Cardiac mitochondria exhibit dynamic functional clustering

Multi-oscillatory behavior of mitochondrial inner membrane potential ΔΨm in self-organized cardiac mitochondrial networks can be triggered by metabolic or oxidative stress. Spatio-temporal analyses of cardiac mitochondrial networks have shown that mitochondria are heterogeneously organized in synchronously oscillating clusters in which the mean cluster frequency and size are inversely correlated, thus suggesting a modulation of cluster frequency through local inter-mitochondrial coupling. In this study, we propose a method to examine the mitochondrial network's topology through quantification of its dynamic local clustering coefficients. Individual mitochondrial ΔΨm oscillation signals were identified for each cardiac myocyte and cross-correlated with all network mitochondria using previously described methods (Kurz et al., 2010a). Time-varying inter-mitochondrial connectivity, defined for mitochondria in the whole network whose signals are at least 90% correlated at any given time point, allowed considering functional local clustering coefficients. It is shown that mitochondrial clustering in isolated cardiac myocytes changes dynamically and is significantly higher than for random mitochondrial networks that are constructed using the Erdös–Rényi model based on the same sets of vertices. The network's time-averaged clustering coefficient for cardiac myocytes was found to be 0.500 ± 0.051 (N = 9) vs. 0.061 ± 0.020 for random networks, respectively. Our results demonstrate that cardiac mitochondria constitute a network with dynamically connected constituents whose topological organization is prone to clustering. Cluster partitioning in networks of coupled oscillators has been observed in scale-free and chaotic systems and is therefore in good agreement with previous models of cardiac mitochondrial networks

Methodologies and MR Parameters in Quantitative Magnetic Resonance Neurography: A Scoping Review Protocol.

Magnetic resonance neurography (MRN), the MR imaging of peripheral nerves, is clinically used for assessing and monitoring peripheral neuropathies based on qualitative, weighted MR imaging. Recently, quantitative MRN has been increasingly reported with various MR parameters as potential biomarkers. An evidence synthesis mapping the available methodologies and normative values of quantitative MRN of human peripheral nerves, independent of the anatomical location and type of neuropathy, is currently unavailable and would likely benefit this young field of research. Therefore, the proposed scoping review will include peer-reviewed literature describing methodologies and normative values of quantitative MRN of human peripheral nerves. The literature search will include the databases MEDLINE (PubMed), EMBASE (Ovid), Web of Science, and Scopus. At least two independent reviewers will screen the titles and abstracts against the inclusion criteria. Potential studies will then be screened in full against the inclusion criteria by two or more independent reviewers. From all eligible studies, data will be extracted by two or more independent reviewers and presented in a diagrammatic or tabular form, separated by MR parameter and accompanied by a narrative summary. The reporting will follow the guidelines of the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). Upon completion, the scoping review will provide a map of the available literature, identify possible gaps, and inform on possible future research. SCOPING REVIEW REGISTRATION: Open Science Framework 9P3ZM

Mitochondrial redox and pH signaling occurs in axonal and synaptic organelle clusters

Redox switches are important mediators in neoplastic, cardiovascular and neurological disorders. We recently identified spontaneous redox signals in neurons at the single mitochondrion level where transients of glutathione oxidation go along with shortening and re-elongation of the organelle. We now have developed advanced image and signal-processing methods to re-assess and extend previously obtained data. Here we analyze redox and pH signals of entire mitochondrial populations. In total, we quantified the effects of 628 redox and pH events in 1797 mitochondria from intercostal axons and neuromuscular synapses using optical sensors (mito-Grx1-roGFP2; mito-SypHer). We show that neuronal mitochondria can undergo multiple redox cycles exhibiting markedly different signal characteristics compared to single redox events. Redox and pH events occur more often in mitochondrial clusters (medium cluster size: 34.1 ± 4.8 μm2). Local clusters possess higher mitochondrial densities than the rest of the axon, suggesting morphological and functional inter-mitochondrial coupling. We find that cluster formation is redox sensitive and can be blocked by the antioxidant MitoQ. In a nerve crush paradigm, mitochondrial clusters form sequentially adjacent to the lesion site and oxidation spreads between mitochondria. Our methodology combines optical bioenergetics and advanced signal processing and allows quantitative assessment of entire mitochondrial populations

