Automatic speech analysis for detecting cognitive decline of older adults.

Background: Speech analysis has been expected to help as a screening tool for early detection of Alzheimer's disease (AD) and mild-cognitively impairment (MCI). Acoustic features and linguistic features are usually used in speech analysis. However, no studies have yet determined which type of features provides better screening effectiveness, especially in the large aging population of China. Objective: Firstly, to compare the screening effectiveness of acoustic features, linguistic features, and their combination using the same dataset. Secondly, to develop Chinese automated diagnosis model using self-collected natural discourse data obtained from native Chinese speakers. Methods: A total of 92 participants from communities in Shanghai, completed MoCA-B and a picture description task based on the Cookie Theft under the guidance of trained operators, and were divided into three groups including AD, MCI, and heathy control (HC) based on their MoCA-B score. Acoustic features (Pitches, Jitter, Shimmer, MFCCs, Formants) and linguistic features (part-of-speech, type-token ratio, information words, information units) are extracted. The machine algorithms used in this study included logistic regression, random forest (RF), support vector machines (SVM), Gaussian Naive Bayesian (GNB), and k-Nearest neighbor (kNN). The validation accuracies of the same ML model using acoustic features, linguistic features, and their combination were compared. Results: The accuracy with linguistic features is generally higher than acoustic features in training. The highest accuracy to differentiate HC and AD is 80.77% achieved by SVM, based on all the features extracted from the speech data, while the highest accuracy to differentiate HC and AD or MCI is 80.43% achieved by RF, based only on linguistic features. Conclusion: Our results suggest the utility and validity of linguistic features in the automated diagnosis of cognitive impairment, and validated the applicability of automated diagnosis for Chinese language data.

A sensitive "turn-on" Schiff-base fluorescent probe for the selective detection of Fe3+ and bio-imaging.

A novel Schiff-base fluorescent probe, 4-(N-(2- hydroxyl-1-naphthalymethylimino)-ethylamino) -7-nitro-1,2,3-benzoxadiazole (HENB) was synthesized and utilized for spectral sensing of Fe3+ ions at neutral pH. The binding of Fe3+ to HENB in C2H5OH-HEPES buffer (1:1 v/ v, 25 mM, pH 7.2) resulted in a pronounced emission enhancement at 530 nm, which is possibly due to the inhibition of photo-induced electron transfer (PET) process as well as the chelation enhanced fluorescence (CHEF) effect. HENB shows good selectivity and sensitivity toward Fe3+ with the detection limit as low as 4.51 nM. Test strips made of HENB was used for rapid "naked-eye" detection of Fe3+ ions in aqueous medium. Moreover, HENB was successfully applied in fluorescence imaging of exogenous and endogenous Fe3+ in live Hela cells as well as zebrafish. Importantly, HENB is capable of effectively monitoring the variations of Fe3+ in living cells during ferroptosis process.