Citations

A 10-year-old girl with a past medical history significant for prematurity at 29 weeks, short bowel syndrome secondary to necrotizing enterocolitis, prior episodes of small intestinal bacterial overgrowth complicated by D-lactic acidosis, and gastric tube dependence presented to an emergency department with emesis, slurred speech, somnolence, and unsteady gait. Workup was initiated, and she was started on broad-spectrum antibiotics and transferred to our pediatric intensive care unit for further evaluation and treatment. Differential diagnosis on admission was broad, and, through thorough laboratory evaluation, imaging, and multiple subspecialty inputs, an unusual diagnosis was confirmed.

Background: Pancreatic ductal adenocarcinoma (PDAC) is characterized by a dense, collagen-rich stroma that impedes drug delivery. We investigated how iRGD-mediated nanoparticle delivery modifies collagen architecture using humanized mouse models. Computational analysis of Picro-Sirius Red-stained whole-slide images (WSIs) using curvelet transform-based fiber extraction method (CT-FIRE) was performed to quantitatively examine changes in collagen features, thereby understanding treatment-induced alterations in the tumor microenvironment (TME). Methods: Humanized PDAC mice were prepared with either the human PANC-1 cell line or 0779 primary human PDAC cells and treated with or without iRGD monotherapy for two months (n=12). Primary tumors were sectioned and stained with Picro-Sirius Red to visualize collagen structures. WSIs of Picro-Sirius Red-stained tissues were analyzed with CT-FIRE to extract six morphological features of collagen, including average fiber length, waviness, and density to capture structural changes. WSIs were divided into 256×256 pixel tiles with consistent sampling through bootstrapping, followed by permutation testing for statistical validation (1,000 permutations, p<0.01). Results: Computational analysis revealed significant iRGD-induced collagen remodeling with quantifiable changes in key structural and morphological features. Permutation testing showed that, compared to controls, average fiber length decreased by 3.27% (p<0.01), waviness reduced by 3.25% (p<0.01), and density diminished by 36.3% (p<0.01), compared to controls. The structural changes were more notable in 0779-derived tumors compared to PANC-1, with reductions in average fiber length (1.03%, p<0.01), waviness (8.78%, p<0.01), and collagen density (24.27%, p<0.01), demonstrating cell-line dependent stromal remodeling. Conclusions: Our findings suggest that long-term iRGD therapy alters collagen morphology in PDAC. Further investigation is warranted to understand how these alterations contribute to enhanced penetration of drugs and immune cells observed in iRGD-treated PDAC tumors. The observed cell-line dependent variations in stromal remodeling suggest tumor heterogeneity influences treatment response, highlighting the potential for personalized therapeutic approaches in stromal-rich tumors like PDAC. Citation Format: Yoon Ho Choi, Minji Kim, Hotaka Kawai, Yukihito Kuroda, Norio Miyamura, Seock-Jin Chung, Kazuki N. Sugahara, Tae Hyun Hwang. Altered collagen morphology in pancreatic cancer treated with the iRGD tumor-penetrating peptide [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2698.

