Comparative Effects of Applied Behavior Analysis on Male and Female Individuals With Autism Spectrum Disorder.

Introduction Current evidence-based treatments for autism spectrum disorder (ASD) are based on applied behavior analysis (ABA). However, research on gender differences in ABA therapy response is limited. This study seeks to (1) confirm the 4:1 male-to-female ratio reported in the literature and (2) identify any possible gender differences in target behaviors over seven timepoints measured every two weeks. Materials and methods  For three months, from March 19, 2023, to June 11, 2023, a team of 3-5 behavioral technicians per individual collected daily data on general target mastery for 100 individuals with ASD treated with ABA. Data was collected at seven timepoints every two weeks. Descriptive demographics were computed. Two independent sample t-tests were performed to determine significant or nonsignificant gender differences with the seven timepoint variables.  Results Nonstatistically significant gender differences (p > .05) were found on all seven cumulative target behavior timepoints measured at two-week intervals. For targets mastered Time 1, baseline between males and females, there was no significant difference in the means for males (M = 1.0571, SD = 1.9196) and females (M = 2.0455, SD = 3.9457) (t(90) = -1.591, p = 0.115, confidence interval (CI) = -2.2223, 0.2456, d = -0.389). For targets mastered Time 2, two weeks between males and females, there was no significant difference in the means for males (M = 3.7132; SD = 4.5065) and females (M = 4.0682, SD = 5.1508) (t(88) = -0.310, p = 0.757, CI = -2.6305, 1.92056, d = -0.076). For targets mastered Time 3, four weeks between males and females, there was no significant difference in the means for males (M = 7.0956; SD = 8.7781) and females (M = 8.6136; SD = 11.2799) (t(88) = -0.656, p = 0.514, CI = -6.1173, 3.0811, d = -0.161). For targets mastered Time 4, six weeks between males and females, there was no significant difference in the means for males (M = 13.1728, SD = 16.2003) and females (M = 13.0682, SD = 16.9272) (t(88) = 0.026, p = 0.979, CI = -7.8779, 8.0871, d = 0.006). For targets mastered Time 5, eight weeks between males and females, there was no significant difference in the means for males (M = 17.2096; SD = 18.8546) and females (M = 17.4286, SD = 22.1683) (t(87) = -0.045, p = 0.965, CI = -9.9773, 9.5393, d = -0.011). For targets mastered Time 6, 10 weeks between males and females, there was no significant difference in the means for males (M = 21.0074, SD = 21.3329) and females (M = 20.6818, SD = 26.1231) (t(88) = 0.059, p = 0.953, CI = -10.6752, 11.3262, d = 0.014). For targets mastered Time 7, 12 weeks between males and females, there was no significant difference in the means for males (M = 26.1196, SD = 24.2235) and females (M = 29.6364, SD = 33.7406) (t(89) = -0.536, p = 0.593, CI = -16.5431, 9.5094, d = -0.131). Conclusions The study indicates that ABA treatments may be equally beneficial for both genders with ASD, showing no significant gender differences. However, the broad CIs in this study imply a level of statistical uncertainty, indicating potential gender differences, suggesting the results may not be uniform across genders. These findings challenge assumptions on gender-specific treatment responses, implying that ABA treatments shouldn't be recommended based on gender. Instead, individual needs should guide treatment recommendations. Future research could consider other response moderators like age, ASD severity, or coexisting mental health conditions.

A Non-parametric Analysis of Gender Differences With the Impacts of Applied Behavior Analysis on Autistic Individuals.

