Mumps is an acute and self-limiting disease characterized by parotitis, however in some cases it leads to aseptic meningitis, deafness, encephalitis and orchitis, which is a serious health concern. MMR vaccination was successful in eradicating the disease however, recent reports question the efficacy of MMR vaccine and countless outbreaks are observed in vaccinated populations throughout the world. Lack of specific treatment methods for mumps infection and inefficiency of MMR vaccine in vaccinated populations accentuates the need for the development of novel drugs to control mumps virus mediated serious infections. It was with this backdrop of information that the anti-mumps virus activity of Mimosa pudica was evaluated. Suspected mumps cases were collected to isolate a standard mumps virus by systematic laboratory testing which included IgM antibody assays, virus isolation, RT-PCR and phylogenetic analysis. The virus was quantified by TCID 50 assay and anti-mumps virus property was evaluated by CPE reduction assay and cytotoxicity of the extract was measured by MTT assay and phytochemical analysis was done by gas chromatographymass spectroscopy. The RT-PCR and phylogenetic tree analysis of the SH gene sequence of the clinical isolate showed it to be mumps virus genotype C. 150 lg/ml concentration of M. pudica completely inhibited mumps virus and the drug was found to be non-toxic up to 2 mg/ml. M. pudica was thus found to be a potent inhibitor of MuV.

Understanding the complex biology of the tumor microenvironment (TME) is necessary to understand the mechanisms of action of immuno-oncology therapies and to match the right therapies to the right patients. Multiplex immunofluorescence (mIF) is a useful technology that has tremendous potential to further our understanding of cancer patho-biology; however, tools that fully leverage the high dimensionality of this data are still in their infancy. We describe here a novel deep learning pipeline aimed to allow Graph-based Inspection of Tissues via Embeddings, GraphITE. GraphITE transforms mIF data into a graph representation, where unsupervised learning algorithms can be utilised to generate embeddings representing cellular `neighbourhoods'. The embeddings can be downprojected and explored for clustering analysis, and patterns can be mapped back to the image as well as interrogated for phenotypical, morphological, or structural distinctiveness. GraphITE supports the extraction of information not only on the phenotypes of individual cells or the relationships between specific cell types, but is able to characterize cell neighborhoods to look for more complex interactions, thereby allowing pathologists and data scientists to explore mIF data sets, uncovering patterns that are otherwise obscured by the high-dimensionality of the data. In this work, we showcase the current setup of the system, going from raw input data all the way to a user friendly exploration tool. Using this tool, we show how the data can be navigated in a way previously not possible.

Characterization of the location and phenotype of cells in the tumor microenvironment (TME) is important to inform the development and monitoring of anti-cancer therapeutic interventions, especially immunotherapies designed to stimulate the immune system to have an anti-cancer effect. Multiplex immunofluorescence (mIF) imaging is being increasingly employed to simultaneously label multiple cell types and subtypes in the tumor microenvironment, but interpretation of these images to gain a robust understanding of tumor and immune cell interactions remains a complicated and challenging process. The rich phenotypic information contained in mIF images has to be taken into account with the spatial topology of the cells in order to be able to distil potential predictive indicators of patient response to therapies as well as prognostic indicators of outcome. While contemporary computational methods allow pathologists to view aggregated phenotypical information and cell interactions on a limited, generally one-to-one basis, these methods have been largely descriptive and geared toward addressing hypotheses as opposed to holistically leveraging the spatial and phenotypic data into a single predictive model. Additional methods are needed to provide a fuller picture of the spatial structure of the TME as captured in mIF images. In this work, we propose a novel pipeline that uses graphs generated from image analysis results and user-defined distance criteria to represent the tumor cellular microstructure. This graph-based approach complements existing mIF analysis techniques by providing information on the spatial, phenotypic, and morphological features of cells in the context of their neighborhood. These graphs subsequently enable characterization of protein expression in detail, description of interactions between individual cells or cell types and their neighbors, interactive tissue querying, and exploration of the cell-level biodiversity. The graph approach not only allows pathologists to efficiently interrogate data contained in mIF images in a hypothesis-driven manner, but importantly also supports more holistic data-driven approaches which, by leveraging state of the art graph convolutional neural networks to obtain numerical embeddings representing each graph and its nodes, enable additional downstream activities such as cell similarity search, and the development of predictive models for patient outcomes and response to therapies. Citation Format: Jason Hipp, Christopher Innocenti, Zhenning Zhang, Jake Cohen-Setton, Balaji Selvaraj, Michalis Frangos, Carlos Pedrinaci, Michael Surace, Laura Dillon, Khan Baykaner. Leveraging graphs to do novel hypothesis and data-driven research using multiplex immunofluorescence images [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PR-05.

