Segmentation of 3D images to identify cells and their molecular outputs can be difficult and tedious. Machine learning algorithms provide a promising alternative to manual analysis as emerging 3D image processing technology can save considerable time. For those unfamiliar with machine learning or 3D image analysis, the rapid advancement of the field can make navigating the newest software options confusing. In this paper, two open-source machine learning algorithms, Cellpose and Stardist, are compared in their application on a 3D light sheet dataset counting fluorescently stained proliferative cell nuclei. The effects of image tiling and background subtraction are shown through image analysis pipelines for both algorithms. Based on our analysis, the relative ease of use of Cellpose and the absence of need to train a model leaves it a strong option for 3D cell segmentation despite relatively longer processing times. When Cellpose's pretrained model yields results that are not of sufficient quality, or the analysis of a large dataset is required, Stardist may be more appropriate. Despite the time it takes to train the model, Stardist can create a model specialized to the users' dataset that can be iteratively improved until predictions are satisfactory with far lower processing time relative to other methods.
Colorectal cancer (CRC) is a significant contributor to cancer-related mortality, emphasizing the need for advanced biomarkers to guide treatment. As part of an international consortium, we previously categorized CRCs into four consensus molecular subtypes (CMS1-CMS4), showing promise for outcome prediction. To facilitate clinical integration of CMS classification in settings where formalin-fixed paraffin-embedded (FFPE) samples are routinely used, we developed NanoCMSer, a NanoString-based CMS classifier using 55 genes.
In the rapid urbanization process in China, due to reasons such as employment, education, and family reunification, the number of mobile population without registered residence in the local area has increased significantly. By 2020, the group had a population of 276 million, accounting for over 20% of the total population, making significant contributions to urban economic development and resource optimization. However, the health status of migrant populations is affected by unique issues such as occupational risks and socio-economic disparities, which play an important role in personal welfare, social stability, and sustainable economic growth.
The enormous diversity of bacteriophages and their bacterial hosts presents a significant challenge to predict which phages infect a focal set of bacteria. Infection is largely determined by complementary-and largely uncharacterized-genetics of adsorption, injection, cell take-over, and lysis. Here we present a machine learning approach to predict phage-bacteria interactions trained on genome sequences of and phenotypic interactions among 51 strains and 45 phage λ strains that coevolved in laboratory conditions for 37 days.
Improved surgical skill is generally associated with improved patient outcomes, although assessment is subjective, labour intensive, and requires domain-specific expertise. Automated data-driven metrics can alleviate these difficulties, as demonstrated by existing machine learning instrument tracking models. However, these models are tested on limited datasets of laparoscopic surgery, with a focus on isolated tasks and robotic surgery.
Breast cancer is one of the most common causes of death in women in the modern world. Cancerous tissue detection in histopathological images relies on complex features related to tissue structure and staining properties. Convolutional neural network (CNN) models like ResNet50, Inception-V1, and VGG-16, while useful in many applications, cannot capture the patterns of cell layers and staining properties.
