Search

This study explored the related factors of FQOL in parents of children with ASD and examined whether social support mediates the relationship between parental self-efficacy and FQOL in parents of children with ASD.Using a cross-sectional design, a total of 260 parents of children with ASD were recruited from September 2019 to November 2020. They completed the Beach Center Family Quality of Life Scale, the Parenting Sense of Competence Scale, and the Social Support Rating Scale.Parental self-efficacy and social support explained approximately 49.5% of the variance in FQOL. After controlling for the confounding influence of parents' education level, parental self-efficacy had a direct effect on FQOL (β = 0.292, SE = 0.108, P 0.01) and an indirect effect on FQOL (β = 0.165, SE = 0.069, P 0.01). Effects were mediated through social support, with partial mediating effects accounting for 36.11% of the total effect.Both parental self-efficacy and social support are critical to promoting FQOL, and a partial mediating effect of social support was established.Interventions for families with children with ASD should focus on enhancing parental self-efficacy, followed by a perceived social support and FQOL prompt.

Parents of children with autism spectrum disorder (ASD) experience posttraumatic growth (PTG). No study has investigated the moderating effect of social support and family function between symptom severity and PTG. The study aims to examine whether social support and family function moderate the relationship between symptom severity and PTG among parents of children with ASD. Using a cross-sectional design, a total of 385 parents of children with ASD were recruited from September 2019 to November 2020 by convenience sampling. Participants completed the Posttraumatic Growth Inventory, Social Support Rating Scale, Autism Behavior Checklist, and Family Apgar Index. Both social support (r = 0.354, p 0.01) and family function (r = 0.379, p 0.05) were significantly related to PTG. Although symptom severity was not significantly related to PTG (p 0.05), social support moderated the correlation between symptom severity and PTG [β(SE) = -0.134 (0.719), p 0.01, 95% CI = (-3.552, -0.723)]; the positive association was stronger for low social support [β(SE) = 0.145 (0.054), t = 2.675, p 0.01, 95% CI = (0.038, 0.252)], while the negative association was weaker for high social support [β(SE) = -0.121 (0.051), t = -2.378, p 0.05, 95% CI = (-0.221, -0.021)]. Family function did not moderate the relationship (p 0.05). Higher social support appears to buffer the detrimental effect of symptom severity on PTG, and social support seems to be an important factor when delivering interventions aimed at decreasing symptom severity and improving positive growth. LAY SUMMARY: Both social support and family function were positively associated with PTG. Providing sufficient perceived social support and enhancing family function promoted parents' positive psychological experience. Higher social support seemed to buffer the detrimental effect of symptom severity on PTG, and it could be an important intervention target for improving the psychological growth of parents of children with ASD.

The genotype-first approach has been successfully applied and has elucidated several subtypes of autism spectrum disorder (ASD). However, it requires very large cohorts because of the extensive genetic heterogeneity. We investigate the alternate possibility of whether phenotype-specific genes can be identified from a small group of patients with specific phenotype(s). To identify novel genes associated with ASD and abnormal head circumference using a phenotype-to-genotype approach, we performed whole-exome sequencing on 67 families with ASD and abnormal head circumference. Clinically relevant pathogenic or likely pathogenic variants account for 23.9% of patients with microcephaly or macrocephaly, and 81.25% of those variants or genes are head-size associated. Significantly, recurrent pathogenic mutations were identified in two macrocephaly genes (PTEN, CHD8) in this small cohort. De novo mutations in several candidate genes (UBN2, BIRC6, SYNE1, and KCNMA1) were detected, as well as one new candidate gene (TNPO3,) implicated in ASD and related neurodevelopmental disorders. We identify genotype–phenotype correlations for head-size-associated ASD genes and novel candidate genes for further investigation. Our results also suggest a phenotype-to-genotype strategy would accelerate the elucidation of genotype–phenotype relationships for ASD by using phenotype-restricted cohorts.

