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- ItemFiber-based SORS-SERDS system and chemometrics for the diagnostics and therapy monitoring of psoriasis inflammatory disease in vivo(Washington, DC : Optica, 2021-1-28) Schleusener, Johannes; Guo, Shuxia; Darvin, Maxim E.; Thiede, Gisela; Chernavskaia, Olga; Knorr, Florian; Lademann, Jürgen; Popp, Jürgen; Bocklitz, Thomas W.Psoriasis is considered a widespread dermatological disease that can strongly affect the quality of life. Currently, the treatment is continued until the skin surface appears clinically healed. However, lesions appearing normal may contain modifications in deeper layers. To terminate the treatment too early can highly increase the risk of relapses. Therefore, techniques are needed for a better knowledge of the treatment process, especially to detect the lesion modifications in deeper layers. In this study, we developed a fiber-based SORS-SERDS system in combination with machine learning algorithms to non-invasively determine the treatment efficiency of psoriasis. The system was designed to acquire Raman spectra from three different depths into the skin, which provide rich information about the skin modifications in deeper layers. This way, it is expected to prevent the occurrence of relapses in case of a too short treatment. The method was verified with a study of 24 patients upon their two visits: the data is acquired at the beginning of a standard treatment (visit 1) and four months afterwards (visit 2). A mean sensitivity of ≥85% was achieved to distinguish psoriasis from normal skin at visit 1. At visit 2, where the patients were healed according to the clinical appearance, the mean sensitivity was ≈65%.
- ItemAssessing agreement between preclinical magnetic resonance imaging and histology: An evaluation of their image qualities and quantitative results(San Francisco, California, US : PLOS, 2017) Elschner, Cindy; Korn, Paula; Hauptstock, Maria; Schulz, Matthias C.; Range, Ursula; Jünger, Diana; Scheler, UlrichOne consequence of demographic change is the increasing demand for biocompatible materials for use in implants and prostheses. This is accompanied by a growing number of experimental animals because the interactions between new biomaterials and its host tissue have to be investigated. To evaluate novel materials and engineered tissues the use of nondestructive imaging modalities have been identified as a strategic priority. This provides the opportunity for studying interactions repeatedly with individual animals, along with the advantages of reduced biological variability and decreased number of laboratory animals. However, histological techniques are still the golden standard in preclinical biomaterial research. The present article demonstrates a detailed method comparison between histology and magnetic resonance imaging. This includes the presentation of their image qualities as well as the detailed statistical analysis for assessing agreement between quantitative measures. Exemplarily, the bony ingrowth of tissue engineered bone substitutes for treatment of a cleft-like maxillary bone defect has been evaluated. By using a graphical concordance analysis the mean difference between MRI results and histomorphometrical measures has been examined. The analysis revealed a slightly but significant bias in the case of the bone volume ðbiasHisto MRI: Bonevolume = 2: 40 %, p < 0: 005) and a clearly significant deviation for the remaining defect width ðbiasHisto MRI: Defectwidth = 6: 73 %, p 0: 005Þ: But the study although showed a considerable effect of the analyzed section position to the quantitative result. It could be proven, that the bias of the data sets was less originated due to the imaging modalities, but mainly on the evaluation of different slice positions. The article demonstrated that method comparisons not always need the use of an independent animal study, additionally.
- ItemEvolutionary design of explainable algorithms for biomedical image segmentation([London] : Nature Publishing Group UK, 2023) Cortacero, Kévin; McKenzie, Brienne; Müller, Sabina; Khazen, Roxana; Lafouresse, Fanny; Corsaut, Gaëlle; Van Acker, Nathalie; Frenois, François-Xavier; Lamant, Laurence; Meyer, Nicolas; Vergier, Béatrice; Wilson, Dennis G.; Luga, Hervé; Staufer, Oskar; Dustin, Michael L.; Valitutti, Salvatore; Cussat-Blanc, SylvainAn unresolved issue in contemporary biomedicine is the overwhelming number and diversity of complex images that require annotation, analysis and interpretation. Recent advances in Deep Learning have revolutionized the field of computer vision, creating algorithms that compete with human experts in image segmentation tasks. However, these frameworks require large human-annotated datasets for training and the resulting “black box” models are difficult to interpret. In this study, we introduce Kartezio, a modular Cartesian Genetic Programming-based computational strategy that generates fully transparent and easily interpretable image processing pipelines by iteratively assembling and parameterizing computer vision functions. The pipelines thus generated exhibit comparable precision to state-of-the-art Deep Learning approaches on instance segmentation tasks, while requiring drastically smaller training datasets. This Few-Shot Learning method confers tremendous flexibility, speed, and functionality to this approach. We then deploy Kartezio to solve a series of semantic and instance segmentation problems, and demonstrate its utility across diverse images ranging from multiplexed tissue histopathology images to high resolution microscopy images. While the flexibility, robustness and practical utility of Kartezio make this fully explicable evolutionary designer a potential game-changer in the field of biomedical image processing, Kartezio remains complementary and potentially auxiliary to mainstream Deep Learning approaches.
