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Implications of Advanced Glycation Endproducts in Oxidative Stress and Neurodegenerative Disorders
(2001)
The reactions of reducing sugars with primary amino groups are the most common nonenzymatic modifications of proteins. Subsequent rearrangements, oxidations, and dehydrations yield a heterogeneous group of mostly colored and fluorescent compounds, termed "Maillard products" or advanced glycation end products (AGEs). AGE formation has been observed on long-lived proteins such as collagen, eye lens crystalline, and in pathological protein deposits in Alzheimer's (AD) and Parkinson's disease (PD) and dialysis-related amyloidosis. AGE-modified proteins are also involved in the complications of diabetes. AGEs accumulate in the the ß-amyloid plaques and neurofibrillary tangles (NFT) associated with AD and in the Lewy bodies characteristic of PD. Increasing evidence supports a role for oxidative stress in neurodegenerative disorders such as AD and PD. AGEs have been shown to contribute towards oxidative damage and chronic inflammation, whereby activated microglia secrete cytokines and free radicals, including nitric oxide (NO). Roles proposed for NO in the pathophysiology of the central nervous system are increasingly diverse and range from intercellular signaling, through necrosis of cells and invading pathogens, to the involvement of NO in apoptosis. Using in vitro experiments, it was shown that AGE-modified bovine serum albumin (BSA-AGE) and AGE-modified ß-amyloid, but not their unmodified proteins, induce NO production in N-11 murine microglia cells. This was mediated by the receptor for AGEs (RAGE) and upregulation of the inducible nitric oxide synthase (iNOS). AGE-induced enzyme activation and NO production could be blocked by intracellular-acting antioxidants: Ginkgo biloba special extract EGb 761, the estrogen derivative, 17ß-estradiol, R-(+)-thioctic acid, and a nitrone-based free radical trap, N-tert.-butyl-*-phenylnitrone (PBN). Methylglyoxal (MG) and 3-deoxyglucosone (3-DG), common precursors in the Maillard reaction, were also tested for their ability to induce the production of NO in N-11 microglia. However, no significant changes in nitrite levels were detected in the cell culture medium. The significance of these findings was supported by in vivo immunostaining of AD brains. Single and double immunostaining of cryostat sections of normal aged and AD brains was performed with polyclonal antibodies to AGEs and iNOS and monoclonal antibodies to Aß and PHF-1 (marker for NFT) and reactive microglia. In aged normal individuals as well as early stage AD brains (i.e. no pathological findings in isocortical areas), a few astrocytes showed co-localisation of AGE and iNOS in the upper neuronal layers of the temporal (Area 22) and entorhinal (Area 28, 34) cortices compared with no astrocytes detected in young controls. In late AD brains, there was a much denser accumulation of astrocytes co-localised with AGE and iNOS in the deeper and particularly upper neuronal layers. Also, numerous neurons with diffuse AGE but not iNOS reactivity and some AGE and iNOS-positive microglia were demonstrated, compared with only a few AGE-reactive neurons and no microglia in controls. Finally, astrocytes co-localised with AGE and iNOS as well as AGE and ß-amyloid were found surrounding mature but not diffuse ß-amyloid plaques in the AD brain. Parts of NFT were AGE-immunoreactive. Immunohistochemical staining of cryostat sections of normal aged and PD brains was performed with polyclonal antibodies to AGEs. The sections were counterstained with monoclonal antibodies to neurofilament components and a-synuclein. AGEs and a-synuclein were colocalized in very early Lewy bodies in the substantia nigra of cases with incidental Lewy body disease. These results support an AGE-induced oxidative damage due to the action of free radicals, such as NO, occurring in the AD and PD brains. Furthermore, the involvement of astrocytes and microglia in this pathological process was confirmed immunohistochemically in the AD brain. It is suggested that oxidative stress and AGEs participate in the very early steps of Lewy body formation and resulting cell death in PD. Since the iNOS gene can be regulated by redox-sensitive transcription factors, the use of membrane permeable antioxidants could be a promising strategy for the treatment and prevention of chronic inflammation in neurodegenerative disorders.
Multiple myeloma is a bone marrow plasma cell tumor which is supported by the external growth factors APRIL and IL-6, among others. Recently, we identified eosinophils and megakaryocytes to be functional components of the micro-environmental niches of benign bone marrow plasma cells and to be important local sources of these cytokines. Here, we investigated whether eosinophils and megakaryocytes also support the growth of tumor plasma cells in the MOPC315. BM model for multiple myeloma. As it was shown for benign plasma cells and multiple myeloma cells, IL-6 and APRIL also supported MOPC315. BM cell growth in vitro, IL-5 had no effect. Depletion of eosinophils in vivo by IL-5 blockade led to a reduction of the early myeloma load. Consistent with this, myeloma growth in early stages was retarded in eosinophil-deficient Delta dblGATA-1 mice. Late myeloma stages were unaffected, possibly due to megakaryocytes compensating for the loss of eosinophils, since megakaryocytes were found to be in contact with myeloma cells in vivo and supported myeloma growth in vitro. We conclude that eosinophils and megakaryocytes in the niches for benign bone marrow plasma cells support the growth of malignant plasma cells. Further investigations are required to test whether perturbation of these niches represents a potential strategy for the treatment of multiple myeloma.
