Informatics in Medicine Unlocked

The -Bonacci weighted sequence space {ell}(,) is introduced for [&ge;] 2, 1 [&le;] < {infty}, and > 0). It is proved to be a Banach space which is isometrically isomorphic to {ell}, with an unconditional Schauder basis. The space is also reflexive and uniformly convex. Its strict inclusion chain, continuous dual, and asymptotic truncation estimate of order - / are also established. Based on this framework, Scaled &#119898-BonacciNet is proposed as a lightweight CNN that rescales Fibonacci-Net channels using the dominant &#119898-Bonacci root and employs depth wise separable convolutions. Tribonacci-Net achieved 96.09% accuracy and a 0.9581 macro F1-score on the 15-class Brain MRI dataset using 0.85 million parameters and 0.366 GFLOPs. Pentanacci-Net attained 95.87% accuracy on the 44-class dataset while reducing parameters and FLOPs by 47.10% and 44.48%, respectively. On the CKD ultrasound dataset, Hexanacci-Net achieved 83.75% accuracy and a 0.7941 macro F1-score, with corresponding reductions of 50.37% and 45.10%. These findings support dominant-root scaling as a structured approach to lightweight CNN design.

Purpose: Posterior pharyngeal wall (PPW) thickening is a common structural manifestation of treatment-related internal lymphedema in patients with head and neck cancer and can be evaluated using lateral videofluoroscopic images acquired during modified barium swallow (MBS) studies. However, no standardized method currently exists for quantitative measurement. Methods: This two-phase methodological study first conducted a scoping review to characterize existing approaches to PPW thickness measurement and identify the principal factors contributing to variation in PPW thickness measurement. Eight studies were identified with variation observed across these measurement factors. An empirical reliability study was then performed using 107 MBS studies from 93 Head and Neck Cancer patients. Two independent raters annotated 10 anatomical landmarks on pre-swallow and post-swallow resting frames, and a computer-assisted algorithm quantitatively generated seven candidate PPW thickness measurements based on different vertebral reference points and measurement directions. Reliability was assessed intraclass correlation coefficients (ICCs). Results: Inter-rater reliability was highest for the measurement originating from the midpoint of C3 and extending perpendicular to the anterior C3 surface (ICC, 0.78; 95% CI, 0.72-0.83). Among the normalization references, C2-C4 vertebral length demonstrated greater reliability than C3 vertebral length, with an ICC of 0.88 (95% CI, 0.85-0.91). No significant differences were observed between pre-swallow and post-swallow PPW thickness measurements across the evaluated approaches. Conclusions: These findings provide a methodological foundation for more standardized and reliable quantification of PPW thickness, with potential to expand the role of MBS from the functional evaluation of swallowing to include quantitative characterization of pharyngeal structural abnormalities.

Awareness of comorbidities in people with dementia is vital context for dementia research and clinical management. Natural Language Processing (NLP) applied to free text in the electronic health record (EHR) may help ascertain comorbidity presence. Our aim was to assess the validity of the MedCAT Comorbidity NLP app in a cohort of people with dementia. We used text from the EHR of South London and Maudsley NHS Mental Health Trust via Clinical Records Interactive Search (CRIS), linked to structured data from local primary care (Lambeth DataNet) and hospital admission data in a cohort of 3,543 people with dementia. We evaluated MedCAT Comorbidity, examining internal validity via manual review of text and external validity by cross-validation against linked primary care and hospital data; and comparison of the predictive value of comorbidity measures for mortality. Internal precision was highest for atrial fibrillation (100%) and lowest for diabetes (78%). Cross-validation with linked data varied substantially across conditions, with precision from 96% to 59% and recall 77% to 4%. The median number of comorbidities identified was two using any single source, or three when combined. MedCAT Comorbidity demonstrated predictive ability for mortality (HR 1.11 per condition), although the model fit was better using comorbidity from hospital admission data. In people with dementia, the MedCAT Comorbidity app showed high precision for most conditions during internal validation. The variability of precision and recall on external validity assessment partly reflects incomplete documentation. Results from the MedCAT comorbidity app can assist researchers in understanding comorbidity of cohorts, particularly when primary care or hospital data is not available. Our findings highlight the need to develop quality assurance protocols that include internal and external validity checks before deploying general data extraction apps for specific clinical subpopulations.

