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Frontiers in Neuroscience 2024The Human Connectome Project (HCP) has become a keystone dataset in human neuroscience, with a plethora of important applications in advancing brain imaging methods and...
INTRODUCTION
The Human Connectome Project (HCP) has become a keystone dataset in human neuroscience, with a plethora of important applications in advancing brain imaging methods and an understanding of the human brain. We focused on tractometry of HCP diffusion-weighted MRI (dMRI) data.
METHODS
We used an open-source software library (pyAFQ; https://yeatmanlab.github.io/pyAFQ) to perform probabilistic tractography and delineate the major white matter pathways in the HCP subjects that have a complete dMRI acquisition ( = 1,041). We used diffusion kurtosis imaging (DKI) to model white matter microstructure in each voxel of the white matter, and extracted tract profiles of DKI-derived tissue properties along the length of the tracts. We explored the empirical properties of the data: first, we assessed the heritability of DKI tissue properties using the known genetic linkage of the large number of twin pairs sampled in HCP. Second, we tested the ability of tractometry to serve as the basis for predictive models of individual characteristics (e.g., age, crystallized/fluid intelligence, reading ability, etc.), compared to local connectome features. To facilitate the exploration of the dataset we created a new web-based visualization tool and use this tool to visualize the data in the HCP tractometry dataset. Finally, we used the HCP dataset as a test-bed for a new technological innovation: the TRX file-format for representation of dMRI-based streamlines.
RESULTS
We released the processing outputs and tract profiles as a publicly available data resource through the AWS Open Data program's Open Neurodata repository. We found heritability as high as 0.9 for DKI-based metrics in some brain pathways. We also found that tractometry extracts as much useful information about individual differences as the local connectome method. We released a new web-based visualization tool for tractometry-"Tractoscope" (https://nrdg.github.io/tractoscope). We found that the TRX files require considerably less disk space-a crucial attribute for large datasets like HCP. In addition, TRX incorporates a specification for grouping streamlines, further simplifying tractometry analysis.
PubMed: 38933816
DOI: 10.3389/fnins.2024.1389680 -
F1000Research 2023Developing countries like India are rapidly transitioning from traditional energy sources to sustainable energy sources, due to the increase in demand and the depletion... (Comparative Study)
Comparative Study
Developing countries like India are rapidly transitioning from traditional energy sources to sustainable energy sources, due to the increase in demand and the depletion of fossil fuels. Grid-connected photovoltaic (PV) systems attract many investors, organizations, and institutions for deployment. This article studies and compares the performance evaluations of three 52-kW PV plants installed at an educational institution, SRMIST (SRM Institute of Science and Technology), in Tamil Nadu, India. This site receives an annual average temperature of 28.5°C and an average global horizontal irradiation of 160 kWh/m2/m. The prediction model for the 52-kW power plant is obtained using solar radiation, temperature, and wind speed. Linear regression model-based prediction equations are derived using the Minitab 16.2.1 software, and the results are compared with the real-time AC energy yield acquired from the three 52-kW plants for the year 2020. Furthermore, this 52-kW plant is designed using PVsyst V7.1.8 version software. The simulation results are compared with the energy yield from the plants in 2020 to identify the shortfall in the plant performance. The loss analysis for the plant is performed by obtaining the loss diagram from the PVsyst software. This study also proposes a methodology to study the commissioned PV plant's performance and determine the interaction between variables such as direct and diffused solar radiations, air temperature, and wind speed for forecasting hourly produced power. This article will motivate researchers to analyze installed power plants using modern technical tools.
Topics: India; Power Plants; Solar Energy
PubMed: 38933489
DOI: 10.12688/f1000research.134731.1 -
Health Science Reports Jun 2024Impaired lung function has been observed in patients following COVID-19 infection, with studies reporting persistent lung volume and diffusing capacity impairments. Some...
INTRODUCTION
Impaired lung function has been observed in patients following COVID-19 infection, with studies reporting persistent lung volume and diffusing capacity impairments. Some studies have demonstrated significantly higher small airway resistance in COVID-19 positive cases. This retrospective study aims to examine impulse oscillometry (IOS) data of patients with persistent symptoms after COVID-19 infection, focusing on the relationship between time and symptoms.
