Search results for: persistent photoconductivity

Authors: Mohammadjavad Sotoudeheian, Navid Farahmandian

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Cardiac hypertrophy arises in response to persistent increases in hemodynamic loads. In comparison, diabetic cardiomyopathy is defined by an abnormal myocardial changes without other cardiac-related risk factors. Pathological cardiac hypertrophy and myocardial remodeling are hallmarks of cardiovascular diseases and are risk factors for heart failure. The transcription factor forkhead box class O (FOXOs) can protect heart tissue by hostile oxidative stress and stimulating apoptosis and autophagy. FOXO proteins, as sensitive elements and mediators in response to environmental changes, have been revealed to prevent and inverse cardiac hypertrophy. FOXOs are inhibited by insulin and are critical mediators of insulin action. Insulin deficiency and uncontrolled diabetes lead to a catabolic state. FOXO1 acts downstream of the insulin-dependent pathways, which are dysregulated in diabetes. It regulates cardiomyocyte hypertrophy downstream of IGF1R/PI3K/Akt activation, which are critical regulators of cardiac hypertrophy. The complex network of signaling pathways comprising insulin/IGF-1 signaling, AMPK, JNK, and Sirtuins regulate the development of cardiovascular dysfunction by modulating the activity of FOXOs. Insulin receptors and IGF1R act via the PI3k/Akt and the MAPK/ERK pathways. Activation of Akt in response to insulin or IGF-1 induces phosphorylation of FOXOs. Increased protein synthesis induced by activation of the IGF-I/Akt/mTOR signaling pathway leads to hypertrophy. This pathway and the myostatin/Smad pathway are potent negative muscle development regulators. In cardiac muscle, insulin receptor substrates (IRS)-1 or IRS-2 activates the Akt signaling pathway and inactivate FOXO1. Under metabolic stress, p38 MAPK promotes degradation of IRS-1 and IRS-2 in cardiac myocytes and activates FOXO1, leading to cardiomyopathy. Sirt1 and FOXO1 interaction play an essential role in starvation-induced autophagy in cardiac metabolism. Inhibition of Angiotensin-II induced cardiomyocyte hypertrophy is associated with reduced FOXO1 acetylation and activation of Sirt1. The NF-κB, ERK, and FOXOs are de-acetylated by SIRT1. De-acetylation of FOXO1 induces the expression of genes involved in autophagy and stimulates autophagy flux. Therefore, under metabolic stress, FOXO1 can cause diabetic cardiomyopathy. The overexpression of FOXO1 leads to decreased cardiomyocyte size and suppresses cardiac hypertrophy through inhibition of the calcineurin–NFAT pathway. Diabetes mellitus is associated with elevation of O-GlcNAcylation. Some of its binding partners regulate the substrate selectivity of O-GlcNAc transferase (OGT). O-GlcNAcylation of essential contractile proteins may inhibit protein-protein interactions, reduce calcium sensitivity, and modulate contractile function. Uridine diphosphate (UDP)-GlcNAc is the obligatory substrate of OGT, which catalyzes a reversible post-translational protein modification. The increase of O-GlcNAcylation is accompanied by impaired cardiac hypertrophy in diabetic hearts. Inhibition of O-GlcNAcylation blocks activation of ERK1/2 and hypertrophic growth. O-GlcNAc modification on NFAT is required for its translocation from the cytosol to the nucleus, where NFAT stimulates the transcription of various hypertrophic genes. Inhibition of O-GlcNAcylation dampens NFAT-induced cardiac hypertrophic growth. Transcriptional activity of FOXO1 is enriched by improved O-GlcNAcylation upon high glucose stimulation or OGT overexpression. In diabetic conditions, the modification of FOXO1 by O-GlcNAc is promoted in cardiac troponin I and myosin light chain 2. Therefore targeting O-GlcNAcylation represents a potential therapeutic option to prevent hypertrophy in the diabetic heart.

Keywords: diabetes, cardiac hypertrophy, O-GlcNAcylation, FOXO1, Akt, PI3K, AMPK, insulin

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Authors: Denis Jordan, Daniel Golkowski, Mathias Lukas, Katharina Merz, Caroline Mlynarcik, Max Maurer, Valentin Riedl, Stefan Foerster, Eberhard F. Kochs, Andreas Bender, Ruediger Ilg