Introduction: This study aimed to characterize the neurochemical profile of Alzheimer s disease (AD) continuum stages using proton magnetic resonance spectroscopy (1H-MRS) data for brain metabolites and macromolecules. We used Orthogonal Projection to Latent Structures Discriminant Analysis (OPLS-DA) to classify diagnostic groups and assess feature importance. Methods: Our dataset consisted of 7 T short-echo time 1H-MRS spectra of the posterior cingulate cortex/precuneus of individuals diagnosed with AD (n = 27) or Mild Cognitive Impairment (MCI, n = 27) and Healthy Controls (HC, n = 31). Classification used 100-round Monte Carlo cross-validation based on metabolite concentrations and macromolecular peak intensities. Variable importance in projection (VIP) was assessed across classification models to identify the most relevant neurochemical features. The resulting metabolite rankings were compared with previous literature to evaluate their biological plausibility. Results: Classification was strongest for AD versus HC (AUC=0.88) and lower for comparisons involving MCI. Metabolites contributing most strongly to classification included myo-inositol, ascorbate, and choline-containing compounds, consistent with previous studies on AD-related neurochemical alterations. In addition, specific macromolecular components were identified as relevant features, suggesting a potential contribution of MM signals to the characterization of AD-related neurochemistry. Discussion: These findings support the use of short-echo-time 1H-MRS as a complementary tool for characterizing neurochemical changes in the AD continuum, with potential applications in disease characterization and monitoring, warranting validation in larger cohorts.

Background: Plasma biomarkers offer a minimally invasive alternative for detecting Alzheimer disease (AD) pathology, yet the genetic determinants influencing their levels remain largely underexplored. Objective: To identify genetic variants associated with AD-related plasma biomarker levels in a Midwestern Amish cohort. Methods: We analyzed plasma levels of amyloid beta (A{beta}42/A{beta}40), phosphorylated tau 181 (p-tau181), and the p-tau181/A{beta}42 ratio in the Midwestern Amish. Genome-wide association studies (GWAS) were used to examine associations with common genetic variants, while genetic linkage analysis was used to assess the potential impact of rare variants. Results: GWAS found associations between plasma biomarkers and the APOE gene. Additionally, novel candidate genes were implicated, including MME for A{beta}42/A{beta}40 ratio, and VEGFC and LRRTM4 for p-tau181. Linkage analysis identified significant signals for p-tau181 on chromosome 1, potentially involving the CSF1 and AHCYL1genes. Conclusions: The novel genetic variants associated with AD pathology implicate metabolic, synaptic, neuronal, and immune pathways. The use of GWAS and linkage analysis sheds light on the genetic architecture of AD-related plasma biomarkers.

Longitudinal clinical cohorts contain heterogeneous progression patterns that are obscured by coarse diagnostic labels. Discovering these latent progression phenotypes requires models that distinguish patients not only by their outcomes, but also by how their clinical trajectories evolve. This paper proposes TRACE, an interpretable temporal phenotyping method that enables the learning of outcome-informed progression phenotypes from longitudinal trajectories. TRACE combines a trajectory- and outcome-aware contrastive objective with a probabilistic predictive phenotype layer, encouraging phenotypes to capture progression structure while remaining predictive of diagnostic state. A dual-attention mechanism further supports interpretation by identifying the visits and clinical variables most associated with each phenotype assignment. TRACE is evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort for cognitive disease modeling, showing improved performance in both diagnostic prediction and phenotype discovery compared with other temporal phenotyping baselines. Finally, by allowing the number of phenotypes to exceed the diagnostic labels, TRACE enables discovering additional progression patterns that are not captured by labels defined a priori.

