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PURPOSE: Autistic adolescents commonly experience sleep-related difficulties and prior studies have sought to separately examine physiological and family-level predictors of their sleep quality. The current study aimed to conceptually replicate and extend to an adolescent sample a prior study that found respiratory sinus arrhythmia was associated with sleep quality in autistic children. In addition, the current study also examined whether the quality of the family environment was associated with sleep quality in autistic adolescents. METHODS: The sample consisted of 107 autistic adolescents who completed a baseline measure of respiratory sinus arrhythmia and then watched a video of their parents engaged in a discussion about a topic of disagreement while their respiratory sinus arrhythmia reactivity was measured. Adolescents also completed questionnaires measuring their sleep quality and family environment. RESULTS: In regression models, adolescents' physiological functioning was not a significant predictor of their sleep quality; however, adolescents living in poorer quality family environments reported worse sleep quality after controlling for their physiological functioning. The interaction between physiological functioning and the family environment predicting sleep quality was non-significant. CONCLUSION: Although the current study did not conceptually replicate prior work, the findings highlight the importance of the family environment for adolescents' sleep. Implications and future directions are discussed.

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Background: The aging spine often presents multifaceted surgical challenges for the surgeon because it can directly and indirectly impact a patient's spinal alignment and quality of life. Elderly and osteoporotic patients are predisposed to progressive spinal deformities and potential neurologic compromise and surgical management can be difficult because these patients often present with greater frailty. Methods: This was a literature review of spinal alignment changes, preoperative considerations, and spinal alignment considerations for surgical strategies. Results: Many factors impact spinal alignment as we age including lumbar lordosis flexibility, hip flexion, deformity, and osteoporosis. Preoperative considerations are required to assess the patient's overall health, bone mineral density, and osteoporosis medications. Careful radiographic assessment of the spinopelvic parameters using various classification/scoring systems provide the surgeon with goals for surgical treatment. An individualized surgical strategy can be planned for the patient including extent of surgery, surgical approach, extent of the constructs, fixation techniques, vertebral augmentation, ligamentous augmentation, and staging surgery. Conclusions: Surgical treatment should only be considered after a thorough assessment of the patient's health, deformity, bone quality and corresponding age matched alignment goals. An individualized treatment approach is often required to tackle the deformity and minimize the risk of hardware related complications and pseudarthrosis. Anabolic agents offer a promising benefit in this patient population by directly addressing and improving their bone quality and mineral density preoperatively and postoperatively.

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Sleep problems significantly affect the quality of life of autism spectrum disorder (ASD) children. This study aimed to evaluate the effects of a 12-week ball combination exercise, continuous theta burst stimulation (cTBS) stimulation, and combined intervention on sleep problems in children with ASD. Forty-five ASD children were divided into three intervention groups (ball combination exercise(n = 12), cTBS stimulation(n = 10), combined (n = 12) and a control group (n = 11). The intervention groups underwent intervention, while the control group maintained daily activities. The effects were assessed using the Children's Sleep Habits Questionnaire (CSHQ) scale. The results revealed that after 12 weeks of intervention, three programs reduced sleep problems in children with ASD. The post-test scores of the cTBS group (p = 0.002) and the combined group (p < 0.001) were significantly lower than the baseline scores on the CSHQ scale. The exercise group (p = 0.002) and the combined group (p < 0.001) showed significant improvement in sleep anxiety, while there was no statistically significant difference in the effectiveness of the three interventions for sleep-onset delay. The combined group outperformed the single intervention groups in the CSHQ score and sleep anxiety sub-dimensions. The combined intervention group showed slightly superior performance in sleep onset latency, however, there was no significant difference. Three interventions alleviated sleep issues in ASD children, with the combined method proving more effective. This study validates non-pharmacologic and combined approaches for ASD sleep problems. Future research should delve deeper into the mechanisms of these interventions in ASD children's sleep improvement.