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Introduction: So far, clinical assessments that rely on behavioral responses to differentiate coma states or even predict outcome in coma patients are unreliable, e.g. because of some patients’ motor disabilities. The present study was aimed to provide prognosis in coma patients using markers from electroencephalogram (EEG), blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) and [18F]-fluorodeoxyglucose (FDG) positron emission tomography (PET). Unsuperwised principal component analysis (PCA) was used for multimodal integration of markers. Methods: Approved by the local ethics committee of the Technical University of Munich (Germany) 20 patients (aged 18-89) with severe brain damage were acquired through intensive care units at the Klinikum rechts der Isar in Munich and at the Therapiezentrum Burgau (Germany). At the day of EEG/fMRI/PET measurement (date I) patients (<3.5 month in coma) were grouped in the minimal conscious state (MCS) or vegetative state (VS) on the basis of their clinical presentation (coma recovery scale-revised, CRS-R). Follow-up assessment (date II) was also based on CRS-R in a period of 8 to 24 month after date I. At date I, 63 channel EEG (Brain Products, Gilching, Germany) was recorded outside the scanner, and subsequently simultaneous FDG-PET/fMRI was acquired on an integrated Siemens Biograph mMR 3T scanner (Siemens Healthineers, Erlangen Germany). Power spectral densities, permutation entropy (PE) and symbolic transfer entropy (STE) were calculated in/between frontal, temporal, parietal and occipital EEG channels. PE and STE are based on symbolic time series analysis and were already introduced as robust markers separating wakefulness from unconsciousness in EEG during general anesthesia. While PE quantifies the regularity structure of the neighboring order of signal values (a surrogate of cortical information processing), STE reflects information transfer between two signals (a surrogate of directed connectivity in cortical networks). fMRI was carried out using SPM12 (Wellcome Trust Center for Neuroimaging, University of London, UK). Functional images were realigned, segmented, normalized and smoothed. PET was acquired for 45 minutes in list-mode. For absolute quantification of brain’s glucose consumption rate in FDG-PET, kinetic modelling was performed with Patlak’s plot method. BOLD signal intensity in fMRI and glucose uptake in PET was calculated in 8 distinct cortical areas. PCA was performed over all markers from EEG/fMRI/PET. Prognosis (persistent VS and deceased patients vs. recovery to MCS/awake from date I to date II) was evaluated using the area under the curve (AUC) including bootstrap confidence intervals (CI, *: p<0.05). Results: Prognosis was reliably indicated by the first component of PCA (AUC=0.99*, CI=0.92-1.00) showing a higher AUC when compared to the best single markers (EEG: AUC<0.96*, fMRI: AUC<0.86*, PET: AUC<0.60). CRS-R did not show prediction (AUC=0.51, CI=0.29-0.78). Conclusion: In a multimodal analysis of EEG/fMRI/PET in coma patients, PCA lead to a reliable prognosis. The impact of this result is evident, as clinical estimates of prognosis are inapt at time and could be supported by quantitative biomarkers from EEG, fMRI and PET. Due to the small sample size, further investigations are required, in particular allowing superwised learning instead of the basic approach of unsuperwised PCA.

Keywords: coma states and prognosis, electroencephalogram, entropy, functional magnetic resonance imaging, machine learning, positron emission tomography, principal component analysis

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Authors: Lauti Dwita Santy, Hermes Panggabean, Syahrir Andi Mangga

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The Bengkulu Basin is part of the Sunda Arc system, which is a classic convergent type margin that occur around the southern rim of the Eurasian continental (Sundaland) plate. The basin is located between deep sea trench (Mentawai Outer Arc high) and the volvanic/ magmatic Arc of the Barisan Mountains Range. To the northwest it is bounded by Padang High, to the northest by Barisan Mountains (Sumatra Fault Zone) to the southwest by Mentawai Fault Zone and to the southeast by Semangko High/ Sunda Strait. The stratigraphic succession and tectonic development can be broadly divided into four stage/ periods, i.e Late Jurassic- Early Cretaceous, Late Eocene-Early Oligocene, Late Oligocene-Early Miocene, Middle Miocene-Late Miocene and Pliocene-Plistocene, which are mainly controlled by the development of subduction activities. The Pre Tertiary Basement consist of sedimentary and shallow water limestone, calcareous mudstone, cherts and tholeiitic volcanic rocks, with Late Jurassic to Early Cretaceous in age. The sedimentation in this basin is depend on the relief of the Pre Tertiary Basement (Woyla Terrane) and occured into two stages, i.e. transgressive stage during the Latest Oligocene-Early Middle Miocene Seblat Formation, and the regressive stage during the Latest Middle Miocene-Pleistocene (Lemau, Simpangaur and Bintunan Formations). The Pre-Tertiary Faults were more intensive than the overlying cover, The Tertiary Rocks. There are two main fault trends can be distinguished, Northwest–Southwest Faults and Northeast-Southwest Faults. The NW-SE fault (Ketaun) are commonly laterally persistent, are interpreted to the part of Sumatran Fault Systems. They commonly form the boundaries to the Pre Tertiary basement highs and therefore are one of the faults elements controlling the geometry and development of the Tertiary sedimentary basins.The Northeast-Southwest faults was formed a conjugate set to the Northwest–Southeast Faults. In the earliest Tertiary and reactivated during the Plio-Pleistocene in a compressive mode with subsequent dextral displacement. The Block Faulting accross these two sets of faults related to approximate North–South compression in Paleogene time and produced a series of elongate basins separated by basement highs in the backarc and forearc region. The Bengkulu basin is interpreted having evolved from pull apart feature in the area southwest of the main Sumatra Fault System related to NW-SE trending in dextral shear.Based on Pyrolysis Yield (PY) vs Total Organic Carbon (TOC) diagram show that Seblat and Lemau Formation belongs to oil and Gas Prone with the quality of the source rocks includes into excellent and good (Lemau Formation), Fair and Poor (Seblat Formation). The fine-grained carbonaceous sediment of the Seblat dan Lemau Formations as source rocks, the coarse grained and carbonate sediments of the Seblat and Lemau Formations as reservoir rocks, claystone bed in Seblat and Lemau Formation as caprock. The source rocks maturation are late immature to early mature, with kerogen type II and III (Seblat Formation), and late immature to post mature with kerogen type I and III (Lemau Formation). The burial history show to 2500 m in depthh with paleo temperature reached 80oC. Trapping mechanism occur during Oligo–Miocene and Middle Miocene, mainly in block faulting system.