The investigation of interacting multi-agent models is a new field of mathematical research with application to the study of behavior in groups of animals or community of people. One interesting feature of multi-agent systems is collective behavior. From the mathematical point of view, one of the challenging issues considering with these dynamical models is development of control mechanisms that are able to influence the time evolution of these systems.
In this thesis, we focus on the study of controllability, stabilization and optimal control problems for multi-agent systems considering three models as follows: The first one is the Hegselmann Krause opinion formation (HK) model. The HK dynamics describes how individuals' opinions are changed by the interaction with others taking place in a bounded domain of confidence. The study of this model focuses on determining feedback controls in order to drive the agents' opinions to reach a desired agreement. The second model is the Heider social balance (HB) model. The HB dynamics explains the evolution of relationships in a social network. One purpose of studying this system is the construction of control function in oder to steer the relationship to reach a friendship state. The third model that we discuss is a flocking model describing collective motion observed in biological systems. The flocking model under consideration includes self-propelling, friction, attraction, repulsion, and alignment features. We investigate a control for steering the flocking system to track a desired trajectory. Common to all these systems is our strategy to add a leader agent that interacts with all other members of the system and includes the control mechanism.
Our control through leadership approach is developed using classical theoretical control methods and a model predictive control (MPC) scheme. To apply the former method, for each model the stability of the corresponding linearized system near consensus is investigated. Further, local controllability is examined. However, only in the
Hegselmann-Krause opinion formation model, the feedback control is determined in order to steer agents' opinions to globally converge to a desired agreement. The MPC approach is an optimal control strategy based on numerical optimization. To apply the MPC scheme, optimal control problems for each model are formulated where the objective functions are different depending on the desired objective of the problem. The first-oder necessary optimality conditions for each problem are presented. Moreover for the numerical treatment, a sequence of open-loop discrete optimality systems is solved by accurate Runge-Kutta schemes, and in the optimization procedure, a nonlinear conjugate gradient solver is implemented. Finally, numerical experiments are performed to investigate the properties of the multi-agent models and demonstrate the ability of the proposed control strategies to drive multi-agent systems to attain a desired consensus and to track a given trajectory.
How insects promote crop pollination remains poorly understood in terms of the contribution of functional trait differences between species. We used meta-analyses to test for correlations between community abundance, species richness and functional trait metrics with oilseed rape yield, a globally important crop. While overall abundance is consistently important in predicting yield, functional divergence between species traits also showed a positive correlation. This result supports the complementarity hypothesis that pollination function is maintained by non-overlapping trait distributions. In artificially constructed communities (mesocosms), species richness is positively correlated with yield, although this effect is not seen under field conditions. As traits of the dominant species do not predict yield above that attributed to the effect of abundance alone, we find no evidence in support of the mass ratio hypothesis. Management practices increasing not just pollinator abundance, but also functional divergence, could benefit oilseed rape agriculture.
Background
Human African Trypanosomiasis (HAT) also called sleeping sickness is an infectious disease in humans caused by an extracellular protozoan parasite. The disease, if left untreated, results in 100% mortality. Currently available drugs are full of severe drawbacks and fail to escape the fast development of trypanosoma resistance. Due to similarities in cell metabolism between cancerous tumors and trypanosoma cells, some of the current registered drugs against HAT have also been tested in cancer chemotherapy. Here we demonstrate for the first time that the simple ester, ethyl pyruvate, comprises such properties.
Results
The current study covers the efficacy and corresponding target evaluation of ethyl pyruvate on T. brucei cell lines using a combination of biochemical techniques including cell proliferation assays, enzyme kinetics, phasecontrast microscopic video imaging and ex vivo toxicity tests. We have shown that ethyl pyruvate effectively kills trypanosomes most probably by net ATP depletion through inhibition of pyruvate kinase (Ki = 3.0\(\pm\)0.29 mM). The potential of ethyl pyruvate as a trypanocidal compound is also strengthened by its fast acting property, killing cells within three hours post exposure. This has been demonstrated using video imaging of live cells as well as concentration and time dependency experiments. Most importantly, ethyl pyruvate produces minimal side effects in human red cells and is known to easily cross the blood-brain-barrier. This makes it a promising candidate for effective treatment of the two clinical stages of sleeping sickness. Trypanosome drug-resistance tests indicate irreversible cell death and a low incidence of resistance development under experimental conditions.
