Citations

This paper summarizes the presentations and panel discussion provided at Plenary Panel #2 on the first day of the 16 th World Congress of the International Radiation Protection Association (IRPA) jointly organized with the 69th Annual Meeting of the Health Physics Society (HPS) in Orlando/Florida in July 2024. International organizations that play a key role in radiation protection and are close in contact with IRPA by official agreements or even MoU’s (Memorandum of Understanding) presented highlights of their projects in radiation protection as well as results. Topical issues on their current work program as seen in a list of statements, including future efforts, were presented and discussed. The needs of practitioners were stressed for recognition by the representatives of the international organizations to be included in their projects. Requested and presented topics were: • Key challenges for radiation protection practitioners in the next 10 y. • Potential effects of emerging science on radiation protection. • Challenges for implementation of IAEA Safety Standards in low- and middle-income countries. • Challenges in occupational radiation protection. • Broadening the process of optimization. • Impact of emerging technologies on radiation protection. • Ensuring radiation protection workforce for the future.

Abstract Rapid and profound reduction of free light chains (FLCs) improves outcomes in AL amyloidosis; however, early FLC kinetics with daratumumab‐based frontline therapy remain undefined. We retrospectively analyzed 315 patients treated with daratumumab–bortezomib–cyclophosphamide–dexamethasone (Dara–VCd) or Dara–Vd at eight centers. Involved FLC (iFLC), the difference between iFLC and uninvolved FLC (dFLC), and hematologic response (≥very good partial response [VGPR]) were assessed at baseline and at 1, 3, and 6 months, and correlated with light chain isotype, disease burden (bone‐marrow plasma‐cell percentage [%BMPC], baseline dFLC), and cytogenetics. Hematological ≥VGPR increased from 49.8% at 1 month to 66.0% at 3 months. The kappa isotype showed slower responses, with higher iFLC/dFLC and lower ≥VGPR at each time point compared to lambda. Higher %BMPC or baseline dFLC predicted persistently elevated iFLC/dFLC and reduced ≥VGPR. The t(11;14) translocation was associated with lower baseline FLC but similar subsequent kinetics. BMPC < 10% and dFLC < 18 mg/dL independently predicted early ≥VGPR (in ≤1 month). Early ≥VGPR conferred superior hematologic event‐free survival (heme‐EFS) and overall survival (OS) versus later responders (P < 0.001). At a 3‐month landmark, achieving dFLC < 1 mg/dL further stratified prognosis, conferring longer heme‐EFS and OS (P < 0.01). Frontline Dara‐based therapy elicits rapid, deep responses in AL amyloidosis; early ≥VGPR (within 1 month) and dFLC < 1 mg/dL by 3 months predict durable EFS and OS, whereas higher baseline disease burden delays hematologic response.

Abstract Background and Aims Celiac disease (CeD) is a chronic digestive autoimmune disorder affecting approximately 1% of the worldwide population. It is driven by T cells activated by specific HLA-DQ2 or HLA-DQ8 molecules leading to the destruction of intestinal villi. We aimed to characterize shared CeD-specific T cells in patients on and off a gluten-free diet (GFD) from a large cohort of cases and controls. Methods We performed bulk TCRβ immune sequencing of the peripheral blood of 1,604 biopsy-confirmed CeD patients (1,339 on GFD, 265 on normal diet) and 1,100 controls. We identified over 300 TCRβs enriched in CeD cases versus controls in an HLA-aware manner, controlling for CeD risk alleles. Results CeD-associated TCRβs were found to be more predictive of disease than previously characterized gliadin and glutenin-binding TCRs in a validation cohort. Furthermore, the clonal breadth of these TCRβs was associated with increased intestinal damage. Immune sequencing of the peripheral blood also uncovered repertoire-level differences between CeD patients and controls. CeD patients displayed significantly higher productive clonality compared to age-matched controls as well as expansion of TCRβs specific to cytomegalovirus (CMV) and Epstein-Barr Virus (EBV). Conclusions These findings underscore the value of unbiased immune repertoire sequencing to identify novel biomarkers for autoimmune disease and to discover new disease mechanisms which can improve both diagnosis and treatment of disease.