BackgroundPredictive biomarkers for response to IO therapies remain insufficient. Although multiplex immunofluorescence has the potential to provide superior biomarkers, the information garnered from these studies is frequently underleveraged. Due to the large number of markers that must be analyzed (6 - 40 +), and the complexity of the spatial information, the number of hypotheses is large and must be tested systematically and automatically. GraphITE (Graphs-based Investigation of Tissues with Embeddings) is a novel method of converting multiplex IF image analysis results into embeddings, numerical vectors which represent the phenotype of each cell as well as the immediate neighborhood. This allows for the clustering of embeddings based on similarity as well as the discovery of novel predictive biomarkers based on both the spatial and multimarker data in multiplex IF images. Here we demonstrate initial observations from deployment of GraphITE on 564 commercially-sourced NSCLC and HNSCC resections stained with a multiplex IF panel containing CD8, PDL1, PD1, CD68, Ki67, and CK.Methods4 μm FFPE tumor sections were stained with CD8, PDL1, PD1, CD68, Ki67, and CK at Akoya Biosciences using OPAL TSA-linked fluorophores and imaged on a Vectra Polaris. Images were analyzed by Computational Biology (AstraZeneca). Graphs were built by mapping each cell in the mIF image as a node, using the X, Y coordinates and connecting nodes with edges according to distance. 64-dimensional embeddings were generated using Deep Graph InfoMax (DGI).1 Embeddings are downprojected to 2 dimensions using UMAP.2. Details are available in the preprint of the GraphITE methods manuscript.3ResultsA single downprojection was developed using embeddings from 158 HNSCC and 406 NSCLC cases. 60–80 distinct clusters were observed, some of which contained embeddings from both indications and others which were exclusive to one indication. Exclusive clusters describe tissue neighborhoods observed only in one indication. Drivers of cluster exclusivity included increased cell density in HNSCC as compared to NSCLC both in PD-L1- tumor centers with few infiltrating lymphocytes as well as in PD-L1- macrophagedominated neighborhoods. HNSCC and NSCLC embeddings were more colocalized in PD-L1+ tumor centers and in tumor stroma with high CD8+ or CD68+ immune cell content and high PD-L1+ expression.ConclusionsThis study demonstrates the utility and potential of the GraphITE platform to discriminate between and describe both unique and common neighborhood-level features of the tumor microenvironment. Deploying GraphITE across multiple indications effectively leverages spatial heterogeneity and multimarker information from multiplex IF panels.References1. Veličković P, Fedus W, Hamilton WL, Liò P, Bengio Y, DevonHjelm R. Deep Graph Infomax. 2018. arxiv:1809.10341 [stat.ML].2. McInnes L, Healy J, Melville J. UMAP: Uniform manifold approximationand projection for dimension reduction. 2020; arxiv:1802.03426 [stat.ML].3. Innocenti C, Zhang Z, Selvaraj B, Gaffney I, Frangos M, Cohen-Setton J, Dillon LAL, Surace MJ, Pedrinaci C, Hipp J, Baykaner K. An unsupervised graph embeddings approach to multiplex immunofluorescence image explorationbioRxiv 2021.06.09.447654; doi: https://doi.org/10.1101/2021.06.09.447654Ethics ApprovalThe study was approved by AstraZeneca.

Introduction: Depression is the more common mental health condition found among the chronic diseases. The prevalence of both diabetes and depression are rapidly increasing and the presence of depression in patients with type 2 diabetes could hinder the adherence and effectiveness of treatment. Objective: This study aimed to estimate the prevalence of depression and to identify the factors influencing depression among patients with type 2 diabetes mellitus in NCD clinic Method: A cross – sectional study was conducted among Type 2 diabetes mellitus attending the NCD clinic of the urban health training centre between January and April 2021. Demographic, clinical and diabetes related information was collected through a semi – structured questionnaire. Level of depression was assessed using a standard questionnaire (PHQ – 9 questionnaire). The total score of 5 – 9, 10 – 14 and >15 were graded as mild, moderate and severe forms of depression respectively. Data analysis was done using SPSS software version 21. Results: The mean age of the study subjects was 53+7 yrs and majority (60%) were males. The prevalence of depression was 30.8 % and among them 71.7 % had mild depression while 12.8 % had severe form of depression. Factors such as female gender, higher educational status and substance use such as alcohol and smoking were found to be significantly associated with the presence of depression. Conclusion: It is imperative to screen for depression and lay emphasis on counseling services for the effective management of diabetes thereby improve the quality of their life.