BackgroundThe major birch pollen allergen Bet v 1 primarily affects the nose and eyes of allergic patients. After crossing the epithelial barrier, it causes IgE-mediated effector cell release, triggering inflammatory responses. We sought to develop bispecific nanobodies that bind Bet v 1 on mucosal surfaces to prevent allergen penetration and subsequent activation of effector cells. Intercellular adhesion molecule 1 (ICAM-1), highly upregulated on epithelial cells of allergic individuals, was chosen as an anchor.MethodsGenetically fused nanobodies targeting Bet v 1 and ICAM-1 were generated and characterized for specificity, affinity, Bet v 1 immobilization on the surface of the human bronchial epithelial cell line, 16HBE14o-, and their potential to inhibit Bet v 1 penetration through a cell monolayer.ResultsBispecific nanobodies recognized ICAM-1, Bet v 1 and related tree pollen allergens (Aln g 1, Cor a 1). While we observed a clear co-localization of nanobodies and Bet v 1 on the 16HBE14o-cell surface after 30 min, Bet v 1 was slightly internalized after 4h at 32 °C. Dissociation rate constants between bispecific nanobodies and antigens were 3–6 times faster at 32 °C compared to 25 °C. Bispecific nanobodies reduced Bet v 1 penetration and hence decreased basophil activation.ConclusionWe generated bivalent nanobodies specific for ICAM-1 and Bet v 1. These nanobodies diminished trans-epithelial transport of Bet v 1 by ICAM-1-mediated immobilization at the cell surface. Reduced Bet v 1 penetration resulted in decreased basophil activation, demonstrating the potential of local administration of nanobodies for the prevention of pollen allergy.

Preeclampsia (PE) is a complex disease with unclear etiology. It is the most dangerous human pregnancy disease, causing morbidity and mortality in thousands of women and newborns worldwide. The soluble fms-like tyrosine kinase-1 (sFlt-1) to placental growth factor (PlGF) ratio is currently the best and only predictive biomarker. The higher the ratio, the more likely the pregnant women will develop PE. The molecular mechanism underlying the increased sFlt-1/PlGF ratio is not known. Here, we show that amino acid transporter LAT1 (SLC7A5) and transcription factor NRF2 regulate this ratio via a previously unknown mechanism to produce sFlt-1 and PlGF in an anti-angiogenic ratio as observed in PE. In addition, we show that PE-associated oxidative stress, whose origin was unknown, is a secondary phenomenon caused by reduced NRF2 and LAT1 activity. The interdependence of the involved proteins, including also ATF4, Flt-1 and Akt, indicates that any disruption of the interaction would ultimately lead to a PE-like phenotype.

Advanced glycation end products (AGEs) and reactive intermediates, such as methylglyoxal, are formed during thermal processing of foods and have been implicated in the pathogenesis of a series of chronic inflammatory diseases. AGEs are thought to directly interact with the intestinal epithelium upon ingestion of thermally processed foods, but their effects on intestinal epithelial cells are poorly understood. This study investigated transcriptomic changes in human intestinal epithelial FHs 74 Int cells after exposure to AGE-modified human serum proteins (AGE-HS), S100A12, a known RAGE ligand, and unmodified human serum proteins (HS). In contrast to previous studies employing cancer cell lines, RNA sequencing of FHs 74 Int cells treated with AGE-HS did not reveal transcriptional changes associated with increased proliferation, increased expression of tight junction proteins or proinflammatory responses. Surprisingly, neither AGE-HS nor S100A12 treatments resulted in significant differential gene expression at standard analysis thresholds, while unmodified HS induced minor transcriptional changes. Gene set enrichment analysis revealed that AGE-HS treatment induced downregulation of gene sets linked to MYC, interferon responses, and oxidative phosphorylation, as well as pathways related to neurodegenerative diseases such as Alzheimer's, Parkinson's, and Huntington's disease, paralleling some effects observed with S100A12. This is the first global transcriptomic analysis of FHs 74 Int cells and the first unbiased investigation of signaling pathway alterations in intestinal epithelial cells exposed to AGEs. In contrast to previous studies, this analysis did not reveal any significantly differentially expressed genes, thus challenging previous reports of robust AGE-induced inflammatory and proliferative effects and emphasizing the importance of an isolated experimental setting and rigorous endotoxin testing.

