Citations

ABSTRACT Metastatic cancer to the pancreas is uncommon, accounting for only 2%–4% of pancreatic malignancies. Prostate cancer spreading to the pancreas is exceptionally rare. We present 2 cases of metastatic castration-resistant prostate cancer with histologic transformation from adenocarcinoma to neuroendocrine prostate cancer. Both patients developed new pancreatic lesions, in which endoscopic ultrasound-guided fine-needle biopsy confirmed the diagnosis. These cases highlight therapy-emergent neuroendocrine prostate cancer with loss of prostate lineage markers (prostate-specific antigen, NKX3.1), positive neuroendocrine markers (synaptophysin, INSM1, chromogranin), very high proliferative indices, and serum marker discordance that can mimic primary pancreatic cancer. These scenarios present a diagnostic challenge with an aggressive and complex nature, requiring multidisciplinary management. With newer therapies and prolonged survival of patients with prostate cancer, clinicians will likely encounter more cases in the future. Gastroenterologists can play a key role in diagnosis and palliative treatment.

Background Tumors with mismatch repair deficiency (MMRd) classically display concomitant loss of MLH1/PMS2 or MSH2/MSH6 on immunohistochemistry (IHC) with microsatellite instability-high (MSI-High) status on molecular testing. Nevertheless, a different phenotype can occur in up to 15% of MMRd tumors (unusual phenotype). Data on the efficacy of immunotherapy in this population remain scarce. Methods We conducted a retrospective study within the IMMUNODIG MSI cohort including patients with advanced gastrointestinal MMRd tumors treated with immune checkpoint blockade in the real-world setting. We selected patients with both IHC and MSI assays data available. Unusual MMRd tumors were classified into four distinct groups: (1) isolated loss of PMS2 or MSH6 with MSI-H (isolated/MSI-H), (2) complex loss of MMR proteins with MSI-H (complex/MSI-H), (3) loss of one or more MMR proteins without MSI-H (MMRd-IHC/microsatellite stability (MSS) or MSI-low (MSI-L)), and (4) four MMR proteins retained with MSI-H (retained IHC/MSI-H). Results Out of 759 patients in the IMMUNODIG-MSI cohort, 571 patients met inclusion criteria. Of these, 90 (15.8%) had an unusual phenotype (47 isolated/MSI-H, 19 complex/MSI-H, 16 MMRd-IHC/MSS or MSI-L and 8 retained IHC/MSI-H). Compared with classical phenotypes, patients with a tumor harboring an unusual phenotype had a younger age at treatment (p=0.005), increased RAS mutation (p=0.005), reduced BRAF p.V600E mutation rates (p<0.001), a higher proportion of Lynch syndrome (p<0.001) and a higher prevalence of non-colorectal cancers (p=0.021). After a median follow-up of 28.1 months (mo), there was a significant difference in progression-free survival, with median values of not reached, 66.4 mo, 37.2 mo, 18.3 mo and 5.5 mo for complex/MSI-H, isolated/MSI-H, classical, retained IHC/MSI-H and MMRd-IHC/MSS or MSI-L subgroups, respectively (p<0.001). Notably, objective response rates were 59.1%, 58.7%, and 63.2% for complex/MSI-H, isolated/MSI-H and classical contrasting with 50% and 25% for retained IHC/MSI-H and MMRd-IHC/MSS or MSI-L, with no complete response observed in the latter two. Conclusion Our findings underscore the need for dual testing and advocate for the presence of both MMRd-IHC and MSI-H for optimal immunotherapy response. Of note, complex MMR aberrations and isolated PMS2/MSH6 losses with MSI-H may represent promising candidates for enhanced immunotherapy efficacy.

Abstract INTRODUCTION M0 images were missing in Siemens ASL data in Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, prohibiting cerebral blood flow (CBF) quantification. METHODS A conditional latent diffusion model was trained and evaluated on in‐house datasets, then applied to the Siemens data in ADNI‐3. Regional CBF differences by Alzheimer's disease (AD) stages, their accuracy for AD classification, and CBF trajectory slopes were compared between generated data (Siemens) and acquired data (General Electric). RESULTS The diffusion model generated M0 images with high fidelity (SSIM = 0.918 ± 0.023, PSNR = 31.361 ± 2.537) and minimal CBF bias (mean difference is 0.21 ± 1.58 mL/100 g/min). Both generated and acquired CBF showed similar spatial patterns and decreasing trends with AD progression in specific AD‐related regions. Generated CBF also improved accuracy in classifying AD stages compared to qualitative perfusion images. CONCLUSION This study shows the potential of diffusion models for imputing missing modalities in large‐scale studies exploring the use of ASL as a biomarker of AD. Highlights Using latent diffusion model, we can generate M0 image from control image in arterial spin labeling (ASL) with high fidelity. The generated M0 can be used for cerebral blood flow (CBF) quantification in Alzheimer's Disease Neuroimaging Initiative dataset. The performance of classification between Alzheimer's disease (AD) patients and cognitive normal people is better when using generated CBF maps than using non‐quantitative perfusion images. ASL CBF decreases with AD progression in key AD‐related brain regions.