ABSTRACT Introduction Metabolic dysfunction‐associated steatotic liver disease (MASLD) and steatohepatitis (MASH) represent a spectrum of liver conditions that can gradually progress to cirrhosis. Sodium‐glucose co‐transporter‐2 (SGLT‐2) inhibitors have shown benefits in reducing hepatic steatosis and liver‐related events in MASLD. This study aims to assess whether SGLT‐2 inhibitors are associated with a reduced risk of all‐cause mortality and disease‐specific outcomes in patients with MASH cirrhosis and type 2 diabetes (T2D). Methods A retrospective cohort study was performed using TriNetX. Patients with T2D and MASH cirrhosis on SGLT‐2 inhibitors were matched 1:1 with other glucose‐lowering drugs (oGLDs) based on demographics, comorbidities and medications. Primary outcomes included all‐cause mortality, hepatic decompensation and major adverse liver outcomes (MALO). Cox‐proportional hazards models were used to estimate hazard ratios (HRs) with 95% confidence interval (CI). Results A total of 51 427 patients with MASH cirrhosis and T2D were identified, of which 6833 (13.28%) were on SGLT‐2 inhibitors. Patients on SGLT‐2 inhibitors ( n = 6449, mean age 63.7 years, 52.9% female) were matched with 6449 individuals (mean age 63.9 years, 53.5% female) on oGLDs. The SGLT‐2 inhibitors cohort had statistically significantly lower risk of all‐cause mortality (HR: 0.58, 95% CI: 0.53–0.63), hepatic decompensation (HR: 0.85, 95% CI: 0.81–0.90) and MALO (HR: 0.88, 95% CI: 0.83–0.93). Conclusion SGLT‐2 inhibitors are associated with a reduced risk of all‐cause mortality in patients with MASH cirrhosis and T2DM, which may be partly attributable to a lower risk of hepatic decompensation and subsequent events. Further studies are warranted as SGLT‐2 inhibitors may serve as an adjunctive therapy for patients with MASH cirrhosis.

We describe an unusual case of a woman in her 30s who had recently undergone Roux-en-Y gastric bypass and presented to the emergency department with a 6-week history of intractable nausea and vomiting, accompanied by progressively worsening bilateral leg weakness. Examination revealed persistent upbeat nystagmus, paraparesis, areflexia in the lower extremities, brisk reflexes in the upper extremities and diminished sensation across all modalities in lower limbs. Brain and spine MRIs were unremarkable, there was no nerve root enhancement. Laboratory tests revealed thiamine deficiency, with a level of 28 nmol/L (reference range: 70–108 nmol/L). No gangliocyte antibodies were detected. Cerebrospinal fluid study showed no albuminocytological dissociation. Electrodiagnostic studies showed evidence of lumbosacral polyradiculopathy. The patient was eventually diagnosed with thiamine deficiency-related acute nutritional axonal neuropathy and Wernicke’s encephalopathy. After aggressive thiamine supplementation and 8 weeks of physical rehabilitation, her leg strength improved, but she still requires assistance to ambulate.

ABSTRACT Aims To investigate whether the association between the nitrite‐generating capacity of the subgingival microbiome and early cardiometabolic risk biomarkers varies by dietary nitrate intake. Materials and Methods Cross‐sectional data from 668 participants (mean age 31 ± 9 years, 73% women) were analysed. Dietary nitrate intake was calculated from food frequency questionnaires. Subgingival 16S rRNA sequencing (Illumina, MiSeq) and PICRUSt2 estimated microbial genes. The Microbiome‐Induced Nitric Oxide Enrichment Score (MINES) was calculated as a ratio of microbial gene abundances representing enhanced net capacity for NO generation. Adjusted multivariable linear models regressed cardiometabolic risk biomarkers (HbA1c, glucose, insulin, insulin resistance (HOMA‐IR), blood pressure) on nitrate intake and MINES together with a MINES × nitrate intake interaction term. Results Mean nitrate intake was 190 ± 171 mg/day. Significant interactions of MINES and nitrate intake were observed for insulin and HOMA‐IR ( p < 0.05). Among participants with a low MINES, higher nitrate intake was associated with lower HOMA‐IR (1.2 [1.1–1.4] vs. 1.5 [1.3–1.6]; p = 0.002), but levels were similar in those with high MINES ( p = 0.84). Conclusions A biomarker of higher microbial NO‐generating capacity in subgingival plaque is associated with lower insulin and insulin resistance among individuals with lower dietary nitrate intake. Future trials evaluating the cardiometabolic benefits of nitrate‐rich diets should incorporate measures of the entire oral microbiome.

