Biology subjects

Buelow, R. D.

Publications and source records attributed to Buelow, R. D..

5 recordsLinked to original sources

bioRxiv2026

Hypothermic and Normothermic Machine Perfusion Differentially Shape the Renal Cellular Landscape

The shortage of donor organs has led to an increased use of extended-criteria donors and donation after circulatory death for kidney transplantation, both of which are associated with graft injury. Although machine perfusion (MP) offers a preservation strategy, the most beneficial approach for kidneys with respect to effective graft preservation remains unclear. We compared oxygenated hypothermic (HMP) and normothermic machine perfusion (NMP) using a murine kidney perfusion model regarding intra-graft immune cell composition, viability, and inflammatory responses. NMP resulted in more severe tubular injury, accompanied by increased immune cell mobilization out of the graft. NMP increased early apoptosis among kidney-resident immune-cell populations, whereas endothelial cells showed increased late apoptosis after both strategies. Proximal tubular-cell apoptosis remained comparable to naive tissue, and neither modality reduced reactive oxygen species levels. Both protocols induced antioxidant and hypoxia-responsive genes compared with static cold storage, while NMP elicited a stronger inflammatory response. Treatment with the senolytic ABT-263 demonstrated the models feasibility for investigating renal injury, cellular viability, and immune activation during MP.

immunology↗

bioRxiv2026

A mixture of plant polyphenols unexpectedly aggravates liver metastasis of colorectal cancer in mice

Epidemiological studies suggest that vegetarian diets are associated with lower cancer incidence and mortality, an effect attributed in part to phytochemicals such as polyphenols and carotenoids. Although numerous in vitro experiments and investigations using immunodeficient rodent models report tumor-suppressive activities of phytochemicals, their impact on tumor progression in immunocompetent hosts remains insufficiently understood. Here, we examined the influence of a defined plant phytochemical mixture (PPM) on the growth of colon cancer liver metastases, both in vitro and in immunocompetent mice. Consistent with the prevailing literature, treatment of the murine colon cancer cell line MC38 with the PPM significantly reduced cell proliferation and survival in vitro. Strikingly, however, administration of the PPM to mice bearing MC38-derived hepatic metastases markedly accelerated tumor growth. Immunohistochemical analyses revealed a significantly increased accumulation of immune cells--specifically CD45 leukocytes and F4/80 macrophages--at the periphery of the metastatic lesions in PPM-treated animals. To assess the functional relevance of this inflammatory response, the PPM was combined with the anti-inflammatory drug prednisolone. This intervention resulted in significantly reduced metastatic burden, supporting the notion that the PPM exacerbates tumor progression through enhanced peritumoral inflammation. These findings highlight the importance of validating observations from cell culture and immunodeficient models in fully immunocompetent systems. They further emphasize that the immunomodulatory effects of plant phytochemicals warrant careful and comprehensive investigation.

cancer biology↗

bioRxiv2024

Rewired type I IFN signaling is linked to age-dependent differences in COVID-19

Advanced age is the most important risk factor for severe disease or death from COVID-19, but a thorough mechanistic understanding of the molecular and cellular underpinnings is lacking. Multi-omics analysis of samples from SARS-CoV-2 infected persons aged 1 to 84 years, revealed a rewiring of type I interferon (IFN) signaling with a gradual shift from signal transducer and activator of transcription 1 (STAT1) to STAT3 activation in monocytes, CD4+ T cells and B cells with increasing age. Diversion of interferon IFN signaling was associated with increased expression of inflammatory markers, enhanced release of inflammatory cytokines, and delayed contraction of infection-induced CD4+ T cells. A shift from IFN-responsive germinal center B (GCB) cells towards CD69high GCB and atypical B cells corresponded to the formation of IgA in children while complement fixing IgG was dominant in adults. Our data provide a mechanistic basis for inflammation-prone responses to infections and associated pathology during aging. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=144 SRC="FIGDIR/small/619479v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@723896org.highwire.dtl.DTLVardef@e12b2aorg.highwire.dtl.DTLVardef@d8de22org.highwire.dtl.DTLVardef@1df96ec_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2022

Adversarial attacks and adversarial robustness in computational pathology

Artificial Intelligence (AI) can support diagnostic workflows in oncology by aiding diagnosis and providing biomarkers. AI applications are therefore expected to evolve from academic prototypes to commercial products in the coming years. However, AI applications are vulnerable to adversarial attacks, such as malicious interference with test data aiming to cause misclassifications. Therefore, it is essential for the use of AI-based diagnostic devices to secure them against such attacks before widespread use. Unfortunately, no resistant systems exist in computational pathology so far. To address this problem, we investigate the susceptibility of convolutional neural networks (CNNs) to multiple types of white- and black-box attacks. We demonstrate that both attacks can easily confuse CNNs in clinically relevant pathology tasks and impair classification performance. Classical adversarially robust training and dual batch normalization (DBN) are possible mitigation strategies but require precise knowledge of the type of attack used in the inference. We demonstrate that vision transformers (ViTs) perform equally well compared to CNNs at baseline and are orders of magnitude more robust to different types of white-box and black-box attacks. At a mechanistic level, we show that this is associated with a more robust latent representation of clinically relevant categories in ViTs compared to CNNs. Our results are in line with previous theoretical studies. We show that ViTs are robust learners in computational pathology. This implies that large-scale rollout of AI models in computational pathology should rely on ViTs rather than CNN-based classifiers to provide inherent protection against adversaries.

bioinformatics↗

bioRxiv2021

Benchmarking artificial intelligence methods for end-to-end computational pathology

Artificial intelligence (AI) can extract subtle visual information from digitized histopathology slides and yield scientific insight on genotype-phenotype interactions as well as clinically actionable recommendations. Classical weakly supervised pipelines use an end-to-end approach with residual neural networks (ResNets), modern convolutional neural networks such as EfficientNet, or non-convolutional architectures such as vision transformers (ViT). In addition, multiple-instance learning (MIL) and clustering-constrained attention MIL (CLAM) are being used for pathology image analysis. However, it is unclear how these different approaches perform relative to each other. Here, we implement and systematically compare all five methods in six clinically relevant end-to-end prediction tasks using data from N=4848 patients with rigorous external validation. We show that histological tumor subtyping of renal cell carcinoma is an easy task which approaches successfully solved with an area under the receiver operating curve (AUROC) of above 0.9 without any significant differences between approaches. In contrast, we report significant performance differences for mutation prediction in colorectal, gastric and bladder cancer. Weakly supervised ResNet-and ViT-based workflows significantly outperformed other methods, in particular MIL and CLAM for mutation prediction. As a reason for this higher performance we identify the ability of ResNet and ViT to assign high prediction scores to highly informative image regions with plausible histopathological image features. We make all source codes publicly available at https://github.com/KatherLab/HIA, allowing easy application of all methods on any end-to-end problem in computational pathology.

bioinformatics↗