Laboratories

The Joint Committee for Guides in Metrology (JCGM) today presents a definition of measurement uncertainty that modifies the previous one and improves the management of scenarios other than scalar (quantitative) measurements, such as classificatory or qualitative (nominal and ordinal) properties. Nominal results are often found in biology and medicine. For the accreditation of medical laboratories and testing laboratories, both ISO 15189 and ISO 17025 require the management of these situations, using the professional expertise of specialists with the support of manufacturers. Some of the members of JCGM WG2 developed a discussion on the concept of measurement uncertainty and raised some criticisms. ISO produces detailed guides for this purpose, such as ISO 20914, ISO 27877, ISO 16393, ISO 20397-2, and ISO 22692. Laboratories now have all the tools they need to meet accreditation requirements on uncertainty.

The development of an immersive virtual reality laboratory in the facilities of a private Mexican university is presented. This laboratory contemplates the use of different disciplines and different student profiles, for which it was developed considering technological, ergonomic, educational, and disciplinary requirements. A primary assessment of a selected group of students was developed to find out the initial level of satisfaction with the user experience in the laboratory and the improvements to be proposed for future adaptations.

Exposure to chemical irritants in laboratory and medical environments poses significant health risks to workers, particularly in relation to asthma-like symptoms. Routine cleaning practices, which often involve the use of strong chemical agents to maintain hygienic settings, have been shown to contribute to respiratory issues. Laboratories, where chemicals such as hydrochloric acid and ammonia are frequently used, represent an underexplored context in the study of occupational asthma. While much of the research on chemical exposure has focused on industrial and high-risk occupations or large cohort populations, less attention has been given to the risks in laboratory and medical environments, particularly for professional cleaning staff. Given the growing reliance on cleaning agents to maintain sterile and safe workspaces in scientific research and healthcare facilities, this gap is concerning. This study developed an exploratory simulation tool, using a simulated cohort based on key demographic and exposure patterns from foundational research, to assess the impact of chemical exposure from cleaning products in laboratory environments. Four supervised machine learning models were applied to evaluate the relationship between chemical exposures and asthma-like symptoms: (1) Decision Trees, (2) Random Forest, (3) Gradient Boosting, and (4) XGBoost. High exposures to hydrochloric acid and ammonia were found to be significantly associated with asthma-like symptoms, and workplace type also played a critical role in determining asthma risk. This research provides a data-driven framework for assessing and predicting asthma-like symptoms in professional cleaning workers exposed to cleaning agents and highlights the potential for integrating predictive modeling into occupational health and safety monitoring. Future work should explore dose–response relationships and the temporal dynamics of chemical exposure to further refine these models and improve understanding of long-term health risks.