Biology subjects

Prasanna, P.

Publications and source records attributed to Prasanna, P..

4 recordsLinked to original sources

bioRxiv2025

Effect of biogel C addition on biochar degradation and microbial activities

Biochar contributes to long-term soil carbon stabilization (C) by acting both as a stable carbon pool and as a sorbent for labile compounds. Biogels from roots and microbes are known to form persistent surface coatings with soil sorbents, where they host microbial hotspots. Yet research has mainly focused on their interactions with minerals. The interactive effects of biogels and biochar on soil carbon dynamics remain largely unexplored. This study aimed to assess the effects of biogel coatings on fresh and aged biochar, particularly under drought, with a focus on biochar degradation and microbial responses. We conducted a three-factorial soil incubation study to examine the effects of fresh and aged biochar, with or without biogel amendment, under two moisture levels (30% and 70% water holding capacity, WHC). Using 14C labelled biochar, we quantified biochar degradation and biochar- induced priming effects by measuring 14CO2respiration, microbial biomass carbon (MBC) and its 14C incorporation, hydrolytic and oxidative enzyme activities, and changes in biochar surface area. Mucilage strongly enhanced microbial incorporation of biochar-derived C, specifically from aged biochar under drought conditions, while the opposite effect was observed in soils amended with fresh biochar. This demonstrates the greater microbial accessibility of aged biochar surfaces and limited use of fresh biochar due to inaccessibility of the hydrophobic surfaces, and in consequence the preference for more easily accessible C sources. Additionally, mucilage significantly reduced the Michaelis Menten constant (Km) of {beta}-glucosidase by up to 58% in soils amended with aged biochar under drought. This indicates that under these conditions, the interaction of the aged biochar with the biogel may have created a unique habitat requiring specific enzyme systems with higher affinity. Our findings highlight the role of mucilage in regulating microbial surface access and thus the decomposition of biochar, particularly under moisture- limited conditions. The synergy between aged biochar and biogels provides a promising new perspective to further enhance biochar-based drought mitigation, specifically for managing microbial activity in drought-prone soils.

ecology↗

bioRxiv2024

Eco-evolutionary Guided Pathomic Analysis to Predict DCIS Upstaging

Cancers evolve in a dynamic ecosystem. Thus, characterizing cancers ecological dynamics is crucial to understanding cancer evolution and can lead to discovering novel biomarkers to predict disease progression. Ductal carcinoma in situ (DCIS) is an early-stage breast cancer characterized by abnormal epithelial cell growth confined within the milk ducts. In this study, we show that ecological analysis of hypoxia and acidosis biomarkers can significantly improve prediction of DCIS upstaging. First, we developed a novel eco-evolutionary designed approach to define habitats in the tumor intraductal microenvironment based on oxygen diffusion distance. Then, we identified cancer cells with metabolic phenotypes attributed to their habitats, including CA9 for hypoxia responding phenotype, and LAMP2b for acid adapted phenotype. Traditionally these markers have shown limited predictive capabilities for DCIS progression, if any. However, when analyzed from an ecological perspective, their power to differentiate between non-upstaged and upstaged DCIS increased significantly. Second, we discovered distinct niches with spatial patterns of these biomarkers and used the distribution of such niches to predict patient upstaging. The niches were characterized by pattern analysis of both cellular and spatial features. With a 5-fold validation on the biopsy cohort, we trained a random forest classifier to achieve the area under curve (AUC) of 0.74. Our results affirm the importance of tumor ecological features in eco-evolutionary-designed approaches for novel biomarkers discovery. SignificanceOur results affirm the importance of spatial and ecological features in eco-evolutionary-designed biomarkers discovery studies in the era of digital pathology.

cancer biology↗

bioRxiv2024

Performance of GPT-4 on the American College of Radiology In-Service Examination

ObjectivesNo study has evaluated the ability of ChatGPT-4 to answer image-rich diagnostic radiology board exam questions or assessed for model drift in GPT-4s image interpretation abilities. In our study we evaluate GPT-4s performance on the American College of Radiology (ACR) 2022 Diagnostic Radiology In-Training Examination (DXIT). MethodsQuestions were sequentially input into GPT-4 with a standardized prompt. Each answer was recorded and overall accuracy was calculated, as was logic-adjusted accuracy, and accuracy on image-based questions. This experiment was repeated several months later to assess for model drift. ResultsGPT-4 achieved 58.5% overall accuracy, lower than the PGY-3 average (61.9%) but higher than the PGY-2 average (52.8%). Adjusted accuracy was 52.8%. GPT-4 showed significantly higher (p = 0.012) confidence for correct answers (87.1%) compared to incorrect (84.0%). Performance on image-based questions was notably poorer (p < 0.001) at 45.4% compared to text-only questions (80.0%), with adjusted accuracy for image questions of 36.4%. When the questions were repeated, GPT-4 chose a different answer 25.5% of the time and there was a small but insignificant decrease in accuracy. DiscussionGPT-4 performed between PGY-2 and PGY-3 levels on the 2022 DXIT, but significantly poorer on image-based questions, and with large variability in answer choices across time points. This study underscores the potential and risks of using minimally-prompted general AI models in interpreting radiologic images as a diagnostic tool. Implementers of general AI radiology systems should exercise caution given the possibility of spurious yet confident responses.

bioinformatics↗

bioRxiv2022

Deep learning-based approach for the characterization and quantification of histopathology in mouse models of colitis

Inflammatory bowel disease (IBD) is a chronic immune-mediated disease of the gastrointestinal tract. While therapies exist, response can be limited within the patient population. As such, researchers have studied mouse models of colitis to further understand its pathogenesis and identify new treatment targets. Although bench methods like flow cytometry and RNA-sequencing can characterize immune responses with single-cell resolution, whole murine colon specimens are processed at once. Given the simultaneous presence of colonic regions that are involved or uninvolved with abnormal histology, processing whole colons may lead to a loss of spatial context. Detecting these regions in hematoxylin and eosin (H&E)-stained colonic tissues offers the downstream potential of quantifying immune populations in areas with and without disease involvement by immunohistochemistry on serially sectioned slides. This could provide a complementary, spatially-aware approach to further characterize populations identified by other methods. However, detection of such regions requires expert interpretation by pathologists and is a tedious process that may be difficult to perform consistently across experiments. To this end, we have trained a deep learning model to detect Involved and Uninvolved regions from H&E-stained colonic slides across controls and three mouse models of colitis - the dextran sodium sulfate (DSS) chemical induction model, the recently established intestinal epithelium-specific, inducible Klf5{Delta}IND (Villin-CreERT2;Klf5fl/fl) genetic model, and one that combines both induction methods. The trained classifier allows for extraction of Involved colonic regions across mice to cluster and identify histological classes. Here, we show that quantification of Involved and Uninvolved image patch classes in swiss rolls of colonic specimens can be utilized to train a linear determinant analysis classifier to distinguish between mouse models. Such an approach has the potential for revealing histological links and improving synergy between various colitis mouse model studies to identify new therapeutic targets and pathophysiological mechanisms.

immunology↗