Infrared Spectroscopic Electronic Noses: An Innovative Approach for Exhaled Breath Sensing.

Gastric cancer remains a leading cause of cancer-related mortality, requiring the urgent development of innovative diagnostic tools for early detection. This study presents an integrated infrared spectroscopic electronic nose system, a novel device that combines infrared (IR) spectroscopy and electronic nose (eNose) concepts for analyzing volatile organic compounds (VOCs) in exhaled breath. This system was calibrated using relevant gas mixtures and then tested during a feasibility study involving 26 gastric cancer patients and 32 healthy controls using chemometric analyses to distinguish between exhaled breath profiles.

Identification and Characterization of Chemotherapy-Resistant High-Risk Neuroblastoma Persister Cells.

Relapse rates in high-risk neuroblastoma remain exceedingly high. The malignant cells that are responsible for relapse have not been identified, and mechanisms of therapy resistance remain poorly understood. In this study, we used single-nucleus RNA sequencing and bulk whole-genome sequencing to identify and characterize the residual malignant persister cells that survive chemotherapy from a cohort of 20 matched diagnosis and definitive surgery tumor samples from patients treated with high-risk neuroblastoma induction chemotherapy.

Pro-cycling team cyclist assignment for an upcoming race.

Professional bicycle racing is a popular sport that has attracted significant attention in recent years. The evolution and ubiquitous use of sensors allow cyclists to measure many metrics including power, heart rate, speed, cadence, and more in training and racing. In this paper we explore for the first time assignment of a subset of a team's cyclists to an upcoming race.

Advancing Colorectal Cancer Diagnosis with AI-Powered Breathomics: Navigating Challenges and Future Directions.

Early detection of colorectal cancer is crucial for improving outcomes and reducing mortality. While there is strong evidence of effectiveness, currently adopted screening methods present several shortcomings which negatively impact the detection of early stage carcinogenesis, including low uptake due to patient discomfort. As a result, developing novel, non-invasive alternatives is an important research priority.

The Detection of Colorectal Cancer through Machine Learning-Based Breath Sensor Analysis.

Colorectal cancer (CRC) is the third most common malignancy and the second most common cause of cancer-related deaths worldwide. While CRC screening is already part of organized programs in many countries, there remains a need for improved screening tools. In recent years, a potential approach for cancer diagnosis has emerged via the analysis of volatile organic compounds (VOCs) using sensor technologies.