Conclusion
Our results present ethyl pyruvate as a safe and fast acting trypanocidal compound and show that it inhibits the enzyme pyruvate kinase. Competitive inhibition of this enzyme was found to cause ATP depletion and cell death. Due to its ability to easily cross the blood-brain-barrier, ethyl pyruvate could be considered as new candidate agent to treat the hemo-lymphatic as well as neurological stages of sleeping sickness.
Aim of this thesis was to study the contribution of the hosts immune system during tumor regression. A wild-type rejection model was studied in which tumor regression is mediated through an adaptive, T cell host response (Research article 1). Additionally, the relationship between VACV infection and cancer rejection was assessed by applying organism-specific microarray platforms to infected and non-infected xenografts. It could be shown that tumor rejection in this nude mouse model was orchestrated solely by the hosts innate immune system without help of the adaptive immunity. In a third study the inflammatory baseline status of 75 human cancer cell lines was tested in vitro which was correlated with the susceptibility to VACV and Adenovirus 5 (Ad5) replication of the respective cell line (Manuscript for Research article 3). Although xenografts by themselves lack the ability to signal danger and do not provide sufficient proinflammatory signals to induce acute inflammation, the presence of viral replication in the oncolytic xenograft model provides the "tissue-specific trigger" that activates the immune response and in concordance with the hypothesis, the ICR is activated when chronic inflammation is switched into an acute one. Thus, in conditions in which a switch from a chronic to an acute inflammatory process can be induced by other factors like the immune-stimulation induced by the presence of a virus in the target tissue, adaptive immune responses may not be necessary and immune-mediated rejection can occur without the assistance of T or B cells. However, in the regression study using neu expressing MMC in absence of a stimulus such as a virus and infected cancer cells thereafter, adaptive immunity is needed to provoke the switch into an acute inflammation and initiate tissue rejection. Taken together, this work is supportive of the hypothesis that the mechanisms prompting TSD differ among immune pathologies but the effect phase converges and central molecules can be detected over and over every time TSD occurs. It could be shown that in presence of a trigger such as infection with VACV and functional danger signaling pathways of the infected tumor cells, innate immunity is sufficient to orchestrate rejection of manifested tumors.
The minimum energy path for the reaction O(\(^3\)P\(_g\)) + C\(_2\)H\(_4\)(\(^1\)A\(_g\)) has been calculated by optimizing all relevant geometrical parameters along the approach of oxygen to ethene. A barrier of 4.7 kcal/mol in the \(^3\)A"( ... 9a'\(^2\)- 10a'3a") potential energy surface and an energy difference of 14.4 kcal/mol between the product and the fragments is found at the multireference-configuration interaction level. The corresponding values at the lower-level treatment CASSCF are 9 kcal/mol for the barrier and 9 kcal/mol for the depth of the potential; this shows the importance of inclusion of electron correlation. The barrier for CH\(_2\) rotation for the lowestenergy structure (asymmetric OC\(_2\)H\(_4\)) is around 5 kcal/mol. The energy gap to the first excited state \(^3\)A'( ... 9a'l0a'3a'12) is found tobe 3.6 kcal/mol in MRD-CI calculations at the ground-state minimum. Comparison with \(^3\)CH\(_2\) + C\(_2\)H\(_4\) shows that in this system the lowest-energy surface is \(^3\)A', i.e., the state which is the excited state in 0 + C\(_2\)H\(_4\). This difference in energy ordering of \(^3\)A' and \(^3\)A" states results from the fact that the p\(_x\), p\(_y\), p\(_z\) degeneracy of oxygen orbitals is lifted in \(^3\)CH\(_2\)leading to b\(_1\), b\(_2\). and a\(_1\) MOs whereby the lowest b\(_2\) (a") remains doubly occupied; as a consequence, the reaction pattem between the oxygen and \(^3\)CH\(_2\) approach is different, which is also quite apparent in the calculated charge transfer.
Purpose
Machine learning based on radiomics features has seen huge success in a variety of clinical applications. However, the need for standardization and reproducibility has been increasingly recognized as a necessary step for future clinical translation. We developed a novel, intuitive open-source framework to facilitate all data analysis steps of a radiomics workflow in an easy and reproducible manner and evaluated it by reproducing classification results in eight available open-source datasets from different clinical entities.