MATERIAL AND METHOD
The study analyzed data from adult patients with persistent symptoms who underwent IOS testing within and after 84 days from the diagnosis date.
RESULT
The results showed that patients within 84 days and those between 31 and 84 days had higher small airway resistance values, indicating peripheral airway disease. Patients with dyspnea exhibited higher IOS values compared to those with cough symptoms, suggesting more significant impairment in the peripheral airways.
CONCLUSION
The study highlights the importance of using comprehensive diagnostic tools like IOS to assess respiratory impairments in post-COVID-19 patients, particularly in the small airways. Understanding the relationship between time and symptoms can provide valuable insights for the treatment of peripheral airway dysfunction in post-COVID-19 patients.
PubMed: 38933420
DOI: 10.1002/hsr2.2191 -
Frontiers in Neurology 2024Acute Ischemic Stroke (AIS) remains a leading cause of mortality and disability worldwide. Rapid and precise prognostication of AIS is crucial for optimizing treatment...
BACKGROUND
Acute Ischemic Stroke (AIS) remains a leading cause of mortality and disability worldwide. Rapid and precise prognostication of AIS is crucial for optimizing treatment strategies and improving patient outcomes. This study explores the integration of machine learning-derived radiomics signatures from multi-parametric MRI with clinical factors to forecast AIS prognosis.
OBJECTIVE
To develop and validate a nomogram that combines a multi-MRI radiomics signature with clinical factors for predicting the prognosis of AIS.
METHODS
This retrospective study involved 506 AIS patients from two centers, divided into training (n = 277) and validation ( = 229) cohorts. 4,682 radiomic features were extracted from T1-weighted, T2-weighted, and diffusion-weighted imaging. Logistic regression analysis identified significant clinical risk factors, which, alongside radiomics features, were used to construct a predictive clinical-radiomics nomogram. The model's predictive accuracy was evaluated using calibration and ROC curves, focusing on distinguishing between favorable (mRS ≤ 2) and unfavorable (mRS > 2) outcomes.
RESULTS
Key findings highlight coronary heart disease, platelet-to-lymphocyte ratio, uric acid, glucose levels, homocysteine, and radiomics features as independent predictors of AIS outcomes. The clinical-radiomics model achieved a ROC-AUC of 0.940 (95% CI: 0.912-0.969) in the training set and 0.854 (95% CI: 0.781-0.926) in the validation set, underscoring its predictive reliability and clinical utility.
CONCLUSION
The study underscores the efficacy of the clinical-radiomics model in forecasting AIS prognosis, showcasing the pivotal role of artificial intelligence in fostering personalized treatment plans and enhancing patient care. This innovative approach promises to revolutionize AIS management, offering a significant leap toward more individualized and effective healthcare solutions.
PubMed: 38933326
DOI: 10.3389/fneur.2024.1379031 -
Frontiers in Immunology 2024Succinate, traditionally viewed as a mere intermediate of the tricarboxylic acid (TCA) cycle, has emerged as a critical mediator in inflammation. Disruptions within the... (Review)
Review
Succinate, traditionally viewed as a mere intermediate of the tricarboxylic acid (TCA) cycle, has emerged as a critical mediator in inflammation. Disruptions within the TCA cycle lead to an accumulation of succinate in the mitochondrial matrix. This excess succinate subsequently diffuses into the cytosol and is released into the extracellular space. Elevated cytosolic succinate levels stabilize hypoxia-inducible factor-1α by inhibiting prolyl hydroxylases, which enhances inflammatory responses. Notably, succinate also acts extracellularly as a signaling molecule by engaging succinate receptor 1 on immune cells, thus modulating their pro-inflammatory or anti-inflammatory activities. Alterations in succinate levels have been associated with various inflammatory disorders, including rheumatoid arthritis, inflammatory bowel disease, obesity, and atherosclerosis. These associations are primarily due to exaggerated immune cell responses. Given its central role in inflammation, targeting succinate pathways offers promising therapeutic avenues for these diseases. This paper provides an extensive review of succinate's involvement in inflammatory processes and highlights potential targets for future research and therapeutic possibilities development.
