Disease Models & Mechanisms

Pain in pancreatic ductal adenocarcinoma (PDAC) is associated with poor survival, but whether altered pain processing carries prognostic significance is unknown. We analyzed a prospective cohort of 143 patients with PDAC who underwent pancreatic quantitative sensory testing (PQST) after diagnosis. Patients were classified as having normal pain processing (n=84), segmental hyperalgesia (n=30), or widespread hyperalgesia (n=29). Survival was measured from the date of P-QST assessment. During follow-up, 70 deaths occurred. Widespread hyperalgesia was associated with increased mortality in unadjusted Cox analysis (HR 1.96, 95% CI 1.14,3.35) and after adjustment for age, sex, tumor stage, comorbidity, opioid treatment, and body mass index (adjusted HR 2.33, 95% CI 1.30,4.15). Segmental hyperalgesia was not associated with mortality. Kaplan Meier analysis demonstrated lower survival probability in the widespread hyperalgesia group (log rank p=0.025). These findings suggest that widespread hyperalgesia, reflecting altered central pain processing, identifies a subgroup of PDAC patients at increased risk of mortality independent of conventional clinical factors.

Wolfram syndrome (WFS) is characterized by youth-onset insulin-dependent diabetes and neurological deficits. Brain white matter deficiency has been reported, but its trajectory remains unclear. Applying diffusion basis spectrum imaging models longitudinally in 29 individuals with WFS (baseline ages, 5.2 to 25.8 years; maximum 7 visits) and 52 matched controls, we found that WFS is associated with microstructural alterations suggesting diminished axonal integrity, myelin content, and cellularity. These changes were present and stable early in the disease progression in visual and auditory-related regions, whereas abnormalities in the corpus callosum appeared later in adolescence and adulthood. Our results support developmental hypomyelination as a neurophenotype of WFS.

Background: Intrauterine contraceptives (IUCs) are effective, but effects on genital inflammation among women living with HIV (WLHIV) by antiretroviral therapy (ART) use are unclear. We evaluated the longitudinal effects of copper IUC (C IUC) and the levonorgestrel intrauterine system (LNG IUS) on cervicovaginal cytokine profiles in a secondary analysis of a randomized trial (NCT01721798, 2013 to 2016). Methods: Cervicovaginal secretions were collected from 100 WLHIV (non ART users; ART users) randomized 1:1 to C IUC or LNG IUS. Twenty eight cytokines were measured prior to insertion and 3 and 6 months post insertion. Cytokine concentrations at each follow up visit were compared with baseline, using participant fixed effects models stratified by ART status. Results: At enrolment, non ART users had higher average concentrations of most cytokines (21/28) than women using ART. Among non-ART users, IUC use was not associated with cytokine increases; only MCP1 increased significantly at 3 months among C IUC users (log10 geometric mean ratio 0.77, 95%CI 0.38 to 1.17), while none increased with LNG IUS use. Among ART users, C IUC insertion resulted in broad and sustained cytokine increases at both 3 (16/28) and 6 months (15/28). At month 3, the largest increases in log10 geometric mean were observed for IL6 (1.04, 0.72 to 1.36), RANTES (0.97, 0.54 to 1.40), MCP1 (0.71, 0.46 to 0.96), MIP1; (0.66, 0.37 to 0.94), and GCSF (0.63, 0.36 to 0.89), which was maintained until month 6. Cytokine changes following LNG IUS insertion were minimal (IL5, month 3). Conclusions: Among ART users, C IUC is associated with increases in cervicovaginal cytokines, across functional classes. This supports LNG IUS as a less inflammatory option for WLHIV to minimize genital immune activation.

Patients with primary immunodeficiency (PID) often face prolonged diagnostic delays and may increasingly turn to large language models (LLMs) to interpret their symptoms during this period. We evaluated whether an LLM could recognize PID from symptom descriptions derived from interviews with 21 PID patients. In a prior study, we showed that GPT-4o identified PID in 96% of cases when prompted with physician-written patient histories (Rider et al., JACI, 2024). Here, when prompted with symptom descriptions in patients' own words, GPT-5 identified PID in only 7 cases (33%), although it more broadly suggested immune system issues in 18 cases (81%). The gap between these findings indicates that LLMs are sensitive to the language and framing of symptom descriptions, performing substantially worse when patients describe their own symptoms in everyday language than when clinicians summarize patient histories in structured medical terms. This study underscores the need to carefully evaluate how LLMs are used in patient-facing applications.

