Introduction: Diabetes may affect the human body’s systems and organs, including the eye. Diabetic retinopathy is the 5th leading cause of blindness globally. Diabetic subjects demonstrated dry eye symptoms that were also supported by the low values of the clinical tests. Purpose: This study aimed to compare the dry eye symptoms and signs between diabetics and non-diabetics and tear functions between diabetic subjects with and without dry eye. Methods: This retrospective study was based on the observation of 643 medical files. Using a convenience sampling method, 88 subjects were found to report diabetes mellitus. The information extracted from the files included: date of first examination, age at first visit, gender, past ocular history, systemic disease, symptoms of dry eye disease and details of clinical diagnostic signs. Noncontact lens wearers were excluded. A group of 88, age and gender matched, control subjects were included for this comparison study. Results: The percentage of dry eye symptoms was higher in diabetic subjects (15.9%) compared with non-diabetic subjects (13.6%; p<0.001). The percentage of dry eye symptoms was also higher in diabetics with dry eye (63%) than in diabetics without dry eye (36.9%; p<0.001). Tear break up time was significantly different between diabetics and non-diabetics (p<0.001) and between diabetics with and without dry eye (p=0.046). The corneal staining was significantly different between diabetic subjects with and without dry eye (p=0.028). Conclusion: Dry eye symptoms were significantly associated with diabetics. Tear break up time was significantly shorter in diabetics with dry eye compared to diabetics without dry eye.

The aim of this paper is twofold: Firstly, to provide introductory knowledge to the reader who has little orno knowledge of machine learning with examples of applications in clinical and biomedical domains, andsecondly, to compare and contrast the concept of Artificial Neural Network (ANN) and the Qur’anic conceptof intellect (aql) in the Qur’an. Learning algorithm can generally be categorised into supervised andunsupervised learning. To better understand the machine learning concept, hypothetical data of glaucomacases are presented. ANN is then selected as an example of supervised learning and the underlyingprinciples in ANN are presented with general audience in mind with an attempt to relate the mechanismemployed in the algorithm with Qur’anic verses containing the verbs derived from aql. The applications ofmachine learning in clinical and biomedical domains are briefly demonstrated based on the author’s ownresearch and most recent examples available from University of California, Irvine Machine LearningRepository. Selected verses which indicate motivation to use the intellect in positive manners and rebuke tothose who do not activate the intellect are presented. The evidence found from the verses suggests thatANN shares similar learning process to achieve belief (iman) by analysing the similitudes (amsal) introducedto the algorithm.

Background and Objectives: Auditory sensory gating deficits are abnormalities in patients with autism spectrum disorder (ASD) that may lead to sensory processing difficulties. It is particularly difficult for children with ASD to distinguish multiple auditory stimuli, which hinders them from focusing on a single auditory stimulus and separating unnecessary sounds. Suppression of otoacoustic emission (OAE) is an approach used to identify sensory gating deficits in the general population, specifically for children with ASD. This study aimed to investigate the suppression effect of various types of sound suppressors to measure their sensory gating capacity in children with ASD. Subjects and Methods: Twenty children including 10 with ASD and 10 normally developing children aged 6-12 years were recruited for this study. One accessible ear was exposed to transient-evoked OAE, whereas the other was exposed to suppressor tones. Contralateral suppressors included white noise, Quranic recitations, environmental noise, and natural sound. The magnitude of OAE suppression was determined from the difference between the OAE amplitude with and without the masker (in dB sound pressure levels) for all sound types. The sound with the highest suppression effect was determined using effect size calculation and repeated-measures analysis of variance at a 95% confidence level. A high suppression effect may suggest a high sensory gating performance, whereas low suppression may indicate low sensory gating performance. Results: Based on the analysis, the sound with the highest suppression effect was that of the waterfall. The suppression results were supported by descriptive analysis findings and effect-size calculations. Conclusions This study provides a better understanding of the alternative sound stimuli, besides the standard white noise tone, for the assessment of sensory gating deficits among children with ASD. Sounds with a high suppression effect have the potential to be used as sound therapy interventions for children with ASD as part of rehabilitation and therapy.

Background and Objectives: Auditory sensory gating deficits are abnormalities in patients with autism spectrum disorder (ASD) that may lead to sensory processing difficulties. It is particularly difficult for children with ASD to distinguish multiple auditory stimuli, which hinders them from focusing on a single auditory stimulus and separating unnecessary sounds. Suppression of otoacoustic emission (OAE) is an approach used to identify sensory gating deficits in the general population, specifically for children with ASD. This study aimed to investigate the suppression effect of various types of sound suppressors to measure their sensory gating capacity in children with ASD. Subjects and Methods: Twenty children including 10 with ASD and 10 normally developing children aged 6-12 years were recruited for this study. One accessible ear was exposed to transient-evoked OAE, whereas the other was exposed to suppressor tones. Contralateral suppressors included white noise, Quranic recitations, environmental noise, and natural sound. The magnitude of OAE suppression was determined from the difference between the OAE amplitude with and without the masker (in dB sound pressure levels) for all sound types. The sound with the highest suppression effect was determined using effect size calculation and repeated-measures analysis of variance at a 95% confidence level. A high suppression effect may suggest a high sensory gating performance, whereas low suppression may indicate low sensory gating performance. Results: Based on the analysis, the sound with the highest suppression effect was that of the waterfall. The suppression results were supported by descriptive analysis findings and effect-size calculations. Conclusions This study provides a better understanding of the alternative sound stimuli, besides the standard white noise tone, for the assessment of sensory gating deficits among children with ASD. Sounds with a high suppression effect have the potential to be used as sound therapy interventions for children with ASD as part of rehabilitation and therapy.

Background and Objectives: Auditory sensory gating deficits are abnormalities in patients with autism spectrum disorder (ASD) that may lead to sensory processing difficulties. It is particularly difficult for children with ASD to distinguish multiple auditory stimuli, which hinders them from focusing on a single auditory stimulus and separating unnecessary sounds. Suppression of otoacoustic emission (OAE) is an approach used to identify sensory gating deficits in the general population, specifically for children with ASD. This study aimed to investigate the suppression effect of various types of sound suppressors to measure their sensory gating capacity in children with ASD. Subjects and Methods: Twenty children including 10 with ASD and 10 normally developing children aged 6-12 years were recruited for this study. One accessible ear was exposed to transient-evoked OAE, whereas the other was exposed to suppressor tones. Contralateral suppressors included white noise, Quranic recitations, environmental noise, and natural sound. The magnitude of OAE suppression was determined from the difference between the OAE amplitude with and without the masker (in dB sound pressure levels) for all sound types. The sound with the highest suppression effect was determined using effect size calculation and repeated-measures analysis of variance at a 95% confidence level. A high suppression effect may suggest a high sensory gating performance, whereas low suppression may indicate low sensory gating performance. Results: Based on the analysis, the sound with the highest suppression effect was that of the waterfall. The suppression results were supported by descriptive analysis findings and effect-size calculations. Conclusions This study provides a better understanding of the alternative sound stimuli, besides the standard white noise tone, for the assessment of sensory gating deficits among children with ASD. Sounds with a high suppression effect have the potential to be used as sound therapy interventions for children with ASD as part of rehabilitation and therapy.