Computed tomography (CT) interpretation requires recognition of the anatomical level of each slice. Classification may facilitate preprocessing and AI-assisted fracture diagnosis. We developed deep learning models to classify craniofacial CT slices into six anatomical categories. A total of 11,769 images were collected from a multicenter database involving 10 Japanese university hospitals and the public Head-Neck-Radiomics-HN1 dataset. Patients were divided into training (70%) and evaluation (30%) sets. Five CNN architectures were trained from scratch, and hard- and soft-voting ensembles were evaluated. Individual-model accuracy ranged from 94.72% to 96.09%; the hard-voting ensemble achieved 96.46% accuracy (Cohen's k = 0.954). Although performance differed among individual models (Cochran's Q = 99.29, p < 0.001), the ensemble did not significantly outperform EfficientNetV2B1 (McNemar's p = 0.163). Errors occurred mainly between adjacent anatomical regions. Deep learning can classify craniofacial CT slices accurately and may support automated dataset organization and AI-assisted fracture diagnosis. External validation is needed.

INTRODUCTION: The digital clock drawing test (dCDT) is a cognitive screening tool capturing high-resolution pen-movement data. Existing approaches for classifying cognitive impairment from dCDTs rely on selected features or image-based methods. We propose representing dCDTs as structural networks to classify cognitive impairment and characterize spatial organization in clock drawings. METHODS: Weighted networks were constructed from 3,362 Framingham Heart Study participants, with and without cognitive impairment. Network representations were generated separately for command and copy conditions, with nodes representing pre-defined clock components (digits, hands, clock face) and edges capturing pairwise spatial relationships between components. Random forest models and graph convolutional networks were trained to predict cognitive impairment from the networks. Models were benchmarked against demographics-only and established summary feature baselines. RESULTS: Network-based models achieved AUC values comparable to the established summary feature baseline across command-only and copy-only conditions. Differences in spatial organization between the cognitively intact and impaired groups were greatest in the upper-right clock region, involving digits 1 and 2, the clock hands, and the surrounding clock face. Differences between the command and copy networks were larger among cognitively impaired participants. DISCUSSION: The proposed network representations provide an objective, scalable, and flexible framework for representing clock drawings for use in classification models. dCDT networks matched the predictive performance of established summary feature-based approaches and identified clock regions where spatial organization differed by cognitive status. After external validation, this approach has the potential to be incorporated within a clinical workflow to support automated and interpretable cognitive screening.

Introduction: Lichen sclerosus (LS) is a chronic inflammatory dermatosis predominantly affecting the anogenital region of both women and men. Sexual dysfunction remains among the most debilitating and overlooked consequences of the disease, as available patient-reported outcome measures are either restricted to one sex or capture sexual health only partially. The aim of the present work is to validate the Qualisex questionnaire (in its the Italian version) in patients with LS and to evaluate its psychometric properties, factor structure, convergent, discriminant validity, and sex symmetry. Methods: A cross-sectional study was conducted on 211 patients (167 women, 44 men; mean age 52.9 years) with a confirmed diagnosis of LS. Participants completed the Italian Qualisex, Skindex-29, DASS-21, and sexual-health-specific scales, the Female Sexual Function Index (FSFI) for women, and the International Index of Erectile Function (IIEF) for men. Construct validity was assessed via Principal Component Analysis (PCA), and reliability via Cronbach's alpha. Convergent and discriminant validities were evaluated against standard psychometric batteries. Results: Item 2 was removed due to low communality (0.167). PCA on the remaining 9 items revealed a two-factor structure accounting for 72.5% of total variance: Component 1 (Sexual Functioning and Satisfaction, 59.89%) and Component 2 (Partner Devaluation, 12.66%). Internal consistency was high (alpha= 0.911 overall; alpha= 0.841 in women; alpha= 0.899 in men). Qualisex scores correlated strongly with Skindex-29 Social Functioning (r = 0.582) and Emotions (r = 0.531) subscales, as well as FSFI (r = -0.391) and IIEF (alpha= -0.566). Discriminant validity was supported by low-to-moderate correlations with DASS-21 domains (r < 0.38). While FSFI and IIEF showed significant negative correlations with age (p = 0.002), Qualisex remained age-independent across sexes (p > 0.05). Conclusion. The 9-item Italian Qualisex is a reliable, sex-symmetrical, and age-independent patient-reported outcome measure for assessing sexual health impairment in LS, successfully isolating disease-specific distress from physiological aging and general distress.