Abstract Coarsening Visium HD resolution from 8 to 64 µ m can flip cell-type co-localization from negative to positive ( r = −0.12 → +0.80 ), yet many widely used compositional deconvolution workflows require coarsening or subsampling at million-bin scale. Here we introduce FlashDeconv, which combines leverage-score importance sampling with sparse spatial regularization to achieve competitive benchmark accuracy while processing 1.6 million bins in 153 seconds on commodity hardware. Systematic multi-resolution analysis of Visium HD mouse intestine reveals a tissue-specific resolution horizon (8–16 µ m)—the scale at which this sign inversion occurs—validated by Xenium ground truth. Below this horizon, FlashDeconv provides, to our knowledge, the first sequencing-based quantification of Tuft cell chemosensory niches (15.3-fold stem cell enrichment). In a 1.6-million-bin human colorectal cancer cohort, FlashDeconv uncovers neutrophil inflammatory microdomains co-localized with immunoregulatory dendritic cells (mRegDC) at the tumor–stroma interface—spatial niches largely missed by discrete-label summaries, with RCTD doublet mode labeling only 2.3% of hotspot bins as neutrophil singlets.

Abstract Background Most existing biomarkers for Alzheimer's disease (AD) inform on core AD pathologies in the central nervous system, namely amyloid, tau and neurodegeneration (ATN). However, molecular disease mechanisms underlying AD are complex and heterogeneous, highlighting the need for cost‐effective blood‐based biomarkers that reflect brain molecular changes in AD beyond ATN. Method Using peripheral blood transcriptome data from the Mayo Clinic Study of Aging(MCSA) and the Alzheimer's Disease Neuroimaging Initiative, we identified genes associated with AD or mild cognitive impairment (MCI) diagnosis, cognitive, and harmonized neuroimaging measures in two cohorts, followed by meta‐analysis. We identified consensus gene co‐expression modules in MCSA and ADNI using WGCNA and assessed the association between module eigengenes and aforementioned measures. Using seven bulk brain RNAseq cohorts, we confirmed the preservation of coexpression modules across blood and brain. Enrichment analyses of Gene Ontology (GO) terms and cell‐types were performed using significant and/or hub genes. The MCSA transcriptome was further deconvoluted to obtain cell‐type proportions and cell‐type specific expression. Result Blood transcriptome modules M5 and M8 are upregulated in AD/MCI and associate with worse cognition, whereas M1, M10, M13, M16, and M21 are downregulated in AD/MCI and associate with better cognition. M17 associates with more microhemorrhages. Cell‐type enrichment analysis revealed that M5 is enriched in basophils, monocytes, and neutrophils; M8 in monocytes and neutrophils; M13 in natural kills cells; M17 in megakaryocytes; M21 in B cells. GO analysis shows broad consistency with the cell‐type enrichment analysis. M1, 5, and 8 are preserved in the brain network modules. From these modules, we prioritized transcripts significantly associated with at least one AD‐related phenotype in both blood and brain. Cell‐type deconvolution analysis shows that prioritized transcripts in M1 associate with better cognition and higher metabolic activity in B cells. Conclusion We identified blood transcripts and modules associated with AD/MCI, cognition, or neuroimaging, with three preserved in brain networks. These three modules represent centrally linked peripheral molecular signatures that can reveal perturbed brain pathways and cell level transcriptional changes in AD/MCI. We aim to further investigate the prioritized transcripts in additional datasets to establish them as potential novel biomarkers and therapeutic targets.

