Sigma-1 receptor (σ ₁R) has become a focus point of drug discovery for central nervous system (CNS) diseases. A series of novel 1-phenylethan-1-one O-(2-aminoethyl) oxime derivatives were synthesized. In vitro biological evaluation led to the identification of 1a, 14a, 15d and 16d as the most high-affinity (K _i < 4 nmol/L) and selective σ ₁R agonists. Among these, 15d, the most metabolically stable derivative exhibited high selectivity for σ ₁R in relation to σ ₂R and 52 other human targets. In addition to low CYP450 inhibition and induction, 15d also exhibited high brain permeability and excellent oral bioavailability. Importantly, 15d demonstrated effective antipsychotic potency, particularly for alleviating negative symptoms and improving cognitive impairment in experimental animal models, both of which are major challenges for schizophrenia treatment. Moreover, 15d produced no significant extrapyramidal symptoms, exhibiting superior pharmacological profiles in relation to current antipsychotic drugs. Mechanistically, 15d inhibited GSK3β and enhanced prefrontal BDNF expression and excitatory synaptic transmission in pyramidal neurons. Collectively, these in vivo proof-of-concept findings provide substantial experimental evidence to demonstrate that modulating σ ₁R represents a potential new therapeutic approach for schizophrenia. The novel chemical entity along with its favorable drug-like and pharmacological profile of 15d renders it a promising candidate for treating schizophrenia.

Breastfeeding is essential for the healthy growth and development of infants. The ongoing advancements in technology, such as the emergence of artificial intelligence (AI), have introduced innovative opportunities to support and optimize breastfeeding practices. This review systematically analyzes the progression of AI applications in this field, including intelligent educational platforms, monitoring feeding behavior, analysis of breast milk composition, and clinical decision support systems. Studies demonstrate that machine learning-based technologies can significantly enhance the accuracy of feeding assessments and provide more personalized guidance for caregivers. These innovations not only improve traditional evaluation methods but also pioneer a novel model of precision feeding management. This review highlights the potential of AI in improving breastfeeding quality while addressing challenges in technological implementation, providing critical insights for future research and clinical translation.

Breastfeeding is essential for the healthy growth and development of infants. The ongoing advancements in technology, such as the emergence of artificial intelligence (AI), have introduced innovative opportunities to support and optimize breastfeeding practices. This review systematically analyzes the progression of AI applications in this field, including intelligent educational platforms, monitoring feeding behavior, analysis of breast milk composition, and clinical decision support systems. Studies demonstrate that machine learning-based technologies can significantly enhance the accuracy of feeding assessments and provide more personalized guidance for caregivers. These innovations not only improve traditional evaluation methods but also pioneer a novel model of precision feeding management. This review highlights the potential of AI in improving breastfeeding quality while addressing challenges in technological implementation, providing critical insights for future research and clinical translation.