Objective. Temperature readings are routinely stratified by race and ethnicity, though significant group differences may reflect physiology, instrument noise, or categorization rather than biology. This study tests whether these categories partition facial infrared thermography measurements beyond individual variation and baseline device noise. Approach. A secondary analysis of thermography data (n = 951 adults) compares inner-canthus maximum (TCEmax) and full-face maximum (Tmax) temperatures across five administrative categories via one-way analysis of variance (ANOVA), mixed-effects modeling, Bland-Altman analysis against oral reference, and Monte Carlo sensitivity analysis. Main results. ANOVA returns significant group differences (p < 0.001; eta-squared = 0.04 for TCEmax, 0.03 for Tmax), but the total spread (0.2 to 0.3 degrees Celsius) is comparable to device accuracy and within-subject variability. Individual variation accounts for 95% of TCEmax variance, and adding simulated Gaussian noise to participant-level temperatures (sigma = 0.10 to 0.30 degrees Celsius) preserves the complete group-mean rank order in as few as 37.5% of iterations. Significance. Testing whether a group difference survives perturbation at the scale of instrument accuracy offers a check against treating significance alone as evidence of physiology. Here, observed group differences are small relative to measurement variability, and their physiological interpretation remains uncertain, with implications for clinical tools that treat administrative categories as biologically meaningful.

Introduction: Acute gastroenteritis (AGE) is a cause of childhood morbidity and may be associated with human adenovirus (HAdV) infection. This study aimed to investigate the occurrence of HAdV in fecal samples from children with AGE in Salvador, Bahia, Brazil, as well as to molecularly characterize the identified strains and evaluate their cytopathic effects in cell culture. Methods: A total of 131 samples were analyzed using the nested polymerase chain reaction (Nested-PCR) technique. Molecular characterization was performed through Sanger sequencing and phylogenetic analysis, while Vero cell cultures were used to investigate the occurrence of cytopathic effects. Results: Of the samples analyzed, 13 (9.9%) tested positive for HAdV, while 118 (90.1%) were negative. The highest detection frequency was observed in children aged three years or younger, particularly those under one year of age. Phylogenetic analysis identified variants related to HAdV-F41 and HAdV-B3, revealing genetic differences and distinct clustering patterns compared with reference sequences. In cell culture, cytopathic effects were observed four days after inoculation, although further analyses are required to confirm productive viral replication. Conclusion: These findings demonstrate the presence of HAdV in the samples studied and contribute to understanding its genetic diversity and circulation in Salvador, highlighting the importance of epidemiological surveillance and further studies on its role in the etiology of childhood AGE.

Type 1 diabetes mellitus (T1DM) remains a growing global health priority, with disproportionate underdiagnosis, delayed diagnosis, and mortality in sub-Saharan Africa. Despite the introduction of the Life for a Child (LFaC) programme in Kenya, which provides free insulin, glucose-monitoring supplies, and specialist education, no published studies have examined the determinants of glycaemic control among Kenyan children and adolescents with T1DM. This study aimed to determine the proportion of children and adolescents achieving good glycaemic control and to identify the socio-demographic and clinical determinants of glycaemic control at a tertiary hospital in Kenya. This cross-sectional study enrolled 160 children and adolescents with T1DM attending the paediatric diabetes clinic at Kenyatta National Hospital, Nairobi, between September 2025 and May 2026. Sociodemographic and clinical data were collected through structured interviews and physical examination. Poor glycaemic control was defined as HbA1c >7.5%. Associations between candidate determinants and glycaemic control were assessed using chi-square or Fisher's exact tests. Variables with p<0.2 on bivariate analysis were entered into a multivariable logistic regression model; Firth's penalized-likelihood method was used given the small number of participants achieving good control. Only 9 participants (5.6%) achieved the target HbA1c of [&le;]7.5% (mean HbA1c 11.02%, SD {+/-}2.29), and only 5 of 133 patients (3.8%) had [&ge;]70% of glucometer readings within the target blood glucose range (3.9-10.0 mmol/L).On bivariate analysis, household income (p=0.026) and self-monitored blood glucose (SMBG) frequency (p=0.018) were significantly associated with glycaemic control. In an exploratory multivariable regression analysis (Firth penalized logistic regression, given the small number of participants with good control), only SMBG frequency [&ge;]4 tests/day remained independently associated with lower odds of poor control (adjusted OR 0.19, 95% CI 0.04-0.84, p=0.028); the effect of household income was attenuated after adjustment. Glycaemic control remains markedly poor among children and adolescents with T1DM in this Kenyan tertiary hospital setting despite structured programmatic support. Frequency of self-monitoring, rather than household income alone, was the factor most strongly associated with glycaemic control, suggesting that strengthening access to glucose-testing supplies and supporting their consistent use should be prioritised in care, alongside attention to socioeconomic barriers. Longitudinal studies are needed to confirm these associations.

