The Anthelmintic Quassinoids Ailanthone and Bruceine a Induce Infertility in the Model Organism Caenorhabditis elegans by an Apoptosis-like Mechanism Induced in Gonadal and Spermathecal Tissues

In continuation of the search for new anthelmintic natural products, the study at hand investigated the nematicidal effects of the two naturally occurring quassinoids ailanthone and bruceine A against the reproductive system of the model nematode Caenorhabditis elegans to pinpoint their anthelmintic mode of action by the application of various microscopic techniques. Differential Interference Contrast (DIC) and the epifluorescence microscopy experiments used in the presented study indicated the genotoxic effects of the tested quassinoids (c ailanthone = 50 µM, c bruceine A = 100 µM) against the nuclei of the investigated gonadal and spermathecal tissues, leaving other morphological key features such as enterocytes or body wall muscle cells unimpaired. In order to gain nanoscopic insight into the morphology of the gonads as well as the considerably smaller spermathecae of C. elegans, an innovative protocol of polyethylene glycol embedding, ultra-sectioning, acridine orange staining, tissue identification by epifluorescence, and subsequent AFM-based ultrastructural data acquisition was applied. This sequence allowed the facile and fast assessment of the impact of quassinoid treatment not only on the gonadal but also on the considerably smaller spermathecal tissues of C. elegans. These first-time ultrastructural investigations on C. elegans gonads and spermathecae by AFM led to the identification of specific quassinoid-induced alterations to the nuclei of the reproductive tissues (e.g., highly condensed chromatin, impaired nuclear membrane morphology, as well as altered nucleolus morphology), altogether implying an apoptosis-like effect of ailanthone and bruceine A on the reproductive tissues of C. elegans.

Jointly Optimized Spatial Histogram UNET Architecture (JOSHUA) for Adipose Tissue Segmentation

Objective: We quantify adipose tissue deposition at surgical sites as a function of biomaterial implantation. Impact Statement: To our knowledge, this study is the first investigation to apply convolutional neural network (CNN) models to identify and segment adipose tissue in histological images from silk fibroin biomaterial implants. Introduction: When designing biomaterials for the treatment of various soft tissue injuries and diseases, one must consider the extent of adipose tissue deposition. In this work, we implant silk fibroin biomaterials in a rodent subcutaneous injury model. Current strategies for quantifying adipose tissue after biomaterial implantation are often tedious and prone to human bias during analysis. Methods: We used CNN models with novel spatial histogram layer(s) that can more accurately identify and segment regions of adipose tissue in hematoxylin and eosin (H&E) and Masson's Trichrome stained images, allowing for determination of the optimal biomaterial formulation. We compared the method, Jointly Optimized Spatial Histogram UNET Architecture (JOSHUA), to the baseline UNET model and an extension of the baseline model, Attention UNET, as well as to versions of the models with a supplemental "attention"-inspired mechanism (JOSHUA+ and UNET+). Results: The inclusion of histogram layer(s) in our models shows improved performance through qualitative and quantitative evaluation. Conclusion: Our results demonstrate that the proposed methods, JOSHUA and JOSHUA+, are highly beneficial for adipose tissue identification and localization. The new histological dataset and code for our experiments are publicly available.

Dynamic Infrared Thermography (DIRT) in Biomedical Applications: DIEP Flap Breast Reconstruction and Skin Cancer

Infrared thermography technology has improved drastically in recent years and is regaining interest in medicine for applications such as deep inferior epigastric perforate flap breast reconstruction, breast cancer diagnosis, skin tissue identification, psoriasis detection, etc. However, there is still a need for an optimised measurement setup and protocol in order to capture the most suitable images for decision making and further processing. Nowadays, different cooling methods are being used; nevertheless, a general optimised cooling protocol is not yet defined. In this manuscript, several cooling techniques, as well as the measurement setups, are reviewed and optimised. It is possible to enhance the thermal images by selecting an appropriate cooling method and duration, and additionally, an optimised measurement setup enables a comparison between different inspections.