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This article delves into the intricate relationship between oral health, quality of life, and behavioral characteristics in individuals with autism spectrum disorder (ASD). Background/Objectives: Autism has been associated with various challenges, and this study seeks to elucidate the impact of ASD on oral health outcomes and overall well-being. The research focuses on assessing overall oral health by evaluating various parameters, such as the condition of lips, tongue, gums and tissues, natural teeth, dentures, oral hygiene, and dental pain in individuals with ASD. Additionally, the study explores the influence of age, sex, and certain variables, like basic daily living skills on oral health practices, aiming to identify potential correlations between these factors and oral health outcomes. Methods: We employed standardized instruments to quantitatively measure and analyze the impact of oral health status on the overall quality of life experienced by individuals with ASD. Results: The study found a statistically significant positive association between oral health, measured by the Oral Health Assessment Tool (OHAT), and quality of life, as indicated by EuroQol 5-Dimensions Youth version (EQ-5D-Y) total scores (ß = 0.13045, p = 0.00271). This suggests that better oral health is linked to higher quality of life. When adjusting for age and sex in a multiple linear regression model, the association remained significant but with a slightly reduced effect size (ß = 0.10536, p = 0.0167). Age also showed a marginally significant positive association with quality-of-life scores. ANOVA results indicated that participants with advanced oral health status reported significantly higher quality-of-life scores than those with poorer oral health (p = 0.00246). The study also found that intelligence quotient (IQ) does not substantially influence dental health status, while the "Autonomy" subscale of the EQ-5D-Y is positively related to the OHAT. Conclusions: Unhealthy oral conditions significantly impact the overall quality of life in individuals with ASD. Therefore, it is crucial to include regular dental assessments and treatments in therapeutic protocols for patients with ASD.

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Autism Spectrum Disorder (ASD) is a multifactorial disorder involving genetic and environmental factors leading to pathophysiologic symptoms and comorbidities including neurodevelopmental disorders, anxiety, immune dysregulation, and gastrointestinal (GI) abnormalities. Abnormal intestinal permeability has been reported among ASD patients and it is well established that disturbances in eating patterns may cause gut microbiome imbalance (i.e., dysbiosis). Therefore, studies focusing on the potential relationship between gut microbiota and ASD are emerging. We compared the intestinal bacteriome and mycobiome of a cohort of ASD subjects with their non-ASD siblings. Differences between ASD and non-ASD subjects include a significant decrease at the phylum level in Cyanobacteria (0.015% vs. 0.074%, p < 0.0003), and a significant decrease at the genus level in Bacteroides (28.3% vs. 36.8%, p < 0.03). Species-level analysis showed a significant decrease in Faecalibacterium prausnitzii, Prevotella copri, Bacteroides fragilis, and Akkermansia municiphila. Mycobiome analysis showed an increase in the fungal Ascomycota phylum (98.3% vs. 94%, p < 0.047) and an increase in Candida albicans (27.1% vs. 13.2%, p < 0.055). Multivariate analysis showed that organisms from the genus Delftia were predictive of an increased odds ratio of ASD, whereas decreases at the phylum level in Cyanobacteria and at the genus level in Azospirillum were associated with an increased odds ratio of ASD. We screened 24 probiotic organisms to identify strains that could alter the growth patterns of organisms identified as elevated within ASD subject samples. In a preliminary in vivo preclinical test, we challenged wild-type Balb/c mice with Delftia acidovorans (increased in ASD subjects) by oral gavage and compared changes in behavioral patterns to sham-treated controls. An in vitro biofilm assay was used to determine the ability of potentially beneficial microorganisms to alter the biofilm-forming patterns of Delftia acidovorans, as well as their ability to break down fiber. Downregulation of cyanobacteria (generally beneficial for inflammation and wound healing) combined with an increase in biofilm-forming species such as D. acidovorans suggests that ASD-related GI symptoms may result from decreases in beneficial organisms with a concomitant increase in potential pathogens, and that beneficial probiotics can be identified that counteract these changes.

