Machine learning to improve the understanding of rabies epidemiology in low surveillance settings.

In low and middle-income countries, a large proportion of animal rabies investigations end without a conclusive diagnosis leading to epidemiologic interpretations informed by clinical, rather than laboratory data. We compared Extreme Gradient Boosting (XGB) with Logistic Regression (LR) for their ability to estimate the probability of rabies in animals investigated as part of an Integrated Bite Case Management program (IBCM). To balance our training data, we used Random Oversampling (ROS) and Synthetic Minority Oversampling Technique.

Comparative transcriptome of normal and cancer-associated fibroblasts.

Background: The characteristics of a tumor are largely determined by its interaction with the surrounding micro-environment (TME). TME consists of both cellular and non-cellular components. Cancer-associated fibroblasts (CAFs) are a major component of the TME.

Gene expression signature of castrate resistant prostate cancer.

Prostate gland is a highly androgen dependent gland and hence the first line of treatment for metastatic prostate cancer happens to be androgen ablation. This is achieved by multiple non-surgical methods. However, most of these cancers although respond well initially, become resistant to androgen ablation sooner or later.

Mechanical and Wear Studies of Boron Nitride-Reinforced Polymer Composites Developed via 3D Printing Technology.

In the realm of 3D printing, polymers serve as fundamental materials offering versatility to cater to a diverse array of final product properties and tailored to the specific needs of the creator. Polymers, as the building blocks of 3D printing, inherently possess certain mechanical and wear properties that may fall short of ideal. To address this limitation, the practice of reinforcing polymer matrices with suitable materials has become a common approach.

Predicting Kyasanur forest disease in resource-limited settings using event-based surveillance and transfer learning.

In recent years, the reports of Kyasanur forest disease (KFD) breaking endemic barriers by spreading to new regions and crossing state boundaries is alarming. Effective disease surveillance and reporting systems are lacking for this emerging zoonosis, hence hindering control and prevention efforts. We compared time-series models using weather data with and without Event-Based Surveillance (EBS) information, i.