Tauopathy in the brain of patients with Alzheimer's disease starts in the entorhinal cortex (EC) and spreads anatomically in a defined pattern. To test whether pathology initiating in the EC spreads through the brain along synaptically connected circuits, we have generated a transgenic mouse model that differentially expresses pathological human tau in the EC and we have examined the distribution of tau pathology at different timepoints. In relatively young mice (10–11 months old), human tau was present in some cell bodies, but it was mostly observed in axons within the superficial layers of the medial and lateral EC, and at the terminal zones of the perforant pathway. In old mice (>22 months old), intense human tau immunoreactivity was readily detected not only in neurons in the superficial layers of the EC, but also in the subiculum, a substantial number of hippocampal pyramidal neurons especially in CA1, and in dentate gyrus granule cells. Scattered immunoreactive neurons were also seen in the deeper layers of the EC and in perirhinal and secondary somatosensory cortex. Immunoreactivity with the conformation-specific tau antibody MC1 correlated with the accumulation of argyrophilic material seen in old, but not young mice. In old mice, axonal human tau immunoreactivity, especially at the endzones of the perforant pathway, was greatly reduced. Relocalization of tau from axons to somatodendritic compartments and propagation of tauopathy to regions outside of the EC correlated with mature tangle formation in neurons in the EC as revealed by thioflavin-S staining. Our data demonstrate propagation of pathology from the EC and support a trans-synaptic mechanism of spread along anatomically connected networks, between connected and vulnerable neurons. In general, the mouse recapitulates the tauopathy that defines the early stages of AD and provides a model for testing mechanisms and functional outcomes associated with disease progression.
Nasir Rajpoot, Richard Haworth, Xavier Palazzi
et al.
As the volume and complexity of nonclinical toxicology studies continue to increase, toxicologic pathology reporting faces persistent challenges, including fragmented sources of data (e.g., histopathology images, clinical pathology and other study data, adverse effects database, mechanistic literature), variable reporting timelines and heightened regulatory expectations. This white paper examines the emerging role of agentic artificial intelligence (AI) in addressing these issues through coordinated workflow orchestration, data integration, and pathologist-in-the-loop report generation. Based on a closed-door roundtable held during the 2025 Society of Toxicologic Pathology (STP) Annual Meeting and follow-on discussions, this paper synthesizes the perspectives of leading toxicologic pathologists, toxicologists, and AI developers. It outlines the key pain points in current reporting workflows, identifies realistic near-term use cases for agentic AI, and describes major adoption barriers including requirements for transparency, validation, and organizational readiness. A phased adoption roadmap and pilot design considerations are proposed to help support responsible evaluation and deployment of agentic AI system in nonclinical settings. The paper concludes by emphasizing the need for coordinated efforts across pharmaceutical organizations, CROs, academia, and regulators to establish shared standards, benchmarks, and governance frameworks that will lead to safe, transparent, and trustworthy integration of AI into toxicologic science.
Juampablo E. Heras Rivera, Daniel K. Low, Xavier Xiong
et al.
Accurate brain tumor typing requires integrating heterogeneous clinical evidence, including magnetic resonance imaging (MRI), histopathology, and pathology reports, which are often incomplete at the time of diagnosis. We introduce CoRe-BT, a cross-modal radiology-pathology-text benchmark for brain tumor typing, designed to study robust multimodal learning under missing modality conditions. The dataset comprises 310 patients with multi-sequence brain MRI (T1, T1c, T2, FLAIR), including 95 cases with paired H&E-stained whole-slide pathology images and pathology reports. All cases are annotated with tumor type and grade, and MRI volumes include expert-annotated tumor masks, enabling both region-aware modeling and auxiliary learning tasks. Tumors are categorized into six clinically relevant classes capturing the heterogeneity of common and rare glioma subtypes. We evaluate tumor typing under variable modality availability by comparing MRI-only models with multimodal approaches that incorporate pathology information when present. Baseline experiments demonstrate the feasibility of multimodal fusion and highlight complementary modality contributions across clinically relevant typing tasks. CoRe-BT provides a grounded testbed for advancing multimodal glioma typing and representation learning in realistic scenarios with incomplete clinical data.
Masanori Sudo, Chihiro Sakurazawa, Yuichi Sakamaki
et al.
We report a case of membranoproliferative glomerulonephritis (MPGN) with dominant C3 and uniquely organized deposits complicated by pulmonary non-tuberculous mycobacterial (NTM) infection. A 64-year-old man developed persistent urinary abnormalities following treatment with Mycobacterium malmoense. A renal biopsy revealed MPGN with characteristic fibrous organized deposits and strong complement C3c and fibrinogen positivity, but IgG, IgA, IgM, and C1q negativity. Immunosuppressive therapy was not administered. The patient’s renal function gradually declined, but subsequently stabilized with prolonged antibiotic therapy. This case highlights the importance of considering infection-related glomerulonephritis with C3-dominant organized deposits, particularly in the absence of any immunoglobulin involvement, as well as the need to distinguish this entity from primary C3 glomerulopathy.
Sri Raksha Siva, Nived Suthahar, Prakash Boominathan
et al.
Voice disorders significantly affect communication and quality of life, requiring an early and accurate diagnosis. Traditional methods like laryngoscopy are invasive, subjective, and often inaccessible. This research proposes a noninvasive, machine learning-based framework for detecting voice pathologies using phonation data. Phonation data from the Saarbrücken Voice Database are analyzed using acoustic features such as Mel Frequency Cepstral Coefficients (MFCCs), chroma features, and Mel spectrograms. Recurrent Neural Networks (RNNs), including LSTM and attention mechanisms, classify samples into normal and pathological categories. Data augmentation techniques, including pitch shifting and Gaussian noise addition, enhance model generalizability, while preprocessing ensures signal quality. Scale-based features, such as Hölder and Hurst exponents, further capture signal irregularities and long-term dependencies. The proposed framework offers a noninvasive, automated diagnostic tool for early detection of voice pathologies, supporting AI-driven healthcare, and improving patient outcomes.