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  1. Book: Dear D[octo]r Watson

    Späte, Helmut / Patzer, Christine

    Leitfaden für Wissenschaftler zum Verfassen von Briefen in englischer Sprache

    (Wissenschaftliche Beiträge der Friedrich-Schiller-Universität Jena)

    1989  

    Author's details erarb. von Helmut Späte und Christine Patzer
    Series title Wissenschaftliche Beiträge der Friedrich-Schiller-Universität Jena
    Size XII, 117 S
    Publishing place Jena
    Document type Book
    Database Former special subject collection: coastal and deep sea fishing

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  2. Article: ResNetFed: Federated Deep Learning Architecture for Privacy-Preserving Pneumonia Detection from COVID-19 Chest Radiographs.

    Riedel, Pascal / von Schwerin, Reinhold / Schaudt, Daniel / Hafner, Alexander / Späte, Christian

    Journal of healthcare informatics research

    2023  Volume 7, Issue 2, Page(s) 203–224

    Abstract: Personal health data is subject to privacy regulations, making it challenging to apply centralized data-driven methods in healthcare, where personalized training data is frequently used. Federated Learning (FL) promises to provide a decentralized ... ...

    Abstract Personal health data is subject to privacy regulations, making it challenging to apply centralized data-driven methods in healthcare, where personalized training data is frequently used. Federated Learning (FL) promises to provide a decentralized solution to this problem. In FL, siloed data is used for the model training to ensure data privacy. In this paper, we investigate the viability of the federated approach using the detection of COVID-19 pneumonia as a use case. 1411 individual chest radiographs, sourced from the public data repository COVIDx8 are used. The dataset contains radiographs of 753 normal lung findings and 658 COVID-19 related pneumonias. We partition the data unevenly across five separate data silos in order to reflect a typical FL scenario. For the binary image classification analysis of these radiographs, we propose
    Language English
    Publishing date 2023-06-14
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2895595-X
    ISSN 2509-498X ; 2509-4971
    ISSN (online) 2509-498X
    ISSN 2509-4971
    DOI 10.1007/s41666-023-00132-7
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  3. Article ; Online: Suicide signaling by GSDMA

    Judit Symmank / Collin Jacobs / Ulrike Schulze-Späte

    Signal Transduction and Targeted Therapy, Vol 7, Iss 1, Pp 1-

    a single-molecule mechanism for recognition and defense against SpeB-expressing GAS

    2022  Volume 2

    Keywords Medicine ; R ; Biology (General) ; QH301-705.5
    Language English
    Publishing date 2022-05-01T00:00:00Z
    Publisher Nature Publishing Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article: A Critical Assessment of Generative Models for Synthetic Data Augmentation on Limited Pneumonia X-ray Data.

    Schaudt, Daniel / Späte, Christian / von Schwerin, Reinhold / Reichert, Manfred / von Schwerin, Marianne / Beer, Meinrad / Kloth, Christopher

    Bioengineering (Basel, Switzerland)

    2023  Volume 10, Issue 12

    Abstract: In medical imaging, deep learning models serve as invaluable tools for expediting diagnoses and aiding specialized medical professionals in making clinical decisions. However, effectively training deep learning models typically necessitates substantial ... ...

    Abstract In medical imaging, deep learning models serve as invaluable tools for expediting diagnoses and aiding specialized medical professionals in making clinical decisions. However, effectively training deep learning models typically necessitates substantial quantities of high-quality data, a resource often lacking in numerous medical imaging scenarios. One way to overcome this deficiency is to artificially generate such images. Therefore, in this comparative study we train five generative models to artificially increase the amount of available data in such a scenario. This synthetic data approach is evaluated on a a downstream classification task, predicting four causes for pneumonia as well as healthy cases on 1082 chest X-ray images. Quantitative and medical assessments show that a Generative Adversarial Network (GAN)-based approach significantly outperforms more recent diffusion-based approaches on this limited dataset with better image quality and pathological plausibility. We show that better image quality surprisingly does not translate to improved classification performance by evaluating five different classification models and varying the amount of additional training data. Class-specific metrics like precision, recall, and F1-score show a substantial improvement by using synthetic images, emphasizing the data rebalancing effect of less frequent classes. However, overall performance does not improve for most models and configurations, except for a DreamBooth approach which shows a +0.52 improvement in overall accuracy. The large variance of performance impact in this study suggests a careful consideration of utilizing generative models for limited data scenarios, especially with an unexpected negative correlation between image quality and downstream classification improvement.
    Language English
    Publishing date 2023-12-14
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2746191-9
    ISSN 2306-5354
    ISSN 2306-5354
    DOI 10.3390/bioengineering10121421
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  5. Article ; Online: Leveraging human expert image annotations to improve pneumonia differentiation through human knowledge distillation.