In computational pathology, automatic nuclei instance segmentation plays an essential role in whole slide image analysis. While many computerized approaches have been proposed for this task, supervised deep learning (DL) methods have shown superior segmentation performances compared to classical machine learning and image processing techniques. However, these models need fully annotated datasets for training which is challenging to acquire, especially in the medical domain. In this work, we release one of the biggest fully manually annotated datasets of nuclei in Hematoxylin and Eosin (H&E)-stained histological images, called NuInsSeg. This dataset contains 665 image patches with more than 30,000 manually segmented nuclei from 31 human and mouse organs. Moreover, for the first time, we provide additional ambiguous area masks for the entire dataset. These vague areas represent the parts of the images where precise and deterministic manual annotations are impossible, even for human experts. The dataset and detailed step-by-step instructions to generate related segmentation masks are publicly available on the respective repositories.

Microbial biofilm formation creates a persistent and resistant environment in which microorganisms can survive, contributing to antibiotic resistance and chronic inflammatory diseases. Increasingly, biofilms are caused by multi-drug resistant microorganisms, which, coupled with a diminishing supply of effective antibiotics, is driving the search for new antibiotic therapies. In this respect, antimicrobial peptides (AMPs) are short, hydrophobic, and amphipathic peptides that show activity against multidrug-resistant bacteria and biofilm formation. They also possess broad-spectrum activity and diverse mechanisms of action. In this comprehensive review, 150 publications (from January 2020 to September 2023) were collected and categorized using the search terms ‘polypeptide antibiotic agent’, ‘antimicrobial peptide’, and ‘biofilm’. During this period, a wide range of natural and synthetic AMPs were studied, of which LL-37, polymyxin B, GH12, and Nisin were the most frequently cited. Furthermore, although many microbes were studied, Staphylococcus aureus and Pseudomonas aeruginosa were the most popular. Publications also considered AMP combinations and the potential role of AMP delivery systems in increasing the efficacy of AMPs, including nanoparticle delivery. Relatively few publications focused on AMP resistance. This comprehensive review informs and guides researchers about the latest developments in AMP research, presenting promising evidence of the role of AMPs as effective antimicrobial agents.

Wound care professionals provide proper diagnosis and treatment with heavy reliance on images and image documentation. Segmentation of wound boundaries in images is a key component of the care and diagnosis protocol since it is important to estimate the area of the wound and provide quantitative measurement for the treatment. Unfortunately, this process is very time-consuming and requires a high level of expertise, hence the need for automatic wound measurement methods. Recently, automatic wound segmentation methods based on deep learning have shown promising performance; yet, they heavily rely on large training datasets. A few wound image datasets were published including the Diabetic Foot Ulcer Challenge dataset, the Medetec wound dataset, and WoundDB. Existing public wound image datasets suffer from small size and a lack of annotation. There is a need to build a fully annotated dataset to benchmark wound segmentation methods. To address these issues, we propose the Foot Ulcer Segmentation Challenge (FUSeg), organized in conjunction with the 2021 International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). It contains 1210 pixel-wise annotated foot ulcer images collected over 2 years from 889 patients. The submitted algorithms are reviewed in this paper and the dataset can be accessed through the Foot Ulcer Segmentation Challenge website.

With the advent of digital pathology and microscopic systems that can scan and save whole slide histological images automatically, there is a growing trend to use computerized methods to analyze acquired images. Among different histopathological image analysis tasks, nuclei instance segmentation plays a fundamental role in a wide range of clinical and research applications. While many semi- and fully-automatic computerized methods have been proposed for nuclei instance segmentation, deep learning (DL)-based approaches have been shown to deliver the best performances. However, the performance of such approaches usually degrades when tested on unseen datasets. In this work, we propose a novel method to improve the generalization capability of a DL-based automatic segmentation approach. Besides utilizing one of the state-of-the-art DL-based models as a baseline, our method incorporates non-deterministic train time and deterministic test time stain normalization, and ensembling to boost the segmentation performance. We trained the model with one single training set and evaluated its segmentation performance on seven test datasets. Our results show that the proposed method provides up to 4.9%, 5.4%, and 5.9% better average performance in segmenting nuclei based on Dice score, aggregated Jaccard index, and panoptic quality score, respectively, compared to the baseline segmentation model.

