Biology of GDNF and its receptors — Relevance for disorders of the central nervous system
Neurobiology of Disease · 2016
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Astrocytes: biology and pathology
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Factors influencing knowledge about childhood autism among final year undergraduate Medical, Nursing and Psychology students of University of Nigeria, Enugu State, Nigeria
BACKGROUND: Knowledge and awareness about childhood autism is low among health care workers and the general populace in Nigeria. Poor knowledge about childhood autism among final year medical, nursing and psychology students who would form tomorrow's child health care professionals can compromise early recognition and interventions that are known to improve prognosis in childhood autism. Educational factors that could be influencing knowledge about childhood autism among these future health care professionals are unknown. This study assessed knowledge about childhood autism among final year undergraduate medical, nursing and psychology students in south-eastern Nigeria and determined the factors that could be influencing such knowledge. METHODS: One hundred final year undergraduate students were randomly selected from each of the Departments of Medicine, Nursing Science and Psychology respectively of University of Nigeria, Enugu State, Nigeria making a sample size of three hundred. A socio-demographic questionnaire and knowledge about childhood autism among health workers (KCAHW) questionnaire were administered to the students. RESULTS: The total mean score for the three groups of students on the KCAHW questionnaire was 10.67+/-3.73 out of a possible total score of 19, with medical, nursing and psychology students having total mean scores of 12.24+/-3.24, 10.76+/-3.50 and 9.01+/-3.76 respectively. The mean scores for the three groups showed statistically significant difference for domain 1 (p=0.000), domain 3 (p=0.029), domain 4 (p=0.000) and total score (p=0.000), with medical students more likely to recognise symptoms and signs of autism compared to nursing and psychology students. The mean score in domain 2 did not show statistically significant difference among the three groups (p=0.769). The total score on the KCAHW questionnaire is positively correlated with the number of weeks of posting in psychiatry (r=0.319, p=0.000) and the number of weeks of posting in paediatrics (r=0.372, p=0.000). The total score is also positively correlated with the number of credit hours of lectures in psychiatry/abnormal psychology (r=0.324, p=0.000) and the number of credit hours of lectures in paediatrics (r=0.372, p=0.000). The field of study also influenced knowledge about childhood autism (p=0.000). CONCLUSION: Peculiar situation in this environment as signified by inadequate human resources needed in the area of clinical psychology training often times necessitates employing first degree graduates in psychology into clinical positions. This calls for additional exposure of the undergraduate psychology students to training curriculum aimed at improving their early recognition of symptoms of autism spectrum disorders in this environment.
Gene-Environment Interactions and Epigenetic Regulation in Autism Etiology through Multi-Omics Integration and Computational Biology Approaches
Autism Spectrum Disorder (ASD) is a multifactorial neurodevelopmental condition characterized by substantial genetic heterogeneity and complex environmental influences. Emerging evidence suggests that gene-environment interactions, mediated through dynamic epigenetic mechanisms, play a critical role in modulating neurodevelopmental trajectories implicated in ASD. This review synthesizes current advances in understanding the etiological interplay between genetic variants, environmental exposures, and epigenetic regulation, with a focus on DNA methylation, histone modifications, and non-coding RNAs. We explore how these layers of molecular control intersect to dysregulate neurodevelopmental gene networks and contribute to ASD pathophysiology. Central to this investigation is the integration of multi-omics platforms— encompassing genomics, transcriptomics, epigenomics, proteomics, and metabolomics—supported by computational biology, machine learning, and systems-level modeling frameworks. These technologies facilitate the identification of molecular subtypes, predictive biomarkers, and regulatory circuits associated with ASD. Furthermore, we examine the translational implications of these findings in the context of precision medicine, including early diagnosis, patient stratification, and individualized therapeutic development. Despite the challenges of data heterogeneity, scalability, and interpretability, the integration of high-dimensional biological data holds transformative potential for elucidating ASD etiology and advancing targeted interventions.