Abstract Background: Pancreatic ductal adenocarcinoma (PDAC) exhibits a complex, immunosuppressive tumor immune microenvironment (TIME), necessitating molecular-level understanding to identify therapeutic targets. We investigated how iRGD peptide therapy remodels the immunosuppressive TIME to an immune-favorable state using single-cell spatial molecular analysis. Methods: Single-cell level in situ molecular imaging was performed using Xenium analyzer with 5K gene panel on tumor samples from humanized PDAC mice (n=7) treated with iRGD monotherapy (n=3) or control (n=4), enabling comprehensive spatial molecular characterization of the TIME. Results: iRGD therapy significantly transformed the immunosuppressive TIME to an immune-favorable state through T-cell composition remodeling. We observed increased populations of CD4+ memory T cells, CD4+ helper T cells, and cytokine-secreting effector T cells, indicating enhanced immune memory and effector functions. Concurrent decreases in regulatory T cells and activated CD4+ T cells reflected reduced immune suppression. Additionally, decreased TGF-β signaling in the iRGD cohort suggested TGF-β antagonism as a mechanism for overcoming immunosuppression. Conclusions: iRGD therapy effectively converted the immunosuppressive PDAC microenvironment to an immune-favorable state by enhancing beneficial T cell populations and reducing immunosuppression through TGF-β pathway modulation. These findings support previous work by Kuroda et al. and provide mechanistic rationale for the ongoing iRGD-based immunotherapy clinical trial (iLSTA:ACTRN12623000223639).Acknowledgments: We thank DeepL for the English language editing. Citation Format: Minji Kim, Yoon Ho Choi, Hotaka Kawai, Yukihito Kuroda, Norio Miyamura, Kazuki N. Sugahara, Tae Hyun Hwang. iRGD peptide therapy transforms immunosuppressive microenvironment to immune-favorable state in pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2701.

ABSTRACT Aims Muscle morphology provides important information in differentiating disease aetiology, but its measurement remains challenging because of the lack of an efficient and objective method. This study aimed to quantitatively refine the morphological features of muscle fibres in neuromuscular diseases using machine learning. Methods In this retrospective study, we analysed muscle biopsy specimens on haematoxylin and eosin‐staining. Machine learning–based software was developed to segment muscle fibre contours and perform automated muscle morphometry and subsequent graph theory–based spatial analysis of atrophied fibre grouping. A decision tree–based framework, LightGBM, was trained to predict underlying aetiologies based on morphometric and spatial variables. Results The study included 100 muscle samples, including 20 normal muscles, 49 myopathies and 19 neuropathies. The fine‐tuned segmentation model, YOLOv8, achieved a mask average precision of 0.819. The muscle morphometry revealed the significance of fibre circularity. The mean circularity was higher in the myopathy group, and the SD of circularity was elevated in the neuropathy group. Although most cases were consistent with textbook findings, atypical presentations, such as dermatomyositis with angular atrophy and amyotrophic lateral sclerosis with round atrophy, were objectively documented. Spatial analysis quantified grouped atrophy, showing the potential to feature specific atrophy patterns. The LightGBM model successfully predicted the final clinical diagnosis of the myopathies and neuropathies with an accuracy of 0.852, which exceeded that of 0.808 by human annotation. Conclusion Automated muscle morphometry and spatial analysis provide quantification of muscle morphology and patterns of atrophy, which will facilitate objective and efficient investigation of neuromuscular diseases.

AbstractObjectiveAlzheimer's disease (AD) often coexists with cerebrovascular diseases. However, the impact of cerebrovascular diseases such as stroke on AD pathology remains poorly understood.MethodsThis study examines the correlation between cerebrovascular diseases and AD pathology. The research was carried out using clinical and neuropathological data collected from the National Alzheimer's Coordinating Center (NACC) database and an animal model in which bilateral common carotid artery stenosis surgery was performed, following the injection of tau seeds into the brains of wild‐type mice.ResultsAnalysis of the NACC database suggests that clinical stroke history and lacunar infarcts are associated with lower neurofibrillary tangle pathology. An animal model demonstrates that chronic cerebral hypoperfusion reduces tau pathology, which was observed in not only neurons but also astrocytes, microglia, and oligodendrocytes. Furthermore, we found that astrocytes and microglia were activated in response to tau pathology and chronic cerebral hypoperfusion. Additionally, cerebral hypoperfusion increased a lysosomal enzyme, cathepsin D.InterpretationThese data together indicate that cerebral hypoperfusion reduces tau accumulation likely through an increase in microglial phagocytic activity towards tau and an elevation in degradation through cathepsin D. This study contributes to understanding the relationship between tau pathology and cerebrovascular diseases in older people with multimorbidity.