Background: Individuals with body dysmorphic disorder misperceive defects of their physical appearance. Current evidence suggests that visual processing abnormalities may underlie this core symptom. Separate pre-clinical studies testing perceptual and attentional interventions and non-invasive neuromodulation suggest that these visual processing abnormalities may be modifiable, but their combined effects on neural connectivity and perceptual processing remain unclear. Methods: Thirty-nine unmedicated men and women with body dysmorphic disorder or subclinical body dysmorphic disorder received intermittent theta burst stimulation and continuous theta burst stimulation targeting the lateral parietal cortex combined with a visual attention modification paradigm during functional magnetic resonance imaging, in a crossover design. Dynamic effective connectivity within dorsal and ventral visual stream pathways was calculated, and global visual processing biases were assessed using the face inversion effect before and after stimulation plus attention modification. Results: Intermittent theta burst stimulation resulted in increased connectivity in higher-level dorsal visual stream pathways during naturalistic viewing following attention modification, whereas continuous theta burst stimulation was associated with reduced connectivity in lower-level dorsal pathways and increased connectivity in ventral stream pathways. These changes were accompanied by differential effects on global visual processing, with stimulation type modulating the magnitude of the face inversion effect. Conclusions: Combined neuromodulation and visual attention modification modulate visual system connectivity and perceptual processing in individuals with body dysmorphic disorder symptoms. These findings support a mechanistic link between dorsal-ventral stream dynamics and perceptual biases. Integrating neuromodulation with perceptual retraining may represent a viable approach for targeting core symptoms of distorted appearance perception.

Objective To examine the effects of combined oral contraceptive (OC) use on clinical markers of ovarian reserve by comparing Anti-Muellerian Hormone (AMH), antral follicle count (AFC), and ovarian volume (OV) before and after starting or stopping OC. Methods This analysis is based on data from a prospective cohort study conducted at the University Hospital Tubingen, Germany, as part of the IRTG-2804 project. A total of 54 healthy women were included and categorized into three groups based on their OC use status: OC starters (n = 12), stoppers (n = 16), and long-term OC-users (n = 26). Each participant underwent a transvaginal ultrasound (including AFC and OV) and serum sampling (including AMH) at two time points (S1 and S2), three to six months apart. OC starters were assessed first during the early follicular phase (day 1-7) and then during active OC intake (day 8-21), while stoppers were assessed in the reverse order. Long-term users were assessed twice during active OC intake. Results OC stoppers showed significant within-group increases in all ovarian reserve markers, including AMH ({Delta} = 2.57 ng/mL, p < .001), AFC ({Delta} = 3.88, p = .004), and OV, which almost doubled (1.94-fold increase; 95% CI [1.35, 2.80], p < .001). In contrast, OC starters exhibited a significant decline in AMH ({Delta} = -1.25 ng/mL, p = .013), but no changes in AFC or OV. No significant longitudinal changes were observed among long-term OC users. Conclusion AMH levels decrease after starting OC use whereas AFC and OV are not affected. In contrast, AMH, AFC, and OV recover within three to six months after stopping OC, suggesting a reversible suppression of ovarian reserve markers during OC use. These findings are clinically relevant for fertility counseling and for the interpretation of ovarian reserve markers in women using hormonal contraception.

Spousal caregivers of individuals with Alzheimers disease and related dementias frequently experience elevated perceived stress, caregiver burden, and loneliness, which are associated with adverse health outcomes. Early identification is therefore critical for timely intervention. Existing approaches commonly rely on wearable sensor data and standardized psychological questionnaires, while recent multimodal methods aim to improve prediction by integrating behavioral and linguistic information. In this study, we explored three modality configurations, wearable-derived features, interview-based text, and their combination, to classify caregiver psychological risk using the Perceived Stress Scale (PSS), Zarit Burden Interview, and UCLA Loneliness Scale. We compared traditional machine learning models and large language models (LLMs) (Gemini 2.0, Llama 4, and GPT-4o) under psychometrician-centered and caregiver-centered prompting strategies. Traditional machine learning models performed better under multimodal settings, while LLMs achieved stronger performance with Interview-Only input. We further demonstrate that PSS was the most predictable construct and prompting strategies substantially influenced LLM performance.