Topics: Humans; Succinic Acid; Inflammation; Signal Transduction; Animals; Citric Acid Cycle; Receptors, G-Protein-Coupled
PubMed: 38933270
DOI: 10.3389/fimmu.2024.1404441 -
Frontiers in Neuroinformatics 2024Quantitative maps obtained with diffusion weighted (DW) imaging, such as fractional anisotropy (FA) -calculated by fitting the diffusion tensor (DT) model to the...
BACKGROUND
Quantitative maps obtained with diffusion weighted (DW) imaging, such as fractional anisotropy (FA) -calculated by fitting the diffusion tensor (DT) model to the data,-are very useful to study neurological diseases. To fit this map accurately, acquisition times of the order of several minutes are needed because many noncollinear DW volumes must be acquired to reduce directional biases. Deep learning (DL) can be used to reduce acquisition times by reducing the number of DW volumes. We already developed a DL network named "one-minute FA," which uses 10 DW volumes to obtain FA maps, maintaining the same characteristics and clinical sensitivity of the FA maps calculated with the standard method using more volumes. Recent publications have indicated that it is possible to train DL networks and obtain FA maps even with 4 DW input volumes, far less than the minimum number of directions for the mathematical estimation of the DT.
METHODS
Here we investigated the impact of reducing the number of DW input volumes to 4 or 7, and evaluated the performance and clinical sensitivity of the corresponding DL networks trained to calculate FA, while comparing results also with those using our one-minute FA. Each network training was performed on the human connectome project open-access dataset that has a high resolution and many DW volumes, used to fit a ground truth FA. To evaluate the generalizability of each network, they were tested on two external clinical datasets, not seen during training, and acquired on different scanners with different protocols, as previously done.
RESULTS
Using 4 or 7 DW volumes, it was possible to train DL networks to obtain FA maps with the same range of values as ground truth - map, only when using HCP test data; pathological sensitivity was lost when tested using the external clinical datasets: indeed in both cases, no consistent differences were found between patient groups. On the contrary, our "one-minute FA" did not suffer from the same problem.
CONCLUSION
When developing DL networks for reduced acquisition times, the ability to generalize and to generate quantitative biomarkers that provide clinical sensitivity must be addressed.
PubMed: 38933144
DOI: 10.3389/fninf.2024.1415085 -
World Journal of Nuclear Medicine Jun 2024Extranodal diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease process and an aggressive form of non-Hodgkin's lymphoma. We present a case of multiorgan...
Extranodal diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease process and an aggressive form of non-Hodgkin's lymphoma. We present a case of multiorgan involvement of DLBCL in a patient with documented risk factors, including [ F] fluorodeoxyglucose positron emission tomography/magnetic resonance imaging findings highlighting striking perineural spread involving intracranial and extracranial segments of the bilateral trigeminal nerves.
PubMed: 38933069
DOI: 10.1055/s-0044-1779751 -
Frontiers in Cardiovascular Medicine 2024The long-term impact of type 2 diabetes mellitus (T2DM) after an acute myocardial infarction (AMI) has not been thoroughly investigated yet. This study aimed to assess...
OBJECTIVE
The long-term impact of type 2 diabetes mellitus (T2DM) after an acute myocardial infarction (AMI) has not been thoroughly investigated yet. This study aimed to assess the long-term impact of T2DM after AMI.
RESEARCH DESIGN AND METHODS
We analyzed the data of three nationwide observational studies from the French Registry of Acute ST-elevation and non-ST-elevation Myocardial Infarction (FAST-MI) program, conducted over a 1-month period in 2005, 2010, and 2015. Patients presenting T2DM were classified as diabetic, and patients presenting type 1 diabetes mellitus were excluded. We identified factors related to all-cause death at 1-year follow-up and divided 1,897 subjects into two groups, paired based on their estimated 1-year probability of death as determined by a logistic regression model.
RESULTS
A total of 9,181 AMI patients were included in the analysis, among them 2,038 (22.2%) had T2DM. Patients with diabetes were significantly older (68.2 ± 12.0 vs. 63.8 ± 14.4, < 0.001) and had a higher prevalence of a prior history of percutaneous coronary intervention (PCI), coronary artery bypass grafting (CABG), or heart failure (22.5% vs. 13.0%, 7.1% vs. 3.1% and 6.7 vs. 3.8% respectively, < 0.001 for all). Even after matching two groups of 1,897 patients based on propensity score for their 1-year probability of death, diabetes remained associated with long-term mortality, with an HR of 1.30, 95%CI (1.17-1.45), < 0.001.