    Schaudt, Daniel / von Schwerin, Reinhold / Hafner, Alexander / Riedel, Pascal / Späte, Christian / Reichert, Manfred / Hinteregger, Andreas / Beer, Meinrad / Kloth, Christopher

    Scientific reports

    2023  Volume 13, Issue 1, Page(s) 9203

    Abstract: In medical imaging, deep learning models can be a critical tool to shorten time-to-diagnosis and support specialized medical staff in clinical decision making. The successful training of deep learning models usually requires large amounts of quality data, ...

    Abstract In medical imaging, deep learning models can be a critical tool to shorten time-to-diagnosis and support specialized medical staff in clinical decision making. The successful training of deep learning models usually requires large amounts of quality data, which are often not available in many medical imaging tasks. In this work we train a deep learning model on university hospital chest X-ray data, containing 1082 images. The data was reviewed, differentiated into 4 causes for pneumonia, and annotated by an expert radiologist. To successfully train a model on this small amount of complex image data, we propose a special knowledge distillation process, which we call Human Knowledge Distillation. This process enables deep learning models to utilize annotated regions in the images during the training process. This form of guidance by a human expert improves model convergence and performance. We evaluate the proposed process on our study data for multiple types of models, all of which show improved results. The best model of this study, called PneuKnowNet, shows an improvement of + 2.3% points in overall accuracy compared to a baseline model and also leads to more meaningful decision regions. Utilizing this implicit data quality-quantity trade-off can be a promising approach for many scarce data domains beyond medical imaging.
    MeSH term(s) Humans ; Deep Learning ; Data Curation ; Pneumonia/diagnostic imaging ; Diagnostic Imaging
    Language English
    Publishing date 2023-06-06
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-023-36148-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Book ; Conference proceedings: Beiträge zu sozialen Problemen depressiver Erkrankungen

    Späte, Helmut F.

    [Arbeitstagung, 15. Juni 1988]

    (Wissenschaftliche Beiträge / Martin-Luther-Universität Halle-Wittenberg : R ; 112 ; Wissenschaftliche Beiträge / Martin-Luther-Universität Halle-Wittenberg ; 1989,2 : R ; 112)

    1989  

    Title variant Die andere Seite der Depression
    Institution Klinik und Poliklinik für Psychiatrie und Neurologie / Forschungsgruppe Psychiatrie
    Author's details [Forschungsgruppe Psychiatrie der Nervenklinik der MLU Halle-Wittenberg ...]. Hrsg. von Helmut F. Späte
    Series title Wissenschaftliche Beiträge / Martin-Luther-Universität Halle-Wittenberg : R ; 112
    Wissenschaftliche Beiträge / Martin-Luther-Universität Halle-Wittenberg ; 1989,2 : R ; 112
    Wissenschaftliche Beiträge / Martin-Luther-Universität, Halle-Wittenberg
    Wissenschaftliche Beiträge / Martin-Luther-Universität Halle-Wittenberg ; R
    Collection Wissenschaftliche Beiträge / Martin-Luther-Universität, Halle-Wittenberg
    Wissenschaftliche Beiträge / Martin-Luther-Universität Halle-Wittenberg ; R
    Keywords Depression
    Subject Depressionen
    Language German
    Size 84 S.
    Publishing place Halle (Saale)
    Publishing country XA-DDDE
    Document type Book ; Conference proceedings
    HBZ-ID HT003650306
    ISBN 3-86010-108-0 ; 978-3-86010-108-7
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

    Shelf markLoan statusLocation
    2009 A 11116 order for loan Magazin

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  7. Article ; Online: Leveraging human expert image annotations to improve pneumonia differentiation through human knowledge distillation

    Daniel Schaudt / Reinhold von Schwerin / Alexander Hafner / Pascal Riedel / Christian Späte / Manfred Reichert / Andreas Hinteregger / Meinrad Beer / Christopher Kloth

    Scientific Reports, Vol 13, Iss 1, Pp 1-

    2023  Volume 13

    Abstract: Abstract In medical imaging, deep learning models can be a critical tool to shorten time-to-diagnosis and support specialized medical staff in clinical decision making. The successful training of deep learning models usually requires large amounts of ... ...

