Examine sex differences in autism spectrum disorder in school-aged children and adolescents with fluent language.

There are noteworthy sex disparities in the prevalence of autism spectrum disorders (ASD), while findings regarding the sex differences in core symptoms are inconsistent. There are few relevant studies on sex differences in mainland China. This study was dedicated to a deeper understanding of the impact of sex differences on the clinical presentation of ASD with fluent language. We retrospectively studied 301 children with ASD (58 females) and utilized raw scores from the ADI-R and ADOS and the intelligence quotient (IQ) to measure symptomatology. Based on the Full-Scale IQ (FS-IQ), a binary split of average, above-average IQ (high-IQ), and below-average IQ (low IQ) occurs at 85. Across the entire sample, males and females are comparable in the FS-IQ, while males scored higher in the Perceptual Reasoning Index (PRI) (F = 7.812, p = 0.006). ADI-R did not find any statistically significant sex differences in the diagnostic cutoff score satisfaction or the raw domain scores. While a significant effect of sex on ADOS social affect domain scores was found in the total sample [λ = 0.970, partial η2 = 0.030, F (3,295) = 3.019, p = 0.030]. Tests of between-subjects effects revealed that males scored higher than females mainly in the ADOS reciprocal social interaction subcategory (partial η2 = 0.022, F = 6.563, p = 0.011). Stratified analysis revealed that the effect of sex on ADOS reciprocal social interaction subcategory scores only significant in the low-IQ children with ASD (partial η2 = 0.092, F = 10.088, p = 0.002). In general, overall cognitive functioning is similar across males and females with ASD, while males have a higher perceptual reasoning ability. Females with ASD are more likely to have comorbid intellectual impairment than males, and they could require additional intervention support. Autistic children with low IQs are more likely to exhibit sex differences in their core symptoms than children with high IQs. Intelligence plays a key role in sex-based differences in the core symptoms of ASD.

A multimodal machine learning system in early screening for toddlers with autism spectrum disorders based on the response to name.

Background: Reduced or absence of the response to name (RTN) has been widely reported as an early specific indicator for autism spectrum disorder (ASD), while few studies have quantified the RTN of toddlers with ASD in an automatic way. The present study aims to apply a multimodal machine learning system (MMLS) in early screening for toddlers with ASD based on the RTN. Methods: A total of 125 toddlers were recruited, including ASD (n = 61), developmental delay (DD, n = 31), and typical developmental (TD, n = 33). Procedures of RTN were, respectively, performed by the evaluator and caregiver. Behavioral data were collected by eight-definition tripod-mounted cameras and coded by the MMLS. Response score, response time, and response duration time were accurately calculated to evaluate RTN. Results: Total accuracy of RTN scores rated by computers was 0.92. In both evaluator and caregiver procedures, toddlers with ASD had significant differences in response score, response time, and response duration time, compared to toddlers with DD and TD (all P-values < 0.05). The area under the curve (AUC) was 0.81 for the computer-rated results, and the AUC was 0.91 for the human-rated results. The accuracy in the identification of ASD based on the computer- and human-rated results was, respectively, 74.8 and 82.9%. There was a significant difference between the AUC of the human-rated results and computer-rated results (Z = 2.71, P-value = 0.007). Conclusion: The multimodal machine learning system can accurately quantify behaviors in RTN procedures and may effectively distinguish toddlers with ASD from the non-ASD group. This novel system may provide a low-cost approach to early screening and identifying toddlers with ASD. However, machine learning is not as accurate as a human observer, and the detection of a single symptom like RTN is not sufficient enough to detect ASD.

Chinese Military Evaluation of a Portable Near-Infrared Detector of Traumatic Intracranial Hematomas.

Introduction: Secondary brain injury is the main cause of mortality from traumatic brain injury (TBI). One hallmark of TBI is intracranial hemorrhage, which occurs in 40-50% of severe TBI cases. Early identification of intracranial hematomas in TBI patients allows early surgical evacuation and can reduce the case fatality rate of TBI. As pre-hospital care is the weakest part of Chinese emergency care, there is an urgent need for a capability to detect brain hematomas early. In China, in addition to preventing injuries and diseases in military staff and in enhancing the military armed forces during war, military medicine participates in actions such as emergency public health crises, natural disasters, emerging conflicts, and anti-terrorist campaigns during peacetime. The purpose of this observational study is to evaluate in the Chinese military general hospital the performance of a near-infrared (NIR)-based portable device, developed for US Military, in the detection of traumatic intracranial hematomas. The endpoint of the study was a description of the test characteristics (sensitivity, specificity, and positive and negative predictive values [NPV]) of the portable NIR-based device in identification of hematomas within its detection limits (volume >3.5 mL and depth <2.5 cm) compared with computed tomography (CT) scans as the gold standard. Materials and Methods: The Infrascanner Model 2000 NIR device (InfraScan, Inc., Philadelphia, PA, USA) was used for hematoma detection in patients sustaining TBI. Data were collected in the People's Liberation Army General Hospital in Beijing using the NIR device at the time of CT scans, which were performed to evaluate suspected TBI. One hundred and twenty seven patients were screened, and 102 patients were included in the per protocol population. Of the 102 patients, 24 were determined by CT scan to have intracranial hemorrhage. The CT scans were read by an independent neuroradiologist who was blinded to the NIR measurements. Results: The NIR device demonstrated sensitivity of 100% (95% confidence intervals [CI] 82.8-100%) and specificity of 93.6% (95%CI 85-97.6%) in detecting intracranial hematomas larger than 3.5 mL in volume and that were less than 2.5 cm from the surface of the brain. Blood contained within scalp hematomas was found to be a major cause of false-positive results with this technology. Conclusion: The study showed that the Infrascanner is a suitable portable device in Chinese population for detecting preoperative intracranial hematomas in remote locations, emergency rooms, and intensive care units. It could aid military medics, physicians, and hospital staff, permitting better triage decisions, earlier treatment, and reducing secondary brain injury caused by acute and delayed hematomas.