Introduction Considering the scarcity of research that directly investigates the differences between genders in their response to applied behavior analysis (ABA) therapy for individuals diagnosed with autism spectrum disorder (ASD), the objective of this study is twofold. First, it aims to reinforce the male-to-female ratio reported in existing scientific literature, thereby contributing to a broader understanding of gender distribution in ABA therapy for ASD. Second, it seeks to identify gender-based differences in aggregate target behaviors at various time intervals using three distinct datasets. The goal is to determine if gender influences the effectiveness of ABA therapy for ASD, which could inform future therapeutic strategies. Ultimately, this study strives to enhance our understanding of gender disparities in ABA therapy response among ASD individuals and hopes to improve therapeutic outcomes for all, regardless of gender. Materials and methods Three to five behavioral technicians per child collected daily general target mastery data for 263 individuals with autism. This data was gathered using a large N design through retrospective chart reviews within the "Catalyst" tracking software (DataFinch Technologies, Atlanta, USA). Three separate datasets were collected from June 7, 2023 to January 7, 2024. Behavior analysts employed a mixed model of discrete trial training, mass trials, and naturalistic environment treatment over seven months. General target mastery data was assembled for 259 children and four adults, with seven data instances missing. Descriptive statistics encompassed central tendency and dispersion measures, including the data distribution's mean, standard deviation, median, and range. Non-parametric inferential analysis was performed with the Mann-Whitney U test. Results Mann-Whitney U computations resulted in non-significant gender differences on all gender comparisons for the three datasets covering the seven-month timeframe. Dataset #1: Time 1-(U=727.5, p=0.846, ή2=0.0002, Time 2-(U=736, p=0.910, ή2=0.00005), Time 3-(U=687.5, p=0.569, ή2=0.001) Dataset #2: Time 1-(U=781, p=0.383, ή2=0.003), Time 2-(U=819.5, p=0.585, ή2=0.001), Time 3-(U=825, p=0.618, ή2=0.001) Dataset #3: Time 1-(U=395, p=0.198, ή2=0.007), Time 2-(U=373.5, p=0.365, ή2 =0.003), Time 3-(U=363, p=0.471, ή2=0.002), Time 4-(U=366.5, p=0.436, ή2 =0.003), Time 5-(U=371, p=0.391, ή2=0.003), Time 6-(U=394, p=0.208, ή2=0.007), Time 7-(U=373, p=0.373, ή2=0.003), Time 8-(U=371.5, p=0.387, ή2=0.003), Time 9-(U=464.5, p=0.512, ή2=0.002), Time 10-(U=356.5, p=0.546, ή2=0.002), Time 11-(U=357.5, p=0.535, ή2=0.002), Time 12-(U=350.5, p=0.346, ή2=0.004) Conclusions This study suggests no significant gender differences in response to ABA therapy among individuals with autism, indicating its potential effectiveness for both genders. However, these findings should be interpreted cautiously due to statistical uncertainties reflected in the broad confidence intervals as they hint at possible substantial gender differences. Further research, including an extension study, must confirm these results and understand potential gender nuances in ABA therapy response. This could help tailor more effective, personalized therapeutic strategies for individuals with autism.

An Internal Consistency Reliability Study of the Catalyst Datafinch Applied Behavior Analysis Data Collection Application With Autistic Individuals.

Introduction Many psychometric studies have scrutinized the dependability of different instruments for evaluating and treating autism using applied behavior analysis (ABA). However, there has been no exploration into the psychometric attributes of the Catalyst Datafinch Applied Behavior Analysis Data Collection Application, namely, internal consistency reliability measures. Materials and methods  Four datasets were extracted (n=100, 98, 103, and 62) from published studies at The Oxford Center, Brighton, MI, ranging from March 19, 2023, through January 8, 2024, using Catalyst Datafinch as the data collection tool. All data were gathered by Board Certified Behavior Analysts (BCBAs) and behavioral technicians and designed to replicate how practitioners collect traditional paper and pencil data. SPSS Statistics (v. 29.0) computed internal consistency reliability measures, including Cronbach's alpha, inter-item, split-half, and interclass correlation coefficients. Results  Dataset #1: Cronbach's alpha was 0.916 with seven items, indicating excellent reliability. Cronbach's split-half reliability for Part 1 was 0.777, indicating good reliability, and for Part 2 was 0.972, indicating excellent reliability. Guttman split-half coefficient was 0.817, indicating good reliability. Inter-item correlation coefficients ranged from 0.474 to 0.970. The average measures interclass correlation (ICC) was 0.916, indicating excellent reliability. Single measures (ICC) reliability was 0.609, indicating acceptable reliability. Dataset #2: Cronbach's alpha was 0.954 with three items, indicating excellent reliability. Cronbach's split-half reliability for Part 1 was 0.912, indicating excellent reliability, and for Part 2 was 0.975, indicating excellent reliability. Guttman split-half coefficient was 0.917, indicating excellent reliability. Inter-item correlation coefficients ranged from 0.827 to 0.977. Average measures (ICC) was 0.954, indicating excellent reliability. Single measures (ICC) reliability was 0.875, indicating good reliability. Dataset #3: Cronbach's alpha was 0.974 with three items, indicating excellent reliability. Cronbach's split-half reliability for Part 1 was 0.978, indicating excellent reliability. Split-half reliability for Part 2 was 0.970, indicating excellent reliability. Guttman split-half coefficient was 0.935, indicating excellent reliability. Inter-item correlation coefficients ranged from 0.931 to 0.972. The average measures (ICC) was 0.974, indicating excellent reliability. Single measures (ICC) reliability was 0.926, indicating excellent reliability. Dataset #4: Cronbach's alpha was 0.980 with 12 items, indicating excellent reliability. Cronbach's split-half reliability for Part 1 was 0.973, indicating excellent reliability. Split-half reliability for Part 2 was 0.996, indicating excellent reliability. Guttman split-half coefficient was 0.838, indicating good reliability. Inter-item correlation coefficients ranged from 0.692 to 0.999. The average measures (ICC) was 0.980, indicating excellent reliability. Single measures (ICC) reliability was 0.804, indicating good reliability. Conclusions These results suggest that Catalyst Datafinch demonstrates high internal consistency reliability when used with individuals with autism. This indicates that the application is reliable for collecting and analyzing behavioral data in this population. The ratings ranged from good to excellent, indicating a high consistency in the measurements.

