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    A novel theanine complex, Mg-L-Theanine improves sleep quality via regulating brain electrochemical activity
    (Frontiers Media S.A., 2022) Daşdelen, Muhammed Furkan; Er, Sezgin; Kaplan, Berkan; Çelik, Süleyman; Beker, Mustafa Çağlar; Orhan, Cemal; Tuzcu, Mehmet; Şahin, Nurhan; Mamedova, Havakhanum; Sylla, Sarah; Komorowski, James; Ojalvo, Sara Perez
    L-Theanine is commonly used to improve sleep quality through inhibitory neurotransmitters. On the other hand, Mg2+, a natural NMDA antagonist and GABA agonist, has a critical role in sleep regulation. Using the caffeine-induced brain electrical activity model, here we investigated the potency of L-theanine and two novel Mg-L-theanine compounds with different magnesium concentrations on electrocorticography (ECoG) patterns, GABAergic and serotonergic receptor expressions, dopamine, serotonin, and melatonin levels. Furthermore, we evaluated the sleep latency and duration in the pentobarbital induced sleep model. We herein showed that L-theanine, particularly its various complexes with magnesium increases the expression of GABAergic, serotonergic, and glutamatergic receptors, which were associated with decreased ECoG frequency, increased amplitude, and enhanced delta wave powers. Besides increased dopamine, serotonin, and melatonin; decreased MDA and increased antioxidant enzyme levels were also observed particularly with Mg-complexes. Protein expression analyses also showed that Mg-L-theanine complexes decrease inducible nitric oxide synthase (iNOS) and endothelial nitric oxide synthase (eNOS) levels significantly. In accordance with these results, Mg complexes improved the sleep latency and duration even after caffeine administration. As a result, our data indicate that Mg-L-theanine compounds potentiate the effect of L-theanine on sleep by boosting slow-brain waves, regulating brain electrical activity, and increasing neurotransmitter and GABA receptor levels.
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    Ct2rep: automated radiology report generation for 3d medical imaging
    (2024) Hamamcı, İbrahim Ethem; Er, Sezgin; Menze, Bjoern
    Medical imaging plays a crucial role in diagnosis, with radiology reports serving as vital documentation. Automating report generation has emerged as a critical need to alleviate the workload of radiologists. While machine learning has facilitated report generation for 2D medical imaging, extending this to 3D has been unexplored due to computational complexity and data scarcity. We introduce the first method to generate radiology reports for 3D medical imaging, specifically targeting chest CT volumes. Given the absence of comparable methods, we establish a baseline using an advanced 3D vision encoder in medical imaging to demonstrate our method’s effectiveness, which leverages a novel auto-regressive causal transformer. Furthermore, recognizing the benefits of leveraging information from previous visits, we augment CT2Rep with a cross-attention-based multi-modal fusion module and hierarchical memory, enabling the incorporation of longitudinal multimodal data. Access our code at https://github.com/ibrahimethemhamamci/CT2Rep.
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    Diffusion-based hierarchical multi-label object detection to analyze panoramic dental X-rays
    (Springer Science and Business Media Deutschland GmbH, 2023) Hamamcı, İbrahim Ethem; Er, Sezgin; Simsar, Enis; Sekuboyina, Anjany; Gündoğar, Mustafa; Stadlinger, Bernd; Mehl, Albert; Menze, Bjoern
    Due to the necessity for precise treatment planning, the use of panoramic X-rays to identify different dental diseases has tremendously increased. Although numerous ML models have been developed for the interpretation of panoramic X-rays, there has not been an end-to-end model developed that can identify problematic teeth with dental enumeration and associated diagnoses at the same time. To develop such a model, we structure the three distinct types of annotated data hierarchically following the FDI system, the first labeled with only quadrant, the second labeled with quadrant-enumeration, and the third fully labeled with quadrant-enumeration-diagnosis. To learn from all three hierarchies jointly, we introduce a novel diffusion-based hierarchical multi-label object detection framework by adapting a diffusion-based method that formulates object detection as a denoising diffusion process from noisy boxes to object boxes. Specifically, to take advantage of the hierarchically annotated data, our method utilizes a novel noisy box manipulation technique by adapting the denoising process in the diffusion network with the inference from the previously trained model in hierarchical order. We also utilize a multi-label object detection method to learn efficiently from partial annotations and to give all the needed information about each abnormal tooth for treatment planning. Experimental results show that our method significantly outperforms state-of-the-art object detection methods, including RetinaNet, Faster R-CNN, DETR, and DiffusionDet for the analysis of panoramic X-rays, demonstrating the great potential of our method for hierarchically and partially annotated datasets. The code and the datasets are available at https://github.com/ibrahimethemhamamci/HierarchicalDet.