Comparison of the temporal release pattern of copeptin with conventional biomarkers in acute myocardial infarction

Background Early detection of acute myocardial infarction (AMI) using cardiac biomarkers of myocardial necrosis remains limited since these biomarkers do not rise within the first hours from onset of AMI. We aimed to compare the temporal release pattern of the C-terminal portion of provasopressin (copeptin) with conventional cardiac biomarkers, including creatine kinase isoenzyme (CK-MB), cardiac troponin T (cTnT), and high-sensitivity cTnT (hs-cTnT), in patients with ST-elevation AMI. Methods We included 145 patients undergoing successful primary percutaneous coronary intervention (PCI) for a first ST-elevation AMI presenting within 12 h of symptom onset. Blood samples were taken on admission and at four time points within the first 24 h after PCI. Results In contrast to all other markers, copeptin levels were already elevated on admission and were higher with a shorter time from symptom onset to reperfusion and lower systolic blood pressure. Copeptin levels peaked immediately after symptom onset at a maximum of 249 pmol/L and normalized within 10 h. In contrast, CK-MB, cTnT, and hs-cTnT peaked after 14 h from symptom onset at a maximum of 275 U/L, 5.75 lg/L, and 4.16 lg/L, respectively, and decreased more gradually. Conclusions Copeptin has a distinct release pattern in patients with ST-elevation AMI, peaking within the first hour after symptom onset before conventional cardiac biomarkers and falling to normal ranges within the first day. Further studies are required to determine the exact role of copeptin in AMI suspects presenting within the first hours after symptom onset

Correlated MRI and Ultramicroscopy (MR-UM) of Brain Tumors Reveals Vast Heterogeneity of Tumor Infiltration and Neoangiogenesis in Preclinical Models and Human Disease

Diffuse tumor infiltration into the adjacent parenchyma is an effective dissemination mechanism of brain tumors. We have previously developed correlated high field magnetic resonance imaging and ultramicroscopy (MR-UM) to study neonangiogenesis in a glioma model. In the present study we used MR-UM to investigate tumor infiltration and neoangiogenesis in a translational approach. We compare infiltration and neoangiogenesis patterns in four brain tumor models and the human disease: whereas the U87MG glioma model resembles brain metastases with an encapsulated growth and extensive neoangiogenesis, S24 experimental gliomas mimic IDH1 wildtype glioblastomas, exhibiting infiltration into the adjacent parenchyma and along white matter tracts to the contralateral hemisphere. MR-UM resolves tumor infiltration and neoangiogenesis longitudinally based on the expression of fluorescent proteins, intravital dyes or endogenous contrasts. Our study demonstrates the huge morphological diversity of brain tumor models regarding their infiltrative and neoangiogenic capacities and further establishes MR-UM as a platform for translational neuroimaging

Deep-learning-based reconstruction of undersampled MRI to reduce scan times:a multicentre, retrospective, cohort study