A Descriptive Study on the Impacts of Hyperbaric Oxygen Therapy on Autistic Individuals Using Parent Testimonies.

Introduction Hyperbaric oxygen therapy (HBOT) has been influential in treating many physical and psychological ailments, including the symptoms of autism. This current study aims to evaluate HBOT parents' goals and exit interviews describing the positive, negative, or no impacts experienced from the HBOT dives, asking the question, "Are your child's symptoms improving?" Methods Between January 2020 and July 2023, a Class B monoplace hyperbaric chamber (Sechrist 3300H, Sechrist Industries, Inc., Anaheim, California, United States) was used to administer HBOT sessions to patients with autism. Medical-grade oxygen was pressurized to 1.5-2.0 atmospheres absolute at a rate of 1-2 psi/min, with an average oxygen percentage of 100%, for up to five sessions per week. Retrospective descriptive data and patient information through parent testimonials were collected through a chart review of 30 children and one adult with autism who experienced HBOT sessions. Data were presented through exit interviews describing how parents felt about their child's progress toward goals. Four raters rated parent testimonies on a 5-point Likert scale (1 = Much worse, 2 = Somewhat worse, 3 = Stayed the same, 4 = Somewhat improved, and 5 = Much improved), and an inter-rater reliability estimate using interclass correlation (2) (r = 0.831) was derived, indicating excellent agreement between raters. Results Parents/caregivers provided testimony in an exit interview with a registered nurse after the individual with autism received an entire course of HBOT dives. Descriptive statistics resulted in Rater #1 (M = 4.19, median = 4, SD = 0.654): 87.1% of Rater #1 ratings were Somewhat improved and Much improved; Rater #2 (M = 4.23, median = 4, SD = 0.717): 83.9% of Rater #2 ratings were Somewhat improved and Much improved; Rater #3 (M = 4.23, median = 4, SD = 0.560): 93.5% of Rater #3 ratings were Somewhat improved and Much improved; and Rater #4 (M = 4.26, median = 4, SD = 0.631): 90.3% of Rater #4 ratings were Somewhat improved and Much improved. One-way ANOVA resulted in F (3,123) = 0.052, p = 0.984, which indicated a nonstatistically significant mean difference between rater groups. Conclusions The current study assessed HBOT parents'/caregivers' goals and exit interviews, describing the effects experienced from the complete course of HBOT dives on their children/individuals. A majority of parents/caregivers declared that their condition had "Much improved" or "Somewhat improved," based on the 5-point Likert scale. Based on parents'/caregivers' testimonies, HBOT was demonstrated as a safe and effective intervention, and side effects were primarily mild and did not lead to treatment discontinuation. As a result of this analysis, we recommend continued use of HBOT for treatment.