Objective: To address the need for improved measurement of the ways contraception impacts the baseline menstrual cycle (i.e., contraceptive-induced menstrual changes; CIMCs) by assembling an interdisciplinary, global research collective to rigorously develop a person-centered measure for CIMCs in multiple languages. As the first step, this paper reports on our conceptual model development, which is the foundation for ongoing measure development. Study design: We conducted 18 focus groups with 106 people experiencing CIMCs while using hormonal or intrauterine contraception in Durban, South Africa, Santo Domingo, Dominican Republic, and Portland Oregon, United States. We used a virtual affinity mapping approach to analyze qualitative data, which was the basis of our conceptual model along with relevant theory and related models in the literature. Results: The conceptual model of experiences with CIMCs depicts the baseline menstrual cycle, including CIMCs and conceptually-linked effects and the impacts and perceptions of those CIMCs. We found key domains of changes in pain, bleeding volume, bleeding patterns, and characteristics of blood. Conclusion: Our CIMC conceptual model will inform development of a measure with evidence of validation across three language and global contexts. Adoption of a person-centered, standardized CIMC measurement across trials will improve knowledge and decision-making between methods.

Background: Conventional psychiatric screening instruments summarize symptoms within individual scales and prioritize cases with high single-instrument additive score severity. This design treats items as independent within instruments and ignores cross-instrument covariance structure, making it insensitive to respondents whose responses are distributed across multiple domains in unusual combinations that remain below threshold on every individual scale. Methods: We analyzed two cohorts spanning older and younger adults. Item prompts from depression, stress, anxiety, and sleep instruments were embedded into a shared semantic space using a pretrained sentence encoder. Principal component analysis of the item-prompt embeddings alone---with no use of respondent data at this stage---was used to construct a low-dimensional subspace retaining 80\% of variance in the item embedding matrix. Normalized participant responses were then projected into this subspace, with Jaccard-based stability analysis used as a check on dimensional robustness. Multivariate deviation from the cohort norm was quantified with Mahalanobis distance using Ledoit-Wolf covariance regularization. Candidate outliers were defined by the empirical 95th percentile of the cohort-specific distance distribution. To isolate response configurations not already captured by conventional single-instrument extreme-value logic, we excluded all outlier respondents who had endorsed any individual item at the maximum value of its Likert scale on any instrument. For the remaining outliers, anomalous components were backtracked to their original item loadings for interpretation. Results: In the older-adult Health and Retirement Study (HRS) cohort, principal component analysis of 27 item-prompt embeddings showed that a 10-dimensional subspace provided a stable representation of cross-instrument semantic structure. In the younger-adult Xinxiang cohort the corresponding stable solution was 16-dimensional. In each cohort, seven respondents remained as multivariate outliers despite falling below every single-instrument extreme-value threshold. These cases were not characterized by uniformly severe symptom scores but by unusual cross-domain response configurations that became visible only in the shared semantic covariance subspace. The response structure of the retained configurations differed across cohorts: older-adult cases more often involved weak endorsement of mood-labeled items alongside nonzero body- and sleep-related responses, whereas younger-adult cases more often involved incomplete response configurations spanning mood, sleep, stress, and self-harm-related items. Conclusions: A semantically aligned, auditable covariance subspace provides a practical tool for flagging unusual multivariate response configurations that single-instrument additive screening may not flag. The method is interpretable at the level of original item contributions. It should be understood as a hypothesis-generating screen for unusual response configurations requiring further clinical assessment, not as a diagnostic instrument. Outcome validity remains to be established by prospective study.