Aims: To understand how users believe continuous glucose monitoring (CGM) changes their behaviour, which app features they credit, and whether endorsing more mechanisms of action (MoAs) is linked to reported diet and physical activity change. Methods: We surveyed 112 U.S. adults with type 1 or type 2 diabetes who had used a Dexcom or Abbott Libre CGM within 12 months. Participants rated 26 MoA statements mapped to the Capability, Opportunity, Motivation-Behaviour (COM-B) model, identified contributing app features, and reported CGM-attributed diet and physical activity change. Logistic regression examined MoA count and COM-B domains as predictors of behaviour change. Results: Participants (mean age 54.5 years; 60.7% female) reported dietary (82.1%) and physical activity (46.4%) change and endorsed a median of 18 MoAs, most often relating to knowledge, skills and behavioural consequences. Real-time glucose and graphs were attributed to many MoAs. Each additional MoA was associated with higher odds of dietary (OR 1.27, 95% CI 1.15-1.44) and physical activity change (OR 1.27, 95% CI 1.16-1.43). Motivation made the strongest distinct contribution among COM-B domains. Conclusions: Users perceive CGM as engaging many behavioural mechanisms; endorsing more was linked to behaviour change. Motivation may be a key target for CGM-based behavioural support.

Biomedical artificial intelligence (AI) increasingly relies on heterogeneous data sources, yet differences in data structure, terminology, evidence quality, and provenance complicate their integration into reliable knowledge resources. Here, we develop an evidence-aware framework for processing and integrating heterogeneous pharmacovigilance data from FDA Adverse Event Reporting System (FAERS) reports, biomedical literature, clinical trials, and electronic health records in Mass General Brigham. The framework combines large language model (LLM)-based information extraction, ontology-driven semantic normalization, cross-source evidence assessment, statistical and machine learning-based prioritization, and ingredient-based adverse drug event (ADE) relationship inference to transform heterogeneous evidence into a structured and evidence-based knowledge resource. The constructed knowledge system contains 11,588 drug nodes, 44,843 ingredient nodes, 3,275 ADE nodes, 993 indication nodes, and more than 2.47 million relationships, with each drug-ADE relationship assigned a confidence score. We further developed a drug-level web interface for accessing the knowledge resource and validated its utility in downstream AI tasks. Specifically, incorporating the developed knowledge resource improved the comprehensiveness, relevance, and overall quality of LLM-based drug-specific ADE synthesis and improved the precision and F1 score of clinical ADE relation extraction. These results demonstrate that AI-assisted processing combined with evidence-aware curation can transform heterogeneous biomedical evidence into reusable, AI-ready knowledge with explicit provenance and evidence strength information. The framework provides a general strategy for integrating heterogeneous biomedical data to support reproducible knowledge construction and downstream biomedical AI applications.