The Effects of Applied Behavior Analysis on Verbal Behavior With Autistic Individuals Using the Verbal Behavior Milestones Assessment and Placement Program (VBMAPP) and the Assessment of Basic Language and Learning Skills (ABLLS).

Introduction  Applied behavior analysis (ABA) is a fundamental practice-based intervention for treating autism spectrum disorder (ASD). Few studies have directly measured and evaluated the effects of ABA on verbal behaviors, mainly using the Verbal Behavior Milestones Assessment and Placement Program (VBMAPP) and the Assessment of Basic Language and Learning Skills (ABLLS) as outcome measures. This study aims to fill this gap by examining the relationship between ABA interventions and the enhancement of verbal skills, as measured by the VBMAPP and the ABLLS, in a convenience sample of individuals with ASD.  Materials and methods At The Oxford Centers (TOCs) in Brighton and Troy, Michigan, USA, 33 individuals with autism received treatment from January 2018 to July 2021, spanning 43 months. A pretest-posttest design was employed to retrospectively examine any impacts between ABA interventions and alterations in verbal scores among individuals with ASD. Depending on developmental age, all subjects underwent two verbal assessments with a six-month interval in-between. Twelve children were administered the VBMAPP, while 21 were given the ABLLS. Results Paired t-tests for pretest and posttest VBMAPP subscales resulted in statistically significant effects (p<0.05) for (VBMAPP - Mand), (VBMAPP - Tact), (VBMAPP - Listener Responding), (VBMAPP - Visual Perceptual Skills and Matching-to-Sample), (VBMAPP -Independent Play), (VBMAPP - Social Play), (VBMAPP - Motor Imitation), (VBMAPP - Spontaneous Vocalization), (VBMAPP - Intraverbal), (VBMAPP - Group Behavior), and (VBMAPP - Linguistic Structure). As measured by Cohen's d, effect sizes were moderate to mostly high (-0.623 to -1.688). There were non-significant results (p>0.05) for (VBMAPP - Listener Responding by Feature, Function, and Class) and (VBMAPP - Echoic). Paired t-tests for pretest and posttest ABLLS subscales resulted in statistically significant effects (p<.05) for all ABLLS scales: (ABLLS - Receptive Language), (ABLLS - Requests), (ABLLS - Labeling), (ABLLS - Intraverbals), (ABLLS - Spontaneous Vocalizations), (ABLLS - Syntax Grammar), (ABLLS - Social Interactions), and (ABLLS - Generalized Responding). As measured by Cohen's d, effect sizes were moderate to mostly high (-0.656 to -1.372). Conclusions  The administration of ABA treatments had a noteworthy influence, with statistically significant impacts on improving verbal behaviors on 11 of the 13 VBMAPP scales and all of the ABLLS scales. As measured by Cohen's d, effect sizes were moderate to high for both scales. These findings underscore the importance and effectiveness of ABA interventions in enhancing verbal skills in children with ASD. However, it's crucial to note that further confirmatory studies are required to verify the reliability of these original findings, emphasizing the ongoing need for research in this field.

Replicative Study of the Impacts of Applied Behavior Analysis on Target Behaviors in Individuals With Autism Using Repeated Measures.

