المؤلفون: Norris, Caroline Nicole

مرشدي الرسالة: Department of Biomedical Engineering and Mechanics, VandeVord, Pamela J., Lee, Yong Woo, Theus, Michelle H., DeMar, James C., Swanger, Sharon A.

مصطلحات موضوعية: Traumatic Brain Injury, Blast-Induced Neurotrauma, Intracranial Pressure, Neurometabolism, Glutamine-Glutamate/GABA Cycle

الوصف: Blast-induced traumatic brain injury (bTBI) remains a significant problem among military populations. When an explosion occurs, a high magnitude positive pressure rapidly propagates away from the detonation source. Upon contact, biological tissues throughout the body undergo deformation at high strain rates and then return to equilibrium following a brief negative pressure phase. This mechanical disruption of the tissue is known to cause oxidative stress and neuroinflammation in the brain, which can lead to neurodegeneration and consequently poor cognitive and behavioral outcomes. Further, these clinical outcomes, which can include chronic headaches, problems with balance, light and noise sensitivity, anxiety, and depression, may be sustained years following blast exposure and there are currently no effective treatments. Thus, there is a need to investigate the acute molecular responses following bTBI in order to motivate the development of effective therapeutic strategies and ultimately improve or prevent long-term patient outcomes. It is important to not only understand the acute molecular response, but how the brain tissue mechanics drive these metabolic changes. The objective of this work was to identify the interplay between the tissue-level biomechanics and the acute bTBI pathophysiology. In a rodent bTBI model, using adult rats, intracranial pressure was mapped throughout the brain during blast exposure where frequency contributions from skull flexure and wave dynamics were significantly altered between brain regions and were largely dependent on blast magnitude. These findings informed the subsequent spatial and temporal changes in neurometabolism. Amino acid molecular precursor concentrations decreased at four hours post-blast in the cortex and hippocampus regions. This motivates further investigation of amino acids as therapeutic targets aimed to reduce oxidative stress and prevent prolonged injury cascades. However, neurochemical changes were not consistent across blast magnitudes, which may be explained by the disparities in biomechanics at lower blast pressures. Lastly, we investigated the acute changes in metabolic regulators influencing excitotoxicity where it was found that astrocytes maintained normal clearance of excitatory and inhibitory neurotransmitters prior to astrocyte reactivity. Outcomes of this work provide improved understanding of blast mechanics and associated acute pathophysiology and inform future therapeutic and diagnostic approaches following bTBI.
Doctor of Philosophy
Blast-induced traumatic brain injury (bTBI) remains a significant problem among military populations. When an explosion occurs, a high magnitude positive pressure wave rapidly propagates away from the detonation source. Upon contact, biological tissues throughout the body undergo deformation that can cause injury. This mechanical disruption of the tissue is known to trigger negative biological processes that lead to persistent cognitive and behavioral deficits. Further, these clinical outcomes, which can include chronic headaches, problems with balance, light and noise sensitivity, anxiety, and depression, may be sustained years following blast exposure. There are currently no effective treatments that can help those afflicted, and biomarkers for injury diagnostics are limited. Thus, there is a great need to investigate the early biological responses following bTBI in order to motivate the development of effective therapeutic strategies and ultimately improve or prevent long-term patient outcomes. It is important to not only understand the immediate responses, but also how the brain tissue mechanics drive these metabolic changes. The objective of this work was to identify the interplay between the brain biomechanics and the acute bTBI pathophysiology. Using a translational animal model, pressure inside the brain was measured with pressure sensors during blast exposure. Subsequent spatial and temporal changes in neurochemical concentrations were quantified. The results showed (1) significant disparities in the pressure dynamics inside the brain and it varied across brain regions, (2) neurochemical precursors may have therapeutic potential post-injury, and (3) biomechanical and neurochemical responses were dependent on blast severity. Outcomes of this work provide improved understanding of blast mechanics and associated pathophysiology and inform future therapeutic and diagnostic approaches to prevent prolonged injury cascades.