The receptor for advanced glycation end products (RAGE) is encoded by AGER, a gene that is subjected to tissue-specific alternative splicing. Splice variants of RAGE in intestine and placenta are unknown and contradictory data concerning RAGE protein expression in these tissues have been published. As a basis for future functional studies, we examined RAGE expression in small intestine, colon and placentas. PCR cloning revealed that full-length RAGE is the only RAGE transcript isoform expressed in placenta. In the small intestine, the major transcript isoform detected was RAGE_v1 encoding the C-terminally truncated soluble receptor. In the colon, both full-length RAGE as well as several splice variants were identified. Four antibodies were used to study protein expression by immunoblotting and were carefully validated. Appropriate controls were essential to avoid misinterpretation of bands caused by non-specific reactivity of antibodies. Only one of four antibodies tested detected full-length RAGE in placenta, whereas no RAGE-specific band was detected in intestinal tissues despite loading >30-fold more intestinal tissue than the positive control, human lung. RAGE expression levels in the placenta were 100-fold lower compared with human lung when analyzed by ELISA, and no significant differences in RAGE expression were detected between healthy placentas and placentas from women with preeclampsia, gestational diabetes mellitus, or fetal growth restriction. We conclude that healthy placental chorionic tissue expresses low levels of full-length RAGE, whereas expression of the tissue-specific intestinal isoforms is below the limit of detection. Low RAGE expression levels in combination with a lack of antibody validation may explain the conflicting published results on RAGE protein expression in intestine and placenta.

Allergy and rhinovirus (RV) infections are major triggers for rhinitis and asthma, causing a socioeconomic burden. As RVs and allergens may act synergistically to promote airway inflammation, simultaneous treatment strategies for both causative agents would be innovative. We have previously identified the transmembrane glycoprotein intercellular adhesion molecule 1 (ICAM-1) as an anchor for antibody conjugates bispecific for ICAM-1 and Phleum pratense (Phl p) 2, a major grass pollen allergen, to block allergen transmigration through the epithelial barrier. Since ICAM-1 is a receptor for the major group RVs, we speculated that our bispecific antibody conjugates may protect against RV infection. Therefore, we created antibody conjugates bispecific for ICAM-1 and the major grass pollen allergen Phl p 5 and analyzed their capacity to affect allergen penetration and RV infection. Bispecific antibody conjugates significantly reduced the trans-epithelial migration of Phl p 5 and thus the basolateral Phl p 5 concentration and allergenic activity as determined by humanized rat basophilic leukemia cells and inhibited RV infection of cultured epithelial cells. A reduction in allergenic activity was obtained only through the prevention of allergen transmigration because the Phl p 5-specific IgG antibody did not block the allergen–IgE interaction. Our results indicate the potential of allergen/ICAM-1-specific antibody conjugates as a topical treatment strategy for allergy and RV infections.

Background: Peanut-allergic individuals react upon their first known ingestion of peanuts, suggesting sensitization occurs through non-oral exposure. Increasing evidence suggests that the respiratory tract is a probable site for sensitization to environmental peanuts. However, the response of the bronchial epithelium to peanut allergens has never been explored. Furthermore, food matrix-derived lipids play an important role in allergic sensitization.Objective: To contribute to a better understanding of the mechanisms of allergic sensitization to peanuts via inhalation, by exploring the direct effect of the major peanut allergens Ara h 1 and Ara h 2 and peanut lipids on bronchial epithelial cells.Methods: Polarized monolayers of the bronchial epithelial cell line 16HBE14o- were stimulated apically with peanut allergens and/or peanut lipids (PNL). Barrier integrity, transport of allergens across the monolayers, and release of mediators were monitored.Results: Ara h 1 and Ara h 2 impacted the barrier integrity of the 16HBE14o- bronchial epithelial cells and crossed the epithelial barrier. Ara h 1 also induced the release of pro-inflammatory mediators. PNL improved the barrier function of the cell monolayers, decreased paracellular permeability and reduced the amount of allergens crossing the epithelial layer.Conclusion: Our study provides evidence of the transport of Ara h 1 and Ara h 2 across the airway epithelium, of the induction of a pro-inflammatory milieu, and identifies an important role for PNL in controlling the amount of allergens that can cross the epithelial barrier. These, all together, contribute to a better understanding of the effects of peanuts exposure on the respiratory tract.

Summary In view of the recent revival of interest in circadian biology and circadian epidemiology at the Medical University of Vienna, it seems appropriate to highlight the rich and pioneering history of circadian research in Austria. Among the forefathers of circadian research in Vienna are Otto Marburg (1874–1948), who discovered important elements of the pineal gland physiology, Robert Hofstätter (1883–1970), who used pineal gland extract in obstetrics/gynecology, and Paul Engel (1907–1997), who discovered that the pineal gland was controlled by light. More recently, Vera Lapin (1920–2007) showed that surgical removal of the pineal gland increased tumor growth, while Franz Waldhauser (*1946) investigated melatonin in conjunction with night work. Michael Kundi (*1950) and his team conducted among the first studies demonstrating differences in rhythms of night workers and early evidence for health impairments among them. Furthermore, Vienna-born Erhard Haus (1926–2013) pioneered the discovery of the role and importance of melatonin in relation to numerous diseases. This rich pioneering contribution of scientists in Vienna or with roots in Vienna is continued today by a new generation of chronobiologists, epidemiologists and clinicians in Vienna whose new insights contribute to the rapidly developing field of circadian rhythms research. Current topics and contributions relate to the impact of circadian rhythm disruption on health, and the application of chronotherapeutic approaches in clinical and preventive settings.

