Search Results (3)

The application of deep learning algorithms to predict the molecular profiles of various cancers from digital images of hematoxylin and eosin (H&E)-stained slides has been reported in recent years, mainly for gastric and colon cancers. In this study, we investigated the potential use of H&E-stained endometrial cancer slide images to predict the associated mismatch repair (MMR) status. H&E-stained slide images were collected from 127 cases of the primary lesion of endometrial cancer. After digitization using a Nanozoomer virtual slide scanner (Hamamatsu Photonics), we segmented the scanned images into 5397 tiles of 512 × 512 pixels. The MMR proteins (PMS2, MSH6) were immunohistochemically stained, classified into MMR proficient/deficient, and annotated for each case and tile. We trained several neural networks, including convolutional and attention-based networks, using tiles annotated with the MMR status. Among the tested networks, ResNet50 exhibited the highest area under the receiver operating characteristic curve (AUROC) of 0.91 for predicting the MMR status. The constructed prediction algorithm may be applicable to other molecular profiles and useful for pre-screening before implementing other, more costly genetic profiling tests. Full article

Wound healing is a highly regulated multi-step process that involves a plethora of signals. Blood perfusion is crucial in wound healing and abnormalities in the formation of new blood vessels define the outcome of the wound healing process. Thy-1 has been implicated in angiogenesis and silencing of the Thy-1 gene retards the wound healing process. However, the role of Thy-1 in blood perfusion during wound closure remains unclear. We proposed that Thy-1 regulates vascular perfusion, affecting the healing rate in mouse skin. We analyzed the time of recovery, blood perfusion using Laser Speckle Contrast Imaging, and tissue morphology from images acquired with a Nanozoomer tissue scanner. The latter was assessed in a tissue sample taken with a biopsy punch on several days during the wound healing process. Results obtained with the Thy-1 knockout (Thy-1^−/−) mice were compared with control mice. Thy-1^−/− mice showed at day seven, a delayed re-epithelialization, increased micro- to macro-circulation ratio, and lower blood perfusion in the wound area. In addition, skin morphology displayed a flatter epidermis, fewer ridges, and almost no stratum granulosum or corneum, while the dermis was thicker, showing more fibroblasts and fewer lymphocytes. Our results suggest a critical role for Thy-1 in wound healing, particularly in vascular dynamics. Full article

Eradication of malaria, a mosquito-borne parasitic disease that hijacks human red blood cells, is a global priority. Microscopy remains the gold standard hallmark for diagnosis and estimation of parasitemia for malaria, to date. However, this approach is time-consuming and requires much expertise especially in malaria-endemic countries or in areas with low-density malaria infection. Thus, there is a need for accurate malaria diagnosis/parasitemia estimation with standardized, fast, and more reliable methods. To this end, we performed a proof-of-concept study using the automated imaging (NanoZoomer) platform to detect the malarial parasite in infected blood. The approach can be used as a steppingstone for malaria diagnosis and parasitemia estimation. Additionally, we created an algorithm (ParasiteMacro) compatible with free online imaging software (ImageJ) that can be used with low magnification objectives (e.g., 5×, 10×, and 20×) both in the NanoZoomer and routine microscope. The novel approach to estimate malarial parasitemia based on modern technologies compared to manual light microscopy demonstrated 100% sensitivity, 87% specificity, a 100% negative predictive value (NPV) and a 93% positive predictive value (PPV). The manual and automated malaria counts showed a good Pearson correlation for low- (R² = 0.9377, r = 0.9683 and p < 0.0001) as well as high- parasitemia (R² = 0.8170, r = 0.9044 and p < 0.0001) with low estimation errors. Our robust strategy that identifies and quantifies malaria can play a pivotal role in disease control strategies. Full article