Autism Spectrum Disorder (ASD), a neurodevelopmental disability, has become one of the high incidence diseases among children. Studies indicate that early diagnosis and intervention treatments help to achieve positive longitudinal outcomes. In this paper, we focus on the speech and language abnormalities of young children with ASD and present an automated assessment framework in quantifying atypical prosody and stereotyped idiosyncratic phrases related to ASD. For detecting atypical prosody from speech, we propose both the hand-crafted feature based method as well as the end-to-end deep learning framework. First, we use the OpenSMILE toolkit to extract utterance level high dimensional acoustic features followed by a support vector machine (SVM) backend as the conventional baseline. Second, we propose several end-to-end deep neural network setups and configurations to model the atypical prosody label directly from the constant Q transform spectrogram of speech. Third, we apply cross-validation on the training data to perform segments selection and enhance the subject level classification performance. Fourth, we fuse the deep learning based methods with the conventional baseline at the score level to further enhance the overall system performance. For detecting the stereotyped idiosyncratic usage of words or phrases from speech transcripts, we adopt language model, dependency treebank and Term Frequency–Inverse Document Frequency (TF–IDF) in addition to Linguistic Inquiry and Word Count software (LIWC) methods to extract a set of text features followed by a standard SVM backend. We collect a database of spontaneous Mandarin speech recorded during the Autism Diagnostic Observation Schedule (ADOS) Module 2 and Module 3 sessions. The Module 2 part consists of 118 children while the Module 3 part includes 71 children. Experimental results on this database show that our proposed methods can effectively predict the atypical prosody and stereotyped idiosyncratic phrases codes for young children with the risk of ASD. On the two categories classification task, the unweighted accuracy of the aforementioned two tasks are 88.1% and 77.8%, respectively.

The heterogeneity of disease is a major concern in medical research and is commonly characterized as subtypes with different pathogeneses exhibiting distinct prognoses and treatment effects. The classification of a population into homogeneous subgroups is challenging, especially for complex diseases. Recent studies show that gut microbiome compositions play a vital role in disease development, and it is of great interest to cluster patients according to their microbial profiles. There are a variety of beta diversity measures to quantify the dissimilarity between the compositions of different samples for clustering. However, using different beta diversity measures results in different clusters, and it is difficult to make a choice among them. Considering microbial compositions from 16S rRNA sequencing, which are presented as a high-dimensional vector with a large proportion of extremely small or even zero-valued elements, we set up three simulation experiments to mimic the microbial compositional data and evaluate the performance of different beta diversity measures in clustering. It is shown that the Kullback-Leibler divergence-based beta diversity, including the Jensen-Shannon divergence and its square root, and the hypersphere-based beta diversity, including the Bhattacharyya and Hellinger, can capture compositional changes in low-abundance elements more efficiently and can work stably. Their performance on two real datasets demonstrates the validity of the simulation experiments.

Objective This study was conducted to examine the posttraumatic growth (PTG) of parents of children with autism spectrum disorder (ASD) and the association among family function, mental resilience and PTG. Design and methods This descriptive cross-sectional study was conducted with 205 parents of children with ASD in the clinical department of a university-affiliated hospital in Guangzhou from January to October 2019. The Posttraumatic Growth Inventory, Family Assessment Device, and Connor-Davidson Resilience Scale were employed for data collection. Results The mean PTG score of parents was 59.56±18.46; and 24.63%, 43.84%, and 32.51% of parents exhibited a high level, moderate level and low level, respectively, of PTG. The problem-solving dimension of family function (p = 0.005) and the strength dimension of mental resilience (p ≤0.001) were positively related to PTG. Conclusions Parents of children with ASD experienced moderate PTG overall. The problem-solving dimension of family function and the strength dimension of mental resilience were significant predictors of PTG. Practical implications Interventions to improve family function and the mental resilience of parents with ASD children could contribute to improving the PTG of parents.

Background: Heterogeneity of disease is a major concern in medical research, which is commonly recognized as subtypes with different pathogenesis, exhibiting distinct prognosis and treatment effects. The classification of population into homogenous subgroups is challenging, especially for the complex diseases. Recent studies show that gut microbiome compositions play a vital role in disease development, and it is of great interest to cluster the patients according to their microbial profiles. There are a variety of beta diversity measures to quantify the dissimilarity between the compositions of different samples for clustering. However, using different beta diversity measures results in different clusters, and it is difficult to make a choice. Results: Considering the microbial compositions from 16S rRNA sequencing, which are presented as a high-dimensional vector with large proportion of extremely small or even zero-valued elements, we set up three simulation experiments to mimic the microbial compositional data and evaluate the performance of different beta diversity measures in clustering. Their performance in two real datasets demonstrates the validity of the simulation experiments. Conclusion: It is shown that J-divergence/Jensen-Shannon divergence/square root of Jensen-Shannon divergence and Bhattacharyya/Hellinger can capture the compositional changes at low abundance elements more efficiently, and Bhattacharyya/Hellinger is suggested.