Methods
The framework performs image preprocessing, feature extraction, feature selection, modeling, and model evaluation, and can automatically choose the optimal parameters for a given task. All analysis steps can be reproduced with a web application, which offers an interactive user interface and does not require programming skills. We evaluated our method in seven different clinical applications using eight public datasets: six datasets from the recently published WORC database, and two prostate MRI datasets—Prostate MRI and Ultrasound With Pathology and Coordinates of Tracked Biopsy (Prostate-UCLA) and PROSTATEx.
Results
In the analyzed datasets, AutoRadiomics successfully created and optimized models using radiomics features. For WORC datasets, we achieved AUCs ranging from 0.56 for lung melanoma metastases detection to 0.93 for liposarcoma detection and thereby managed to replicate the previously reported results. No significant overfitting between training and test sets was observed. For the prostate cancer detection task, results were better in the PROSTATEx dataset (AUC = 0.73 for prostate and 0.72 for lesion mask) than in the Prostate-UCLA dataset (AUC 0.61 for prostate and 0.65 for lesion mask), with external validation results varying from AUC = 0.51 to AUC = 0.77.
Conclusion
AutoRadiomics is a robust tool for radiomic studies, which can be used as a comprehensive solution, one of the analysis steps, or an exploratory tool. Its wide applicability was confirmed by the results obtained in the diverse analyzed datasets. The framework, as well as code for this analysis, are publicly available under https://github.com/pwoznicki/AutoRadiomics.
Objectives
Open-access cancer imaging datasets have become integral for evaluating novel AI approaches in radiology. However, their use in quantitative analysis with radiomics features presents unique challenges, such as incomplete documentation, low visibility, non-uniform data formats, data inhomogeneity, and complex preprocessing. These issues may cause problems with reproducibility and standardization in radiomics studies.
Methods
We systematically reviewed imaging datasets with public copyright licenses, published up to March 2023 across four large online cancer imaging archives. We included only datasets with tomographic images (CT, MRI, or PET), segmentations, and clinical annotations, specifically identifying those suitable for radiomics research. Reproducible preprocessing and feature extraction were performed for each dataset to enable their easy reuse.
Results
We discovered 29 datasets with corresponding segmentations and labels in the form of health outcomes, tumor pathology, staging, imaging-based scores, genetic markers, or repeated imaging. We compiled a repository encompassing 10,354 patients and 49,515 scans. Of the 29 datasets, 15 were licensed under Creative Commons licenses, allowing both non-commercial and commercial usage and redistribution, while others featured custom or restricted licenses. Studies spanned from the early 1990s to 2021, with the majority concluding after 2013. Seven different formats were used for the imaging data. Preprocessing and feature extraction were successfully performed for each dataset.
Conclusion
RadiomicsHub is a comprehensive public repository with radiomics features derived from a systematic review of public cancer imaging datasets. By converting all datasets to a standardized format and ensuring reproducible and traceable processing, RadiomicsHub addresses key reproducibility and standardization challenges in radiomics.
Critical relevance statement
This study critically addresses the challenges associated with locating, preprocessing, and extracting quantitative features from open-access datasets, to facilitate more robust and reliable evaluations of radiomics models.
Key points
- Through a systematic review, we identified 29 cancer imaging datasets suitable for radiomics research.
- A public repository with collection overview and radiomics features, encompassing 10,354 patients and 49,515 scans, was compiled.
- Most datasets can be shared, used, and built upon freely under a Creative Commons license.
- All 29 identified datasets have been converted into a common format to enable reproducible radiomics feature extraction.
(1) Background: To evaluate radiomics features as well as a combined model with clinical parameters for predicting overall survival in patients with bladder cancer (BCa). (2) Methods: This retrospective study included 301 BCa patients who received radical cystectomy (RC) and pelvic lymphadenectomy. Radiomics features were extracted from the regions of the primary tumor and pelvic lymph nodes as well as the peritumoral regions in preoperative CT scans. Cross-validation was performed in the training cohort, and a Cox regression model with an elastic net penalty was trained using radiomics features and clinical parameters. The models were evaluated with the time-dependent area under the ROC curve (AUC), Brier score and calibration curves. (3) Results: The median follow-up time was 56 months (95% CI: 48–74 months). In the follow-up period from 1 to 7 years after RC, radiomics models achieved comparable predictive performance to validated clinical parameters with an integrated AUC of 0.771 (95% CI: 0.657–0.869) compared to an integrated AUC of 0.761 (95% CI: 0.617–0.874) for the prediction of overall survival (p = 0.98). A combined clinical and radiomics model stratified patients into high-risk and low-risk groups with significantly different overall survival (p < 0.001). (4) Conclusions: Radiomics features based on preoperative CT scans have prognostic value in predicting overall survival before RC. Therefore, radiomics may guide early clinical decision-making.