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BACKGROUND: Finding effective pharmacological interventions to address the complex array of neurodevelopmental disorders is currently an urgent imperative within the scientific community as these conditions present significant challenges for patients and their families, often impacting cognitive, emotional, and social development. In this study, we aimed to explore non-invasive method to diagnose autism spectrum disorders (ASD) within Pakistan children population and to identify clinical drugs for its treatment. AIMS: The current report outlines a comprehensive bidirectional investigation showcasing the successful utilization of saliva samples to quantify the expression patterns of profilins (PFN1, 2, and 3); and ERM (ezrin, radixin, and moesin) proteins; and additionally moesin pseudogene 1 and moesin pseudogene 1 antisense (MSNP1AS). Subsequently, these expression profiles were employed to forecast interactions between drugs and genes in children diagnosed with ASD. METHODS: This study sought to delve into the intricate gene expression profiles using qualitative polymerase chain reaction of profilin isoforms (PFN1, 2, and 3) and ERM genes extracted from saliva samples obtained from children diagnosed with ASD. Through this analysis, we aimed to elucidate potential molecular mechanisms underlying ASD pathogenesis, shedding light on novel biomarkers and therapeutic targets for this complex neurological condition. (n = 22). Subsequently, we implemented a diagnostic model utilizing sparse partial least squares discriminant analysis (sPLS-DA) to predict drugs against our genes of interest. Furthermore, connectivity maps were developed to illustrate the predicted associations of 24 drugs with the genes expression. RESULTS: Our study results showed varied expression profile of cytoskeleton linked genes. Similarly, sPLS-DA model precisely predicted drug to genes response. Sixteen of the examined drugs had significant positive correlations with the expression of the targeted genes whereas eight of the predicted drugs had shown negative correlations. CONCLUSION: Here we report the role of cytoskeleton linked genes (PFN and ERM) in co-relation to ASD. Furthermore, variable yet significant quantitative expression of these genes successfully predicted drug-gene interactions as shown with the help of connectivity maps that can be used to support the clinical use of these drugs to treat individuals with ASD in future studies.

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The integration of artificial intelligence (AI) into the diagnosis and treatment of autism spectrum disorder (ASD) represents a promising frontier in healthcare. This review explores the current landscape and future prospects of AI technologies in ASD diagnostics and interventions. AI enables early detection and personalized assessment of ASD through the analysis of diverse data sources such as behavioural patterns, neuroimaging, genetics, and electronic health records. Machine learning algorithms exhibit high accuracy in distinguishing ASD from neurotypical development and other developmental disorders, facilitating timely interventions. Furthermore, AI-driven therapeutic interventions, including augmentative communication systems, virtual reality-based training, and robot-assisted therapies, show potential in improving social interactions and communication skills in individuals with ASD. Despite challenges such as data privacy and interpretability, the future of AI in ASD holds promise for refining diagnostic accuracy, deploying telehealth platforms, and tailoring treatment plans. By harnessing AI, clinicians can enhance ASD care delivery, empower patients, and advance our understanding of this complex condition.

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Hot melt extrusion (HME) has been widely used as a continuous and highly flexible pharmaceutical manufacturing process for the production of a variety of dosage forms. In particular, HME enables preparation of amorphous solid dispersions (ASDs) which can improve bioavailability of poorly water-soluble drugs. The rheological properties of drug-polymer mixtures can significantly influence the processability of drug formulations via HME and eventually the end-use product properties such as physical stability and drug release. The objective of this review is to provide an overview of various rheological techniques and properties that can be used to evaluate the flow behavior and processability of the drug-polymer mixtures as well as formulation characteristics such as drug-polymer interactions, miscibility/solubility, and plasticization to improve the HME processability. An overview of the thermodynamics and kinetics of ASD processing by HME is also provided, as well as aspects of scale-up and process modeling, highlighting rheological properties on formulation design and process development. Overall, this review provides valuable insights into critical rheological properties which can be used as a predictive tool to optimize the HME processing conditions.