- ItemA new human adipocyte model with PTEN haploinsufficiency(Abingdon : Taylor and Francis Inc., 2020) Kässner F.; Kirstein A.; Händel N.; Schmid G.L.; Landgraf K.; Berthold A.; Tannert A.; Schaefer M.; Wabitsch M.; Kiess W.; Körner A.; Garten A.Few human cell strains are suitable and readily available as in vitro adipocyte models. We used resected lipoma tissue from a patient with germline phosphatase and tensin homolog (PTEN) haploinsufficiency to establish a preadipocyte cell strain termed LipPD1 and aimed to characterize cellular functions and signalling pathway alterations in comparison to the established adipocyte model Simpson-Golabi-Behmel-Syndrome (SGBS) and to primary stromal-vascular fraction cells. We found that both cellular life span and the capacity for adipocyte differentiation as well as adipocyte-specific functions were preserved in LipPD1 and comparable to SGBS adipocytes. Basal and growth factor-stimulated activation of the PI3 K/AKT signalling pathway was increased in LipPD1 preadipocytes, corresponding to reduced PTEN levels in comparison to SGBS cells. Altogether, LipPD1 cells are a novel primary cell model with a defined genetic lesion suitable for the study of adipocyte biology. © 2020, © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
- ItemComputational tissue staining of non-linear multimodal imaging using supervised and unsupervised deep learning(Washington, DC : OSA, 2021) Pradhan, Pranita; Meyer, Tobias; Vieth, Michael; Stallmach, Andreas; Waldner, Maximilian; Schmitt, Michael; Popp, Juergen; Bocklitz, ThomasHematoxylin and Eosin (H&E) staining is the 'gold-standard' method in histopathology. However, standard H&E staining of high-quality tissue sections requires long sample preparation times including sample embedding, which restricts its application for 'real-time' disease diagnosis. Due to this reason, a label-free alternative technique like non-linear multimodal (NLM) imaging, which is the combination of three non-linear optical modalities including coherent anti-Stokes Raman scattering, two-photon excitation fluorescence and second-harmonic generation, is proposed in this work. To correlate the information of the NLM images with H&E images, this work proposes computational staining of NLM images using deep learning models in a supervised and an unsupervised approach. In the supervised and the unsupervised approach, conditional generative adversarial networks (CGANs) and cycle conditional generative adversarial networks (cycle CGANs) are used, respectively. Both CGAN and cycle CGAN models generate pseudo H&E images, which are quantitatively analyzed based on mean squared error, structure similarity index and color shading similarity index. The mean of the three metrics calculated for the computationally generated H&E images indicate significant performance. Thus, utilizing CGAN and cycle CGAN models for computational staining is beneficial for diagnostic applications without performing a laboratory-based staining procedure. To the author's best knowledge, it is the first time that NLM images are computationally stained to H&E images using GANs in an unsupervised manner.
- ItemTissue Tolerable Plasma (TTP) induces apoptosis in pancreatic cancer cells in vitro and in vivo(London : BioMed Central, 2012) Partecke, L.I.; Evert, K.; Haugk, J.; Doering, F.; Normann, L.; Diedrich, S.; Weiss, F.-U.; Evert, M.; Huebner, N.O.; Guenther, C.; Heidecke, C.D.; Kramer, A.; Bussiahn, R.; Weltmann, K.-D.; Pati, O.; Bender, C.; von Bernstorff, W.Background: The rate of microscopic incomplete resections of gastrointestinal cancers including pancreatic cancer has not changed considerably over the past years. Future intra-operative applications of tissue tolerable plasmas (TTP) could help to address this problem. Plasma is generated by feeding energy, like electrical discharges, to gases. The development of non-thermal atmospheric plasmas displaying spectra of temperature within or just above physiological ranges allows biological or medical applications of plasmas.Methods: We have investigated the effects of tissue tolerable plasmas (TTP) on the human pancreatic cancer cell line Colo-357 and PaTu8988T and the murine cell line 6606PDA in vitro (Annexin-V-FITC/DAPI-Assay and propidium iodide DNA staining assay) as well as in the in vivo tumour chorio-allantoic membrane (TUM-CAM) assay using Colo-357.Results: TTP of 20 seconds (s) induced a mild elevation of an experimental surface temperature of 23.7 degree Celsius up to 26.63+/-0.40 degree Celsius. In vitro TTP significantly (p=0.0003) decreased cell viability showing the strongest effects after 20s TTP. Also, TTP effects increased over time levelling off after 72 hours (30.1+/-4.4% of dead cells (untreated control) versus 78.0+/-9.6% (20s TTP)). However, analyzing these cells for apoptosis 10s TTP revealed the largest proportion of apoptotic cells (34.8+/-7.2%, p=0.0009 versus 12.3+/-6.6%, 20s TTP) suggesting non-apoptotic cell death in the majority of cells after 20s TTP. Using solid Colo-357 tumours in the TUM-CAM model TUNEL-staining showed TTP-induced apoptosis up to a depth of tissue penetration (DETiP) of 48.8+/-12.3μm (20s TTP, p<0.0001). This was mirrored by a significant (p<0.0001) reduction of Ki-67+ proliferating cells (80.9+/-13.2% versus 37.7+/-14.6%, p<0.0001) in the top cell layers as well as typical changes on HE specimens. The bottom cell layers were not affected by TTP.Conclusions: Our data suggest possible future intra-operative applications of TTP to reduce microscopic residual disease in pancreatic cancer resections. Further promising applications include other malignancies (central liver/lung tumours) as well as synergistic effects combining TTP with chemotherapies. Yet, adaptations of plasma sources as well as of the composition of effective components of TTP are required to optimize their synergistic apoptotic actions.