Breast cancer is the most common cancer detected in women and current screening methods for the disease are not sensitive. Volatile organic compounds (VOCs) include endogenous metabolites that provide information about health and disease which might be useful to develop a better screening method for breast cancer. The goal of this study was to classify mice with and without tumors and compare tumors localized to the mammary pad and tumor cells injected into the iliac artery by differences in VOCs in urine. After 4T1.2 tumor cells were injected into BALB/c mice either in the mammary pad or into the iliac artery, urine was collected, VOCs from urine headspace were concentrated by solid phase microextraction and results were analyzed by gas chromatography-mass spectrometry quadrupole time-of-flight. Multivariate and univariate statistical analyses were employed to find potential biomarkers for breast cancer and metastatic breast cancer in mice models. A set of six VOCs classified mice with and without tumors with an area under the receiver operator characteristic (ROC AUC) of 0.98 (95% confidence interval [0.85, 1.00]) via five-fold cross validation. Classification of mice with tumors in the mammary pad and iliac artery was executed utilizing a different set of six VOCs, with a ROC AUC of 0.96 (95% confidence interval [0.75, 1.00]).

Diabetes is a disease that involves dysregulation of metabolic processes. Patients with type 1 diabetes (T1D) require insulin injections and measured food intake to maintain clinical stability, manually tracking their results by measuring blood glucose levels. Low blood glucose levels, hypoglycemia, can be extremely dangerous and can result in seizures, coma, or even death. Canines trained as diabetes alert dogs (DADs) have demonstrated the ability to detect hypoglycemia from breath, which led us to hypothesize that hypoglycemia, a metabolic dysregulation leading to low blood glucose levels, could be identified through analyzing volatile organic compounds (VOCs) contained within breath. We hoped to replicate the canines’ detection ability and success by analytically using gas chromatography/mass spectrometry of VOCs in 128 breath samples collected from 52 youths with T1D at two different diabetes camps. We used different tests for significance including Ranksum, Student’s T-test, and difference between means, and found a subset of 56 traces of potential metabolites. Principle component and linear discriminant analysis (LDA) confirmed a hypoglycemic signature likely resides within this group. Supervised machine learning combined with LDA narrowed the list of likely components to seven. The technique of leave one out cross validation demonstrated the model thus developed has a sensitivity of 91% (95% confidence interval (CI) [57.1, 94.7]) and a specificity of 84% (95% CI [73.0, 92.7]) at identifying hypoglycemia. Confidence intervals were obtained by bootstrapping. These results demonstrate that it is possible to differentiate breath samples obtained during hypoglycemic events from all other breath samples by analytical means and could lead to developing a simple analytical monitoring device as an alternative to using DADs.

Organic–inorganic hybrid perovskites, direct band-gap semiconductors, have shown tremendous promise for optoelectronic device fabrication. We report the first colloidal synthetic approach to prepare ultrasmall (∼1.5 nm diameter), white-light emitting, organic–inorganic hybrid perovskite nanoclusters. The nearly pure white-light emitting ultrasmall nanoclusters were obtained by selectively manipulating the surface chemistry (passivating ligands and surface trap-states) and controlled substitution of halide ions. The nanoclusters displayed a combination of band-edge and broadband photoluminescence properties, covering a major part of the visible region of the solar spectrum with unprecedentedly large quantum yields of ∼12% and photoluminescence lifetime of ∼20 ns. The intrinsic white-light emission of perovskite nanoclusters makes them ideal and low cost hybrid nanomaterials for solid-state lighting applications.