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The prevalence of neurological dieases, including neurodegenerative, neurotraumatic disorders, and neuroinflammatory conditions, has been rising due to global population and aging demographics. A key factor in the pathogenesis of these disorders is the hyperaccumulation of reactive oxygen and nitrogen species (RONS). Nanozymes have emerged as promising candidates for neurotherapeutic applications owing to their exceptional catalytic activity and stability. Of particular note is their ability to cross the blood‐brain barrier and counteract the production of reactive oxygen species via their enzyme‐mimicking characteristics. In this review, the latest advancements and theoretical knowledge in this research domain are summarized. Using the inherent functionalities of the Web of Science and bibliometric methodologies, annual publication trends are identified and extensively explored the most researched topics and neurological disorders in this field. The antioxidant reduction chemistry of the nanozymes is discussed, highlighting their ability to mimic natural oxidoreductase activity and inhibit RONS production at the source. Moreover, this review delves into the current limitations and future prospects of these mechanisms in addressing neurological disorders. The significant benefits and recent developments in the use of RONS‐regulating nanozymes for the treatment of neurological diseases are emphasized, offering insights into their therapeutic applications and broader implications for neurology. [ABSTRACT FROM AUTHOR]

The thyroid gland is the largest endocrine organ in the body, and when it behaves abnormally, patient health may suffer. The development of thyroid diagnostic techniques is reviewed considering direct and indirect techniques. Combining spectroscopic approaches with machine deep learning represents a promising diagnostic tool due to its low cost, speed, and good precision. The machine deep learning models for spectral profiling and the evaluation methods are used to test the performance on the bases of precision, sensitivity, specificity, subject operating characteristic (ROC) curve, and F1-score. The analysis of the spectra with machine deep learning models has significant potential for disease diagnosis. [ABSTRACT FROM AUTHOR]