To attain Salmonella detection thresholds in spinach suspensions using enrichment media requires at least 24 hr. Separation and concentration of selected microorganisms via microfiltration and microfugation reduce time for sample preparation, especially when working with large volumes of vegetable suspensions. This facilitates accelerated detection of Salmonella in spinach suspensions, and may contribute to effectively monitoring this pathogen before it reaches the consumer. We report a microfiltration‐based protocol for accelerated sample preparation to concentrate and recover ≤1 colony forming unit (CFU) Salmonella/g pathogen‐free spinach. Store‐bought samples of spinach and a spinach plant subjected to two environmental conditions (temperature and light exposure) during its production were tested. The overall procedure involves extraction with buffer, a short enrichment step, prefiltration using a nylon filter, crossflow hollow fiber microfiltration, and retentate centrifugation to bring microbial cells to detection levels. Based on 1 CFU Salmonella/g frozen spinach, and a Poisson distribution statistical analyses with 99% probability, we calculated that 3 hr of incubation, when followed by microfiltration, is sufficient to reach the 2 log concentration required for Salmonella detection within 7 hr. Longer enrichment times (5 hr or more) is needed for concentrations lower than 1 CFU Salmonella/g of ready to eat spinach. The recovered microbial cells were identified and confirmed as Salmonella using both polymerase chain reaction (PCR) and plating methods. Different environmental conditions tested during production did not affect Salmonella viability; this demonstrated the broad adaptability of Salmonella and emphasized the need for methods that enable efficient monitoring of production for the presence of this pathogen.
IntroductionMelanoma is the fifth most common cancer in US, and the incidence is increasing 1.4% annually. The overall survival rate for early-stage disease is 99.4%. However, melanoma can recur years later (in the same region of the body or as distant metastasis), and results in a dramatically lower survival rate. Currently there is no reliable method to predict tumor recurrence and metastasis on early primary tumor histological images.MethodsTo identify rapid, accurate, and cost-effective predictors of metastasis and survival, in this work, we applied various interpretable machine learning approaches to analyze melanoma histopathological H&E images. The result is a set of image features that can help clinicians identify high-risk-of-metastasis patients for increased clinical follow-up and precision treatment. We use simple models (i.e., logarithmic classification and KNN) and “human-interpretable” measures of cell morphology and tissue architecture (e.g., cell size, staining intensity, and cell density) to predict the melanoma survival on public and local Stage I–III cohorts as well as the metastasis risk on a local cohort.ResultsWe use penalized survival regression to limit features available to downstream classifiers and investigate the utility of convolutional neural networks in isolating tumor regions to focus morphology extraction on only the tumor region. This approach allows us to predict survival and metastasis with a maximum F1 score of 0.72 and 0.73, respectively, and to visualize several high-risk cell morphologies.DiscussionThis lays the foundation for future work, which will focus on using our interpretable pipeline to predict metastasis in Stage I & II melanoma.
Background: Spatial and single-cell transcriptomics have revealed significant heterogeneity in tumor and normal tissues. Each approach has its advantages: The Visium platform for spatial transcriptomics (ST) offers lower resolution than single-cell analysis, but histology enables the examination of cell morphology, tissue architecture, and potential cell-cell interactions. Single-cell transcriptomics (SC) provides high resolution, but manual cell-type annotation depends on incomplete scientific knowledge from heterogeneous experiments. When investigating poorly defined phenomena, such as the transition from normal tissue to cancer and metaplasia, researchers might overlook critical and unexpected findings in downstream analysis if they rely on pre-existing annotations to determine cell types, particularly in the context of phenotypic plasticity. Results: We employ our deep-transfer learning framework, DEGAS, to identify benign morphology glands in normal prostate tissue that are associated with poor progression-free survival in cancer patients and exhibit transcriptional signatures of carcinogenesis and de-differentiation. We confirm this finding in an additional ST dataset and use novel published methods to integrate SC data, showing that cells annotated as cancerous in the SC data map to regions of benign glands in another dataset. We pinpoint several genes, primarily MSMB, with expression closely correlated with progression-free survival scores, which are known markers of de-differentiation, and attribute their expression specifically to luminal epithelia, which are the presumed origin of most prostatic cancers. Discussion: Our work shows that morphologically normal epithelia can have transcriptional signatures like that of frank cancer, and that these tissues are associated with poor progression-free survival. We also highlight a critical gap in single-cell workflows: annotating continuous transitional phenomena like carcinogenesis with discrete labels can result in incomplete conclusions. Two approaches can help mitigate this issue: Tools like DEGAS and Scissor can provide a disease-association score for SC and ST data, independent of cell type and histology. Additionally, researchers should adopt a bidirectional approach, transferring histological labels from ST data to SC data using tools like RCTD, rather than only using SC cell-type assignments to annotate ST data. Employed together, these methods can offer valuable histology and disease-related information to better define tissue subtypes, especially epithelial cells in the process of carcinogenesis. Conclusions: DEGAS is a vital tool for generating clinically-oriented hypotheses from SC and ST data, which are heterogeneous, information-rich assays. In this study, we identify potential signatures of carcinogenesis in morphologically benign epithelia, which may be the precursors to cancer and high-grade pre-malignant lesions. Validating these genes as a panel may help identify patients at high risk for future cancer development, recurrence, and assist researchers in studying the biology of early carcinogenesis by detecting metaplastic changes before they are morphologically identifiable.
