Airborne microplastics (MPs) pose significant respiratory and systemic health risks upon inhalation; however, current assessment methods remain inadequate. This study integrates breathomics and transcriptomics to establish a non-invasive approach for evaluating MP-induced damage to the lungs and heart. C57BL/6 mice were exposed to polystyrene MPs (0.1 μm, 2 μm, and 10 μm), and their exhaled volatile organic compounds (VOCs) were analyzed using photoinduced associative ionization time-of-flight mass spectrometry. Machine learning algorithms identified hydrogen sulfide, acetone, acrolein, propionitrile, and butyronitrile as key VOC biomarkers, linking MP exposure to oxidative stress and metabolic dysregulation. Transcriptomic analysis further revealed significant gene expression alterations in pulmonary and cardiac tissues, implicating immune dysregulation, metabolic disturbance, and cardiac dysfunction. Pathway enrichment analysis, supported by histological and immunohistochemical validation, confirmed pulmonary inflammation and cardiac injury. By integrating exhaled biomarker profiling with transcriptomic insights, this study advances non-invasive detection strategies for MP-related health effects, offering valuable prospects for public health monitoring and early diagnosis.

Persons with type 1 diabetes (T1D) must track/control their blood glucose (BG) levels to avoid hypoglycemic events (BG < 70 mg/dL), which in the most severe cases can lead to seizures or even death. Canines may lead the way toward innovative testing solutions, as they can be trained to identify hypoglycemia simply and noninvasively by smelling exhaled volatile organic compounds (VOCs). To identify breath-based biomarkers of hypoglycemia, samples were collected during two consecutive summers at a diabetes camp (Cohort 1 and Cohort 2), and VOCs were analyzed by gas chromatography-mass spectrometry. Conserved VOCs between the two cohorts were identified, but individual VOCs alone had low accuracies for detection. Therefore, supervised multivariate statistical analysis was undertaken to identify a biosignature in the training data set (Cohort 1) that could detect hypoglycemia with higher accuracy (sensitivity = 94.8%/specificity = 95.0%). When this model was blindly tested on Cohort 2, hypoglycemia was classified with sensitivity = 90.0%/specificity = 89.9%. Ultimately, this study makes strides toward clinical validation through verifying biomarkers of hypoglycemia in hundreds of breath samples. These results may be translated to design a sensor array that could be integrated into a portable breathalyzer to increase glycemic control in persons with T1D.

The multitude of metabolites generated by physiological processes in the body can serve as valuable biomarkers for many clinical purposes. They can provide a window into relevant metabolic pathways for health and disease, as well as be candidate therapeutic targets. A subset of these metabolites generated in the human body are volatile, known as volatile organic compounds (VOCs), which can be detected in exhaled breath. These can diffuse from their point of origin throughout the body into the bloodstream and exchange into the air in the lungs. For this reason, breath VOC analysis has become a focus of biomedical research hoping to translate new useful biomarkers by taking advantage of the non-invasive nature of breath sampling, as well as the rapid rate of collection over short periods of time that can occur. Despite the promise of breath analysis as an additional platform for metabolomic analysis, no VOC breath biomarkers have successfully been implemented into a clinical setting as of the time of this review.

This review aims to summarize the progress made to address the major methodological challenges, including standardization, that have historically limited the translation of breath VOC biomarkers into the clinic. We highlight what steps can be taken to improve these issues within new and ongoing breath research to promote the successful development of the VOCs in breath as a robust source of candidate biomarkers. We also highlight key recent papers across select fields, critically reviewing the progress made in the past few years to advance breath research.

VOCs are a set of metabolites that can be sampled in exhaled breath to act as advantageous biomarkers in a variety of clinical contexts.

The liver is a key organ involved in a wide range of functions, whose damage can lead to chronic liver disease (CLD). CLD accounts for more than two million deaths worldwide, becoming a social and economic burden for most countries. Among the different factors that can cause CLD, alcohol abuse, viruses, drug treatments, and unhealthy dietary patterns top the list. These conditions prompt and perpetuate an inflammatory environment and oxidative stress imbalance that favor the development of hepatic fibrogenesis. High stages of fibrosis can eventually lead to cirrhosis or hepatocellular carcinoma (HCC). Despite the advances achieved in this field, new approaches are needed for the prevention, diagnosis, treatment, and prognosis of CLD. In this context, the scientific com-munity is using machine learning (ML) algorithms to integrate and process vast amounts of data with unprecedented performance. ML techniques allow the integration of anthropometric, genetic, clinical, biochemical, dietary, lifestyle and omics data, giving new insights to tackle CLD and bringing personalized medicine a step closer. This review summarizes the investigations where ML techniques have been applied to study new approaches that could be used in inflammatory-related, hepatitis viruses-induced, and coronavirus disease 2019-induced liver damage and enlighten the factors involved in CLD development.

The global healthcare sector continues to grow rapidly and is reflected as one of the fastest-growing sectors in the fourth industrial revolution (4.0). The majority of the healthcare industry still uses labor-intensive, time-consuming, and error-prone traditional, manual, and manpower-based methods. This review addresses the current paradigm, the potential for new scientific discoveries, the technological state of preparation, the potential for supervised machine learning (SML) prospects in various healthcare sectors, and ethical issues. The effectiveness and potential for innovation of disease diagnosis, personalized medicine, clinical trials, non-invasive image analysis, drug discovery, patient care services, remote patient monitoring, hospital data, and nanotechnology in various learning-based automation in healthcare along with the requirement for explainable artificial intelligence (AI) in healthcare are evaluated. In order to understand the potential architecture of non-invasive treatment, a thorough study of medical imaging analysis from a technical point of view is presented. This study also represents new thinking and developments that will push the boundaries and increase the opportunity for healthcare through AI and SML in the near future. Nowadays, SML-based applications require a lot of data quality awareness as healthcare is data-heavy, and knowledge management is paramount. Nowadays, SML in biomedical and healthcare developments needs skills, quality data consciousness for data-intensive study, and a knowledge-centric health management system. As a result, the merits, demerits, and precautions need to take ethics and the other effects of AI and SML into consideration. The overall insight in this paper will help researchers in academia and industry to understand and address the future research that needs to be discussed on SML in the healthcare and biomedical sectors.