Predicting Prostate Cancer Molecular Subtype With Deep Learning on Histopathologic Images.

Microscopic examination of prostate cancer has failed to reveal a reproducible association between molecular and morphologic features. However, deep-learning algorithms trained on hematoxylin and eosin (H&E)-stained whole slide images (WSI) may outperform the human eye and help to screen for clinically-relevant genomic alterations. We created deep-learning algorithms to identify prostate tumors with underlying ETS-related gene (ERG) fusions or PTEN deletions using the following 4 stages: (1) automated tumor identification, (2) feature representation learning, (3) classification, and (4) explainability map generation. A novel transformer-based hierarchical architecture was trained on a single representative WSI of the dominant tumor nodule from a radical prostatectomy (RP) cohort with known ERG/PTEN status (n = 224 and n = 205, respectively). Two distinct vision transformer-based networks were used for feature extraction, and a distinct transformer-based model was used for classification. The ERG algorithm performance was validated across 3 RP cohorts, including 64 WSI from the pretraining cohort (AUC, 0.91) and 248 and 375 WSI from 2 independent RP cohorts (AUC, 0.86 and 0.89, respectively). In addition, we tested the ERG algorithm performance in 2 needle biopsy cohorts comprised of 179 and 148 WSI (AUC, 0.78 and 0.80, respectively). Focusing on cases with homogeneous (clonal) PTEN status, PTEN algorithm performance was assessed using 50 WSI reserved from the pretraining cohort (AUC, 0.81), 201 and 337 WSI from 2 independent RP cohorts (AUC, 0.72 and 0.80, respectively), and 151 WSI from a needle biopsy cohort (AUC, 0.75). For explainability, the PTEN algorithm was also applied to 19 WSI with heterogeneous (subclonal) PTEN loss, where the percentage tumor area with predicted PTEN loss correlated with that based on immunohistochemistry (r = 0.58, P = .0097). These deep-learning algorithms to predict ERG/PTEN status prove that H&E images can be used to screen for underlying genomic alterations in prostate cancer.

A deep learning system for prostate cancer diagnosis and grading in whole slide images of core needle biopsies.

Gleason grading, a risk stratification method for prostate cancer, is subjective and dependent on experience and expertise of the reporting pathologist. Deep Learning (DL) systems have shown promise in enhancing the objectivity and efficiency of Gleason grading. However, DL networks exhibit domain shift and reduced performance on Whole Slide Images (WSI) from a source other than training data. We propose a DL approach for segmenting and grading epithelial tissue using a novel training methodology that learns domain agnostic features. In this retrospective study, we analyzed WSI from three cohorts of prostate cancer patients. 3741 core needle biopsies (CNBs) received from two centers were used for training. The κquad (quadratic-weighted kappa) and AUC were measured for grade group comparison and core-level detection accuracy, respectively. Accuracy of 89.4% and κquad of 0.92 on the internal test set of 425 CNB WSI and accuracy of 85.3% and κquad of 0.96 on an external set of 1201 images, was observed. The system showed an accuracy of 83.1% and κquad of 0.93 on 1303 WSI from the third institution (blind evaluation). Our DL system, used as an assistive tool for CNB review, can potentially improve the consistency and accuracy of grading, resulting in better patient outcomes.

In-vivo and in-vitro evaluation of therapeutic potential of ß- Carotene in diabetes.

The present study was performed to investigate the therapeutic potential of ß- Carotene against STZ induced diabetes by using in vivo and in vitro models. MTT assay was performed to check the cytotoxic effect of ß- Carotene in HepG2 liver cells which were treated with ß- Carotene (10, 20 µM). The anti-diabetic activity was examined by estimating different enzymes in cell lines. Further, we validated activity by using in vitro models. Male Albino Wistar rats were divided into five groups each group contain six animals (n = 6). The diabetes was induced via intraperitoneal injection of STZ and the ß- Carotene was treated with daily doses of 10 and 20 mg/kg for 14 days. After the last dose of ß- Carotene, rats were sacrificed and the biochemical parameters were estimated in liver homogenate. The disease control group showed an elevation in the level of cytokine as well as ROS and ß- Carotene-treated animals showed a reduction in the level of cytokine and normal content of anti-oxidant enzyme in liver tissue homogenate. We found ß- Carotene had no toxic effect on HepG2 liver cells. In the case of the glucose utilization assay, it was found that glucose uptake level was significantly increased with the increasing concentrations of ß-Carotene. In conclusion ß- Carotene improves glucose metabolism along with oxidative status in STZ-induced diabetic rats.

Characterization, pro-inflammatory response and cytotoxic profile of bioaerosols from urban and rural residential settings in Pune, India.

Microbiota associated with airborne particulate matter (PM) is an important indicator of indoor pollution as they can be pathogenic and cause serious health threats to the exposed occupants. Present study aimed to investigate the level of culturable microbes associated with PM and their toxicological characterization in urban and rural houses of Pune city. Highest concentration of bacterial aerosols observed to be associated with PM10 size fraction in urban site (2136 ± 285 CFU/m3) whereas maximum fungal concentration has been measured in rural houses (1521 ± 302 CFU/m3). Predominantly found bacterial species were Bacillus sp., S. aureus, and Pseudomonas aeruginosa and fungal species were Aspergillus sp., Cladosporium sp., and Penicillium sp. in both urban and rural residential premises. Concentration of endotoxin measured using the kinetic Limulus Amebocyte Lysate assay exhibited that the level of endotoxin in both urban and rural sites are associated with household characteristics and the activities performed in indoor as well as outdoor. Cell free DTT assay confirmed the ability of these airborne microbes to induce the production of reactive oxygen species (ROS) varying along with the types of microorganisms. On exposure of A549 cells to airborne microbes, a significant decrease in cell viability was observed in terms of both necrosis and apoptosis pathway. Elevated production of nitric oxide (NO) and proinflammatory cytokines in epithelial cells and macrophages clearly suggest the inflammatory nature of these airborne microbes. Results derived from the present study demonstrated that the indoor air of urban and rural houses of Pune is contaminated in terms of microbial load. Therefore, attention should be paid to control the factors favoring the microbial growth in order to safeguard the health of exposed inhabitants.