Background  The effectiveness of interventions based on applied behavior analysis (ABA) for individuals with autism has been well documented in numerous meta-analyses, systematic reviews, and cost-benefit analyses. However, an observed 'efficacy-effectiveness gap' exists, which can be attributed to various factors. This third replication study, therefore, has significant implications for the field. By assessing the impact of ABA treatment, specifically involving discrete trial training and mass trials, within a naturalistic environment, the study provides valuable insights that can inform and improve the delivery of ABA treatments in real-world settings. Methods  The study was conducted using a repeated measures research design. Retrospective chart review data were collected from 62 individuals with autism, age (M=8.65, SD=4.53), all of whom were level two autistic and required moderate support in communication, socialization, and daily life. These individuals received ABA treatment over five months. The study measured cumulative target behaviors using a repeated measures design, which allowed for the identification of statistically significant differences across 12 time points. This robust methodology ensures the validity and reliability of the study's findings. Results  Mixed repeated measures analysis of variance (ANOVA) indicated statistical significance (sphericity assumed), F(11,495) = 55.432, p < 0.001 (time). Multiple comparisons using bootstrapped paired t-tests showed p < 0.05 on time points 1-8 and non-significance (p > 0.05) on time points 9-12. There was a significant interaction effect (sphericity assumed) with time x (age category), F(44,495) = 2.338, p < 0.001. Interaction contrasts indicated statistically significant differences over time, mainly within the one-year to four-year-old, five to eight-year-old, and most in the nine to 12-year-old age groups. There was some significance within the 13- to 16-year-old age group and no significance within the 17- to 26-year-old age group. Conclusions  Over five months, individuals with autism who underwent ABA treatments demonstrated a statistically significant enhancement in general target behaviors. This finding is crucial as it underscores the effectiveness of ABA treatments in a naturalistic environment. Moreover, the study's discovery of a significant interaction between time and age in these behaviors provides valuable insights into the impact of age on treatment outcomes. Extensive large-N studies of general ABA broad effectiveness and repeated measures designs are lacking and can lead to further research to improve quality and outcomes. These findings contribute to the body of empirical evidence and emphasize the importance of replicative efficacy studies in ensuring the reliability of research findings.

Examining the Effects of Discrete Trials, Mass Trials, and Naturalistic Environment Training on Autistic Individuals Using Repeated Measures.

INTRODUCTION: Behavioral interventions based on applied behavior analysis (ABA) form current evidence-based practices in treating autism spectrum disorder (ASD). Research is scarce relative to the broad effects of intensive repetitive, discrete trial training, and mass trials combined with a naturalistic environment as measured by overall general target behaviors. The primary objective of this study was to evaluate the effectiveness of a mixed behavioral model consisting of discrete trial training and mass trial interventions in the naturalistic environment, using a repeated measures design with a retrospective snapshot cohort of 93 individuals with autism. METHODS: A repeated measures analysis tracked 89 autistic children with four adult autistic individuals over seven time points during a three-month snapshot period from March 19, 2023, to June 11, 2023. This study determined the effectiveness of applied behavior analysis (ABA) interventions combining discrete trial training, mass trials, and naturalistic environment training on mastered broad target behaviors in autistic individuals using a mixed (between and within) ANOVA statistical design. RESULTS: Mixed (between and within) ANOVA indicated overall statistical significance, F (6,674)=45.447, p<0.001, partial eta squared=0.365 across time. These results indicated a large effect size. Multiple comparisons showed statistical significance (p<0.001) on all 21 multiple comparisons between timepoints. There was also a significant interaction effect with time × age category, F (24,474)=2.961, p<0.001, partial eta squared=0.130. These results also indicated a large effect size. CONCLUSIONS: Autistic individuals who received applied behavior analysis combining discrete trial training, mass trials, and naturalistic environment training intervention demonstrated statistically significant improvement in target behaviors over the three-month snapshot period, the most prominent being in the 13-16 years age category.

Impact of Applied Behavior Analysis on Autistic Children Target Behaviors: A Replication Using Repeated Measures.

INTRODUCTION: Applied behavior analysis (ABA) is a primary evidence-based practice in treating autism spectrum disorder (ASD). Ongoing research is needed to report the results of ABA relative to attaining target behaviors. This study aims to replicate the results of previous research to determine the effectiveness of ABA of target behaviors in autistic children with a new timepoint sample of data.  Materials & methods: A repeated measures analysis tracked 98 autistic children, which included four adult participants, over three timepoints during a one-month snapshot period from 6/7/23 to 7/7/23. This study used a retrospective chart review to gather data on target behaviors to determine the effectiveness of ABA treatments across age categories. A mixed (between x within) analysis of variance (ANOVA) and subsequent post hoc and interaction contrasts were used to determine statistical significance. RESULTS: Mixed (between x within) ANOVA indicated statistical significance (sphericity assumed), F(2,160) = 32.893, and p < 0.05, across time. Using bootstrapped paired t-tests, multiple comparisons indicated p < 0.001 on all three multiple comparisons, with Bonferroni corrected α = 0.017. There was also a non-significant interaction effect (sphericity assumed) with (time) x (age category), F(8,160) = 0.333, p = 0.952, likely due to sizeable within-group variation resulting in a lowered statistical power.  Conclusions: This replication found that autistic children receiving the ABA intervention demonstrated statistically significant improvement in target behaviors over the one-month snapshot period.