PATH-43. RAMAN SPECTROSCOPY AS A TOOL IN NEUROSURGERY

BACKGROUND Raman Spectra have been shown to be sufficiently characteristic to their samples of origin that they can be used in a wide range of applications including distinction of intracranial tumors. While not replacing pathological analysis, the advantage of non-destructive sample analysis and extremely fast feedback make this technique an interesting tool for surgical use. METHODS We sampled intractanial tumors from more than 300 patients at the Centre Hospitalier Luxembourg over a period of three years and compared the spectra of different tumor entities, different tumor subregions and healthy surrounding tissue. We created machine-learning based classifiers that include tissue identification as well as diagnostics. RESULTS To this end, we solved several classes in the intracranial tumor classification, and developed classifiers to distinguish primary central nervous system lymphoma from glioblastoma, which is an important differential diagnosis, as well as meningioma from the surrounding healthy dura mater for identification of tumor tissue. Within glioblastoma, we resolve necrotic, vital tumor tissue and peritumoral infiltration zone.We are currently developing a multi-class classifier incorporating all tissue types measured. CONCLUSIONS Raman Spectroscopy has the potential to aid the surgeon in the surgery theater by providing a quick assessment of the tissue analyzed with regards to both tumor identity and tumor margin identification. Once a reliable classifier based on sufficient patient samples is developed, this may even be integrated into a surgical microscope or a neuronavigation system.

An interactive system for muscle and fat tissue identification of the lumbar spine using semantic segmentation

We introduce a system that allows the immediate identification and inspection of fat and muscle structures around the lumbar spine as a means of orthopaedic diagnostics before surgical treatment. The system comprises a backend component that accepts MRI data from a web-based interactive frontend as REST requests. The MRI data is passed through a U-net model, fine-tuned on lumbar MRI images, to generate segmentation masks of fat and muscle areas. The result is sent back to the frontend that functions as an inspection tool. For the model training, 4000 MRI images from 108 patients were used in a k-fold cross-validation study with k = 10. The model training was performed over 25-30 epochs. We applied shift, scale, and rotation operations as well as elastic deformation and distortion functions for image augmentation and a combined objective function using Dice and Focal loss. The trained models reached a mean dice score of 0.83 and 0.52 and a mean area error tissue of 0.1 and 0.3 for muscle and fat tissue, respectively. The interactive webbased frontend as an inspection tool was evaluated by clinicians to be suitable for the exploration of patient data as well as the assessment of segmentation results. We developed a system that uses semantic segmentation to identify fat and muscle tissue areas in MRI images of the lumbar spine. Further improvements should focus on the segmentation accuracy of fat tissue, as it is a determining factor in surgical decisionmaking. To our knowledge, this is the first system that automatically provides semantic information of the respective lumbar tissues.

A non-toxic, reversibly released imaging probe for oral cancer that is derived from natural compounds

CD44 is emerging as an important receptor biomarker for various cancers. Amongst these is oral cancer, where surgical resection remains an essential mode of treatment. Unfortunately, surgery is frequently associated with permanent disfigurement, malnutrition, and functional comorbidities due to the difficultly of tumour removal. Optical imaging agents that can guide tumour tissue identification represent an attractive approach to minimising the impact of surgery. Here, we report the synthesis of a water-soluble fluorescent probe, namely HA-FA-HEG-OE (compound 1), that comprises components originating from natural sources: oleic acid, ferulic acid and hyaluronic acid. Compound 1 was found to be non-toxic, displayed aggregation induced emission and accumulated intracellularly in vesicles in SCC-9 oral squamous cells. The uptake of 1 was fully reversible over time. Internalization of compound 1 occurs through receptor mediated endocytosis; uniquely mediated through the CD44 receptor. Uptake is related to tumorigenic potential, with non-tumorigenic, dysplastic DOK cells and poorly tumorigenic MCF-7 cells showing only low intracellular levels and highlighting the critical role of endocytosis in cancer progression and metastasis. Together, the recognised importance of CD44 as a cancer stem cell marker in oral cancer, and the reversible, non-toxic nature of 1, makes it a promising agent for real time intraoperative imaging.