Background We previously performed targeted sequencing of autism risk genes in probands from the Autism Clinical and Genetic Resources in China (ACGC) (phase I). Here, we expand this analysis to a larger cohort of patients (ACGC phase II) to better understand the prevalence, inheritance, and genotype–phenotype correlations of likely gene-disrupting (LGD) mutations for autism candidate genes originally identified in cohorts of European descent. Methods We sequenced 187 autism candidate genes in an additional 784 probands and 85 genes in 599 probands using single-molecule molecular inversion probes. We tested the inheritance of potentially pathogenic mutations, performed a meta-analysis of phase I and phase II data and combined our results with existing exome sequence data to investigate the phenotypes of carrier parents and patients with multiple hits in different autism risk genes. Results We validated recurrent, LGD, de novo mutations (DNMs) in 13 genes. We identified a potential novel risk gene (ZNF292), one novel gene with recurrent LGD DNMs (RALGAPB), as well as genes associated with macrocephaly (GIGYF2 and WDFY3). We identified the transmission of private LGD mutations in genes predominantly associated with DNMs and showed that parental carriers tended to share milder autism-related phenotypes. Patients that carried DNMs in two or more candidate genes show more severe phenotypes. Conclusions We identify new risk genes and transmission of deleterious mutations in genes primarily associated with DNMs. The fact that parental carriers show milder phenotypes and patients with multiple hits are more severe supports a multifactorial model of risk. Electronic supplementary material The online version of this article (10.1186/s13229-018-0247-z) contains supplementary material, which is available to authorized users.

Recurrent de novo (DN) and likely gene-disruptive (LGD) mutations contribute significantly to autism spectrum disorders (ASDs) but have been primarily investigated in European cohorts. Here, we sequence 189 risk genes in 1,543 Chinese ASD probands (1,045 from trios). We report an 11-fold increase in the odds of DN LGD mutations compared with expectation under an exome-wide neutral model of mutation. In aggregate, ∼4% of ASD patients carry a DN mutation in one of just 29 autism risk genes. The most prevalent gene for recurrent DN mutations is SCN2A (1.1% of patients) followed by CHD8, DSCAM, MECP2, POGZ, WDFY3 and ASH1L. We identify novel DN LGD recurrences (GIGYF2, MYT1L, CUL3, DOCK8 and ZNF292) and DN mutations in previous ASD candidates (ARHGAP32, NCOR1, PHIP, STXBP1, CDKL5 and SHANK1). Phenotypic follow-up confirms potential subtypes and highlights how large global cohorts might be leveraged to prove the pathogenic significance of individually rare mutations., Recurrent sporadic mutations are important risk factors for autism spectrum disorders (ASDs) but have been primarily investigated in European cohorts. Here, Eichler, Xia and colleagues analyse risk genes in a large Chinese ASD cohort and find novel recurrences of potential pathogenic significance.

Figure S6. Distribution of mutations in some of the top mutated genes (DYRK1A, ASXL3, WDFY3 and MECP2) in the ACGC cohort (above) compared to previously published LGD and missense DNMs identified in the SSC and ASC cohorts. (PDF 896 kb)

Figure S5. Distribution of missense mutations in SCN2A and CHD8. a. Missense DNMs in SCN2A are mainly located in the ion transport domain. Three recurrent missense DNM sites were identified at R937 (4), R379 (2), and G1744 (2). b. Distribution of missense DNMs in CHD8. One recurrent missense DNM site was identified at M904. c. The overall CADD score distributions of the missense DNMs within SCN2A and CHD8 are significantly higher than the distribution of rare missense mutations of SCN2A and CHD8 from ExAC. P values were corrected for the two tests. (PDF 1001 kb)

