and radar data on the Google Earth Engine platform

Sustainable development, a cornerstone in responsible land resource management, occupies a critical role in global efforts to balance economic progress with ecological preservation. This research explores the intricate dynamics of sustainable development and land resource management in the Lake Urmia watershed, northwestern Iran, with a specific focus on Land Use and Land Cover (LULC) alterations. Leveraging cutting-edge methodologies within the Google Earth Engine (GEE) framework, the study employs the Random Forest (RF) model, and multi-sensor satellite data to achieve meticulous LULC classification from the years 2016 to 2020.This strategy incorporates satellite images from Sentinel-1 (S1), Sentinel-2 (S2), coupled with ancillary data, and the implementation of the RF algorithm. It highlighted the intricate synergy between optical and Synthetic Aperture Radar (SAR) data for LULC classification in the Lake Urmia watershed. The Overall Accuracy (OA) values ranging from 88.28 % to 92.00 % affirmed the robustness of the RF model, demonstrating its potential for research objectives. In addition, Dynamic World, a near real-time global dataset, was applied to the study area, provides a convenient view to detect changes from the years 2016 to 2023, while enabling comparison with the widely used RF algorithm in past studies. Notably, this study goes beyond methodological developments to examine the concrete consequences of the observed changes. The decline in water bodies within the Urmia Lake basin and the parallel expansion of cultivated lands underscore the profound impact of human activities, particularly agricultural practices, on the ecological equilibrium. As we navigate the intricate terrain of sustainable development, this research serves as a clarion call for remote sensing approaches to monitor and manage land resources, emphasizing the delicate balance between economic growth and ecological preservation.

Using attentive temporal GNN for dynamic trust assessment in the presence of malicious entities

Trust networks are built on the premise that there is a network of trust among entities. Precisely evaluating pairwise trust in such networks is essential for enhancing the quality of service in various applications, including online marketing, recommender systems, and IoT-based applications. However, it remains challenging to consider dynamic trust and account for malicious behavior in a scalable and effective manner.Recently, Graph Neural Networks (GNN)-based methods have emerged as powerful and scalable tools for trust estimation. Nevertheless, they lack thorough consideration for the time-dependent nature of trust, especially in the presence of malicious behaviors, leading to inaccurate trust predictions. To address this gap, we propose a novel trust assessment model called MATA, which handles the time dependency of trust and exhibits robustness against malicious behaviors. MATA leverages an extended Graph Convolutional Network (GCN) integrated with Recurrent Neural Networks (RNNs) and an attention mechanism to jointly capture time dependencies and trace entities’ behavior over time in complex networks. Additionally, by clustering the learned trust representations over time, we assign reputation values to entities. This allows us to monitor their dynamic behavior and mitigate potential adverse effects on trust assessment.In our experiments on real-world datasets, MATA outperforms the state-of-the-art methods by up to 8.4 % in accuracy. This improvement is a testament to the effectiveness of the proposed approach. Furthermore, our work enhances the robustness of trust assessment models, as evidenced by consistently higher performance metrics in the face of various trust-related attacks.

An automated dynamic quality assessment method for cyber threat intelligence

The emergence of cyber threat intelligence (CTI) is a promising approach for alleviating malicious activities. However, the effectiveness of CTIs is heavily dependent on their quality. Current literature develops the CTI quality assessment ontology mainly from the perspective of CTI source or content separately, regardless of their availability in practice. In this paper, we propose an automated CTI quality assessment method that synthesizes the trustworthiness of CTI sources and the availability of CTI contents. Specifically, we model the interactions of CTI feeds as a correlation graph and propose an iterative algorithm to well discriminate the feeds’ trustworthiness. We elaborate a CTI content assessment together with a machine learning algorithm to automatically classify CTIs’ availability from a set of content metrics. A comprehensive CTI quality assessment is proposed by jointly considering the feed trustworthiness and content availability. Extensive experimental results on real datasets demonstrate that our proposed method can quantitatively as well as effectively assess CTI quality.

