Search

This preliminary study investigated the tolerability and efficacy of a novel mattress technology-the Sound-To-Sleep (STS) system-in the treatment of sleep problems in children with autism.After screening, 45 children, ages 2.5 to 12.9 years, were randomized to order of mattress technology use (On-Off vs. Off-On). Treatment conditions (On vs. Off) lasted two weeks with immediate crossover. Tolerability, including study discontinuation and parent-report of mattress tolerance and ease of use, was tracked throughout the study. Efficacy assessments were obtained at baseline, prior to crossover, and end of study and included measures of autism traits, other psychopathology symptoms, sensory abnormalities, communication difficulties, quality of life, sleep diary parameters, and single-blinded actigraphy-derived sleep parameters. Statistical analyses evaluated differences in tolerability and efficacy when the STS system was on versus off.STS system use was well tolerated (n = 2, 4.4% dropout) and resulted in parent-reported sleep quality improvements (STS off mean = 4.3, 95% CI = 4.05-4.54 vs. on mean = 4.9, 95%CI = 4.67-5.14). The technology was described by parents as very easy to use and child tolerance was rated as good. Parent-diary outcomes indicated improvements in falling asleep and reduced daytime challenging behavior. Actigraphy-derived sleep parameters indicated improved sleep duration and sleep efficiency. Improvements in child and family quality of life were identified on parent questionnaires.A future large sample phase 2 trial of the STS system is warranted and would benefit from extended study duration, an objective primary efficacy outcome, and careful attention to methodological issues that promote compliance with the intervention and study procedures.

Abnormal eye gaze is a hallmark characteristic of autism spectrum disorder (ASD), and numerous studies have identified abnormal attention patterns in ASD. The primary aim of the present study was to create an objective, eye tracking-based autism risk index.In initial and replication studies, children were recruited after referral for comprehensive multidisciplinary evaluation of ASD and subsequently grouped by clinical consensus diagnosis (ASD n = 25/15, non-ASD n = 20/19 for initial/replication samples). Remote eye tracking was blinded to diagnosis and included multiple stimuli. Dwell times were recorded to each a priori-defined region of interest (ROI) and averaged across ROIs to create an autism risk index. Receiver operating characteristic curve analyses examined classification accuracy. Correlations with clinical measures evaluated whether the autism risk index was associated with autism symptom severity independent of language ability.In both samples, the autism risk index had high diagnostic accuracy (area under the curve [AUC] = 0.91 and 0.85, 95% CIs = 0.81-0.98 and 0.71-0.96), was strongly associated with Autism Diagnostic Observation Schedule-Second Edition (ADOS-2) severity scores (r = 0.58 and 0.59, p .001), and not significantly correlated with language ability (r ≤| -0.28|, p.095).The autism risk index may be a useful quantitative and objective measure of risk for autism in at-risk settings. Future research in larger samples is needed to cross-validate these findings. If validated and scaled for clinical use, this measure could inform clinical judgment regarding ASD diagnosis and track symptom improvements.

IntroductionAlterations of serum metabolites may allow us to identify individuals with lung cancer and advance our understanding of the nature and treatment of their cancer. We aimed to identify serum metabolites that differentiate patients with lung cancer from at-risk controls.MethodsSerum samples from patients with biopsy-confirmed untreated stage I through stage III non–small cell lung cancer and at-risk controls were divided into fractions for analysis by ultrahigh-performance liquid chromatography–tandem mass spectrometry and gas chromatography–mass spectrometry. Compounds were identified by comparison with library entries of purified standards. Differences in concentrations of single metabolites and metabolite ratios were identified. Prediction models were developed.ResultsSerum samples from 284 subjects was analyzed. The subjects' mean age was 67 and 48% were female. Ninety-four patients had lung cancer (50 had adenocarcinoma and 44 had squamous cell carcinoma), 44% had stage I disease, 17% had stage II disease, and 39% had stage III disease. The patients with cancer were slightly older than the controls (68.7 versus 66.2 years, p = 0.013). A total of 534 metabolites were identified in eight metabolite superpathways and 73 subpathways. The concentrations of 149 metabolites differed significantly (q values

Volatile organic compounds present in the exhaled breath have shown promise as biomarkers of lung cancer. Advances in colorimetric sensor array technology, breath collection methods, and clinical phenotyping may lead to the development of a more accurate breath biomarker.Perform a discovery-level assessment of the accuracy of a colorimetric sensor array-based volatile breath biomarker.Subjects with biopsy-confirmed untreated lung cancer, and others at risk for developing lung cancer, performed tidal breathing into a breath collection instrument designed to expose a colorimetric sensor array to the alveolar portion of the breath. Random forest models were built from the sensor output of 70% of the study subjects and were tested against the remaining 30%. Models were developed to separate cancer and subgroups from control, and to characterize the cancer. Additional models were developed after matching the clinical phenotypes of cancer and control subjects.Ninety-seven subjects with lung cancer and 182 control subjects participated. The accuracies, reported as C-statistics, for models of cancer and subgroups versus control ranged from 0.794 to 0.861. The accuracy was improved by developing models for cancer and control groups selected through propensity matching for clinical variables. A model built using only subjects from the largest available clinical subgroup (49 subjects) had a C-statistic of 0.982. Models developed and tested to characterize cancer histology, and to compare early- with late-stage cancer, had C-statistics of 0.881-0.960.The colorimetric sensor array signature of exhaled breath volatile organic compounds was capable of distinguishing patients with lung cancer from clinically relevant control subjects in a discovery level trial. The incorporation of clinical phenotypes into the further development of this biomarker may optimize its accuracy.