OBJECTIVES/GOALS: Single-cell and spatial transcriptomics have revealed high heterogeneity in the tumor and microenvironment. Identifying populations of cells that impact a patient’s prognosis is an important research goal, so researchers can generate hypotheses and clinicians can provide targeted treatment. METHODS/STUDY POPULATION: DEGAS uses deep-transfer-learning to identify patterns between patient tumor RNA-seq and clinical outcomes and map these associations on to higher-resolution data like spatial and single-cell transcriptomics. We apply DEGAS to prostate and pancreatic cancer spatial transcriptomics samples, as well as one normal sample of prostate tissue. We used the TCGA prostate cancer cohort to with the accompanying survival information and publicly accessible prostate cancer ST data from 10X Genomics to predict survival associations in the ST slides derived from the TCGA patients. Based on these survival associations, we identify higher risk subsections of ST slides which can be further studied. RESULTS/ANTICIPATED RESULTS: We were able to validate our method by comparing it to Scissor and were able to show that the number of high-risk regions in prostate cancer slides increased with the stage of disease. Furthermore, we identify transcriptomic signatures enriched for ontology terms associated with growth regulation and apoptosis, inflammation, immune signaling, and autophagy in histologically normal prostate tissues and adjacent normal pancreatic cancer tissues that were identified as high-risk by DEGAS. The regions highlighted by DEGAS could reflect transcriptional precursors to intraepithelial neoplasia–a well-recognized premalignant morphological change in glandular epithelium. DISCUSSION/SIGNIFICANCE: Identifying biomarkers of tissue stress that precede morphologic diagnosis of high-grade pre-malignant lesions by a pathologist may help triage patients at high risk for future development of cancer, or aid in better understanding whether histologically normal pre-malignant tissues at tumor margins contribute to recurrence.
Background and Hypothesis Critical limb-threatening ischemia (CLTI) is a severe limitation in perfusion of the lower extremities. It is the most advanced stage of peripheral arterial disease. Characterized by unremitting rest pain and/or gangrene, the CLTI patient population has an excessively high rate of opioid use and addiction. The MOBILE Trial was a Phase II multi-center, double-blinded placebo controlled trial designed to assess the safety and efficacy of autologous bone marrow cells (ABMC) in treating patients with CLTI. Our central hypothesis is that cell therapy may provide trophic effects in the treated limb that may decrease opioid requirements. Project Methods The primary endpoint of the MOBILE Trial was amputation-free survival at 52 weeks. Secondary endpoints included limb perfusion measures, ambulatory function, quality of life (VascuQol), pain assessment with a visual analog score, and opioid requirements, as tabulated by prescriptions reported to us. A post-hoc statistical analysis using ANOVA was conducted to describe changes in pain and quality of life measures along the study, and Fisher’s test to compare the incidence of new opioid prescriptions between treatment groups. Results There were no differences in quality of life and visual analog pain measures between the ABMC and placebo groups (P= 0.42). There was however a 36% decrease in new opioid prescriptions in the ABMC groups as compared to placebo (P = 0.058, HR: -0.44-1.012). This reduction did not correlate with changes in limb perfusion measures, gender, age, diagnosis of diabetes, or Rutherford score. Discussion The results of this analysis demonstrate an effect of ABMC in reducing opioid use in a high-risk addiction patient population. These findings are consistent with other studies assessing mesenchymal stem cells in chronic pain syndromes. The effect of ABMC did not correlate with changes in limb perfusion suggesting other beneficial trophic effects of cells in ischemic induced neuropathies. This discovery suggests a potential therapeutic strategy for reducing opioid dependency.
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