BACKGROUND: The extended acquisition times required for MRI limit its availability in resource-constrained settings. Consequently, accelerating MRI by undersampling k-space data, which is necessary to reconstruct an image, has been a long-standing but important challenge. We aimed to develop a deep convolutional neural network (dCNN) optimisation method for MRI reconstruction and to reduce scan times and evaluate its effect on image quality and accuracy of oncological imaging biomarkers. METHODS: In this multicentre, retrospective, cohort study, MRI data from patients with glioblastoma treated at Heidelberg University Hospital (775 patients and 775 examinations) and from the phase 2 CORE trial (260 patients, 1083 examinations, and 58 institutions) and the phase 3 CENTRIC trial (505 patients, 3147 examinations, and 139 institutions) were used to develop, train, and test dCNN for reconstructing MRI from highly undersampled single-coil k-space data with various acceleration rates (R=2, 4, 6, 8, 10, and 15). Independent testing was performed with MRIs from the phase 2/3 EORTC-26101 trial (528 patients with glioblastoma, 1974 examinations, and 32 institutions). The similarity between undersampled dCNN-reconstructed and original MRIs was quantified with various image quality metrics, including structural similarity index measure (SSIM) and the accuracy of undersampled dCNN-reconstructed MRI on downstream radiological assessment of imaging biomarkers in oncology (automated artificial intelligence-based quantification of tumour burden and treatment response) was performed in the EORTC-26101 test dataset. The public NYU Langone Health fastMRI brain test dataset (558 patients and 558 examinations) was used to validate the generalisability and robustness of the dCNN for reconstructing MRIs from available multi-coil (parallel imaging) k-space data. FINDINGS: In the EORTC-26101 test dataset, the median SSIM of undersampled dCNN-reconstructed MRI ranged from 0·88 to 0·99 across different acceleration rates, with 0·92 (95% CI 0·92-0·93) for 10-times acceleration (R=10). The 10-times undersampled dCNN-reconstructed MRI yielded excellent agreement with original MRI when assessing volumes of contrast-enhancing tumour (median DICE for spatial agreement of 0·89 [95% CI 0·88 to 0·89]; median volume difference of 0·01 cm3 [95% CI 0·00 to 0·03] equalling 0·21%; p=0·0036 for equivalence) or non-enhancing tumour or oedema (median DICE of 0·94 [95% CI 0·94 to 0·95]; median volume difference of -0·79 cm3 [95% CI -0·87 to -0·72] equalling -1·77%; p=0·023 for equivalence) in the EORTC-26101 test dataset. Automated volumetric tumour response assessment in the EORTC-26101 test dataset yielded an identical median time to progression of 4·27 months (95% CI 4·14 to 4·57) when using 10-times-undersampled dCNN-reconstructed or original MRI (log-rank p=0·80) and agreement in the time to progression in 374 (95·2%) of 393 patients with data. The dCNN generalised well to the fastMRI brain dataset, with significant improvements in the median SSIM when using multi-coil compared with single-coil k-space data (p&lt;0·0001). INTERPRETATION: Deep-learning-based reconstruction of undersampled MRI allows for a substantial reduction of scan times, with a 10-times acceleration demonstrating excellent image quality while preserving the accuracy of derived imaging biomarkers for the assessment of oncological treatment response. Our developments are available as open source software and hold considerable promise for increasing the accessibility to MRI, pending further prospective validation. FUNDING: Deutsche Forschungsgemeinschaft (German Research Foundation) and an Else Kröner Clinician Scientist Endowed Professorship by the Else Kröner Fresenius Foundation.</p

Deep-learning-based reconstruction of undersampled MRI to reduce scan times: a multicentre, retrospective, cohort study