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BACKGROUND: Common genetic variation has been shown to account for a large proportion of ASD heritability. Polygenic scores generated for autism spectrum disorder (ASD-PGS) using the most recent discovery data, however, explain less variance than expected, despite reporting significant associations with ASD and other ASD-related traits. Here, we investigate the extent to which information loss on the target study genome-wide microarray weakens the predictive power of the ASD-PGS. METHODS: We studied genotype data from three cohorts of individuals with high familial liability for ASD: The Early Autism Risk Longitudinal Investigation (EARLI), Markers of Autism Risk in Babies-Learning Early Signs (MARBLES), and the Infant Brain Imaging Study (IBIS), and one population-based sample, Study to Explore Early Development Phase I (SEED I). Individuals were genotyped on different microarrays ranging from 1 to 5 million sites. Coverage of the top 88 genome-wide suggestive variants implicated in the discovery was evaluated in all four studies before quality control (QC), after QC, and after imputation. We then created a novel method to assess coverage on the resulting ASD-PGS by correlating a PGS informed by a comprehensive list of variants to a PGS informed with only the available variants. RESULTS: Prior to imputations, None of the four cohorts directly or indirectly covered all 88 variants among the measured genotype data. After imputation, the two cohorts genotyped on 5-million arrays reached full coverage. Analysis of our novel metric showed generally high genome-wide coverage across all four studies, but a greater number of SNPs informing the ASD-PGS did not result in improved coverage according to our metric. LIMITATIONS: The studies we analyzed contained modest sample sizes. Our analyses included microarrays with more than 1-million sites, so smaller arrays such as Global Diversity and the PsychArray were not included. Our PGS metric for ASD is only generalizable to samples of European ancestries, though the coverage metric can be computed for traits that have sufficiently large-sized discovery findings in other ancestries. CONCLUSIONS: We show that commonly used genotyping microarrays have incomplete coverage for common ASD variants, and imputation cannot always recover lost information. Our novel metric provides an intuitive approach to reporting information loss in PGS and an alternative to reporting the total number of SNPs included in the PGS. While applied only to ASD here, this metric can easily be used with other traits.

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BACKGROUND: Reduced social attention - looking at faces - is one of the most common manifestations of social difficulty in autism central to social development. Although reduced social attention is well-characterized in autism, qualitative differences in how social attention unfolds across time remains unknown. METHODS: We used a computational modeling (i.e., hidden Markov modeling) approach to assess and compare the spatiotemporal dynamics of social attention in a large, well-characterized sample of autistic (n = 280) and neurotypical (n = 120) children (ages 6-11) that completed three social eye-tracking assays across three longitudinal time points (Baseline, 6 weeks, 24 weeks). RESULTS: Our analysis supported the existence of two common eye movement patterns that emerged across three ET assays. A focused pattern was characterized by small face regions of interest, which had high probability of capturing fixations early in visual processing. In contrast, an exploratory pattern was characterized by larger face regions of interest, with lower initial probability of fixation, and more non-social regions of interest. In the context of social perception, autistic children showed significantly more exploratory eye movement patterns than neurotypical children across all social perception assays and all three longitudinal time points. Eye movement patterns were associated with clinical features of autism, including adaptive function, face recognition, and autism symptom severity. CONCLUSIONS: Decreased likelihood of precisely looking to faces early in social visual processing may be an important feature of autism that was associated with autism-related symptomology and may reflect less visual sensitivity to face information.