CONCLUSIONS
T2DM has an adverse impact on long-term survival after myocardial infarction. Independently of the risk of short-term mortality, patients with diabetes who survived an AMI have a 30% higher risk of long-term mortality.
PubMed: 38932992
DOI: 10.3389/fcvm.2024.1401569 -
Vaccines May 2024The potency of inactivated seasonal influenza vaccine is harmonised by establishing the haemagglutinin (HA) content using the compendial single radial diffusion (SRD)...
The potency of inactivated seasonal influenza vaccine is harmonised by establishing the haemagglutinin (HA) content using the compendial single radial diffusion (SRD) method. SRD reagents (antigens and antisera) are prepared, calibrated and distributed by regulatory agencies as standards for potency testing, following the biannual World Health Organization (WHO) announcements of the virus strains suitable for inclusion in the vaccine. The generation of a homologous hyperimmune sheep antiserum constrains the time to vaccine release. This study tests the application of heterologous antisera to determine the potency of influenza vaccine compared to that of a standard homologous antiserum. The results indicate that the selected heterologous sheep antisera directed to seasonal H1N1, H3N2 or B Victoria virus strains can be used to determine the accurate potency of inactivated seasonal influenza vaccines. Individually selected antisera could be useful for two to fourteen seasons. A limitation to the heterologous antiserum approach is the diversity of each individual serum, indicating that the empirical determination of a specific serum is required. This application has the potential to enable the earlier availability of a seasonal vaccine and reduce animal usage.
PubMed: 38932325
DOI: 10.3390/vaccines12060596 -
Viruses May 2024Despite their small and simple structure compared with their hosts, virus particles can cause severe harm and even mortality in highly evolved species such as humans. A...
Despite their small and simple structure compared with their hosts, virus particles can cause severe harm and even mortality in highly evolved species such as humans. A comprehensive quantitative biophysical understanding of intracellular virus replication mechanisms could aid in preparing for future virus pandemics. By elucidating the relationship between the form and function of intracellular structures from the host cell and viral components, it is possible to identify possible targets for direct antiviral agents and potent vaccines. Biophysical investigations into the spatio-temporal dynamics of intracellular virus replication have thus far been limited. This study introduces a framework to enable simulations of these dynamics using partial differential equation (PDE) models, which are evaluated using advanced numerical mathematical methods on leading supercomputers. In particular, this study presents a model of the replication cycle of a specific RNA virus, the hepatitis C virus. The diffusion-reaction model mimics the interplay of the major components of the viral replication cycle, including non structural viral proteins, viral genomic RNA, and a generic host factor. Technically, surface partial differential equations (sufPDEs) are coupled on the 3D embedded 2D endoplasmic reticulum manifold with partial differential equations (PDEs) in the 3D membranous web and cytosol volume. The membranous web serves as a viral replication factory and is formed on the endoplasmic reticulum after infection and in the presence of nonstructural proteins. The coupled sufPDE/PDE model was evaluated using realistic cell geometries based on experimental data. The simulations incorporate the effects of non structural viral proteins, which are restricted to the endoplasmic reticulum surface, with effects appearing in the volume, such as host factor supply from the cytosol and membranous web dynamics. Because the spatial diffusion properties of genomic viral RNA are not yet fully understood, the model allows for viral RNA movement on the endoplasmic reticulum as well as within the cytosol. Visualizing the simulated intracellular viral replication dynamics provides insights similar to those obtained by microscopy, complementing data from in vitro/in vivo viral replication experiments. The output data demonstrate quantitative consistence with the experimental findings, prompting further advanced experimental studies to validate the model and refine our quantitative biophysical understanding.
Topics: Virus Replication; Humans; Computer Simulation; Hepacivirus; Endoplasmic Reticulum; RNA, Viral; Models, Biological; Spatio-Temporal Analysis
PubMed: 38932132
DOI: 10.3390/v16060840