With rapid industrial expansion, air pollution’s adverse neurological effects have gained increasing attention. Children face a greater risk of neurological damage because of their higher breathing rates, developing brains, and limited ability to detoxify harmful substances. Fine particulate matter has been identified as a primary neurotoxic contributor affecting developing brains. Strong evidence connects environmental pollutant exposure to the prevalence of Autism Spectrum Disorder (ASD), a neurodevelopmental condition marked by lasting difficulties with social communication and interaction. This study explores the association between long-term PM2.5 exposure and ASD symptom exacerbation, investigating underlying mechanisms. We hypothesize that long-term PM2.5 exposure exacerbates ASD symptoms through neuroinflammatory activation, leading to neuronal damage and impaired synaptic plasticity. Our investigation employs three complementary approaches: First, integrated analysis combining Global Burden of Disease data with Mendelian randomization demonstrates a significant association between PM2.5 exposure and increased ASD severity risk. Second, utilizing the China High-Resolution Air Pollution Database in conjunction with cohort studies, we provide evidence that ambient air pollution substantially influences autism severity, with PM2.5 identified as the predominant environmental determinant. Third, through network toxicology, single-cell transcriptomics, and animal experimentation, we demonstrate that chronic PM2.5 exposure exacerbates valproic acid-induced autism-like behaviors in murine models, identifying CTNNB1, PTEN, CCR2, AKT1, and mTOR as potential core mediating genes. Importantly, these findings represent preliminary results. Several potential confounding factors such as co-exposure to other pollutants and socioeconomic variables have not been fully addressed. While our multi-modal approach provides converging lines of evidence, further validation in larger, more diverse populations with refined control of confounders will be essential to establish causality and elucidate mechanisms. Nonetheless, these early insights advance our understanding of PM2.5-induced neurotoxicity in the context of ASD and offer timely, albeit preliminary, evidence to inform public health policy.

Background/Objectives: Menopause, marked by permanent cessation of menstruation, is a universal transition associated with vasomotor, genitourinary, psychological, and metabolic changes. These conditions significantly affect health-related quality of life (HRQoL) and increase the risk of chronic diseases. Despite their impact, timely diagnosis and individualized management are often limited by delayed care, fragmented health systems, and cultural barriers. Methods: This review summarizes current applications of artificial intelligence (AI) in postmenopausal health, focusing on risk prediction, early detection, and personalized treatment. Evidence was compiled from studies using biomarkers, imaging, wearable sensors, electronic health records, natural language processing, and digital health platforms. Results: AI enhances disease prediction and diagnosis, including improved accuracy in breast cancer and osteoporosis screening through imaging analysis, and cardiovascular risk stratification via machine learning models. Wearable devices and natural language processing enable real-time monitoring of underreported symptoms such as hot flushes and mood disorders. Digital technologies further support individualized interventions, including lifestyle modification and optimized medication regimens. By improving access to telemedicine and reducing bias, AI also has the potential to narrow healthcare disparities. Conclusions: AI can transform postmenopausal care from reactive to proactive, offering personalized strategies that improve outcomes and quality of life. However, challenges remain, including algorithmic bias, data privacy, and clinical implementation. Ethical frameworks and interdisciplinary collaboration among clinicians, data scientists, and policymakers are essential for safe and equitable adoption.

Background: Biobanks often lack standard mechanisms to keep donors connected to their biospecimens, reflecting a broken feedback loop that compromises trust, engagement, and scientific progress. Decentralized biobanking empowers patients to track their donations throughout the research journey, supporting personalized feedback and research collaboration via a privacy-preserving blockchain network. This case study explores operational feasibility of implementing decentralized biobanking for a large breast cancer biobank at a US academic medical center. Methods: A mixed-methods case study of the groundwork, implementation, and stakeholder feedback for a real-world decentralized biobanking app pilot was conducted. Biobank members were recruited from February to April 2023. Operational feasibility was assessed via analysis of institutional stakeholder perspectives, pilot engagement, and de-bi app activity. Findings: Physicians and other biobank stakeholders surfaced challenges surrounding managing expectations, balancing empowerment with clinical and research workflows, and navigating power dynamics between patients, physicians, scientists, and leadership. A total of 1080 participants enrolled over 10 weeks, including nearly 10% of the biobank with about 4000 biospecimens. During the pilot, biobank enrollment increased 65% versus the prior year, and there were no biobank withdrawals during or within 1 year following the pilot ( p < 0.001 for both). The app, which facilitated biospecimen tracking and research engagement, was downloaded by 405 users. A total of 140 users tested the blockchain component, with 89% successfully claiming a nonfungible token representing their unique, immutable connection and access to donated biospecimens. Feedback was solicited to inform potential process improvements and risk assessments related to public relations, systems infrastructure, and ethical governance, illuminating next steps. Conclusions: We established operational feasibility for the first step toward decentralized biobanking, informed by requirements to manage expectations, workflows, and power dynamics. Our technical solution demonstrated robust participant engagement and compatibility with established biobanks, suggesting potential to build trust and align incentives and identifying next steps for communications, sustainability, and governance.