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The S100 calcium-binding protein beta subunit (S100B) protein, which mostly exists in the central nervous system, is commonly noted as a marker of neuronal damage. We conducted the first systematic review with meta-analysis to compare peripheral blood S100B levels in individuals with ASD with those in healthy controls. A systematic search was carried out for studies published before May 5, 2020. In total, this meta-analysis involved ten studies with 822 participants and 451 cases. The meta-analysis revealed that individuals with ASD had higher peripheral blood S100B levels than healthy controls [standardized mean difference (SMD) = 0.97, 95% confidence interval (95% CI) = 0.41–1.53; p  < 0.001]. Peripheral blood S100B levels may have potential as a useful biomarker for ASD.

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.

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.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects about 2% of children. Due to the shortage of clinicians, there is an urgent demand for a convenient and effective tool based on regular videos to assess the symptom. Computer-aided technologies have become widely used in clinical diagnosis, simplifying the diagnosis process while saving time and standardizing the procedure. In this study, we proposed a computer vision-based motion trajectory detection approach assisted with machine learning techniques, facilitating an objective and effective way to extract participants’ movement features (MFs) to identify and evaluate children’s activity levels that correspond to clinicians’ professional ratings. The designed technique includes two key parts: (1) Extracting MFs of participants’ different body key points in various activities segmented from autism diagnostic observation schedule (ADOS) videos, and (2) Identifying the most relevant MFs through established correlations with existing data sets of participants’ activity level scores evaluated by clinicians. The research investigated two types of MFs, i.e., pixel distance (PD) and instantaneous pixel velocity (IPV), three participants’ body key points, i.e., neck, right wrist, and middle hip, and five activities, including Table-play, Birthday-party, Joint-attention, Balloon-play, and Bubble-play segmented from ADOS videos. Among different combinations, the high correlations with the activity level scores evaluated by the clinicians (greater than 0.6 with p < 0.001) were found in Table-play activity for both the PD-based MFs of all three studied key points and the IPV-based MFs of the right wrist key point. These MFs were identified as the most relevant ones that could be utilized as an auxiliary means for automating the evaluation of activity levels in the ASD assessment.

Autism spectrum disorder (ASD) is characterized by deficits in social interactions and repetitive, stereotypic behaviors. Evidence shows that bidirectional communication of the gut-brain axis plays an important role. Here, we recruited 62 patients with ASD in southern China, and performed a cross-sectional study to test the relationship between repeated behavior, gut microbiome composition, and alpha diversity. We divided all participants into two groups based on the clustering results of their microbial compositions and found Veillonella and Ruminococcus as the seed genera in each group. Repetitive behavior differed between clusters, and cluster 2 had milder repetitive symptoms than Cluster 1. Alpha diversity between clusters was significantly different, indicating that cluster 1 had lower alpha diversity and more severe repetitive, stereotypic behaviors. Repetitive behavior had a negative correlation with alpha diversity. We demonstrated that the difference in intestinal microbiome composition and altered alpha diversity can be associated with repetitive, stereotypic behavior in autism. The role of Ruminococcus and Veillonella in ASD is not yet understood.

Autism Spectrum Disorder (ASD) is a group of lifelong neurodevelopmental disorders with complicated causes. A key symptom of ASD patients is their impaired interpersonal communication ability. Recent study shows that face scanning patterns of individuals with ASD are often different from those of typical developing (TD) ones. Such abnormality motivates us to study the feasibility of identifying ASD children based on their face scanning patterns with machine learning methods. In this paper, we consider using the bag-of-words (BoW) model to encode the face scanning patterns, and propose a novel dictionary learning method based on dual mode seeking for better BoW representation. Unlike k-means which is broadly used in conventional BoW models to learn dictionaries, the proposed method captures discriminative information by finding atoms which maximizes both the purity and coverage of belonging samples within one class. Compared to the rich literature of ASD studies from psychology and neural science, our work marks one of the relatively few attempts to directly identify high-functioning ASD children with machine learning methods. Experiments demonstrate the superior performance of our method with considerable gain over several baselines. Although the proposed work is yet too preliminary to directly replace existing autism diagnostic observation schedules in the clinical practice, it shed light on future applications of machine learning methods in early screening of ASD.