Background: The extended acquisition times required for MRI limit its availability in resource-constrained settings. Consequently, accelerating MRI by undersampling k-space data, which is necessary to reconstruct an image, has been a long-standing but important challenge. We aimed to develop a deep convolutional neural network (dCNN) optimisation method for MRI reconstruction and to reduce scan times and evaluate its effect on image quality and accuracy of oncological imaging biomarkers. Methods: In this multicentre, retrospective, cohort study, MRI data from patients with glioblastoma treated at Heidelberg University Hospital (775 patients and 775 examinations) and from the phase 2 CORE trial (260 patients, 1083 examinations, and 58 institutions) and the phase 3 CENTRIC trial (505 patients, 3147 examinations, and 139 institutions) were used to develop, train, and test dCNN for reconstructing MRI from highly undersampled single-coil k-space data with various acceleration rates (R=2, 4, 6, 8, 10, and 15). Independent testing was performed with MRIs from the phase 2/3 EORTC-26101 trial (528 patients with glioblastoma, 1974 examinations, and 32 institutions). The similarity between undersampled dCNN-reconstructed and original MRIs was quantified with various image quality metrics, including structural similarity index measure (SSIM) and the accuracy of undersampled dCNN-reconstructed MRI on downstream radiological assessment of imaging biomarkers in oncology (automated artificial intelligence-based quantification of tumour burden and treatment response) was performed in the EORTC-26101 test dataset. The public NYU Langone Health fastMRI brain test dataset (558 patients and 558 examinations) was used to validate the generalisability and robustness of the dCNN for reconstructing MRIs from available multi-coil (parallel imaging) k-space data. Findings: In the EORTC-26101 test dataset, the median SSIM of undersampled dCNN-reconstructed MRI ranged from 0·88 to 0·99 across different acceleration rates, with 0·92 (95% CI 0·92-0·93) for 10-times acceleration (R=10). The 10-times undersampled dCNN-reconstructed MRI yielded excellent agreement with original MRI when assessing volumes of contrast-enhancing tumour (median DICE for spatial agreement of 0·89 [95% CI 0·88 to 0·89]; median volume difference of 0·01 cm3 [95% CI 0·00 to 0·03] equalling 0·21%; p=0·0036 for equivalence) or non-enhancing tumour or oedema (median DICE of 0·94 [95% CI 0·94 to 0·95]; median volume difference of -0·79 cm3 [95% CI -0·87 to -0·72] equalling -1·77%; p=0·023 for equivalence) in the EORTC-26101 test dataset. Automated volumetric tumour response assessment in the EORTC-26101 test dataset yielded an identical median time to progression of 4·27 months (95% CI 4·14 to 4·57) when using 10-times-undersampled dCNN-reconstructed or original MRI (log-rank p=0·80) and agreement in the time to progression in 374 (95·2%) of 393 patients with data. The dCNN generalised well to the fastMRI brain dataset, with significant improvements in the median SSIM when using multi-coil compared with single-coil k-space data (p<0·0001). Interpretation: Deep-learning-based reconstruction of undersampled MRI allows for a substantial reduction of scan times, with a 10-times acceleration demonstrating excellent image quality while preserving the accuracy of derived imaging biomarkers for the assessment of oncological treatment response. Our developments are available as open source software and hold considerable promise for increasing the accessibility to MRI, pending further prospective validation

Deep-learning-based reconstruction of undersampled MRI to reduce scan times:a multicentre, retrospective, cohort study