وصف الملف: ETD; application/pdf; application/vnd.openxmlformats-officedocument.wordprocessingml.document

الإتاحة: http://hdl.handle.net/10919/115474Test

المؤلفون: Walther, Georg, Martin, Christian, Haase, Amelie, Nestler, Ulf, Schob, Stefan

مصطلحات موضوعية: unruptured intracranial aneurysm, rupture risk, machine learning, PHASES score, info:eu-repo/classification/ddc/610, ddc:610

الوصف: Intracranial aneurysms represent a potentially life-threatening condition and occur in 3–5% of the population. They are increasingly diagnosed due to the broad application of cranial magnetic resonance imaging and computed tomography in the context of headaches, vertigo, and other unspecific symptoms. For each affected individual, it is utterly important to estimate the rupture risk of the respective aneurysm. However, clinically applied decision tools, such as the PHASES score, remain insufficient. Therefore, a machine learning approach assessing the rupture risk of intracranial aneurysms is proposed in our study. For training and evaluation of the algorithm, data from a single neurovascular center was used, comprising 446 aneurysms (221 ruptured, 225 unruptured). The machine learning model was then compared with the PHASES score and proved superior in accuracy (0.7825), F1-score (0.7975), sensitivity (0.8643), specificity (0.7022), positive predictive value (0.7403), negative predictive value (0.8404), and area under the curve (0.8639). The frequency distributions of the predicted rupture probabilities and the PHASES score were analyzed. A symmetry can be observed between the rupture probabilities, with a symmetry axis at 0.5. A feature importance analysis reveals that the body mass index, consumption of anticoagulants, and harboring vessel are regarded as the most important features when assessing the rupture risk. On the other hand, the size of the aneurysm, which is weighted most in the PHASES score, is regarded as less important. Based on our findings we discuss the potential role of the model for clinical practice in geographically confined aneurysm patients.

العلاقة: 943

الإتاحة: https://ul.qucosa.de/id/qucosa%3A87165Test
https://ul.qucosa.de/api/qucosa%3A87165/attachment/ATT-0Test/

المؤلفون: Sasaki, Natsuhi

مرشدي الرسالة: 佐々木, 夏一, ササキ, ナツヒ

مصطلحات موضوعية: Bioresorbable polymer stent, Flow diverter, Animal experiments, Intracranial aneurysm, 490

الوصف: 甲第24966号
医博第5020号
新制||医||1069(附属図書館)
学位規則第4条第1項該当
Doctor of Medical Science
Kyoto University
DFAM

الإتاحة: http://hdl.handle.net/2433/286359Test

المؤلفون: Fehrenbach, Michael Karl, Brock, Hannah, Mehnert-Theuerkauf, Anja, Meixensberger, Jürgen

مصطلحات موضوعية: intracranial neoplasia, psychological distress, self-report questionnaires, depression, anxiety, posttraumatic stress disorder, health-related quality of life, fear of progression, info:eu-repo/classification/ddc/150, ddc:150

الوصف: Objective: We aimed to assess psychological distress in patients with intracranial neoplasia, a group of patients who suffer from severe functional, neurocognitive and neuropsychological side effects, resulting in high emotional distress. Methods: We conducted a cross-sectional study, including inpatients with brain tumours. Eligible patients completed validated self-report questionnaires measuring depression, anxiety, distress, symptoms of posttraumatic stress disorder (PTSD), fear of progression and health-related quality of life. The questionnaire set was completed after brain surgery and receiving diagnosis and before discharge from hospital. Results: A total of n = 31 patients participated in this survey. Fourteen of them suffered from malignant (n = 3 metastatic neoplasia) and 17 from benign brain tumours. Mean values of the total sample regarding depression (M = 9.28, SD = 6.08) and anxiety (M = 6.00, SD = 4.98) remained below the cut-off ≥ 10. Mean psychosocial distress (M = 16.30, SD = 11.23, cut-off ≥ 14) and posttraumatic stress (M = 35.10, SD = 13.29, cut-off ≥ 32) exceeded the clinically relevant cut-off value in all the patients with intracranial tumours. Significantly, more patients with malignant (79%) than benign (29%) brain tumours reported PTSD symptoms (p = 0.006). Conclusion: Distress and clinically relevant PTSD symptoms in patients with intracranial neoplasia should be routinely screened and treated in psycho-oncological interventions immediately after diagnosis. Especially, neuro-oncological patients with malignant brain tumours or metastases need targeted support to reduce their emotional burden.

