Obesity is a complex disease of global epidemic proportions. Adipose tissue expansion and chronic low-grade inflammation, locally and systemically, are hallmark features of obesity. Obesity is associated with several other chronic diseases, which are also characterized by inflammation. Determination of adipocyte size and macrophage content in adipose tissue is a critical step in assessing changes in this tissue with obesity. Here, we introduce a complete standalone software package, AdipoGauge, to analyse microscopic images derived from haematoxylin and eosin (H&E)-stained and immunofluorescently stained histology sections of adipose tissue. The software package is a user-friendly application that does not require a vast knowledge of computer science or costly commercial tools. AdipoGauge includes analysing tools that are capable of cell counting and colour separation. Furthermore, it can quantify the cell data in images both with and without clear boundaries around the cells. It can also remove objects from the image that are not intended for analysis, such as blood vessels or partial cells at edges of slide sections. The simple and state-of-the-art graphical user interface requires minimal time and learning.

Worldwide rates of Western-diet-induced obesity epidemics are growing dramatically. Being linked with numerous comorbidities and complications, including cardiovascular disease, type 2 diabetes, cancer, chronic inflammation, and osteoarthritis (OA), obesity represents one of the most threatening challenges for modern healthcare. Mouse models are an invaluable tool for investigating the effects of diets and their bioactive components against high fat diet (HFD)-induced obesity and its comorbidities. During recent years, very high fat diets (VHFDs), providing 58–60% kcal fat, have become a popular alternative to more traditional HFDs, providing 40–45% total kcal fat, due to the faster induction of obesity and stronger metabolic responses. This project aims to investigate if the 60% fat VHFD is suitable to evaluate the protective effects of curcumin in diet-induced obesity and osteoarthritis. B6 male mice, prone to diet-induced metabolic dysfunction, were supplemented with VHFD without or with curcumin for 13 weeks. Under these experimental conditions, feeding mice a VHFD for 13 weeks did not result in expected robust manifestations of the targeted pathophysiologic conditions. Supplementing the diet with curcumin, in turn, protected the animals against obesity without significant changes in white adipocyte size, glucose clearance, and knee cartilage integrity. Additional research is needed to optimize diet composition, curcumin dosage, and duration of dietary interventions to establish the VHFD-induced obesity for evaluating the effects of curcumin on metabolic dysfunctions related to obesity and osteoarthritis.

Detection of tumor-infiltrating lymphocytes (TILs) in cancer images has gained significant importance as these lymphocytes can be used as a biomarker in cancer detection and treatment procedures. Our goal was to develop and apply a TILs detection tool that utilizes deep learning models, following two sequential steps. First, based on the guidelines from the International Immuno-Oncology Biomarker Working Group (IIOBWG) on Breast Cancer, we labeled 63 large pathology imaging slides and annotated the TILs in the stroma area to create the dataset required for model development. In the second step, various machine learning models were employed and trained to detect the stroma where U-Net deep learning structure was able to achieve 98% accuracy. After detecting the stroma area, a Mask R-CNN model was employed for the TILs detection task. The R-CNN model detected the TILs in various images and was used as the backbone analysis network for the GUI development of the TILs detection tool. This is the first study to combine two deep learning models for TILs detection at the cellular level in breast tumor histopathology slides. Our novel approach can be applied to scoring TILs in large cancer slides. Statistical analysis showed that the output of the implemented approach had 95% concordance with the scores assigned by the pathologists, with a p-value of 0.045 (n = 63). This demonstrated that the results from the developed software were statistically meaningful and highly accurate. The implemented approach in analyzing whole tumor histology slides and the newly developed TILs detection tool can be used for research purposes in biomedical and pathology applications and it can provide researchers and clinicians with the TIL score for various input images. Future research using additional breast cancer slides from various sources for further training and validation of the developed models is necessary for more inclusive, rigorous, and robust clinical applications.

Knowledge regarding complex radiation responses in biological systems can be enhanced using genetically amenable model organisms. In this manuscript, we reviewed the use of the nematode, Caenorhabditis elegans (C. elegans), as a model organism to investigate radiation’s biological effects. Diverse types of experiments were conducted on C. elegans, using acute and chronic exposure to different ionizing radiation types, and to assess various biological responses. These responses differed based on the type and dose of radiation and the chemical substances in which the worms were grown or maintained. A few studies compared responses to various radiation types and doses as well as other environmental exposures. Therefore, this paper focused on the effect of irradiation on C. elegans, based on the intensity of the radiation dose and the length of exposure and ways to decrease the effects of ionizing radiation. Moreover, we discussed several studies showing that dietary components such as vitamin A, polyunsaturated fatty acids, and polyphenol-rich food source may promote the resistance of C. elegans to ionizing radiation and increase their life span after irradiation.

The multiplexed immunofluorescence (mIF) platform enables biomarker discovery through the simultaneous detection of multiple markers on a single tissue slide, offering detailed insights into intratumor heterogeneity and the tumor-immune microenvironment at spatially resolved single cell resolution. However, current mIF image analyses are labor-intensive, requiring specialized pathology expertise which limits their scalability and clinical application. To address this challenge, we developed CellGate, a deep-learning (DL) computational pipeline that provides streamlined, end-to-end whole-slide mIF image analysis including nuclei detection, cell segmentation, cell classification, and combined immuno-phenotyping across stacked images. The model was trained on over 750,000 single cell images from 34 melanomas in a retrospective cohort of patients using whole tissue sections stained for CD3, CD8, CD68, CK-SOX10, PD-1, PD-L1, and FOXP3 with manual gating and extensive pathology review. When tested on new whole mIF slides, the model demonstrated high precision-recall AUC. Further validation on whole-slide mIF images of 9 primary melanomas from an independent cohort confirmed that CellGate can reproduce expert pathology analysis with high accuracy. We show that spatial immuno-phenotyping results using CellGate provide deep insights into the immune cell topography and differences in T cell functional states and interactions with tumor cells in patients with distinct histopathology and clinical characteristics. This pipeline offers a fully automated and parallelizable computing process with substantially improved consistency for cell type classification across images, potentially enabling high throughput whole-slide mIF tissue image analysis for large-scale clinical and research applications.

Much of the complex medical physics work requires radiation dose delivery, which requires dosimeters to accurately measure complex three-dimensional dose distribution with good spatial resolution. MAGIC-f polymer gel is one of the emerging new dosimeters widely used in medical physics research. The purpose of this study was to present an overview of polymer gel dosimetry, using MAGIC-f gel, including its composition, manufacture, imaging, calibration, and application to medical physics research. In this review, the history of polymer gel development is presented, along with the applications so far. Moreover, the most important experiments/applications of MAGIC-f polymer gel are discussed to illustrate the behavior of gel on different conditions of irradiation, imaging, and manufacturing techniques. Finally, various future works are suggested based on the past and present works on MAGIC-f gel and polymer gel in general, with the hope that these bits of knowledge can provide important clues for future research on MAGIC-f gel as a dosimeter.