To best identify tuberculosis and pneumonia diagnoses in chest x-rays, the authors compare different deep learning convolution neural networks.

Despite the prevalence of PD, diagnosing PD is expensive, requires specialized testing, and is often inaccurate. Moreover, diagnosis is often made late in the disease course when treatments are less effective. Using existing voice data from patients with PD and healthy controls, the authors created and trained two different algorithms: one using logistic regression and another employing an artificial neural network (ANN).

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.

Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer, with early diagnosis and treatment challenges. When any of the genes KRAS, SMAD4, TP53, and BRCA2 are heavily mutated, they correlate with PDAC progression. Cellular stress, partly regulated by the gene SERPINA6, also correlates with PDAC progression. When SERPINA6 is highly expressed, corticosteroid-binding globulin inhibits the effect of the stress hormone cortisol. In this study, the authors explored whether there is an inverse correlation between the expression of SERPINA6 and PDAC-linked genes.

In this study, the authors developed and assessed the accuracy of a machine learning algorithm to identify skin cancers using images of biopsies.

Using a convolution neural network, these authors show machine learning can clinically diagnose breast cancer with high accuracy.

This study sought to determine if there is an association between the single nucleotide polymorphism rs7528684 of the Fc receptor-like-3 (FCRL3) gene and asthma or allergic rhinitis (AR). Based on previous studies in an Asian population, we hypothesized that participants with an AA genotype of FCRL3 would be more likely to have asthma and/or allergic rhinitis. To test the hypothesis, surveys were administered to participants, and genotyping was performed on spit samples via PCR, restriction digest, and gel electrophoresis.

The authors looked at previous studies to evaluate the ability to use serum levels of certain cytokines as biomarkers for pneumoconiosis.

Cardiac autonomic and structural changes may occur in temporal lobe epilepsy patients and contribute to the risk of sudden unexpected death in epilepsy patients. Choi and colleagues reviewed clinical charts to obtain patients’ lifetime seizure count, antiepileptic drug use, and history of heart disease, followed by transthoracic echocardiogram to calculate left ventricle dimensions, ejection fraction, and left ventricle mass. By comparing epilepsy patients to control subjects, they found that epilepsy patients had thinner left ventricle walls and smaller ejection fraction, but with no significant difference in left ventricle mass.

Cannabidiol (CBD) is a chemical extracted from cannabis and shown by some studies to alleviate the symptoms of many mental disorders, especially major depressive disorder. The authors hypothesized that chronic treatments with purified CBD through oral administration would relieve depression-associated behaviors in normal healthy rats under adverse conditions. A statistical analysis of the experimental data suggested that long-term consumption of CBD could elicit depression associated symptoms in normal rats without depression. The results imply that people should consume CBD-containing products with extreme caution and highlight the need to carefully monitor the use of CBD in health care products.