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This study aims to employ machine learning for automated age stratification in patients with polycystic ovary morphology (PCOM) and to clarify the age-specific contributions of key clinical characteristics, thereby improving diagnostic accuracy. A total of 192 ovaries with corresponding clinical and ultrasound data were analyzed. Automated age stratification was performed using the K-means unsupervised clustering model along with the elbow method. XGBoost, Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN) algorithms were applied to compare full and selected feature sets. Model performance was evaluated using five-fold cross-validation with metrics including accuracy, sensitivity, F1-score, and area under the curve (AUC), validating the stratification robustness and feature selection strategy. Automated age stratification categorized PCOM patients into three distinct age groups. A stable positive correlation was observed between ovarian volume and follicle count across all strata; however, key diagnostic drivers varied significantly by age. Beyond ovarian volume, follicle count, and BMI, the 18-22 years group was primarily influenced by menstrual cycle phase. The 23-29 years group was characterized by the number of pregnancies and the ovarian stromal artery blood flow RI. In the 30-40 years group, the ovarian stromal artery blood flow S/D ratio and the number of live births showed increasing importance. This study highlights the differential role of age-sensitive indicators in ultrasound-based diagnosis of PCOM and provides a framework for more personalized diagnostic assessment.

Novel PEGylated thermo-sensitive bionic magnetic core-shell structure molecularly imprinted polymers (PMMIPs) for the specific adsorption and separation of bovine serum albumin (BSA) were obtained via a surface-imprinting technique. X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), vibrating sample magnetometry (VSM), fourier transform infrared spectrometry (FT-IR), thermal gravimetric analysis (TGA), and specific surface area (BET), were adopted to demonstrate that novel PMMIPs were successfully synthesized. Subsequently, the prepared PMMIPs were used as the extractor for BSA and were combined with magnetic solid-phase extraction. The concentrations of BSA were detected by UV-vis spectrophotometry at 278 nm. The maximum adsorption capacity of the PMMIPs was 258 mg g−1, which is much higher than that of non-imprinted polymer (PMNIPs). PMMIPs showed favorable selectivity for BSA against reference proteins, i.e., bovine hemoglobin, ovalbumin and lysozyme. PMMIPs were further used to recognize BSA in protein mixtures, milk, urine and sewage, these results revealed that approximately 96% of the ideal-state adsorption capacity of PMMIPs for BSA was achieved under complicated conditions. Regeneration and reusability studies demonstrated that adsorption capacity loss of the PMMIPs was not obvious after recycling for four times. Facile synthesis, excellent adsorption property and efficient selectivity for BSA trapping are features that highlight PMMIPs as an attractive candidate for biomacromolecular purification.

Therapeutic target discovery remains a critical yet intuition-driven bottleneck in drug development, typically relying on disease biologists to laboriously integrate diverse biomedical data into testable hypotheses for experimental validation. Here, we present OriGene, a self-evolving multi-agent system that functions as a virtual disease biologist, systematically identifying original and mechanistically grounded therapeutic targets at scale. OriGene coordinates specialized agents that reason over diverse modalities, including genetic data, protein networks, pharmacological profiles, clinical records, and literature evidence, to generate and prioritize target discovery hypotheses. Through a self-evolving framework, OriGene continuously integrates human and experimental feedback to iteratively refine its core thinking templates, tool composition, and analytical protocols, thereby enhancing both accuracy and adaptability over time. To comprehensively evaluate its performance, we established TRQA, a benchmark comprising over 1,900 expert-level question-answer pairs spanning a wide range of diseases and target classes. OriGene consistently outperforms human experts, leading research agents, and state-of-the-art large language models in accuracy, recall, and robustness, particularly under conditions of data sparsity or noise. Critically, OriGene nominated previously underexplored therapeutic targets for liver (GPR160) and colorectal cancer (ARG2), which demonstrated significant anti-tumor activity in patient-derived organoid and tumor fragment models mirroring human clinical exposures. These findings demonstrate OriGene's potential as a scalable and adaptive platform for AI-driven discovery of mechanistically grounded therapeutic targets, offering a new paradigm to accelerate drug development.Competing Interest StatementThe authors have declared no competing interest.Footnotes* This version of the manuscript has been revised to: (1) expand and clarify the Methods section, with added implementation details including the Tool-RAG component; (2) provide a more complete description of our approach for multi-tool settings, including how tools are selected and invoked when multiple tools are available; (3) improve the organization and clarity of the Results introduction; (4) strengthen the methodological discussion to better highlight the algorithmic innovations and the specific contributions of the proposed framework; and (5) update the author list and affiliations to include additional contributors who made new contributions for this revision.