Diastolic heart failure (HF) in primary cardiomyopathy is under-recognized and often diagnosed late, particularly in children. While recent studies have advanced understanding of HF with preserved ejection fraction in older adults, the prevalence, outcomes and molecular drivers of diastolic HF in pediatric and young adult cardiomyopathy remain poorly defined, where disease is typically driven by primary myocardial disease rather than acquired co-morbidities. The Canadian Cardiomyopathy Collaborative (C3) was assembled to leverage three of Canadas leading pediatric and adult cardiomyopathy biobank registries. Its flagship initiative, Artificial Intelligence to Model Diastolic Heart Failure (AID-HF), aims to integrate deep phenotyping - including comprehensive diastolic function assessment - with genomics, lipidomics and proteomics and apply machine learning to identify biological and clinical signatures that drive cardiac function and outcomes in cardiomyopathy. Harmonized phenotyping and multiomics protocols across registries will create a uniquely integrated national data resource and enable the goals of AID-HF i.e., earlier diagnosis and new therapeutic targets for diastolic HF in cardiomyopathy.

Background. Something in discourse with a person experiencing psychosis often "feels off" before formal assessment is completed, yet this disturbance has not been quantified at the level of ongoing dyadic conversation. Prior work has largely treated patient speech in isolation, limiting our capacity to measure how communicative disruption emerges within clinical exchange. Methods. We applied a three-level decomposition of conversational alignment in 109 patients with psychotic disorders (26 female) and 60 healthy controls (22 female) at baseline and 12 months (n = 115). Register divergence (dAUCnorm) captured lexical distance between interviewer and patient; embedding-based synchrony (rembed) measured semantic trajectory coupling; within-speaker coherence was computed separately for each speaker. We used linear mixed-effects models adjusted for timepoint and participant clustering. Results. Patients showed significantly greater lexical-semantic divergence from the interviewer (d = 0.48, p < .001) and reduced embedding-based synchrony (d = -0.59, p < .001), both effects replicating at each time point. Critically, the interviewer's within-speaker coherence was reduced during conversations with patients (d = -0.33, p = .016), indicating that the disruption extends beyond the patient to the interaction itself. Register divergence tracked impoverished thinking and synchrony tracked disorganized thinking (both FDR-corrected q = .038). Group differences were persistent at 12 months, indicating a partially stable profile. Conclusions. Conversational alignment in psychosis reveals a dyadic failure of semantic coordination that destabilizes the interviewing clinician's coherence even when patient narrative continuity is preserved. These transcript-derived alignment metrics offer a scalable approach to quantifying interpersonal communicative function from routine clinical encounters.

Background: Store-and-forward teledermatology commonly relies on several patient-submitted photographs of the same concern, but most dermatology artificial intelligence models classify single images independently. Objective: To develop and internally validate a case-level diagnostic-support model that aggregates multiple patient-submitted photographs for common dermatologic conditions. Methods: We conducted a retrospective diagnostic-modeling study using the Skin Condition Image Network, a public dataset of deidentified self-taken dermatology images from US adults. We curated 2,336 cases comprising 5,041 images across 10 common inflammatory, allergic, and infectious conditions. Cases were split at the submission level into training, validation, and held-out test sets. Frozen general-purpose and dermatology-specific encoders were compared with image-level classifiers and a gated-attention multiple instance learning model that generated one case-level output from 1-3 images. Results: The strongest image-level baseline, dermatology-specific embeddings with random forest classification, achieved macro/micro ROC-AUCs of 0.797/0.854. Case-level aggregation improved discrimination, with dermatology-specific embeddings plus multiple instance learning achieving mean macro/micro ROC-AUCs of 0.819/0.863 across repeated stratified experiments. The locked final model achieved macro/micro ROC-AUCs of 0.800/0.849 on the held-out test set. Balanced-threshold sensitivity/specificity examples were 0.702/0.688 for eczema and 0.818/0.826 for urticaria. Limitations: Internal validation used a 10-condition subset from a US volunteer dataset; external validation, calibration, subgroup performance analysis, and prospective workflow studies are required. Conclusion: Modeling the teledermatology submission as a multi-image case better reflects asynchronous dermatology workflow than single-image classification. The model is preliminary clinician-facing support for structured review and triage, not autonomous diagnosis.