العلاقة: 664235

الإتاحة: https://ul.qucosa.de/id/qucosa%3A84452Test
https://ul.qucosa.de/api/qucosa%3A84452/attachment/ATT-0Test/

المؤلفون: Jesser, Jessica, Alberalar, Nilüfer D., Kizilkilic, Osman, Saatci, Isil, Baltacioglu, Feyyaz, Özlük, Enes, Killer-Oberpfalzer, Monika, Vollherbst, Dominik F., Islak, Civan, Cekirge, Saruhan H., Bendszus, Martin, Möhlenbruch, Markus, Koçer, Naci

مصطلحات موضوعية: flow diversion, cerebral small vessels, FRED junior, low-profile flow diverter, intracranial aneurysm, info:eu-repo/classification/ddc/610, ddc:610

الوصف: Background and Purpose: Flow diversion is increasingly used as an endovascular treatment for intracranial aneurysms. In this retrospective multicenter study, we analyzed the safety and efficacy of the treatment of intracranial, unruptured, or previously treated but recanalized aneurysms using Flow Re-Direction Endoluminal Device (FRED) Jr with emphasis on midterm results. Materials and Methods: Clinical and radiological records of 150 patients harboring 159 aneurysms treated with FRED Jr at six centers between October 2014 and February 2020 were reviewed and consecutively included. Clinical outcome was measured by using the modified Rankin Scale (mRS). Anatomical results were assessed according to the O’Kelly-Marotta (OKM) scale and the Cekirge-Saatci Classification (CSC) scale. Results: The overall complication rate was 24/159 (16%). Thrombotic-ischemic events occurred in 18/159 treatments (11%). These resulted in long-term neurological sequelae in two patients (1%) with worsening from pre-treatment mRS 0–2 and mRS 4 after treatment. Complete or near-complete occlusion of the treated aneurysm according to the OKM scale was reached in 54% (85/158) at 6-month, in 68% (90/133) at 1-year, and in 83% (77/93) at 2-year follow-up, respectively. The rates of narrowing or occlusion of a vessel branch originating from the treated aneurysm according to the CSC scale were 11% (12/108) at 6-month, 20% (17/87) at 1-year, and 23% (13/57) at 2-year follow-up, respectively, with all cases being asymptomatic. Conclusions: In this retrospective multicenter study, FRED Jr was safe and effective in the midterm occlusion of cerebral aneurysms. Most importantly, it was associated with a high rate of good clinical outcome.

العلاقة: 722183

الإتاحة: https://ul.qucosa.de/id/qucosa%3A84340Test
https://ul.qucosa.de/api/qucosa%3A84340/attachment/ATT-0Test/

المؤلفون: Sobisch, Jannik

مرشدي الرسالة: Hochschule für Technik, Wirtschaft und Kultur

مصطلحات موضوعية: Cerebral angiograms, Intracranial aneurysms, Aneurysm detection, Aneurysm rupture prediction, Point cloud representation learning