Several polyglutamine-encoding repeat expansions cause spinocerebellar ataxias (SCAs) and other movement disorders. SCAs are genetically heterogeneous with a substantial diagnostic gap. We used genome sequencing data from 266 individuals of European and Asian ancestry and screened genome-wide for repeat expansions using STRling, a dedicated repeat expansion caller. The cohort comprised patients with movement disorders, predominantly dystonia and ataxia, as well as unaffected participants. Further, we screened another 436 individuals with ataxia and 427 ethnically matched controls through fragment analysis of the most promising candidate locus in EP400. We applied targeted long-read sequencing to expansion carriers and one control individual to resolve the sequence structure of the repeat locus. Finally, we used microsatellite markers in chromosome 12q24.32-33 to test for a common founder. A genome-wide outlier analysis in STRling revealed a polyglutamine-encoding repeat expansion (GRCh38 chr12:132,062,525-132,062,590) in the E1A binding protein p400 (EP400) gene in two patients with ataxia. Through screening of additional ataxia patients, we identified in total 18 patients from 14 families with heterozygous repeat expansions in EP400. All expansion carriers were from Serbia. The repeat number in affected individuals ranged from 37 to 55, whereas the longest control allele had 32 repeats. We demonstrated that non-expanded alleles carried a CAA interruption within the 5-prime CAG tract; this interruption was lost in expanded alleles. Expansion carriers have pure SCA with onset mainly in their 40s and 50s. There is an inverse correlation between the number of repeats and age at onset. The EP400-encoded protein plays an essential role in chromatin remodeling, epigenetic regulation and DNA repair. Pathogenic sequence variants in EP400 have previously been implicated in a neurodevelopmental disorder. Our study identifies repeat expansions in EP400 as a novel and frequent cause of ataxia in Serbian patients. This finding further increases the diagnostic yield of ataxia testing and warrants investigation in ataxia patients worldwide. We add EP400 to the growing list of ataxia-associated repeat loci, which warrants inclusion in diagnostic ataxia panels.

Objectives To evaluate discrepancies between radiologists and referring physicians in the interpretation of diagnostic probabilities conveyed by common Japanese expressions used in radiology reports. Materials and Methods Fifteen expressions commonly used to convey diagnostic probabilities in radiology reports were selected by five attending radiologists. An anonymous online survey was distributed to 26 radiologists and 33 referring physicians at a single institution. Participants assigned a diagnostic probability (from 0% to 100% in 10% increments) to each expression. Differences in median probabilities between radiologists and referring physicians were analyzed using the Mann-Whitney U test. Variability in interpretation was assessed using interquartile range (IQR), with IQR [&ge;] 30% defined as substantial variability. Results All radiologists (26/26) and 97% of referring physicians (32/33) completed the survey. Seven of the 15 expressions showed statistically significant differences in interpretation between radiologists and referring physicians: "c/w" (80% vs. 60%; p = 0.031), the Japanese equivalents of "present" (100% vs. 100%; p = 0.024), "suspect of" (80% vs. 70%; p = 0.007), "possibly" (50% vs. 70%; p < 0.001), "unlikely" (10% vs. 10%; p = 0.022), "cannot be ruled out" (25% vs. 40%; p < 0.001), and "not inconsistent with" (80% vs. 90%; p = 0.008). Substantial variability in interpretation (IQR [&ge;] 30%) was observed for four expressions: "cannot be ruled out" (IQR, 10%-40%), "s/o" (IQR, 60%-90%), "c/w" (IQR, 50%-90%), and "not inconsistent with" (IQR, 70%-100%). Conclusion This study revealed that nearly half of the Japanese expressions commonly used to convey diagnostic probabilities were interpreted differently by radiologists and referring physicians. These discrepancies highlight the need for a standardized lexicon to improve clarity and consistency in radiology reporting.

Introduction: Acute gastroenteritis (AGE) is a global public health problem and a major cause of mortality in low- and lower-middle-income countries. Human bocavirus (HBoV) is among the viral agents associated with this condition. Therefore, investigating the presence of HBoV in cases of AGE in Salvador, Bahia, as well as its prevalence and circulating strains, is important. Objective: To investigate the occurrence of HBoV infection during an AGE outbreak in Salvador. Methods: A total of 178 samples were analyzed for HBoV using nested polymerase chain reaction (nested PCR), and the amplification products were visualized by agarose gel electrophoresis. HBoV was detected in 5.7% of the samples analyzed. Conclusion: This study contributes to the understanding of HBoV epidemiology as a secondary agent associated with acute gastroenteritis during a norovirus outbreak in Salvador. Continued investigation and identification of the etiological agents involved in outbreaks are essential for evaluating the causes of AGE. These findings also highlight the need for broader diagnostic approaches and the importance of phylogenetic analysis for monitoring and controlling viral infections.