    Abstract Abstract In medical imaging, deep learning models can be a critical tool to shorten time-to-diagnosis and support specialized medical staff in clinical decision making. The successful training of deep learning models usually requires large amounts of quality data, which are often not available in many medical imaging tasks. In this work we train a deep learning model on university hospital chest X-ray data, containing 1082 images. The data was reviewed, differentiated into 4 causes for pneumonia, and annotated by an expert radiologist. To successfully train a model on this small amount of complex image data, we propose a special knowledge distillation process, which we call Human Knowledge Distillation. This process enables deep learning models to utilize annotated regions in the images during the training process. This form of guidance by a human expert improves model convergence and performance. We evaluate the proposed process on our study data for multiple types of models, all of which show improved results. The best model of this study, called PneuKnowNet, shows an improvement of + 2.3% points in overall accuracy compared to a baseline model and also leads to more meaningful decision regions. Utilizing this implicit data quality-quantity trade-off can be a promising approach for many scarce data domains beyond medical imaging.
    Keywords Medicine ; R ; Science ; Q
    Subject code 006
    Language English
    Publishing date 2023-06-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Triple Orthogonal Labeling of Glycans by Applying Photoclick Chemistry.

    Schart, Verena F / Hassenrück, Jessica / Späte, Anne-Katrin / Dold, Jeremias E G A / Fahrner, Raphael / Wittmann, Valentin

    Chembiochem : a European journal of chemical biology

    2018  Volume 20, Issue 2, Page(s) 166–171

    Abstract: Bioorthogonal labeling of multiple biomolecules is of current interest in chemical biology. Metabolic glycoengineering (MGE) has been shown to be an appropriate approach to visualizing carbohydrates. Here, we report that the nitrile imine-alkene ... ...

    Abstract Bioorthogonal labeling of multiple biomolecules is of current interest in chemical biology. Metabolic glycoengineering (MGE) has been shown to be an appropriate approach to visualizing carbohydrates. Here, we report that the nitrile imine-alkene cycloaddition (photoclick reaction) is a suitable ligation reaction in MGE. Using a mannosamine derivative with an acrylamide reporter group that is efficiently metabolized by cells and that quickly reacts in the photoclick reaction, we labeled sialic acids on the surface of living cells. Screening of several alkenes showed that a previously reported carbamate-linked methylcyclopropene reporter that is well suited for the inverse-electron-demand Diels-Alder (DA
    MeSH term(s) Alkenes/chemistry ; Click Chemistry ; Cycloaddition Reaction ; HEK293 Cells ; Humans ; Imines/chemistry ; Molecular Structure ; Nitriles/chemistry ; Photochemical Processes ; Polysaccharides/chemistry
    Chemical Substances Alkenes ; Imines ; Nitriles ; Polysaccharides
    Language English
    Publishing date 2018-12-21
    Publishing country Germany
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2020469-3
    ISSN 1439-7633 ; 1439-4227
    ISSN (online) 1439-7633
    ISSN 1439-4227
    DOI 10.1002/cbic.201800740
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  9. Article ; Online: Systemic vitamin D supplementation and local bone formation after maxillary sinus augmentation - a randomized, double-blind, placebo-controlled clinical investigation.

    Schulze-Späte, Ulrike / Dietrich, Thomas / Wu, Christina / Wang, Kun / Hasturk, Hatice / Dibart, Serge

    Clinical oral implants research

    2016  Volume 27, Issue 6, Page(s) 701–706

    Abstract: ... when serum vitamin D levels were improved (r = 0.92, P ≤ 0.05).: Conclusions: Vitamin D3+ calcium ...