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Introduction  The Verbal Behavior Milestones Assessment and Placement Program (VB-MAPP) is an extensive tool used to assess children with autism and other developmental disabilities who have language delays. Applied behavior analysis (ABA) professionals frequently use the VB-MAPP to create personalized intervention programs catering to each child's needs. The lack of studies examining the VB-MAPP at the pretest, posttest, and differential scores using principal components analysis (PCA) suggests an opportunity to conduct PCAs on these different VB-MAPP scores. In doing so, researchers could better understand the VB-MAPP's dimensionality and factor structure at these levels. This, in turn, could inform the development of more effective assessment strategies and intervention plans for individuals with language and social communication challenges.  Materials and methods From January 2018 to July 2021, The Oxford Center in Brighton and Troy, Michigan, treated autistic children using ABA therapy. A convenience sample of 13 children was retrospectively analyzed using VB-MAPP, which evaluates various behavioral milestones using a pretest-posttest design. Descriptive data analysis and internal consistency reliability estimates (using Cronbach's alpha) were calculated for pretest, posttest, and difference scores. A Wilcoxen signed-rank test was conducted to determine the statistical significance between the pretest and posttest. Correlation matrices were inspected for relevant relationships between VB-MAPP scales, and a PCA with orthogonal rotation was also performed on this pretest, posttest, and difference scores. Results The mean age for the children was 4.083 years ± 1.083 years, (95%CI 3.64, 4.36). Around 66.6% of the children had an autism severity level of three, 33.3% had a severity level of two, and none were at level one. Cronbach's alpha for internal consistency reliability of the pretest, posttest, and difference scores, indicating excellent reliability with values of 0.948 for the pretest and 0.937 for the posttest, respectively. The difference scores had a lower but acceptable reliability coefficient of 0.752. PCA on the pretest scores identified three factors that explain 85.584% of the total variation, indicating that these components capture most of the data's structure. The posttest PCA also identified three factors, accounting for 84.293% of the variance, suggesting a similar complexity and good model fit as the pretest. PCA revealed four factors explaining 82.317% of the variation for the difference scores. The increase in factors suggests that changes between pretest and posttest scores are complex, likely due to the ABA treatment, and require an additional component to represent the data adequately. There is a good model fit; the underlying structure is more complex than the pretest or posttest alone. Conclusions Robust coefficient alphas combined with the shift to a more detailed factor structure post-ABA treatment highlight ABA therapy's diverse and multi-faceted impact on children. The increase from three to four principal components indicates a richer and more nuanced pattern of improvements across different domains of verbal and social behavior. This detailed factor structure is a testament to the comprehensive and individualized nature of ABA treatment, reflecting the therapy's effectiveness in addressing specific needs and fostering broad developmental gains in children.

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Introduction: Clinical heterogeneity is one of the biggest challenges for researchers studying underlying neurobiological mechanisms in Autism Spectrum Disorder (ASD). We aimed to use polyinosinic-polycytidylic acid [Poly (I:C)] induced maternal immune activation mice model to investigate the behavioral variation and the role of brain circuits related to symptom clusters in ASD. For this purpose, behavioral tests were applied to offsprings and regional thickness was measured from histological brain sections in medial prefrontal cortex, hippocampus and striatum. Methods: Pups of intraperitoneal Poly (I:C)-applied mothers (n: 14) and phosphate buffered saline-applied mothers (n: 6) were used for this study. We used three chamber socialization test and social memory test to evaluate social behavior deficit in mice. Marble burying test was used for assessing stereotypic behavior and new object recognition test for learning and cognitive flexibility. Three subgroups (n: 4 for each) were determined according to behavioral test parameters. Regional thickness was measured in medial prefrontal cortex, hippocampus and striatum and compared between subgroups. Results: We detected that the behavioral differences were distributed in a spectrum as expected in the clinic and also detected increased hippocampus thickness in the stereotypic behavior dominant Poly (I:C) subgroup. Conclusion: Poly (I:C) induced maternal immune activation model creates the behavioral variation and cortical development differences that are seen in relation with symptom groups in ASD.