Even in the era of precision medicine, with various molecular tests based on omics technologies available to improve the diagnosis process, microscopic analysis of images derived from stained tissue sections remains crucial for diagnostic and treatment decisions. Among other cellular features, both nuclei number and shape provide essential diagnostic information. With the advent of digital pathology and emerging computerized methods to analyze the digitized images, nuclei detection, their instance segmentation and classification can be performed automatically. These computerized methods support human experts and allow for faster and more objective image analysis. While methods ranging from conventional image processing techniques to machine learning-based algorithms have been proposed, supervised convolutional neural network (CNN)-based techniques have delivered the best results. In this paper, we propose a CNN-based dual decoder U-Net-based model to perform nuclei instance segmentation in hematoxylin and eosin (H&E)-stained histological images. While the encoder path of the model is developed to perform standard feature extraction, the two decoder heads are designed to predict the foreground and distance maps of all nuclei. The outputs of the two decoder branches are then merged through a watershed algorithm, followed by post-processing refinements to generate the final instance segmentation results. Moreover, to additionally perform nuclei classification, we develop an independent U-Net-based model to classify the nuclei predicted by the dual decoder model. When applied to three publicly available datasets, our method achieves excellent segmentation performance, leading to average panoptic quality values of 50.8%, 51.3%, and 62.1% for the CryoNuSeg, NuInsSeg, and MoNuSAC datasets, respectively. Moreover, our model is the top-ranked method in the MoNuSAC post-challenge leaderboard.

Recently, pruning deep neural networks (DNNs) has received a lot of attention for improving accuracy and generalization power, reducing network size, and increasing inference speed on specialized hardwares. Although pruning was mainly tested on computer vision tasks, its application in the context of medical image analysis has hardly been explored. This work investigates the impact of well-known pruning techniques, namely layer-wise and network-wide magnitude pruning, on the nuclei instance segmentation performance in histological images. Our utilised instance segmentation model consists of two main branches: (1) a semantic segmentation branch, and (2) a deep regression branch. We investigate the impact of weight pruning on the performance of both branches separately, and on the final nuclei instance segmentation result. Evaluated on two publicly available datasets, our results show that layer-wise pruning delivers slightly better performance than network-wide pruning for small compression ratios (CRs) while for large CRs, network-wide pruning yields superior performance. For semantic segmentation, deep regression and final instance segmentation, 93.75%, 95%, and 80% of the model weights can be pruned by layer-wise pruning with less than 2% reduction in the performance of respective models.

Foot ulcer is a common complication of diabetes mellitus and, associated with substantial morbidity and mortality, remains a major risk factor for lower leg amputations. Extracting accurate morphological features from foot wounds is crucial for appropriate treatment. Although visual inspection by a medical professional is the common approach for diagnosis, this is subjective and error-prone, and computer-aided approaches thus provide an interesting alternative. Deep learning-based methods, and in particular convolutional neural networks (CNNs), have shown excellent performance for various tasks in medical image analysis including medical image segmentation. In this paper, we propose an ensemble approach based on two encoder-decoder-based CNN models, namely LinkNet and U-Net, to perform foot ulcer segmentation. To deal with a limited number of available training samples, we use pre-trained weights (EfficientNetB1 for the LinkNet model and EfficientNetB2 for the U-Net model) and perform further pre-training using the Medetec dataset while also applying a number of morphological-based and colour-based augmentation techniques. To boost the segmentation performance, we incorporate five-fold cross-validation, test time augmentation and result fusion. Applied on the publicly available chronic wound dataset and the MICCAI 2021 Foot Ulcer Segmentation (FUSeg) Challenge, our method achieves state-of-the-art performance with data-based Dice scores of 92.07% and 88.80%, respectively, and is the top ranked method in the FUSeg challenge leaderboard. The Dockerised guidelines, inference codes and saved trained models are publicly available at https://github.com/masih4/Foot_Ulcer_Segmentation.