The rise of multidrug-resistant microorganisms primarily driven by the misuse and overprescription of antibiotics, is recognized as one of the top ten global health challenges. The identification of novel antimicrobial agents for the development of new drugs has been challenging and natural plant-based bioactive compounds may represent as alternative therapeutic options. Although the antibacterial properties of Allium ursinum (wild garlic) are well documented, no in vivo studies utilizing Caenorhabditis elegans to screen phytochemicals with antimicrobial activity have been conducted. This study aimed to investigate the antimicrobial effects of local wild garlic on C. elegans infected with Pseudomonas aeruginosa. Antimicrobial activity was evaluated through comprehensive analyses of worm morphology, reproductive capacity, and overall fitness. Additionally, the molecular mechanisms underlying these effects were examined at the mRNA and protein levels. Pretreatment of nematodes with wild garlic extract prior to P. aeruginosa infection led to a marked increase in worm length and a significant increase in the number of progeny. Additionally, co-incubation with the extract enhanced the fitness of the worms. Notably, wild garlic treatment significantly changed the expression of key genes associated with bacterial defense, immune responses, and oxidative stress in nematodes infected with P. aeruginosa, as determined by gene expression analyses and fluorescence microscopy. In conclusion, wild garlic exerted beneficial effects on worm size, progeny number, fitness and regulated the expression of key genes in the model organism C. elegans, demonstrating its potential as a natural alternative to synthetic antibiotics.

The recently developed high-throughput system for cell spheroid generation (SpheroWell) is a promising technology for cost- and time-efficient in vitro analysis of, for example, chondrogenic differentiation. It is a compartmental growth surface where spheroids develop from a cell monolayer by self-assembling and aggregation. In order to automatize the analysis of spheroids, we aimed to develop imaging software and improve the localization of cell compartments and fully formed spheroids. Our workflow provides automated detection and localization of spheroids in different formation stages within Petri dishes based on images created with a low-budget camera imaging setup. This automated detection enables a fast and inexpensive analysis workflow by processing a stack of images within a short period of time, which is essential for the extraction of early readout parameters. Our workflow combines image processing algorithms and deep learning-based image localization/segmentation methods like Mask R-CNN and Unet++. These methods are refined by an evolution strategy for automated grid detection, which is able to improve the overall segmentation and classification quality. Besides the already pre-trained neural networks and predefined image processing parameters, our evolution-based post-processing provides the required adaptability for our workflow to deliver a consistent and reproducible quality. This is especially important due to the use of a low-budget imaging setup with various light conditions. The to-be-detected objects of the three different stages show improved results using our evolutionary post-processing for monolayer and starting aggregation with Dice coefficients of 0.7301 and 0.8562, respectively, compared with the raw scores of 0.2879 and 0.8187. The Dice coefficient of the fully formed spheroids in both cases is 0.8829. With our algorithm, we provide automated analyses of cell spheroid by self-assembling in SpheroWell dishes, even if the images are created using a low-budget camera setup.

The outcomes of clinical studies are strongly influenced by their preceding preclinical studies. Due to higher physiological relevance, 3D spheroid arrays can potentially improve drug screening outcomes in preclinical studies. Analytical accessibility is still limited. To further increase usability and simplify subsequent analyses of spheroid arrays, the authors present an automated method for analyzing spheroid viability. The developed easy-to-use workflow provides viability analysis using fluorescence images of cell aggregates. It allows the automated analyses in an early development stage of microarrays with a low amount of available experiment data. The proposed workflow provides an accurate spheroid extraction (with a segmentation accuracy evaluated by Dice score with a score of 0.89) which is compared to the performance of the workflow with the segmentation of a Unet. By reducing human intervention in the analysis task, information extraction and the evaluation process are simplified leading to an overall shorter analysis time, while objectivity and comparability are increased.