Predicting Behavioral Target Mastery With Age, Intensity and Duration, Open Targets, and Maintenance Failure With Applied Behavior Analysis in Individuals With Autism.

Background Researchers have studied the effects of age, treatment intensity, and treatment duration with applied behavior analysis (ABA) outcomes in autistic individuals. This study's primary objective was to evaluate the predictive capabilities of age, intensity, and duration of treatment, open behavioral targets, and behavior maintenance failure on behavioral target mastery. Methods A retrospective cohort of 100 autistic individuals treated with ABA with functional analysis and discrete trial training, mass trials, and naturalistic training were treated and analyzed. Target behavioral mastery data was collected via a retrospective chart review contained within the "Catalyst" tracking software. ABA treatment was administered for three months between March 19, 2023, and June 11, 2023. Multiple linear regression was performed using the percentage of behavioral targets mastered as the dependent variable. The independent variables were age, average trials to behavioral mastery, average teaching days to behavioral mastery, and percentage of behavioral targets that failed in maintenance. Results The multiple linear regression model was statistically significant (R=0.443, R²=0.196, adjusted R2=0.150, F(5, 87)=4.239, p=0.002). The average teaching days to mastery (ß=0.416, p=0.019) and percentage of targets failed in maintenance (ß=0.201, p=0.047) significantly predicted the percentage of behavioral targets mastered. However, age (ß=0.079, p=0.419), average trials to mastery (ß=-0.271, p=0.114), and open targets (ß=0.184, p=0.081) did not significantly predict the percentage of behavioral targets mastered. The analysis of variance (ANOVA) resulted in non-significant (p>0.05) age group differences between the above variables. Conclusions The predictor variables average teaching days to mastery (intensity and duration) and percentage of targets failed in maintenance had a statistically significant effect on the percentage of behavioral targets mastered. The predictor variables age, average trials to behavioral mastery (intensity and duration), and open behavioral targets had a non-significant influence on the percentage of behavioral targets mastered. A non-significant difference between age groups was found in all study variables.

Treating Target Behaviors of Autistic Individuals With Applied Behavior Analysis: An Ongoing Replication Study.

Background Ongoing outcome data replication on target behaviors with autistic individuals using applied behavior analysis (ABA) confirms its effectiveness and remains an essential evidenced-based standard of care. This replication study aims to further confirm the impact of discrete trial training and mass trials on general target behaviors within a naturalistic environment. Methods Data was gathered from 92 children and four adult autistic individuals over one month from 7/7/23 to 8/8/23 using a repeated measures design. This study used a retrospective chart review with general target behaviors to determine the effectiveness of ABA treatments using discrete trial training and mass trials across time and age categories in a naturalistic environment. Results A mixed analysis of variance (ANOVA) indicated statistical significance (sphericity assumed), F(2,168) = 31.663, p < 0.001 (time). Multiple comparisons using bootstrapped paired t-tests indicated p < 0.001 on the three comparisons. There was a significant interaction effect (sphericity assumed) with time x age category, F(8,168) = 2.918, p = 0.004. Interaction contrasts indicated statistically significant differences over time within the 1-4 years, 5-8 years, and a portion of 9-12 years, and not within the 13-16 years and 17-73 years age groups. Conclusions Autistic individuals receiving ABA demonstrated statistically significant improvement in target behaviors over one month. There was a significant interaction between time and age on target behaviors, suggesting a significant association between time and age categories. The reporting of ongoing intervention outcomes provides further justification for continued treatments relative to target behavior mastery with autistic individuals.

The Effects of Hyperbaric Oxygen Treatment on Verbal Scores in Children With Autism Spectrum Disorder: A Retrospective Trial.