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Autism Spectrum Disorder (ASD) is a neurological disorder that influences a person's comprehension and way of behaving. It is a lifetime disability that cannot be completely treated using any therapy up to date. Nevertheless, in time identification and continuous therapies have a huge effect on autism patients. The existing models took a long time to confirm the diagnosis process and also, it is highly complex to differentiate autism from various developmental disorders. To facilitate early diagnosis by providing timely intervention, saving healthcare costs and reducing stress for the family in the long run, this research introduces an affordable and straightforward diagnostic model to detect ASD using EEG and deep learning models. Here, a hybrid deep learning model called Cascade deep maxout fuzzy network (Cascade DMFN) is proposed to identify ASD and it is achieved by the integration of Deep Maxout Network (DMN) and hybrid cascade neuro-fuzzy. Moreover, hybrid similarity measures like Canberra distance and Kumar-hassebrook is employed to conduct the feature selection technique. Also, the EEG dataset and BCIAUT_P300 dataset are used for analyzing the designed Cascade DMFN for detecting Autism Spectrum Disorder. The designed Cascade DMFN has outperformed other classical models by yielding a high accuracy of 0.930, Negative Predictive Value (NPV) of 0.919, Positive Predictive Value (PPV) of 0.923, True Negative Rate (TNR) of 0.926, and True Positive Rate (TPR) of 0.934.

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Pica is known to the medical community as an eating disorder in which individuals may ingest non-food items due to a nutritional deficiency and cause unintentional physical harm to themselves. This article discusses the cases of children with pica in addition to other comorbidities such as trichotillomania, depression, autism, and anxiety. Both patients were trialed on typical first-line treatments to address pica symptoms, including antidepressants, psychotherapy, and neurology consults, which were ineffective in treating pica symptoms. The introduction of naltrexone resulted in significant improvements, including decreased pica symptoms and improvements in depression, anxiety, and overall behaviors. These effects of naltrexone were further bolstered by the effects that occurred when both patients discontinued naltrexone for some time.

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The current diagnostic criteria for the autism spectrum disorder (ASD) include the possibility to specify concomitant language difficulties. Purpose: Our main aim was to explore whether children with ASD-Level 1 (ASD-L1) present difficulties in the acquisition of structural language, as little work has been done in this regard so far. As a secondary aim we evaluated the degree to which the potential language impairment in ASD is directly associated with their social communication deficits or it represents a distinct deficit. Methods: To further clarify the nature and characteristics of putative language difficulties in ASD-L1, we evaluated language skills in 89 children and preadolescents diagnosed with ASD-L1, and a group of typically developing participants (TD). All of them were between 8 and 13 years old and had similar socioeconomic backgrounds. Results: Children with ASD-L1 obtained lower scores than those in TD group in repeating sentences, in finding the semantic relationships between words, and in applying word structure rules (morphology). Congruently, the core language standard score was lower in the ASD-L1 group, and the proportion of language delay was significantly higher in the ASD-L1 group than in the control group. Conclusion: Language scores were associated with autistic traits; thus, language performance in ASD-L1 is closely related to autistic symptoms. These results are discussed according to the literature on linguistic deficits in ASD-L1 and their relations with phonological working memory.

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[This corrects the article DOI: 10.3389/fnsys.2023.1168666.].

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Autism spectrum disorders (ASD) are complex neurodevelopmental conditions characterized by impairments in social communication, repetitive behaviors, and restricted interests. Epigenetic modifications serve as critical regulators of gene expression playing a crucial role in controlling brain function and behavior. Lysine (K)-specific demethylase 6B (KDM6B), a stress-inducible H3K27me3 demethylase, has emerged as one of the highest ASD risk genes, but the precise effects of KDM6B mutations on neuronal activity and behavioral function remain elusive. Here we show the impact of KDM6B mosaic brain knockout on the manifestation of different autistic-like phenotypes including repetitive behaviors, social interaction, and significant cognitive deficits. Moreover, KDM6B mosaic knockout display abnormalities in hippocampal excitatory synaptic transmission decreasing NMDA receptor mediated synaptic transmission and plasticity. Understanding the intricate interplay between epigenetic modifications and neuronal function may provide novel insights into the pathophysiology of ASD and potentially inform the development of targeted therapeutic interventions.

