Several reports have concluded that enzymatic debridement based on Bromelain (NX) is selective and efficient. Although clinical trials showed that viable tissue is not damaged at the macroscopic level, the effect on the cellular level is largely unknown. The current study is meant to close this gap by evaluating whether NX has an effect on vital cells of the human dermis on a cellular level. In an experimental in vitro study design, the effect of NX on human keratinocytes, fibroblasts, and macrophages was analyzed. Enzymatic treatment was performed for 4 hours by using either cell culture medium or phosphate-buffered saline as diluting agent for NX. Cell viability and relative cell number in relation to untreated control cells were determined using a resazurin-based assay. In addition, the development of enzyme activity during clinical treatment was analyzed: wound fluid collected from a burn wound at different points of debridement was applied on collagen-elastin disks to prove enzymatic digestion activity. Both keratinocytes and fibroblasts were damaged by NX even at low concentrations. Both cell types showed improved survival when a medium was used for dissolving NX. Macrophages appeared to resist NX treatment more efficiently than the other cell types. In the clinical trial, NX activity in the wound fluid decreased clearly following 4 hours of enzymatic debridement. NX induces toxicity of vital skin cells in vitro. However, macrophages appear to be more resistant against NX treatment in vitro. The inflammatory responses of vital cells in the burn wound itself are likely to inhibit NX activity. The effect of this inflammatory process on NX activity will have to be investigated in future studies.

Background Fast and accurate diagnostics are key for personalised medicine. Particularly in cancer, precise diagnosis is a prerequisite for targeted therapies, which can prolong lives. In this work, we focus on the automatic identification of gastroesophageal adenocarcinoma (GEA) patients that qualify for a personalised therapy targeting epidermal growth factor receptor 2 (HER2). We present a deep-learning method for scoring microscopy images of GEA for the presence of HER2 overexpression. Methods Our method is based on convolutional neural networks (CNNs) trained on a rich dataset of 1602 patient samples and tested on an independent set of 307 patient samples. We additionally verified the CNN’s generalisation capabilities with an independent dataset with 653 samples from a separate clinical centre. We incorporated an attention mechanism in the network architecture to identify the tissue regions, which are important for the prediction outcome. Our solution allows for direct automated detection of HER2 in immunohistochemistry-stained tissue slides without the need for manual assessment and additional costly in situ hybridisation (ISH) tests. Results We show accuracy of 0.94, precision of 0.97, and recall of 0.95. Importantly, our approach offers accurate predictions in cases that pathologists cannot resolve and that require additional ISH testing. We confirmed our findings in an independent dataset collected in a different clinical centre. The attention-based CNN exploits morphological information in microscopy images and is superior to a predictive model based on the staining intensity only. Conclusions We demonstrate that our approach not only automates an important diagnostic process for GEA patients but also paves the way for the discovery of new morphological features that were previously unknown for GEA pathology.

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BackgroundFast and accurate diagnostics are key for personalized medicine. Particularly in cancer, precise diagnosis is a prerequisite for targeted therapies which can prolong lives. In this work we focus on the automatic identification of gastroesophageal adenocarcinoma (GEA) patients that qualify for a personalized therapy targeting epidermal growth factor receptor 2 (HER2). We present a deep learning method for scoring microscopy images of GEA for the presence of HER2 overexpression.MethodsOur method is based on convolutional neural networks (CNNs) trained on a rich dataset of 1,602 patient samples and tested on an independent set of 307 patient samples. We incorporated an attention mechanism in the CNN architecture to identify the tissue regions in these patient cases which the network has detected as important for the prediction outcome. Our solution allows for direct automated detection of HER2 in immunohistochemistry-stained tissue slides without the need for manual assessment and additional costly in situ hybridization (ISH) tests.ResultsWe show accuracy of 0.94, precision of 0.97, and recall of 0.95. Importantly, our approach offers accurate predictions in cases that pathologists cannot resolve, requiring additional ISH testing. We confirmed our findings in an independent dataset collected in a different clinical center.ConclusionsWe demonstrate that our approach not only automates an important diagnostic process for GEA patients but also paves the way for the discovery of new morphological features that were previously unknown for GEA pathology.