Biological Nanotherapeutics for Liver Disease.

Extracellular vesicles (EVs) are a heterogeneous group of biological nano-sized vesicles that are released from cells and contribute to intercellular communication. Emerging knowledge about their biogenesis, composition, release, and uptake has resulted in broad interest in elucidating their potential roles in disease pathophysiology. The distinct biological properties of these biological nanoparticles emphasize several appealing advantages for potential therapeutic applications compared with the use of synthetic nanoparticles. When administered systemically, EVs are taken up and sequestered within the liver, further emphasizing opportunities for therapeutic use. Consequently, there is growing interest in their use for liver diseases. EVs can be used directly as therapeutics, and several studies have highlighted the intrinsic therapeutic properties of mesenchymal stem cell-derived EVs for chronic and acute liver diseases. Alternatively, EVs can be modified to facilitate their use for the delivery of therapeutic cargo. In this review, we discuss the cellular sources of EV, provide a concise overview of their potential use in diverse processes, and outline several promising applications for the use of EV-based therapeutics for liver diseases. The use of EV-based therapeutics provides a viable approach to target hepatic pathophysiology.

Machine learning analysis of volatolomic profiles in breath can identify non-invasive biomarkers of liver disease: A pilot study.

Disease-related effects on hepatic metabolism can alter the composition of chemicals in the circulation and subsequently in breath. The presence of disease related alterations in exhaled volatile organic compounds could therefore provide a basis for non-invasive biomarkers of hepatic disease. This study examined the feasibility of using global volatolomic profiles from breath analysis in combination with supervised machine learning to develop signature pattern-based biomarkers for cirrhosis. Breath samples were analyzed using thermal desorption-gas chromatography-field asymmetric ion mobility spectroscopy to generate breathomic profiles. A standardized collection protocol and analysis pipeline was used to collect samples from 35 persons with cirrhosis, 4 with non-cirrhotic portal hypertension, and 11 healthy participants. Molecular features of interest were identified to determine their ability to classify cirrhosis or portal hypertension. A molecular feature score was derived that increased with the stage of cirrhosis and had an AUC of 0.78 for detection. Chromatographic breath profiles were utilized to generate machine learning-based classifiers. Algorithmic models could discriminate presence or stage of cirrhosis with a sensitivity of 88-92% and specificity of 75%. These results demonstrate the feasibility of volatolomic profiling to classify clinical phenotypes using global breath output. These studies will pave the way for the development of non-invasive biomarkers of liver disease based on volatolomic signatures found in breath.