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Introduction: Over the past few decades, there has been growing concern about the concurrent trends of increasing Autism Spectrum Disorder (ASD) prevalence and declining sperm quality. These trends represent significant public health challenges that warrant thorough investigation of their underlying causes and implications. Objectives: The primary objectives of this study are to analyze trends in ASD prevalence and sperm quality parameters from 2000 to 2024, assess the statistical significance and effect size of these trends, explore potential correlations between ASD prevalence and sperm quality parameters, and identify significant predictors among sperm quality parameters that influence ASD prevalence. Methods: This study employed a longitudinal approach using multiple regression, time series analysis, ANOVA, Principal Component Analysis (PCA), hierarchical clustering, logistic regression, and cross-correlation analysis. Data on ASD prevalence were sourced from the CDC Autism and Developmental Disabilities Monitoring Network, while sperm quality data were collected from various published studies. Results: The findings reveal significant negative associations between ASD prevalence and sperm quality parameters such as sperm concentration and motility, suggesting that better sperm quality is linked to lower ASD rates. Conversely, parameters like sperm DNA fragmentation (SDF), volume of ejaculate, pH level, and semen viscosity show positive associations with ASD prevalence, indicating higher values in these parameters correlate with higher ASD rates. Conclusion: The study highlights the importance of maintaining reproductive health to potentially mitigate ASD risk and calls for further research to elucidate the underlying mechanisms driving these trends. These findings support the hypothesis that reproductive health factors play a crucial role in ASD etiology and suggest potential biological markers for assessing ASD risk.

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Autism diagnosis in individuals with fragile X syndrome (FXS) or Down syndrome (DS) with co-occurring intellectual disability is complex since clinicians often must consider other co-occurring behavioral features. Understanding how best to assess the features of autism in individuals with these conditions is crucial. In this study, we consider the short-term and long-term psychometric consistency of the Autism Diagnostic Observation Schedule-2 (ADOS-2) calibrated comparison scores (CCSs) and ASD classifications in individuals with FXS or DS. 76 individuals with DS (39 males; Mage = 15.27) and 90 individuals with FXS (71 males; Mage = 14.52 years) completed an assessment battery (ADOS-2, abbreviated IQ assessment and semi-structured language sample) at three timepoints (initial visit, short-term stability visit, long-term stability visit). All CCSs were found to have short-and long-term consistency for both groups, with lowest reliability scores for the repetitive behaviors (RRB) CCSs. Decreased reliability of RRB CCSs was found in the DS group than the FXS group. Variable short- and long-term ASD classifications were observed in both groups, with significantly higher variability in the DS group. Across groups, participants with variable classifications had lower ADOS-2 CCSs and higher language scores than those with stable ASD classifications. In the FXS group, those with variable classifications earned higher cognitive scores than did participants with stable ASD classifications. These findings highlight the high incidence of autism symptomatology in individuals with DS or FXS and co-occurring intellectual disability, while elucidating the short- and long-term variability of symptom expression in the context of structured observational tasks such as the ADOS-2.

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Ebstein anomaly is a congenital heart disease that is considered rare and mostly found in pediatrics population. Symptoms in adults vary depending on the degree of the valve displacement and include difficulty breathing, palpitations, stroke, or even fatigue. However, if it occurs in the elderly, they end up with a good prognosis. A novel calcium sensitizer "levosimendan" has been used perioperatively in heart valve replacement to improve the long-term prognosis of patients. The use of the drug has been shown to reduce postoperative mortality in patients with reduction in ejection fraction. We present the case of a 62-year-old female, a known case of hypothyroidism, bronchial asthma, gastroesophageal reflux disease, and recent diagnosis of Ebstein anomaly, who underwent tricuspid valve repair and atrial septal defect repair on being symptomatic, in addition to the successful use of a novel positive inotropic drug with decrease in the intensive care unit stay.