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Pulmonary diseases like lung cancer and valley fever pose serious health challenges, making accurate and rapid diagnostics essential. This study developed a MATLAB-based software tool that uses computer vision techniques to differentiate between these diseases by analyzing features of lung nodules in CT scans, achieving higher precision than traditional methods.

Osteosarcoma is a type of bone cancer that affects young adults and children. Early diagnosis of osteosarcoma is crucial to successful treatment. The current methods of diagnosis, which include imaging tests and biopsy, are time consuming and prone to human error. Hence, we used deep learning to extract patterns and detect osteosarcoma from histological images. We hypothesized that the combination of two different technologies (transfer learning and data augmentation) would improve the efficacy of osteosarcoma detection in histological images. The dataset used for the study consisted of histological images for osteosarcoma and was quite imbalanced as it contained very few images with tumors. Since transfer learning uses existing knowledge for the purpose of classification and detection, we hypothesized it would be proficient on such an imbalanced dataset. To further improve our learning, we used data augmentation to include variations in the dataset. We further evaluated the efficacy of different convolutional neural network models on this task. We obtained an accuracy of 91.18% using the transfer learning model MobileNetV2 as the base model with various geometric transformations, outperforming the state-of-the-art convolutional neural network based approach.

The authors develop a machine learning method to reduce misclassification of objects in safety-critical applications such as medical diagnosis.

Some cancer treatments lose efficacy when combined with treatments for excessive stomach acid, due to the changes in the stomach environment caused by the stomach acid treatments. Lin and Lin investigate information on oral cancer drugs to see what information is available on interactions of these drugs.

According to the World Health Organization, cancer is a leading cause of death globally. The disease’s prevalence is rapidly increasing in association with factors including the increased use of pesticides and herbicides, such as glyphosate, which is one of the most widely used herbicide ingredients. Natural antioxidants and phytochemicals are being tested as anti-cancer agents due to their antiproliferative, antioxidative, and pro-apoptotic properties. Thus, we aimed to investigate the potential role of S. amara extract as a therapeutic agent against glyphosate-induced toxicity and tumor-like morphologies in regenerating and homeostatic planaria (Dugesia dorotocephala).

A significant percentage of cancer survivors develop a second primary cancer. Using data of deceased patients provided by the Peninsula Regional Medical Center, Li and Holdai conducted a retrospective statistical analysis to investigate whether the type of the first cancer affects the occurrence time and type of the second primary cancer.

The authors assess incidence and mortality rates of two cancer types in relation to trihalomethane pollutant concentrations in drinking water.

In this study, we performed orthotopic auto-transplantation of fresh ovarian tissues by transplanting unilateral half ovarian tissue to the contralateral ovary in the ICR (Institute of Cancer Research) strain of outbred, heterogeneous mice to determine if the transplanted tissue could be functional. We found that the freshly transplanted mouse ovarian tissue survived and functional, as histochemical and immunofluorescence assays have shown that not only both follicles at different developing stages and corpus luteum are available, but the morphology of them are properly maintained within the transplanted tissue.

Andrographolide, a natural compound with anti-inflammatory, antidepressant, and anti-cancer properties, can be chemically modified by adding an acetonide group to form andrographolide acetonide, which is more potent and acts as a pH-dependent prodrug. Researchers investigated the hydrolysis of this acetonide group under mildly acidic conditions.

AI analysis of brain scans offers promise for helping doctors diagnose brain tumors. Haider and Drosis explore this field by developing machine learning models that classify brain scans as "cancer" or "non-cancer" diagnoses.