الوصف: Intracranial Aneurysms are a prevalent vascular pathology present in 3-4% of the population with an inherent risk of rupture. The growing accessibility of angiography has led to a rising incidence of detected aneurysms. An accurate assessment of the rupture risk is of utmost importance for the very high disability and mortality rates in case of rupture and the non-negligible risk inherent to surgical treatment. However, human evaluation is rather subjective, and current treatment guidelines, such as the PHASES score, remain inefficient. Therefore we aimed to develop an automatic machine learning-based rupture prediction model. Our study utilized 686 CTA scans, comprising 844 intracranial aneurysms. Among these aneurysms, 579 were classified as ruptured, while 265 were categorized as non-ruptured. Notably, the CTAs of ruptured aneurysms were obtained within a week after rupture, during which negligible morphological changes were observed compared to the aneurysm’s pre-rupture shape, as established by previous research. Based on this observation, our rupture risk assessment focused on the models’ ability to classify between ruptured and unruptured IAs. In our investigation, we implemented an automated vessel and aneurysm segmentation, vessel labeling, and feature extraction framework. The rupture risk prediction involved the use of deep learning-based vessel and aneurysm shape features, along with a combination of demographic features (patient sex and age) and morphological features (aneurysm location, size, surface area, volume, sphericity, etc.). An ablation-type study was conducted to evaluate these features. Eight different machine learning models were trained with the objective of identifying ruptured aneurysms. The best performing model achieved an area under the receiver operating characteristic curve (AUC) of 0.833, utilizing a random forest algorithm with feature selection based on Spearman’s rank correlation thresholding, which effectively eliminated highly correlated and anti-correlated features...:1 Introduction 1.1 Intracranial aneurysms 1.1.1 Treatment strategy 1.1.2 Rupture risk assesment 1.2 Artificial Intelligence 1.3 Thesis structure 1.4 Contribution of the author 2 Theory 2.1 Rupture risk assessment guidelines 2.1.1 PHASES score 2.1.2 ELAPSS score 2.2 Literature review: Aneurysm rupture prediction 2.3 Machine learning classifiers 2.3.1 Decision Tree 2.3.2 Random Forests 2.3.3 XGBoost 2.3.4 K-Nearest-Neighbor 2.3.5 Multilayer Perceptron 2.3.6 Logistic Regression 2.3.7 Support Vector Machine 2.3.8 Naive Bayes 2.4 Latent feature vectors in deep learning 2.5 PointNet++ 3 Methodology 3.1 Data 3.2 Vessel segmentation 3.3 Feature extraction 3.3.1 Deep vessel features 3.3.2 Deep aneurysm features 3.3.3 Conventional features 3.4 Rupture classification 3.4.1 Univariate approach 3.4.2 Multivariate approach 3.4.3 Deep learning approach 3.4.4 Deep learning amplified multivariate approach 3.5 Feature selection 3.5.1 Correlation-based feature selection 3.5.2 Permutation feature importance 3.6 Implementation 3.7 Evaluation 4 Results 4.1 Univariate approach 4.2 Multivariate approach 4.3 Deep learning approach 4.3.1 Deep vessel features 4.3.2 Deep aneurysm features 4.3.3 Deep vessel and deep aneurysm features 4.4 Deep learning amplified multivariate approach 4.4.1 Conventional and deep vessel features 4.4.2 Conventional and deep aneurysm features 4.4.3 Conventional, deep vessel, and deep aneurysm features 5 Discussion and Conclusions 5.1 Overview of results 5.2 Feature selection 5.3 Feature analysis 5.3.1 Deep vessel features 5.3.2 Deep aneurysm features 5.3.3 Conventional features 5.3.4 Summary 5.4 Comparison to other methods 5.5 Outlook Bibliography
Intrakranielle Aneurysmen sind eine weit verbreitete vaskuläre Pathologie, die bei 3 bis 4% der Bevölkerung auftritt und ein inhärentes Rupturrisiko birgt. Mit der zunehmenden Verfügbarkeit von Angiographie wird eine steigende Anzahl von Aneurysmen entdeckt. Angesichts der sehr hohen permanenten Beeinträchtigungs- und Sterblichkeitsraten im Falle einer Ruptur und des nicht zu vernachlässigenden Risikos einer chirurgischen Behandlung ist eine genaue Bewertung des Rupturrisikos von größter Bedeutung. Die Beurteilung durch den Menschen ist jedoch sehr subjektiv, und die derzeitigen Behandlungsrichtlinien, wie der PHASES-Score, sind nach wie vor ineffizient. Daher wollten wir ein automatisches, auf maschinellem Lernen basierendes Modell zur Rupturvorhersage entwickeln. Für unsere Studie wurden 686 CTA-Scans von 844 intrakraniellen Aneurysmen verwendet, von denen 579 rupturiert waren und 265 nicht rupturiert waren. Dabei ist zu beachten, dass die CTAs der rupturierten Aneurysmen innerhalb einer Woche nach der Ruptur gewonnen wurden, in der im Vergleich zur Form des Aneurysmas vor der Ruptur nur geringfügige morphologische Veränderungen zu beobachten waren, wie in vorhergegangenen Studient festgestellt wurde. Im Rahmen unserer Untersuchung haben wir eine automatische Segmentierung von Adern und Aneurysmen, ein Aderlabeling und eine Merkmalsextraktion implementiert. Für die Vorhersage des Rupturrisikos wurden auf Deep Learning basierende Ader- und Aneurysmaformmerkmale zusammen mit einer Kombination aus demografischen Merkmalen (Geschlecht und Alter des Patienten) und morphologischen Merkmalen (u. A. Lage, Größe, Oberfläche, Volumen, Sphärizität des Aneurysmas) verwendet. Zur Bewertung dieser Merkmale wurde eine Ablationsstudie durchgeführt. Acht verschiedene maschinelle Lernmodelle wurden mit dem Ziel trainiert, rupturierte Aneurysmen zu erkennen...:1 Introduction 1.1 Intracranial aneurysms 1.1.1 Treatment strategy 1.1.2 Rupture risk assesment 1.2 Artificial Intelligence 1.3 Thesis structure 1.4 Contribution of the author 2 Theory 2.1 Rupture risk assessment guidelines 2.1.1 PHASES score 2.1.2 ELAPSS score 2.2 Literature review: Aneurysm rupture prediction 2.3 Machine learning classifiers 2.3.1 Decision Tree 2.3.2 Random Forests 2.3.3 XGBoost 2.3.4 K-Nearest-Neighbor 2.3.5 Multilayer Perceptron 2.3.6 Logistic Regression 2.3.7 Support Vector Machine 2.3.8 Naive Bayes 2.4 Latent feature vectors in deep learning 2.5 PointNet++ 3 Methodology 3.1 Data 3.2 Vessel segmentation 3.3 Feature extraction 3.3.1 Deep vessel features 3.3.2 Deep aneurysm features 3.3.3 Conventional features 3.4 Rupture classification 3.4.1 Univariate approach 3.4.2 Multivariate approach 3.4.3 Deep learning approach 3.4.4 Deep learning amplified multivariate approach 3.5 Feature selection 3.5.1 Correlation-based feature selection 3.5.2 Permutation feature importance 3.6 Implementation 3.7 Evaluation 4 Results 4.1 Univariate approach 4.2 Multivariate approach 4.3 Deep learning approach 4.3.1 Deep vessel features 4.3.2 Deep aneurysm features 4.3.3 Deep vessel and deep aneurysm features 4.4 Deep learning amplified multivariate approach 4.4.1 Conventional and deep vessel features 4.4.2 Conventional and deep aneurysm features 4.4.3 Conventional, deep vessel, and deep aneurysm features 5 Discussion and Conclusions 5.1 Overview of results 5.2 Feature selection 5.3 Feature analysis 5.3.1 Deep vessel features 5.3.2 Deep aneurysm features 5.3.3 Conventional features 5.3.4 Summary 5.4 Comparison to other methods 5.5 Outlook Bibliography