The prevalence of autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) is increasing, with a tendency for co-occurrence. Some studies indicate a connection between atypical sensory processing and executive function. This study aims to explore the distinctive etiology of executive function deficits in children with ASD+ADHD by investigating the relationship between sensory processing and executive function, comparing children with ASD, ASD+ADHD, ADHD, and typically developing children (TD). Method: Sensory Profile 2 (SP-2) and Behavior Rating Inventory of Executive Function 2 (BRIEF-2) were measured in 120 school-aged children. The results of the above scales were compared across these four groups, and correlation and regression analyses between BRIEF2 and SP2 were conducted. Results: Our research revealed varying levels of atypical sensory processing and executive function anomalies across the three neurodevelopmental disorder groups compared to the TD group. The ASD+ADHD group showed particularly significant differences. The heightened emotional problems observed in ASD+ADHD children may be associated with more prominent atypical sensory processing. Variance analysis of inhibitory function revealed differences between ASD+ADHD and ADHD children, suggesting distinct etiological mechanisms for attention issues between ASD+ADHD and ADHD. Conclusions: ASD+ADHD represents a phenotype distinct from both ASD and ADHD. Special consideration should be given to interventions for children with ASD+ADHD. The results of this study may offer a new perspective on understanding the occurrence of ASD+ADHD and potential individualized intervention methods.

Background Gesture difficulties have been reported in later-born siblings of children with autism spectrum disorder (ASD). Careful observation of gesture development during the first two years of children at elevated likelihood (EL) of developing ASD may identify behavioral indicators that facilitate early diagnosis. Methods This study enrolled 47 EL infants and 27 low-likelihood (LL) infants to explore gesture developmental trajectories and the predictive value of gesture to expedite the early detection of core characteristics of ASD. Gesture frequency, communication function, and integration ability were observed and coded from a semi-structured assessment administered longitudinally across 9–19 months of age. We conducted the Autism Diagnostic Observation Schedule assessment at 18–19 months for ASD’s core characteristics. Results The development of joint attention (JA) gestures was slower in the EL than in the LL group. The trajectories of the two groups began to diverge at 14–18 months. Children who reached the diagnostic cutoff point for ASD showed reductions in social interaction gestures at 12–13 months, in gestures integrated with any two communication skills (G-M) at 15–16 months; and in gestures integrated with eye contact (G-E) at 18–19 months. Overall gesture and G-M integration were associated with an overall ADOS communication and social interaction score. Conclusions The developmental trajectories of JA gestures of EL and LL children differed. G-M gestures represent early indicators that may be a predictor of ASD.

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.

Background The elevated prolactin levels in first-episode drug-naïve (FEDN) schizophrenia patients may correlate with long-term stress caused by childhood trauma. This study aimed to assess the relationship between elevated prolactin levels and childhood trauma in FEDN schizophrenia patients, while also considering sex differences. Methods Utilizing a cross-sectional design, the study involved 88 FEDN schizophrenia patients and 76 healthy controls (HCs). Evaluations encompassed measuring prolactin levels in peripheral blood and assessing mental health using the Positive and Negative Syndrome Scale (PANSS), the Childhood Trauma Questionnaire - Short Form (CTQ-SF), as well as evaluating resilience with the Connor-Davidson Resilience Scale (CD-RISC), perceived social support with the Perceived Social Support Scale (PSSS), and demographic characteristics to control for confounding factors. A mediation model was constructed using the RMediation package of the R software. Methods The results suggested prolactin levels in FEDN schizophrenia patients were higher than in HCs(t=-9.938, p  = 0.000). Group classification (HCs vs. FEDN schizophrenia patients) (t = 9.291, p  = 0.000) and sex (t = 3.282, p  = 0.001) were influential factors for prolactin levels. Elevated prolactin(OR = 1.007, p  = 0.000), along with higher scores for childhood emotional(OR = 1.469, p  = 0.006)andsexual abuse(OR = 1.592, p  = 0.018) and lower social support(OR = 0.946, p  = 0.026), were associated with the onset of schizophrenia. Positive correlations were found between prolactin levels and childhood emotional ( r  = 0.268, p  = 0.002) /sexual abuse( r  = 0.264, p  = 0.002), with no sex differences. No significant relationship was observed between prolactin levels and PANSS scores. Mediation analysis revealed that childhood emotional abuse (95% CI: [0.059 ~ 0.293]) and sexual abuse (95% CI: [0.086 ~ 0.439]) had significant indirect effects on schizophrenia, mediated by elevated prolactin levels. Conclusion These findings suggest that childhood trauma may be associated with the onset of schizophrenia by influencing prolactin levels, highlighting the complex interplay between hormonal disruptions and early-life stress in the development of schizophrenia.