Abstract Background Brain cells secrete extracellular vesicles (EVs) containing signaling and pathological proteins related to Alzheimer's disease (AD) and related disorders. Recent studies demonstrate brain‐derived EVs (BDEVs) contain misfolded tau and are transmissible of tau pathology in the brain. However, the underlying mechanism regarding EV‐mediated tau pathology is still largely uncharacterized. Method We conducted the immuno‐affinity purification of tau in human brain‐derived EVs (BDEVs) isolated from age and sex‐matched 14 AD and 14 CTRL cases and performed tandem mass‐tag mass spectrometry to profile unbiased tau interactome in BDEVs. We used Nanotemper Monolith to validate the interaction of EV‐tau interactors with tau, and super‐resolution microscopy (Nanoimager) to detect their colocalization in a single‐EV level. We next designed siRNAs to silence EV‐tau interactors in SH‐SY5Y cells overexpressing human P301L tau (SH‐SY5Y‐P301Ltau) and assessed tau loading to EVs by Flow Nanoanalyzer. These EVs were tested for their uptake and tau seeding by human iPSC‐derived neurons (iNeurons). We finally tested the inhibitors of identified molecules on BDEV‐mediated tau propagation in vivo . Result A total of 764 proteins were identified in BDEV‐associated tau interactome from CTRL and AD patients. Sixty‐five proteins were significantly downregulated in AD BDEVs; whereas 5 proteins were significantly upregulated in AD BDEVs compared to CTRL BDEVs. The most enriched tau‐interacting proteins were significantly positively correlated with Braak stage. We confirmed the direct binding of candidate proteins to tau with micromolar binding affinities by Monolith, and their colocalization with tau in human BDEVs using Nanoimager. Silencing of candidate molecules in human SH‐SY5Y‐P301Ltau cells showed reduced loading of tau in EVs, their reduced uptake by iNeurons, and tau seeding activities. Finally, intracerebroventricular injection of neutralizing antibody against the most promising target results in reduction of tau pathology in PS19 mice expressing P301S human tau mutant. Conclusion Our study identified tau‐interacting molecules highly enriched in AD BDEVs compared to CTRL BDVs. Silencing of candidate molecule reduced tau loading to the EVs and their uptake by iNeurons and neutralizing antibody suppressed tau dissemination, highlighting them as promising therapeutic targets to halt tau pathology in AD and related tauopathy.

Abstract Background Dynamically predicting patients at risk of Alzheimer's disease (AD) is crucial for timely treatment and care in precision medicine. Method We present a novel architecture, a Kolmogorov‐Arnold networks (KANs)‐based joint prediction model of longitudinal and survival data (JM‐KAN). Real‐life data of patients with Mild Cognitive Impairment (MCI) with no prior AD diagnosis from National Alzheimer's Coordinating Center (NACC) dataset was utilized for individualized predictions. We built joint models with landmark times every 1 year up to 4 years after the MCI diagnosis, and predicted survival outcomes one year after each landmark (i.e. 1, 2, 3, and 4 years). Integrated AUC (iAUC), and integrated Brier score (iBS) were measured for existing prediction models and our novel architecture. Result Out of 2711 unique patients with MCI, 821 patients progressed to develop AD. Mean time‐to‐event in years was 3.19 (standard deviation (SD): 2.29). The number of visits prior to AD diagnosis was 4.03 (SD: 2.41). With iAUC of 0.789, iBS of 0.118, JM‐KAN has both the highest iAUC and lowest iBS among joint models, which indicates superiority in discrimination and overall performance. For individualized predictions, the model successfully predicted both longitudinal risk factors and survival probability, as new information came in every year. Conclusion Using neural network‐based Artificial Intelligence (AI) model, we demonstrated that this architecture improves upon prior methods in dynamically updating predictions of AD as new longitudinal data becomes available.