Abstract Aim The morphometry of sural nerve biopsies, such as fibre diameter and myelin thickness, helps us understand the underlying mechanism of peripheral neuropathies. However, in current clinical practice, only a portion of the specimen is measured manually because of its labour‐intensive nature. In this study, we aimed to develop a machine learning‐based application that inputs a whole slide image (WSI) of the biopsied sural nerve and automatically performs morphometric analyses. Methods Our application consists of three supervised learning models: (1) nerve fascicle instance segmentation, (2) myelinated fibre detection and (3) myelin sheath segmentation. We fine‐tuned these models using 86 toluidine blue‐stained slides from various neuropathies and developed an open‐source Python library. Results Performance evaluation showed (1) a mask average precision (AP) of 0.861 for fascicle segmentation, (2) box AP of 0.711 for fibre detection and (3) a mean intersection over union (mIoU) of 0.817 for myelin segmentation. Our software identified 323,298 nerve fibres and 782 fascicles in 70 WSIs. Small and large fibre populations were objectively determined based on clustering analysis. The demyelination group had large fibres with thinner myelin sheaths and higher g‐ratios than the vasculitis group. The slope of the regression line from the scatter plots of the diameters and g‐ratios was higher in the demyelination group than in the vasculitis group. Conclusion We developed an application that performs whole slide morphometry of human biopsy samples. Our open‐source software can be used by clinicians and pathologists without specific machine learning skills, which we expect will facilitate data‐driven analysis of sural nerve biopsies for a more detailed understanding of these diseases.

Abstract To elucidate the molecular basis of multiple system atrophy (MSA), a neurodegenerative disease, we conducted a genome-wide association study (GWAS) in a Japanese MSA case/control series followed by replication studies in Japanese, Korean, Chinese, European, and North American samples. In the GWAS stage, rs2303744 on chromosome 19 showed a suggestive association (P = 6.5 × 10− 7) that was replicated in additional Japanese samples (P = 2.9 × 10− 6, odds ratio (OR) = 1.58; 95% confidence interval (CI), 1.30–1.91), and then confirmed as highly significant in a meta-analysis of East Asian population data (P = 5.0 × 10− 15, OR = 1.49; 95% CI 1.35–1.65). The association of rs2303744 with MSA remained significant in combined European/North American samples (P = 0.017, OR = 1.12; 95% CI 1.02–1.23) despite allele frequencies being quite different between these populations. rs2303744 leads to an amino acid substitution in PLA2G4C that encodes the cPLA2γ lysophospholipase/transacylase. The cPLA2γ-Ile143 isoform encoded by the MSA risk allele has significantly decreased transacylase activity compared with the alternate cPLA2γ-Val143 isoform, and may perturb membrane phospholipids and promote oligodendroglial pathology.

Coronary artery calcification (CAC) accompanies the development of advanced atherosclerosis. Its role in atherosclerosis holds great interest because the presence and burden of coronary calcification provide direct evidence of the presence and extent of coronary artery disease; furthermore, CAC predicts future events independently of concomitant conventional cardiovascular risk factors and to a greater extent than any other noninvasive biomarker of this disease. Nevertheless, the relationship between CAC and the susceptibility of a plaque to provoke a thrombotic event remains incompletely understood. This review summarizes the current understanding and literature on CAC. It outlines the pathophysiology of CAC and reviews laboratory, histopathological, and genetic studies, as well as imaging findings, to characterize different types of calcification and to elucidate their implications. Some patterns of calcification such as microcalcification portend increased risk of rupture and cardiovascular events and may improve prognosis assessment noninvasively. However, contemporary computed tomography cannot assess early microcalcification. Limited spatial resolution and blooming artifacts may hinder estimation of degree of coronary artery stenosis. Technical advances such as photon counting detectors and combination with nuclear approaches (eg, NaF imaging) promise to improve the performance of cardiac computed tomography. These innovations may speed achieving the ultimate goal of providing noninvasively specific and clinically actionable information.