Optimized low pH formulation of niacinamide enhances induction of autophagy marker ATG5 gene expression and protein levels in human epidermal keratinocytes.

BACKGROUND: Macromolecules in skin cells are damaged when exposed to environmental stressors, leading to disrupted cellular function and homeostasis. While epidermal turnover can eliminate some of this damage, autophagy can rapidly remove these defective components. Niacinamide (Nam) is known to induce autophagy and optimizing formulations to maximize this response could provide improved homeostasis in stressed skin. OBJECTIVE: To determine (i) whether Nam can induce autophagy related 5 (ATG5), an autophagy marker, in human keratinocytes and (ii) whether optimized low pH Nam formulations can enhance the response in 3D skin models. METHODS: Human keratinocytes treated with Nam were evaluated for autophagosome accumulation and induction of ATG5 by gene expression, immunoblotting and immune-fluorescence microscopy. 3D skin equivalents were topically treated with Nam formulations at pH 5.8 and 3.8. Gene expression profiling and immunoblot analysis of ATG5 were performed. RESULTS: Nam treatment of keratinocytes led to an accumulation of autophagosomes with a maximal signal at 48 h. Gene expression of ATG5 was induced by Nam, and immunoblots stained for ATG5 showed a significant increase after 6 h of treatment. Gene expression profiling of 3D epidermal skin equivalents treated with Nam at pH 3.8 showed stronger induction of autophagy-related genes, including ATG5, compared with pH 5.8 formulas. Enrichment for gene ontology terms on autophagy showed an increased linkage with Nam formulas at pH 3.8. CONCLUSIONS: We found that Nam induces autophagosome accumulation and ATG5 levels in keratinocytes. We also discovered that a Nam formulation at pH 3.8 can further increase levels of ATG5 in 3D skin models when compared to Nam at pH 5.8. These data support that Nam can induce autophagy in keratinocytes and formulations at pH 3.8 can enhance the impact. We hypothesize that optimized formulations at pH 3.8 can improve skin ageing appearance via autophagy induction.

Cognitive Digital Biomarkers from Automated Transcription of Spoken Language.

BACKGROUND: Although patients with Alzheimer's disease and other cognitive-related neurodegenerative disorders may benefit from early detection, development of a reliable diagnostic test has remained elusive. The penetration of digital voice-recording technologies and multiple cognitive processes deployed when constructing spoken responses might offer an opportunity to predict cognitive status. OBJECTIVE: To determine whether cognitive status might be predicted from voice recordings of neuropsychological testing. DESIGN: Comparison of acoustic and (para)linguistic variables from low-quality automated transcriptions of neuropsychological testing (n = 200) versus variables from high-quality manual transcriptions (n = 127). We trained a logistic regression classifier to predict cognitive status, which was tested against actual diagnoses. SETTING: Observational cohort study. PARTICIPANTS: 146 participants in the Framingham Heart Study. MEASUREMENTS: Acoustic and either paralinguistic variables (e.g., speaking time) from automated transcriptions or linguistic variables (e.g., phrase complexity) from manual transcriptions. RESULTS: Models based on demographic features alone were not robust (area under the receiver-operator characteristic curve [AUROC] 0.60). Addition of clinical and standard acoustic features boosted the AUROC to 0.81. Additional inclusion of transcription-related features yielded an AUROC of 0.90. CONCLUSIONS: The use of voice-based digital biomarkers derived from automated processing methods, combined with standard patient screening, might constitute a scalable way to enable early detection of dementia.