Genetically-targeted neuromodulation, such as opto- or chemogenetics, is important for cell-type specific neuromodulation for different applications. Modulating peripheral nerves is a daunting task due to the wide variety of constraints in the periphery, such as size, location, and relative distance to other nerves. This has resulted in many researchers developing innovative solutions to modulate peripheral nerves, including different types of light-emitting devices for optogenetics, different ligands and designer drugs (DREADDs) for chemogenetics, and use of nanotechnology. Although the spinal cord is part of the central nervous system, it is often a site of stimulation for the modulating function in the periphery and the anatomy of the spinal cord has many of the same obstacles for modulation as peripheral nerves. This review summarizes current efforts in genetically-targeted neuromodulation of the spinal cord and peripheral nerves. This review is grouped by applications, going through major areas in which advancements for peripheral neuromodulation have been made. We focus on in vivo research in rodents but also briefly discuss current work in nonhuman primates (NHPs). Some of the major obstacles to clinical translation, such as long-term adeno-associated virus (AAV) safety, are discussed and some noninvasive, nonspecific technologies for peripheral neuromodulation are briefly mentioned.

Artificial intelligence (AI) is revolutionizing drug development by expediting the discovery, formulation, and testing of potential treatments. By analyzing vast datasets, such as genetic information, AI algorithms pinpoint disease targets and predict drug interactions, accelerating the entire process. This reduces reliance on extensive animal testing, leading to faster development and potentially higher approval rates. AI optimizes costs by streamlining research, predicting drug behavior, and designing better experiments, reducing the need for costly animal testing. Moreover, AI analyzes real-world patient data to personalize drug treatments, potentially improving adherence and outcomes. This comprehensive overview of AI in drug development covers discovery, delivery, dosage form design, process optimization, testing, and pharmacokinetic/pharmacodynamic investigations. It assesses the strengths and weaknesses of AI techniques in pharmaceutical technology while acknowledging potential limitations. The pursuit of more potent and stable drugs to address unmet medical needs is a key goal. However, addressing concerns over toxicity necessitates further investigation. Developing therapeutic molecules with optimal properties for healthcare use remains a priority. Yet, the pharmacy sector faces challenges requiring further development through technology-driven approaches to meet global medical and healthcare demands. This review aims to discuss the role of AI in the field of pharmacy.

Thiolate-protected metal nanoclusters (TPMNCs) exhibit tunable physicochemical properties governed by quantum effects related to size, composition, assembly, and surface ligands. Atomically precise synthesis enables researchers to directly correlate nanostructure with material performance. However, slight variations in structure can lead to significant and nonlinear quantum effects, making macroscopic properties unpredictable. Therefore, nanolevel property tuning remained challenging before the advanced development of machine learning (ML). In contrast to traditional nanodesign methods, algorithm development based on data enables ML approaches to capture the nonlinear behaviors and electronic features of nanoclusters by embedding characteristics into a high-dimensional numerical space, thereby improving the predictability and generative capability for property prediction. TPMNCs are a representative system with atomic precision and distinct optical and catalytic properties. This review explores ML applications in nanocluster research, with a focus on TPMNCs, including synthesis, structure prediction, optical property analysis, and catalytic mechanism discovery. The rapid advancement of ML is propelling progress in this field and paving the way for future directions including active learning, model transferability, autonomous experimentation, and adaptive simulation.

Visual hypersensitivity, including extreme visual sensitivity and avoidance response, is one of the main features of autism spectrum disorder (ASD). It has been proposed to be an effect of aberrant sensory integration and impaired functional brain networks. EEG microstates reflect brain activity fluctuations and offer an innovative way to study the neurological framework of visual hypersensitivity in ASD. The study included 54 children aged 6-9 years: 27 with ASD and 27 typically developing (TD) peers matched for age and gender. 64-channel EEG data were recorded while the subjects were at rest with their eyes open. The duration, occurrence, and coverage of Microstate B were substantially higher in children with ASD than in the TD group (all p < 0.05), suggesting increased stability and activation of the visual network and impaired cross-network resource allocation. ASD participants showed increased transitions from D to B (p = 0.003) and A to B (p = 0.022), indicating more frequent switching to the visual network and excessive visual attention allocation, which maybe the potential neural mechanism of visual hypersensitivity in ASD children. These results indicate disrupted functional network dynamics and increased visual network dominance in ASD, offering insights into the neurological basis of visual hypersensitivity.