Figure S3. Fraction of target based on >â 8-fold sequence coverage by gene. Box and whisker plots show the fraction of a sampleâ s target bases at 8X or greater coverage split by gene. All capture samples are included (along with QC failures). (PDF 109 kb)

Figure S2. QC of MIPs cohort. QC analysis of the percentage of MIPs with at least eight reads per sample. (PDF 84 kb)

Figure S4. Distribution of DNMs for SCN2A and CHD8. LGD (red) and missense (blue) DNMs with respect to the protein model in the ACGC cohort (above the protein model) are compared to previously published DNMs (below the model) primarily from European cohorts. â DNMs unique to Phase II samples; *DNMs from SSC and ASC cohorts. (PDF 931 kb)

This study was designed to compare the effects of alendronate (ALN), strontium ranelate (SR), and zoledronic acid (ZOL) on bone-implant osseointegration in ovariectomized rats. Histological examination and biomechanical tests show that ZOL, ALN, and SR enhance bone-implant osseointegration; ALN and SR have similar effects, while ZOL enhances bone-implant osseointegration more than ALN and SR INTRODUCTION: This study aims to compare the effects of ALN, SR, and ZOL on bone-implant osseointegration in ovariectomized rats.Sixty female Sprague-Dawley rats were included in this study. Of them, 48 rats were ovariectomized (OVX) and assigned to four groups: OVX (OVX + Veh), ALN (OVX + ALN), SR (OVX + SR), and ZOL (OVX + ZOL). And another 12 rats were sham-operated as a control group (Sham). Four weeks after ovariectomy, HA-coated titanium implants were inserted into the tibias bilaterally in all rats. Then the rats in groups ALN, SR, and ZOL were systemically administrated with alendronate (7 mg/kg/week, orally), strontium ranelate (500 mg/kg/day, orally), or a single injection of zoledronic acid (0.1 mg/kg, iv), respectively. Twelve weeks after implantation, all rats were sacrificed to get the femurs and tibias. Histological examination and biomechanical tests were used to evaluate bone-implant osseointegration in all groups.ALN, SR, and ZOL significantly increased distal femoral BMD when compared with group OVX; ZOL increased BMD significantly more than ALN and SR (P 0.05). Significant increase of bone-to-implant contact and peri-implant bone fraction were observed in groups ALN, SR, and ZOL when compared with group OVX (P 0.05). Groups ALN and SR were inferior to groups ZOL and Sham (P 0.05) in bone-to-implant contact and peri-implant bone fraction. Similar results were found in biomechanical testing (max pushout force).In rats losing bone rapidly after ovariectomy, systemic administration of ZOL, ALN, and SR causes better bone-implant osseointegration when compared to OVX; ALN and SR have similar positive effects on osseointegration, while ZOL, that was given in a dose with more positive BMD effect than that of ALN or SR, causes better osseointegration than either ALN or SR.

Figure S8. Multiple-hit model for ASD excluding DNM cases. Shown are comparisons of autism probands and unaffected siblings with one or more DNM. Logistic histograms compare residual DNM counts (DNM number residuals, note: after correction, a residual of 0 does not represent a count of 0) adjusted for the father’s age at birth and gender, and the probability of being a proband or unaffected sibling. This analysis demonstrates an increased burden of multiple hits among affected individuals (OR = 1.15, p = 0.0278) (a). When samples are stratified by genetic sex, we observe a slight increase but no significant effect among males (OR = 1.09, p = 0.6) (b), while females demonstrate a stronger (OR = 1.3, p = 0.037) (c) effect than the grouped analysis. (PDF 957 kb)

Figure S8. Multiple-hit model for ASD excluding DNM cases. Shown are comparisons of autism probands and unaffected siblings with one or more DNM. Logistic histograms compare residual DNM counts (DNM number residuals, note: after correction, a residual of 0 does not represent a count of 0) adjusted for the father’s age at birth and gender, and the probability of being a proband or unaffected sibling. This analysis demonstrates an increased burden of multiple hits among affected individuals (OR = 1.15, p = 0.0278) (a). When samples are stratified by genetic sex, we observe a slight increase but no significant effect among males (OR = 1.09, p = 0.6) (b), while females demonstrate a stronger (OR = 1.3, p = 0.037) (c) effect than the grouped analysis. (PDF 957 kb)