Keywords: fore arc, bengkulu, sumatra, sundaland, hydrocarbon, trapping mechanism

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Authors: Surbhi Kaura, Sachin Kandhari, Shahiduz Zafar

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Chronic stroke, characterized by persistent motor deficits, often necessitates comprehensive rehabilitation interventions to improve functional outcomes and mitigate long-term dependency. Luna neurorobotic devices, integrated with EMG feedback systems, provide an innovative platform for facilitating neuroplasticity and functional improvement in stroke survivors. This retrospective study aims to investigate the impact of EMG-based Luna neurorobotic interventions on the strengthening of the affected side in chronic stroke patients. In rehabilitation, active patient participation significantly activates the sensorimotor network during motor control, unlike passive movement. Stroke is a debilitating condition that, when not effectively treated, can result in significant deficits and lifelong dependency. Common issues like neglecting the use of limbs can lead to weakness in chronic stroke cases. In rehabilitation, active patient participation significantly activates the sensorimotor network during motor control, unlike passive movement. This study aims to assess how electromyographic triggering (EMG-triggered) robotic treatments affect walking, ankle muscle force after an ischemic stroke, and the coactivation of agonist and antagonist muscles, which contributes to neuroplasticity with the assistance of biofeedback using robotics. Methods: The study utilized robotic techniques based on electromyography (EMG) for daily rehabilitation in long-term stroke patients, offering feedback and monitoring progress. Each patient received one session per day for two weeks, with the intervention group undergoing 45 minutes of robot-assisted training and exercise at the hospital, while the control group performed exercises at home. Eight participants with impaired motor function and gait after stroke were involved in the study. EMG-based biofeedback exercises were administered through the LUNA neuro-robotic machine, progressing from trigger and release mode to trigger and hold, and later transitioning to dynamic mode. Assessments were conducted at baseline and after two weeks, including the Timed Up and Go (TUG) test, a 10-meter walk test (10m), Berg Balance Scale (BBG), and gait parameters like cadence, step length, upper limb strength measured by EMG threshold in microvolts, and force in Newton meters. Results: The study utilized a scale to assess motor strength and balance, illustrating the benefits of EMG-biofeedback following LUNA robotic therapy. In the analysis of the left hemiparetic group, an increase in strength post-rehabilitation was observed. The pre-TUG mean value was 72.4, which decreased to 42.4 ± 0.03880133 seconds post-rehabilitation, with a significant difference indicated by a p-value below 0.05, reflecting a reduced task completion time. Similarly, in the force-based task, the pre-knee dynamic force in Newton meters was 18.2NM, which increased to 31.26NM during knee extension post-rehabilitation. The post-student t-test showed a p-value of 0.026, signifying a significant difference. This indicated an increase in the strength of knee extensor muscles after LUNA robotic rehabilitation. Lastly, at baseline, the EMG value for ankle dorsiflexion was 5.11 (µV), which increased to 43.4 ± 0.06 µV post-rehabilitation, signifying an increase in the threshold and the patient's ability to generate more motor units during left ankle dorsiflexion. Conclusion: This study aimed to evaluate the impact of EMG and dynamic force-based rehabilitation devices on walking and strength of the affected side in chronic stroke patients without nominal data comparisons among stroke patients. Additionally, it provides insights into the inclusion of EMG-triggered neurorehabilitation robots in the daily rehabilitation of patients.

Keywords: neurorehabilitation, robotic therapy, stroke, strength, paralysis

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