Introduction Autism spectrum disorder (ASD) is a neurodevelopmental condition that affects millions worldwide. Suggested pathophysiology includes cerebral hypoperfusion, inflammation, mitochondrial and immune dysregulation, and oxidative stress. Debate exists concerning the benefit of hyperbaric oxygen therapy (HBOT) in treating ASD and its impacts on verbal behavior. The present study directly assesses the impacts of HBOT treatments on verbal behavior using a novel and unique manner.  Materials and methods A two-group quasi-experimental trial using a pretest and a posttest was designed to retrospectively assess (n = 65) any association between HBOT and change in verbal scores in children (n = 65) with ASD. All children completed two verbal tests six months apart, either the Verbal Behavior Milestones Assessment and Placement Program (VBMAPP) or the Assessment of Basic Language and Learning Skills (ABLLS), based on their developmental age. The control cohort received applied behavior analysis (ABA) without HBOT. The experimental cohort received ABA and a minimum of 40 HBOT treatments, breathing 100% oxygen at 2.0 atmosphere absolute (ATA) for 60 minutes. Results Sixty-five children were included, of which 32 received HBOT (mean (M) = 5.1, standard deviation (SD) = 2.93), with an age range of two to 17 years. More than 63% of the subjects had an autism severity level of three. The 23 children administered VBMAPP who received HBOT showed substantial mean differences with high effect sizes (ESs) (-0.743 to -1.65) and a total score (TS) ES equal to -1.23 as measured by Cohen's d. There was a statistically significant improvement (p < 0.05) in all VBMAPP milestone domains and TS. TS change from baseline versus those in the non-HBOT (Control-ABA) group (n=12) was 46.41 ± 20.14 vs 14.42 ± 6.99; p < 0.0001, ES = -1.23. The 30 children administered the ABLLS showed substantial mean difference (TS) change from baseline 268.89 ± 182.05 vs 190.81 ± 135.26 and exhibited small to medium (-.114 to -.773) ESs with a TS ES = -0.487. Due to the high within-group variability (low statistical power) within the ABLLS cohort, there was a non-significant mean difference between the control (ABA) and experimental (ABA + HBO2) groups' difference scores (p > 0.2024), despite the medium (TS) ES. Conclusions The child cohorts administered the VBMAPP and the ABLLS demonstrated substantial improvements between the non-HBOT (control-ABA) and HBOT (experimental-ABA + HBO2) groups as measured by the significant mean differences and small to large ESs. Simply put, the children in the experimental cohort acquired more verbal skills than their counterparts in the control group.

Mutations in protein-binding hot-spots on the hub protein Smad3 differentially affect its protein interactions and Smad3-regulated gene expression.

BACKGROUND: Hub proteins are connected through binding interactions to many other proteins. Smad3, a mediator of signal transduction induced by transforming growth factor beta (TGF-ß), serves as a hub protein for over 50 protein-protein interactions. Different cellular responses mediated by Smad3 are the product of cell-type and context dependent Smad3-nucleated protein complexes acting in concert. Our hypothesis is that perturbation of this spectrum of protein complexes by mutation of single protein-binding hot-spots on Smad3 will have distinct consequences on Smad3-mediated responses. METHODOLOGY/PRINCIPAL FINDINGS: We mutated 28 amino acids on the surface of the Smad3 MH2 domain and identified 22 Smad3 variants with reduced binding to subsets of 17 Smad3-binding proteins including Smad4, SARA, Ski, Smurf2 and SIP1. Mutations defective in binding to Smad4, e.g., D408H, or defective in nucleocytoplasmic shuttling, e.g., W406A, were compromised in modulating the expression levels of a Smad3-dependent reporter gene or six endogenous Smad3-responsive genes: Mmp9, IL11, Tnfaip6, Fermt1, Olfm2 and Wnt11. However, the Smad3 mutants Y226A, Y297A, W326A, K341A, and E267A had distinct differences on TGF-ß signaling. For example, K341A and Y226A both reduced the Smad3-mediated activation of the reporter gene by ∼50% but K341A only reduced the TGF-ß inducibilty of Olfm2 in contrast to Y226A which reduced the TGF-ß inducibility of all six endogenous genes as severely as the W406A mutation. E267A had increased protein binding but reduced TGF-ß inducibility because it caused higher basal levels of expression. Y297A had increased TGF-ß inducibility because it caused lower Smad3-induced basal levels of gene expression. CONCLUSIONS/SIGNIFICANCE: Mutations in protein binding hot-spots on Smad3 reduced the binding to different subsets of interacting proteins and caused a range of quantitative changes in the expression of genes induced by Smad3. This approach should be useful for unraveling which Smad3 protein complexes are critical for specific biological responses.