BACKGROUND: The extended acquisition times required for MRI limit its availability in resource-constrained settings. Consequently, accelerating MRI by undersampling k-space data, which is necessary to reconstruct an image, has been a long-standing but important challenge. We aimed to develop a deep convolutional neural network (dCNN) optimisation method for MRI reconstruction and to reduce scan times and evaluate its effect on image quality and accuracy of oncological imaging biomarkers. METHODS: In this multicentre, retrospective, cohort study, MRI data from patients with glioblastoma treated at Heidelberg University Hospital (775 patients and 775 examinations) and from the phase 2 CORE trial (260 patients, 1083 examinations, and 58 institutions) and the phase 3 CENTRIC trial (505 patients, 3147 examinations, and 139 institutions) were used to develop, train, and test dCNN for reconstructing MRI from highly undersampled single-coil k-space data with various acceleration rates (R=2, 4, 6, 8, 10, and 15). Independent testing was performed with MRIs from the phase 2/3 EORTC-26101 trial (528 patients with glioblastoma, 1974 examinations, and 32 institutions). The similarity between undersampled dCNN-reconstructed and original MRIs was quantified with various image quality metrics, including structural similarity index measure (SSIM) and the accuracy of undersampled dCNN-reconstructed MRI on downstream radiological assessment of imaging biomarkers in oncology (automated artificial intelligence-based quantification of tumour burden and treatment response) was performed in the EORTC-26101 test dataset. The public NYU Langone Health fastMRI brain test dataset (558 patients and 558 examinations) was used to validate the generalisability and robustness of the dCNN for reconstructing MRIs from available multi-coil (parallel imaging) k-space data. FINDINGS: In the EORTC-26101 test dataset, the median SSIM of undersampled dCNN-reconstructed MRI ranged from 0·88 to 0·99 across different acceleration rates, with 0·92 (95% CI 0·92-0·93) for 10-times acceleration (R=10). The 10-times undersampled dCNN-reconstructed MRI yielded excellent agreement with original MRI when assessing volumes of contrast-enhancing tumour (median DICE for spatial agreement of 0·89 [95% CI 0·88 to 0·89]; median volume difference of 0·01 cm3 [95% CI 0·00 to 0·03] equalling 0·21%; p=0·0036 for equivalence) or non-enhancing tumour or oedema (median DICE of 0·94 [95% CI 0·94 to 0·95]; median volume difference of -0·79 cm3 [95% CI -0·87 to -0·72] equalling -1·77%; p=0·023 for equivalence) in the EORTC-26101 test dataset. Automated volumetric tumour response assessment in the EORTC-26101 test dataset yielded an identical median time to progression of 4·27 months (95% CI 4·14 to 4·57) when using 10-times-undersampled dCNN-reconstructed or original MRI (log-rank p=0·80) and agreement in the time to progression in 374 (95·2%) of 393 patients with data. The dCNN generalised well to the fastMRI brain dataset, with significant improvements in the median SSIM when using multi-coil compared with single-coil k-space data (p&lt;0·0001). INTERPRETATION: Deep-learning-based reconstruction of undersampled MRI allows for a substantial reduction of scan times, with a 10-times acceleration demonstrating excellent image quality while preserving the accuracy of derived imaging biomarkers for the assessment of oncological treatment response. Our developments are available as open source software and hold considerable promise for increasing the accessibility to MRI, pending further prospective validation. FUNDING: Deutsche Forschungsgemeinschaft (German Research Foundation) and an Else Kröner Clinician Scientist Endowed Professorship by the Else Kröner Fresenius Foundation.</p

Mitochondrial chaotic dynamics: Redox-energetic behavior at the edge of stability

Mitochondria serve multiple key cellular functions, including energy generation, redox balance, and regulation of apoptotic cell death, thus making a major impact on healthy and diseased states. Increasingly recognized is that biological network stability/instability can play critical roles in determining health and disease. We report for the first-time mitochondrial chaotic dynamics, characterizing the conditions leading from stability to chaos in this organelle. Using an experimentally validated computational model of mitochondrial function, we show that complex oscillatory dynamics in key metabolic variables, arising at the “edge” between fully functional and pathological behavior, sets the stage for chaos. Under these conditions, a mild, regular sinusoidal redox forcing perturbation triggers chaotic dynamics with main signature traits such as sensitivity to initial conditions, positive Lyapunov exponents, and strange attractors. At the “edge” mitochondrial chaos is exquisitely sensitive to the antioxidant capacity of matrix Mn superoxide dismutase as well as to the amplitude and frequency of the redox perturbation. These results have potential implications both for mitochondrial signaling determining health maintenance, and pathological transformation, including abnormal cardiac rhythms.publishedVersionKembro, Jackelyn Melissa. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina.Kembro, Jackelyn Melissa. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Biológicas y Tecnológicas; Argentina.Cortassa, Sonia. National Institutes of Health. NIH · NIA Intramural Research Program; Estados Unidos.Lloyd, David. Cardiff University. School of Biosciences 1; Inglaterra.Sollot, Steven. Johns Hopkins University. Laboratory of Cardiovascular Science; Estados Unidos.Sollot, Steven. Johns Hopkins University. Laboratory of Cardiovascular Science; Estados Unidos