Identifying embryos with the highest likelihood of successful implantation is a critical component of the in vitro fertilization (IVF) process. Visual assessments are limited by the subjectivity of embryologists, making consistent evaluation of embryo health challenging with traditional methods. Recent advances in artificial intelligence (AI)—particularly in computer vision and deep learning—have enabled the automated analysis of embryo morphology images, reducing subjectivity and improving evaluation efficiency. Through an extensive literature search using keywords such as “embryo health assessment” and “artificial intelligence,” the present review focuses on AI-driven approaches for automated embryo evaluation. It examines AI techniques applied to embryo assessment across the early development, blastocyst, and full developmental stages. This review indicated the promising potential of AI technologies in enhancing the precision, consistency, and speed of embryo selection. AI models have been reported to outperform manual evaluations across several parameters, offering promising opportunities to improve success rates and operational efficiency in reproductive medicine. Additionally, this review discusses the current limitations of AI implementation in clinical settings and explores future research directions. Overall, the review provides insight into AI’s growing role in advancing embryo selection and highlights the path toward fully automated evaluation systems in assisted reproductive technology. 

Neuromorphic devices have shown great potential in simulating the function of biological neurons due to their efficient parallel information processing and low energy consumption. MXene-Ti₃C₂T_x, an emerging two-dimensional material, stands out as an ideal candidate for fabricating neuromorphic devices. Its exceptional electrical performance and robust mechanical properties make it an ideal choice for this purpose. This review aims to uncover the advantages and properties of MXene-Ti₃C₂T_x in neuromorphic devices and to promote its further development. Firstly, we categorize several core physical mechanisms present in MXene-Ti₃C₂T_x neuromorphic devices and summarize in detail the reasons for their formation. Then, this work systematically summarizes and classifies advanced techniques for the three main optimization pathways of MXene-Ti₃C₂T_x, such as doping engineering, interface engineering, and structural engineering. Significantly, this work highlights innovative applications of MXene-Ti₃C₂T_x neuromorphic devices in cutting-edge computing paradigms, particularly near-sensor computing and in-sensor computing. Finally, this review carefully compiles a table that integrates almost all research results involving MXene-Ti₃C₂T_x neuromorphic devices and discusses the challenges, development prospects, and feasibility of MXene-Ti₃C₂T_x-based neuromorphic devices in practical applications, aiming to lay a solid theoretical foundation and provide technical support for further exploration and application of MXene-Ti₃C₂T_x in the field of neuromorphic devices.

Conductive hydrogels have garnered widespread attention as a versatile class of flexible electronics. Despite considerable advancements, current methodologies struggle to reconcile the fundamental trade-off between high conductivity and effective absorption-dominated electromagnetic interference (EMI) shielding, as dictated by classical impedance matching theory. This study addresses these limitations by introducing a novel synthesis of aramid nanofiber/MXene-reinforced polyelectrolyte hydrogels. Leveraging the unique properties of polyelectrolytes, this innovative approach enhances ionic conductivity and exploits the hydration effect of hydrophilic polar groups to induce the formation of intermediate water. This critical innovation facilitates polarization relaxation and rearrangement in response to electromagnetic fields, thereby significantly enhancing the EMI shielding effectiveness of hydrogels. The electromagnetic wave attenuation capacity of these hydrogels was thoroughly evaluated across both X-band and terahertz band frequencies, with further investigation into the impact of varying water content states—hydrated, dried, and frozen—on their electromagnetic properties. Moreover, the hydrogels exhibited promising capabilities beyond mere EMI shielding; they also served effectively as strain sensors for monitoring human motions, indicating their potential applicability in wearable electronics. This work provides a new approach to designing multifunctional hydrogels, advancing the integration of flexible, multifunctional materials in modern electronics, with potential applications in both EMI shielding and wearable technology.

Salk Institute researchers launch machine learning framework ShortStop, which explores overlooked DNA regions in the "dark side" of the human genome in search of microproteins that may play roles in health or disease. They already used ShortStop to analyze a lung cancer dataset and find microproteins that may serve as biomarkers or therapeutic targets.