Background. The crackles in patients with interstitial pulmonary fibrosis (IPF) can be difficult to distinguish from those heard in patients with congestive heart failure (CHF) and pneumonia (PN). Misinterpretation of these crackles can lead to inappropriate therapy. The purpose of this study was to determine whether the crackles in patients with IPF differ from those in patients with CHF and PN.Methods. We studied 39 patients with IPF, 95 with CHF and 123 with PN using a 16-channel lung sound analyzer. Crackle features were analyzed using machine learning methods including neural networks and support vector machines.Results. The IPF crackles had distinctive features that allowed them to be separated from those in patients with PN with a sensitivity of 0.82, a specificity of 0.88 and an accuracy of 0.86. They were separated from those of CHF patients with a sensitivity of 0.77, a specificity of 0.85 and an accuracy of 0.82.Conclusion. Distinctive features are present in the crackles of IPF that help separate them from the crackles of CHF and PN. Computer analysis of crackles at the bedside has the potential of aiding clinicians in diagnosing IPF more easily and thus helping to avoid medication errors.

Here we report the results of the subgroup analyses of an observational cohort of children whose parents completed the Autism Treatment Evaluation Checklist (ATEC) over the period of several years. A linear mixed effects model was used to evaluate longitudinal changes in ATEC scores within different patient subgroups. All groups decreased their mean ATEC score over time indicating improvement of symptoms, however there were significant differences between the groups. Younger children improved more than the older children. Children with milder ASD improved more than children with more severe ASD in the Communication subscale. There was no difference in improvement between females vs. males. One surprising finding was that children from developed English-speaking countries improved less than children from non-English-speaking countries.

Prefrontal synthesis (PFS) is defined as the ability to juxtapose mental visuospatial objects at will. Paralysis of PFS may be responsible for the lack of comprehension of spatial prepositions, semantically-reversible sentences, and recursive sentences observed in 30 to 40% of individuals with autism spectrum disorder (ASD). In this report we present data from a three-year-long clinical trial of 6454 ASD children age 2 to 12 years, which were administered a PFS-targeting intervention. Tablet-based verbal and nonverbal exercises emphasizing mental-juxtaposition-of-objects were organized into an application called Mental Imagery Therapy for Autism (MITA). The test group included participants who completed more than one thousand exercises and made no more than one error per exercise. The control group was selected from the rest of participants by a matching procedure. Each test group participant was matched to the control group participant by age, gender, expressive language, receptive language, sociability, cognitive awareness, and health score at first evaluation using propensity score analysis. The test group showed a 2.2-fold improvement in receptive language score vs. control group (p < 0.0001) and a 1.4-fold improvement in expressive language (p = 0.0144). No statistically significant change was detected in other subscales not targeted by the exercises. These findings show that language acquisition improves after training PFS and that a further investigation of the PFS-targeting intervention in a randomized controlled study is warranted.

Most early-intervention Autism Spectrum Disorder (ASD) clinical trials are limited by the availability of psychometric technicians who assess each child’s abilities before and after therapeutic intervention. If parents could administer regular psychometric evaluations of their children, then the cost of clinical trials will be reduced, enabling longer clinical trials with the larger number of participants. The Autism Treatment Evaluation Checklist (ATEC) was designed nearly two decades ago to provide such a tool, but the norms on the longitudinal changes in ATEC in the “treatment as usual” population were lacking. Here we report the norms of the observational cohort who voluntarily completed ATEC evaluations over the period of four years from 2013 to 2017.

We were interested in how the transmission of sound through the lung was affected by varying air content in intact humans as a method of monitoring tissue properties noninvasively. To study this, we developed a method of measuring transthoracic sound transit time accurately. We introduced a “coded” sound at the mouth and measured the transit time at multiple microphones placed over the chest wall by using a 16-channel lung sound analyzer (Stethographics). We used a microphone placed over the neck near the trachea as our reference and utilized cross-correlation analysis to calculate the transit times. The use of the coded sound, composed of a mix of frequencies from 130 to 150 Hz, greatly reduced the ambiguity of the cross-correlation function. The measured transit time varied from 1 ms at the central locations to 5 ms at the lung bases. Our results also indicated that transit time at all locations decreased with increasing lung volume. We found that these results can be described in terms of a model in which sound transmission through the lung is treated as a combination of free-space propagation through the trachea and a propagation through a two-phase system in the parenchyma.