Background: Cervical spinal cord (CSC) compression and myelopathy are frequent, severe complications in mucopolysaccharidosis (MPS) types I and VI. Early detection is critical to prevent irreversible neurological deficits and preserve quality of life. This prospective study aimed to identify quantitative neuroimaging biomarkers of structural CSC damage and myelopathy in MPS I and VI. Methods: Structural and diffusion magnetic resonance imaging (MRI) of the CSC was analyzed in 17 MPS I patients, 3 MPS VI patients (19{+/-}7 years), and 19 case-matched healthy controls (20{+/-}7 years). CSC macrostructure (cross-sectional area, diameters) and microstructure (fractional anisotropy [FA], radial [RD], mean, axial diffusivities) were evaluated within and beyond the maximal compression areas. Results: MPS patients demonstrated reduced CSC cross-sectional area and anterior-posterior diameter at upper cervical levels, alongside widespread microstructural alterations (lower FA, higher RD) throughout the CSC versus controls (pFDR<0.05). Restricted foramen magnum space was associated with microstructural damage at lower cervical levels without overt macrostructural compression. Morphological and microstructural metrics were sensitive to CSC compression and myelopathy severity (pFDR<0.05). Lower CSC integrity correlated with more advanced growth retardation (p<0.001). Among patients receiving hematopoietic stem cell transplantation, earlier treatment correlated with preserved upper CSC morphology (p<0.05). Distinct neuroimaging phenotypes were observed across MPS genotypes (pFDR<0.05). Conclusions: CSC MRI provides candidate neuroimaging biomarkers for early detection and characterization of myelopathy in MPS I and VI. Structural damage extends beyond primary maximal compression areas, highlighting these markers for clinical monitoring and future therapeutic trials.

Background: Current Lynch syndrome (LS) prediction models do not account for detailed family structure and tumour mismatch repair (MMR) status. We developed LynchRisk, a Bayesian model that estimates individualized LS probability from pedigree, cancer history and tumour MMR test results. Methods: LynchRisk combines Mendelian inheritance, age- and sex-specific cancer hazards, affected and unaffected relatives, and detailed MMR status. Parameters were derived from the literature. Results: In two simulated cases of probands with LS-associated cancers, family structure and MMR status markedly changed LS probability estimates, with implications for germline testing decisions. LynchRisk also identified situations in which a negative germline result might be considered sufficient to rule out LS or, conversely, prompt further investigations such as somatic MMR or long-read sequencing. Discussion: LynchRisk gives individualized LS probability by combining clinical, family, and tumour data at different stages of the diagnostic process. Further work will focus on recalibrating cancer hazard functions, validating model performance through simulations, and developing an R package for use by interested research groups.

Patients with implantable cardioverter-defibrillators (ICDs) often are excluded from receiving CMR scans at 3.0T due to ICD-induced image artifacts. This work aims to transfer sequence protocols established for CMR imaging in the presence of ICDs at 1.5T to a first human application at 3.0T. All measurements were performed on a clinical 3T MRI scanner. Phantom measurements were used to establish and optimize wideband PSIR LGE imaging at 3.0T. Volunteer measurements with externally placed ICDs were used for optimization of CINE imaging and wideband PSIR imaging. Thirdly, patients with implanted ICD were examined with the optimized protocols. Human measurements showed that SA stacks obtained with spoiled GRE after contrast agent showed a markedly lower artifact burden than the conventional approach. For scar imaging, a wideband PSIR with a bandwidth of 3kHz and a frequency offset of +750Hz allowed a sufficient suppression of relevant artifacts in the majority of scans. No strong correlation between the image quality and the distance between the cardiac silhouette and the device was detected. The presented work shows the feasibility of clinical cardiac MRI at 3.0T including GRE-CINE and wideband PSIR-LGE for patients with ICDs.