Hyperreactivity to weak acoustic stimuli and prolonged acoustic startle latency in children with autism spectrum disorders

BACKGROUND: People with autism spectrum disorders (ASD) are known to have enhanced auditory perception, however, acoustic startle response to weak stimuli has not been well documented in this population. The objectives of this study are to evaluate the basic profile of acoustic startle response, including peak startle latency and startle magnitude to weaker stimuli, in children with ASD and typical development (TD), and to evaluate their relationship to ASD characteristics. METHODS: We investigated acoustic startle response with weak and strong acoustic stimuli in 12 children with ASD and 28 children with TD, analyzing the relationship between startle measures and quantitative autistic traits assessed with the Social Responsiveness Scale (SRS). The electromyographic activity of the left orbicularis oculi muscle to acoustic stimuli of 65 to 115 dB sound pressure level (SPL), in increments of 5 dB, was measured to evaluate acoustic startle response. The average eyeblink magnitude for each acoustic stimuli intensity and the average peak startle latency of acoustic startle response were evaluated. RESULTS: The magnitude of the acoustic startle response to weak stimuli (85 dB or smaller) was greater in children with ASD. The peak startle latency was also prolonged in individuals with ASD. The average magnitude of the acoustic startle response for stimulus intensities greater than 85 dB was not significantly larger in the ASD group compared with the controls. Both greater startle magnitude in response to weak stimuli (particularly at 85 dB) and prolonged peak startle latency were significantly associated with total scores, as well as several subscales of the SRS in the whole sample. We also found a significant relationship between scores on the social cognition subscale of the SRS and the average magnitude of the acoustic startle response for stimulus intensities of 80 and 85 dB in the TD group. CONCLUSIONS: Children with ASD exhibited larger startle magnitude to weak stimuli and prolonged peak startle latency. These startle indices were related to several characteristics of ASD. A comprehensive investigation of acoustic startle response, including the magnitude of startle responses to weak stimuli and peak startle latency, might further our understanding of the neurophysiological impairments underlying ASD

Negatively Skewed Locomotor Activity Is Related to Autistic Traits and Behavioral Problems in Typically Developing Children and Those With Autism Spectrum Disorders

An important objective for researchers and clinicians is to gain a better understanding of the factors that underlie autism spectrum disorders (ASDs). It is possible that investigating objective and quantitative behavioral phenotypes and their relationship to clinical characteristics, such as autistic traits and other emotional/behavioral problems, might facilitate this process. Given this, in the current study we examined the link between locomotor dynamics and clinical characteristics, including autistic traits and emotional/behavioral problems, in children with ASD (n = 14) and typically developing (TD) children (n = 13). A watch-type actigraph was used to continuously measure locomotor activity which was assessed in terms of mean activity levels and the skewness of activity. Parents assessed quantitative autistic traits using the Japanese version of the Social Responsiveness Scale (SRS) and emotional and behavioral problems using the Japanese version of the Strengths and Difficulties Questionnaire (SDQ). Results showed that among all children, all-day activity was more negatively skewed, suggesting sporadic large all-day “troughs” in activity and was significantly correlated with the SRS social awareness subscale score (ρ = −0.446, p = 0.038). In addition, the more negatively skewed daytime locomotor activity was associated with the SDQ Hyperactivity Inattention subscale score (ρ = −0.493, p = 0.020). The results of this study indicate that investigating locomotor dynamics may provide one way to increase understanding of the neurophysiological mechanisms underlying the clinical characteristics of ASD

Terahertz radiation from photoconductive switch fabricated from a zinc oxide single crystal

We present results on the terahertz (THz) radiation from a photoconductive switch fabricated on a zinc oxide (ZnO) single crystal. Due to its high transmittance in the visible, near-infrared, mid-infrared, and THz regions, ZnO may present itself as a viable material for integrated active optics operating in the THz region. © 2007 Springer-Verlag New York

Generation of terahertz radiation using zinc oxide as photoconductive material excited by ultraviolet pulses

Terahertz (THz) radiation generated from photoconductive antenna fabricated on a single crystal zinc oxide (ZnO) is presented. The THz-radiation power is saturated at bias voltages above 800 Vcm and the obtained spectrum extends up to 1 THz. Moreover, ZnO is found to be highly transparent in the visible, near-infrared, mid-infrared and THz frequency regions. The results depicted here will categorically unravel the prospects of using ZnO as a material for integrated active optics. © 2005 American Institute of Physics

A case of fulminant amoebic colitis during systemic chemotherapy for gastric cancer

Amoebiasis is a parasitic infection caused by the protozoan, Entamoeba histolytica. At times, amoebiasis is activated under immunosuppressive conditions such as chemotherapy. We report a case of fulminant amoebic colitis resulting from an asymptomatic Entamoeba histolytica infection, which was activated by chemotherapy for gastric cancer. The patient developed diarrhea and fever after three courses of chemotherapy for gastric cancer and was diagnosed with acute enteritis. A colonoscopy and biopsy were performed because of the bloody stool. Histopathological findings revealed amoebic invasion of the rectum. Therefore, the patient was diagnosed with amoebic colitis and was treated with metronidazole. Emergency surgery was performed because intestinal perforation was suspected after which his general condition improved and was discharged. Subsequently, gastric cancer surgery was performed and the patient was discharged without postoperative complications. Hence, amoebic colitis should be listed as a differential diagnosis, and a colonoscopic biopsy should be performed when colitis occurs during chemotherapy for cancer.This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s12328-023-01835-

Diagnosis of Depth of Submucosal Invasion in Colorectal Cancer with AI Using Deep Learning

The submucosal invasion depth predicts prognosis in early colorectal cancer. Although colorectal cancer with shallow submucosal invasion can be treated via endoscopic resection, colorectal cancer with deep submucosal invasion requires surgical colectomy. However, accurately diagnosing the depth of submucosal invasion via endoscopy is difficult. We developed a tool to diagnose the depth of submucosal invasion in early colorectal cancer using artificial intelligence. We reviewed data from 196 patients who had undergone a preoperative colonoscopy at the Osaka University Hospital and Osaka International Cancer Institute between 2011 and 2018 and were diagnosed pathologically as having shallow submucosal invasion or deep submucosal invasion colorectal cancer. A convolutional neural network for predicting invasion depth was constructed using 706 images from 91 patients between 2011 and 2015 as the training dataset. The diagnostic accuracy of the constructed convolutional neural network was evaluated using 394 images from 49 patients between 2016 and 2017 as the validation dataset. We also prospectively tested the tool from 56 patients in 2018 with suspected early-stage colorectal cancer. The sensitivity, specificity, accuracy, and area under the curve of the convolutional neural network for diagnosing deep submucosal invasion colorectal cancer were 87.2% (258/296), 35.7% (35/98), 74.4% (293/394), and 0.758, respectively. The positive predictive value was 84.4% (356/422) and the sensitivity was 75.7% (356/470) in the test set. The diagnostic accuracy of the constructed convolutional neural network seemed to be as high as that of a skilled endoscopist. Thus, endoscopic image recognition by deep learning may be able to predict the submucosal invasion depth in early-stage colorectal cancer in clinical practice