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    Effects of a novel magnesium-L-theanine compound on brain electrical activity in mice
    (Wiley, 2022) Daşdelen, Muhammed Furkan; Er, Sezgin; Çelik, Süleyman; Kaplan, Berkan; Komorowski, James; Şahin, Kazım; Kılıç, Ertuğrul
    [Abstract Not Available]
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    Generatect: text-conditional generation of 3d chest ct volumes
    (2025) Hamamcı, İbrahim Ethem; Er, Sezgin; Tezcan, Alperen; Şimşek, Ayşe Gülnihan; Esirgün, Şevval Nil; Almas, Furkan; Doğan, İrem; Daşdelen, Muhammed Furkan; Menze, Bjoern
    Text-conditional medical image generation is vital for radiology, augmenting small datasets, preserving data privacy, and enabling patient-specific modeling. However, its applications in 3D medical imaging, such as CT and MRI, which are crucial for critical care, remain unexplored. In this paper, we introduce GenerateCT, the first approach to generating 3D medical imaging conditioned on free-form medical text prompts. GenerateCT incorporates a text encoder and three key components: a novel causal vision transformer for encoding 3D CT volumes, a text-image transformer for aligning CT and text tokens, and a text-conditional super-resolution diffusion model. Without directly comparable methods in 3D medical imaging, we benchmarked GenerateCT against cutting-edge methods, demonstrating its superiority across all key metrics. Importantly, we explored GenerateCT’s clinical applications by evaluating its utility in a multi-abnormality classification task. First, we established a baseline by training a multi-abnormality classifier on our real dataset. To further assess the model’s generalization to external datasets and its performance with unseen prompts in a zero-shot scenario, we employed an external dataset to train the classifier, setting an additional benchmark. We conducted two experiments in which we doubled the training datasets by synthesizing an equal number of volumes for each set using GenerateCT. The first experiment demonstrated an 11% improvement in the AP score when training the classifier jointly on real and generated volumes. The second experiment showed a 7% improvement when training on both real and generated volumes based on unseen prompts. Moreover, GenerateCT enables the scaling of synthetic training datasets to arbitrary sizes. As an example, we generated 100,000 3D CT volumes, fivefold the number in our real dataset, and trained the classifier exclusively on these synthetic volumes. Impressively, this classifier surpassed the performance of the one trained on all available real data by a margin of 8%. Lastly, domain experts evaluated the generated volumes, confirming a high degree of alignment with the text prompts. Access our code, model weights, training data, and generated data at https://github.com/ibrahimethemhamamci/GenerateCT.
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    Social isolation initiated post-weaning augments ischemic brain injury by promoting pro-inflammatory responses
    (Academic Press Inc., 2024) Daşdelen, Muhammed Furkan; Çağlayan, Ahmet Burak; Er, Sezgin; Beker, Mustafa Çağlar; Ateş, Nilay; Gronewold, Janine; Doeppner, Thorsten Roland; Hermann, Dirk M.; Kılıç, Ertuğrul
    Social isolation is associated with poor stroke outcome, but the underlying molecular mechanisms were largely unknown. In male Balb/C mice exposed to transient middle cerebral artery occlusion (MCAo), we examined the effects of social isolation initiated post-weaning on ischemic injury, cytokine/chemokine responses and cell signaling using a broad panel of techniques that involved immunocytochemistry, cytokine/chemokine array and Western blots. Social isolation initiated post-weaning elevated infarct size, brain edema and neuronal injury in the ischemic brain tissue 3 days after MCAo, and increased microglia/ macrophage and leukocyte accumulation. In line with the increased immune cell recruitment, levels of several proinflammatory cytokines (e.g., IL-1?, IL-1?, IL-13, IL-17, TNF-?, IFN-?), chemokines (e.g., CCL3, CCL4, CCL12, CXCL2, CXCL9, CXCL12) and adhesion molecules (i.e., ICAM-1) were increased in the ischemic brain tissue of socially isolated compared with paired housing mice, whereas levels of selected cytokines (IL-5, IL-6, IL-16) and colony-stimulating factors (G-CSF, GM-CSF) were reduced. The activity of the transcription factor nuclear factor-?B (NF-?B), which promotes cell injury via pro-inflammatory responses, was increased by social isolation, whereas that of nuclear factor erythroid related factor-2 (Nrf-2), which mediates anti-oxidative responses under oxidative stress conditions, was reduced. Our study shows that social isolation profoundly alters post-ischemic cell signaling in a way promoting pro-inflammatory responses. Our results highlight the importance of social support in preventing deleterious health effects of social isolation.
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    Two phases of macrophages: Inducing maturation and death of oligodendrocytes in vitro co-culture
    (Elsevier B.V., 2022) Aydınlı, Fatmagül İlayda; Er, Sezgin; Kerman, Bilal Ersen
    Background: The plasticity of macrophages in the immune response is a dynamic situation dependent on external stimuli. The activation of macrophages both has beneficial and detrimental effects on mature oligodendrocytes (OLs) and myelin. The activation towards inflammatory macrophages has a critical role in the immune-mediated oligodendrocytes death in multiple sclerosis (MS) lesions. New method: We established an in vitro co-culture method to study the function of macrophages in the survival and maturation of OLs. Results: We revealed that M1 macrophages decreased the number of mature OLs and phagocytosed the myelin. Interestingly, non-activated as well as M2 macrophages contributed to an increase in the number of mature OLs in our in vitro co-culture platform. Comparison with existing methods: We added an antibody against an OL surface antigen in our in vitro co-cultures. The antibody presents the OLs to the macrophages enabling the investigation of direct interactions between macrophages and OLs. Conclusion: Our co-culture system is a feasible method for the investigation of the direct cell-to-cell interactions between OLs and macrophages. We utilized it to show that M2 and non-activated macrophages may be employed to enhance remyelination.