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The toxicity of heavy metals to marine invertebrates has been widely investigated; however, the effects on marine sedentary polychaetes have largely been ignored. The toxicity of copper, aluminium, lead, nickel, and zinc on fertilization, embryogenesis, and larvae of Hydroides elegans was examined in laboratory acute-toxicity tests. Exposure to metal during fertilization or early developmental stages leads to fertilization block and arrested development, which resulted in morphologic abnormalities in embryo and larvae. Fertilization rate showed a drastic decrease at the highest metal concentration tested. Embryos of H. elegans showed a differential response to metals, and the responses were stage-specific. The different morphologic effects of heavy metals reflect differentiation of the early embryonic cells. For individual metals, the toxicity ranking for 24-hour trochophore larvae was Cu > Al > Pb > Ni > Zn, with EC(50) values of 0.122, 0.210, 0.231, 0.316, and 0.391 mg l(-1), respectively. Rate of larval development and embryogenesis were the most sensitive end points, although the latter is more advisable for routine assessment of seawater quality because of its greater simplicity. In addition to bivalves and sea urchins, polychaete embryos can provide biologic criteria for seawater quality taking into account the sensitivity of a polychaete and contributing to the detection of harmful chemicals with no marked effect on the species currently in use in seawater quality bioassays.

Several variables, for instance, inheritance and surroundings, influence the growth of neurodevelopmental disorders, e.g., autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) during the first 36 months of life. ADHD and ASD diagnosis mainly rely heavily on traditional clinical assessments from the last few decades. These traditional methods are based on massive data collection from multiple respondents’ responses and the extent of various behavioral descriptors, which are then recognized by the researcher while forming a diagnostic criterion. However, opting for traditional diagnostic methods, there is a high possibility of being misdiagnosed, which may lead to the administration of unnecessary long-term pharmaceutical treatment. That may lead to reduction in functioning and an increase in the risk of developing additional social and clinical issues. Moreover, such diagnostic procedures are also time-consuming and costly. In this sense, rapid and advanced criteria are required to be accurate and cost-effective. Consequently, this study emphasizes the application of machine learning (ML) tools and deep learning (DL) techniques such as convolutional neural network (CNN) and Deep Learning APIs (Application Programming Interface), for the early diagnosis and treatment of ADHD and ASD symptoms. From this investigation, it can be concluded that diagnostic techniques based on ML reduce the intervention time and increase the accuracy with simultaneous understanding of the techniques and algorithms applied to different types of Image data. Numerous studies have been done on ASD and ADHD separately, but our investigation also focuses on cooccurrences of these disorders in one individual.