Optimal design of building envelope towards life cycle performance: Impact of considering dynamic grid emission factors

Building-related carbon emissions in the construction and operation stages account for more than one-third of global energy-related emissions. Optimizing during the early design stage is an effective way to reduce building carbon emissions. However, current studies adopt a constant grid emission factor (GEF) to assess the environmental impacts of buildings while ignoring the dynamic changes of the future electricity mix. This will overestimate the operational carbon emissions of the building in the context of the increasing share of renewable energy in power generation. In this study, a dynamic life cycle assessment model based on dynamic GEF is constructed to evaluate the life cycle environmental impacts of buildings. On this basis, a building optimization design framework considering dynamic GEF is proposed, and the impact of considering future variations of GEF on the optimal design of the building envelope is explored. The study is conducted in three steps. First, dynamically changing GEFs are predicted under the future electricity mix based on scenario analysis. Second, a multi-objective optimization framework is established to minimize the life cycle carbon emissions (LCCE) of the building while optimizing its life cycle costs (LCC) and indoor discomfort hours (IDH). Finally, the proposed optimization framework is applied to a prototype office building in Shanghai, and a comparative analysis is conducted for the optimal schemes between a static and two dynamic scenarios. The result reveals that ignoring future variations will lead to overestimating the operational carbon emissions by 49.0%-64.8%, which in turn will result in an over-insulated design of the envelope (i.e., additional material consumption). In addition, compared to the static scenario, consideration of dynamic GEF results in a 38.7%-51.6% reduction in LCCE, a 5.2%-6.1% reduction in LCC, and a 5.3%-9.5% increase in IDH for the optimal solutions. This research illustrates the importance of considering dynamic GEF in identifying the optimal design parameters and can help decision-makers reasonably choose the optimal schemes during early-stage design.

Dynamic Reliability Assessment of Vertical GaN Trench MOSFETs With Thick Bottom Dielectric

Dynamic Reliability Assessment of Vertical GaN Trench MOSFETs With Thick Bottom Dielectric

CSF dynamics throughout the ventricular system using 4D flow MRI: associations to arterial pulsatility, ventricular volumes, and age

BackgroundCerebrospinal fluid (CSF) dynamics are increasingly studied in aging and neurological disorders. Models of CSF-mediated waste clearance suggest that altered CSF dynamics could play a role in the accumulation of toxic waste in the CNS, with implications for Alzheimer’s disease and other proteinopathies. Therefore, approaches that enable quantitative and volumetric assessment of CSF flow velocities could be of value. In this study we demonstrate the feasibility of 4D flow MRI for simultaneous assessment of CSF dynamics throughout the ventricular system, and evaluate associations to arterial pulsatility, ventricular volumes, and age.MethodsIn a cognitively unimpaired cohort (N = 43; age 41–83 years), cardiac-resolved 4D flow MRI CSF velocities were obtained in the lateral ventricles (LV), foramens of Monro, third and fourth ventricles (V3 and V4), the cerebral aqueduct (CA) and the spinal canal (SC), using a velocity encoding (venc) of 5 cm/s. Cerebral blood flow pulsatility was also assessed with 4D flow (venc = 80 cm/s), and CSF volumes were obtained from T1- and T2-weighted MRI. Multiple linear regression was used to assess effects of age, ventricular volumes, and arterial pulsatility on CSF velocities.ResultsCardiac-driven CSF dynamics were observed in all CSF spaces, with region-averaged velocity range and root-mean-square (RMS) velocity encompassing from very low in the LVs (RMS 0.25 ± 0.08; range 0.85 ± 0.28 mm/s) to relatively high in the CA (RMS 6.29 ± 2.87; range 18.6 ± 15.2 mm/s). In the regression models, CSF velocity was significantly related to age in 5/6 regions, to CSF space volume in 2/3 regions, and to arterial pulsatility in 3/6 regions. Group-averaged waveforms indicated distinct CSF flow propagation delays throughout CSF spaces, particularly between the SC and LVs.ConclusionsOur findings show that 4D flow MRI enables assessment of CSF dynamics throughout the ventricular system, and captures independent effects of age, CSF space morphology, and arterial pulsatility on CSF motion.