    Abstract Objectives: Maxillary sinus augmentation procedures with bone replacement grafts aimed to increase bone height in the posterior maxilla. During healing, bone particles are partially resorbed and replaced by the patient's own bone. Vitamin D plays an essential role in calcium homeostasis and is critical for bone formation and remodeling.
    Materials and methods: This randomized, double-blind, placebo-controlled clinical investigation studied whether oral supplementation with vitamin D3 (5000 IU) combined with calcium (600 mg) impacts bone formation and remodeling after maxillary sinus augmentation compared to a placebo medication containing calcium alone (n = 10/group). Bone cores were harvested at the time of implant placement (6-8 months) for histological analysis.
    Results: Serum 25-hydroxyvitamin D (25-OHD) levels were comparable between both groups at the baseline (P = nonsignificant [n.s.]). Vitamin D3+ calcium supplementation improved significantly serum 25-OHD levels (placebo vs. vitamin D3 group: 25-OHD ng/ml: 31.13 ± 7.06 vs. 61.11 ± 20.42, P ≤ 0.01); however, no statistically significant difference in bone formation or graft resorption was detected between groups. However, in the vitamin D3 group, a significant association was found between increased vitamin D levels and number of bone-resorbing osteoclasts around graft particles suggesting that local bone remodeling might be more pronounced when serum vitamin D levels were improved (r = 0.92, P ≤ 0.05).
    Conclusions: Vitamin D3+ calcium supplementation improves serum vitamin D levels and potentially impacts local bone remodeling on a cellular level. However, no statistically significant difference in bone formation or graft resorption was detected between groups.
    MeSH term(s) Bone Density ; Bone Remodeling/drug effects ; Calcium/administration & dosage ; Cholecalciferol/administration & dosage ; Double-Blind Method ; Female ; Humans ; Male ; Middle Aged ; Osteogenesis/drug effects ; Sinus Floor Augmentation ; Vitamin D/blood
    Chemical Substances Vitamin D (1406-16-2) ; Cholecalciferol (1C6V77QF41) ; Calcium (SY7Q814VUP)
    Language English
    Publishing date 2016-06
    Publishing country Denmark
    Document type Journal Article ; Randomized Controlled Trial
    ZDB-ID 1067626-0
    ISSN 1600-0501 ; 0905-7161
    ISSN (online) 1600-0501
    ISSN 0905-7161
    DOI 10.1111/clr.12641
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 3171: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  10. Article ; Online: Conservation and divergence of myelin proteome and oligodendrocyte transcriptome profiles between humans and mice

    Vasiliki-Ilya Gargareta / Josefine Reuschenbach / Sophie B Siems / Ting Sun / Lars Piepkorn / Carolina Mangana / Erik Späte / Sandra Goebbels / Inge Huitinga / Wiebke Möbius / Klaus-Armin Nave / Olaf Jahn / Hauke B Werner

    eLife, Vol

    2022  Volume 11

    Abstract: Human myelin disorders are commonly studied in mouse models. Since both clades evolutionarily diverged approximately 85 million years ago, it is critical to know to what extent the myelin protein composition has remained similar. Here, we use ... ...

    Abstract Human myelin disorders are commonly studied in mouse models. Since both clades evolutionarily diverged approximately 85 million years ago, it is critical to know to what extent the myelin protein composition has remained similar. Here, we use quantitative proteomics to analyze myelin purified from human white matter and find that the relative abundance of the structural myelin proteins PLP, MBP, CNP, and SEPTIN8 correlates well with that in C57Bl/6N mice. Conversely, multiple other proteins were identified exclusively or predominantly in human or mouse myelin. This is exemplified by peripheral myelin protein 2 (PMP2), which was specific to human central nervous system myelin, while tetraspanin-2 (TSPAN2) and connexin-29 (CX29/GJC3) were confined to mouse myelin. Assessing published scRNA-seq-datasets, human and mouse oligodendrocytes display well-correlating transcriptome profiles but divergent expression of distinct genes, including Pmp2, Tspan2, and Gjc3. A searchable web interface is accessible via www.mpinat.mpg.de/myelin. Species-dependent diversity of oligodendroglial mRNA expression and myelin protein composition can be informative when translating from mouse models to humans.
    Keywords Oligodendrocyte ; myelin sheath ; axon-glia interaction ; label-free proteomics ; scRNA-seq ; cross-species comparison ; Medicine ; R ; Science ; Q ; Biology (General) ; QH301-705.5
    Subject code 572
    Language English
    Publishing date 2022-05-01T00:00:00Z
    Publisher eLife Sciences Publications Ltd
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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