Psychophysiological Arousal in Young Children Who Stutter: An Interpretable AI Approach.

The presented first-of-its-kind study effectively identifies and visualizes the second-by-second pattern differences in the physiological arousal of preschool-age children who do stutter (CWS) and who do not stutter (CWNS) while speaking perceptually fluently in two challenging conditions: speaking in stressful situations and narration. The first condition may affect children's speech due to high arousal; the latter introduces linguistic, cognitive, and communicative demands on speakers. We collected physiological parameters data from 70 children in the two target conditions. First, we adopt a novel modality-wise multiple-instance-learning (MI-MIL) approach to classify CWS vs. CWNS in different conditions effectively. The evaluation of this classifier addresses four critical research questions that align with state-of-the-art speech science studies' interests. Later, we leverage SHAP classifier interpretations to visualize the salient, fine-grain, and temporal physiological parameters unique to CWS at the population/group-level and personalized-level. While group-level identification of distinct patterns would enhance our understanding of stuttering etiology and development, the personalized-level identification would enable remote, continuous, and real-time assessment of stuttering children's physiological arousal, which may lead to personalized, just-in-time interventions, resulting in an improvement in speech fluency. The presented MI-MIL approach is novel, generalizable to different domains, and real-time executable. Finally, comprehensive evaluations are done on multiple datasets, presented framework, and several baselines that identified notable insights on CWSs' physiological arousal during speech production.

A sensitive and practical method to detect the T790M mutation in the epidermal growth factor receptor.

The current study aimed to develop a method to rapidly, sensitively and practically screen for the epidermal growth factor receptor (EGFR) T790M mutation. This method combines an allele-specific competitive blocker (ACB) with a TaqMan quantitative polymerase chain reaction (PCR) amplification refractory mutation system (ARMS) in a one-step reaction. Using a mimic of a human genomic DNA panel containing serially diluted mutant alleles, the performance efficacy of this method was assessed. Using this method, the EGFR T790M mutation was detected in tyrosine kinase inhibitor (TKI)-naïve samples obtained from 27 non-small cell lung cancer (NSCLC) patients with EGFR-activating mutations. The association between de novo T790M mutations and the clinical benefit of EGFR-TKI treatment was also analysed. The sensitivity of this method was as low as 0.01%. In the samples from the 27 NSCLC patients, this method identified 6 mutant patients (22.2%), which was higher than the detection rate with scorpion ARMS (0.0%). No clinical variables were associated with the occurrence of a de novo T790M mutation. The median progression-free survival time in the TKI-naïve patients with a T790M mutation was shorter that that of patients without the mutation, but the difference was not significant (3.2 vs. 19.5 months, respectively; P=0.256). The median overall survival time in the groups with or without T790M mutation also did not significantly differ (10 vs. 20 months, respectively; P=0.689). Overall, the ACB-ARMS PCR method could be useful for detecting the EGFR T790M mutation in clinical samples that contain only a small number of mutant alleles. The clinical significance of a de novo T790M mutation should be further investigated.