الإتاحة: https://htwk-leipzig.qucosa.de/id/qucosa%3A87207Test
https://htwk-leipzig.qucosa.de/api/qucosa%3A87207/attachment/ATT-0Test/

المؤلفون: Dai, Honghao

مصطلحات موضوعية: Mechanical Engineering, unsupervised learning, autoencoder, feature extraction, improved PHM, Intracranial pressure signal, fault detection

الوصف: Prognostics and health management (PHM), which aims to convert preventive maintenance (periodical maintenance) into predictive maintenance (condition-based maintenance), has gained increasing attention in the current era of the Internet of Things (IoT), Industry 4.0, and Industrial AI. A significant amount of research has been conducted using a variety of signal processing, statistical analysis, and machine learning algorithms to develop different PHM systems. Feature learning is a crucial task in bridging the gap between data and models. Time-series data in sensor environments exhibit continuous changes and drifts, which require PHM models to balance static and time-independent uncertainty for feature learning. In this dissertation, a novel deep autoencoder with time-lagged regularization is proposed. This method can learn features from the time-domain and frequency-domain and detect underlying weak-sense stationarity. A change point detection strategy is developed by combining the time-lagged autoencoder with a dissimilarity-based anomaly detector. The effectiveness of the proposed change point detection algorithm is validated using public benchmarking datasets, fault detection and prognostics of ion milling etching machine data, non-artificial segments recognition, and long-term assessment of intracranial pressure signals. The proposed methodology is compared with state-of-the-art benchmark approaches and found to establish an improved PHM model with sustainable performance in discovering change point features in time-series signals.

الإتاحة: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1684772500955753Test

المؤلفون: Okada, Akihiro

مرشدي الرسالة: 岡田, 明大, オカダ, アキヒロ

مصطلحات موضوعية: Intracranial aneurysm, Subarachnoid hemorrhage, Macrophage, Nervous system, 490

الوصف: 甲第24785号
医博第4977号
新制||医||1066(附属図書館)
(主査)教授 金子 新, 教授 YOUSSEFIAN Shohab, 教授 阪上 優
学位規則第4条第1項該当
Doctor of Medical Science
Kyoto University
DFAM

الإتاحة: http://hdl.handle.net/2433/284081Test

المؤلفون: Fujimoto, Gaku

مرشدي الرسالة: 藤本, 岳, フジモト, ガク

مصطلحات موضوعية: expected normal healthy volume, intracranial volume, brainstem, posttraumatic amnesia, diffuse axonal injury, 490

الوصف: 甲第24797号
医博第4989号
新制||医||1066(附属図書館)
学位規則第4条第1項該当
Doctor of Medical Science
Kyoto University
DFAM

الإتاحة: http://hdl.handle.net/2433/284093Test

المؤلفون: Ono, Isao

مرشدي الرسالة: 小野, 功朗, オノ, イサオ

مصطلحات موضوعية: 3D-casted mold, Endothelial cell, Hemodynamic force, Intracranial aneurysm, 490

الوصف: 甲第24789号
医博第4981号
新制||医||1066(附属図書館)
学位規則第4条第1項該当
Doctor of Medical Science
Kyoto University
DFAM

الإتاحة: http://hdl.handle.net/2433/284085Test