Background Gait impairment is a central sign of cervical spondylotic myelopathy (CSM) that is typically evaluated through subjective patient-reported questionnaires or objective in-clinic measures. These systems require substantial resources to administer and are poorly suited for longitudinal monitoring, however, emerging smartphone applications present an efficient alternative. We developed and assessed the validity of a data processing framework based on the SynapTrack smartphone application to assess gait function in individuals with CSM. Methods Participants completed walking tasks which were recorded on both the SynapTrack app and a gold standard gait mat. Acceleration data extracted from the smartphone by the app were filtered and processed to produce gait cycle features including velocity, step time, waveform features and frequency domain features. Standard gait features were compared across the two methods by correlation and Bland-Altman plots to assess validity. App-based gait features were then compared to the standard modified Japanese Orthopedic Assessment (mJOA) assessment to determine construct validity through correlation and ability to discriminate between individuals with CSM and healthy controls. Finally, intraclass correlation coefficients and coefficients of variation were used to measure test-retest reliability and standard variation across app features. Results A total of 110 participants were included in this study, of which 55 (50%) had CSM, 24 (22%) had peripheral neuropathy, and 31 (28%) were healthy controls. SynapTrack gait measures including velocity, step time, and double support showed strong validity as indicated through Bland-Altman plots and high correlation (>0.8) with mat features. In addition to the gait features, acceleration root mean square, acceleration crest, spectral entropy, and dominant frequency showed strong construct validity compared to the mJOA across correlation (0.2-0.54), trend test (p < 0.001), and AUROC (0.62-0.79) analyses. ICCs showed moderate test-retest reliability (0.52-0.67). Discussion The proposed framework for processing gait data showed strong validity compared to the gold standard mat and high construct validity compared to the mJOA suggesting the utility of the SynapTrack app as an efficient alternative to existing methods. The confirmation of gait metrics related to CSM severity and identification of relevant waveform and frequency domain features present opportunities to use smartphone apps to develop ecologically valid data driven markers of CSM severity.

Objective. Biomedical knowledge graphs (KGs) such as PrimeKG, Hetionet, UMLS, and PharmGKB are increasingly used as the substrate for downstream machine-learning, retrieval-augmented generation, drug-repurposing, and electronic health record (EHR) augmentation pipelines. The dominant assumption in published work is that integrating two or more such KGs is a tractable engineering step solved by identifier (ID) matching. This paper interrogates that assumption empirically. We quantify how much concept overlap survives realistic alignment, and we characterize the new failure modes introduced by the methods that practitioners reach for when ID matching is insufficient. Materials and Methods. We compared four widely used biomedical KGs (PrimeKG, Hetionet v1.0, the full UMLS Metathesaurus, and PharmGKB) across eleven node types using a tiered alignment pipeline: (1) direct ID matching for nodes sharing a primary vocabulary; (2) cross-ontology bridging using standard mappings (e.g., MONDO-DOID, HPO-UMLS, HPO-UMLS-MeSH for side effects, NCBI Gene-HGNC-UMLS, UBERON-FMA/SNOMEDCT_US/NCI/MeSH for anatomy); (3) ClinicalBERT cosine-similarity grouping at threshold >= 0.98 for over-segmented disease nodes, with a deterministic suffix-stripping canonicalizer; (4) exact name matching for ontology-poor types (anatomy, REACTOME pathways); and (5) embedding-based fuzzy matching with UMLS lookup (SapBERT and ClinicalBERT) for free-text microbiome concepts. We applied the pipeline to a 698-concept gut-microbiome benchmark spanning taxa, pathways, and disease labels, validated grouping decisions against the curated SSSOM mappings released by the MONDO project, and audited the ClinicalBERT consolidation against five clinical-genetics case studies drawn from the literature. Results. Per-type pairwise coverage was strikingly asymmetric. Genes/proteins and the three Gene Ontology categories aligned cleanly across PrimeKG and Hetionet (mutual coverage 94-99%), but disease overlap was sparse: only 0.7% of PrimeKG individual disease nodes mapped to Hetionet, rising to 2.0% after MONDO grouping (versus 78.7% and 18.4% from the Hetionet side). PrimeKG-to-UMLS coverage spanned 100% (effect/phenotype via HPO) down to 20.8% (REACTOME pathways), with drugs at 73.7% and anatomy at 58.8%. PrimeKG-to-PharmGKB drug coverage required up to two bridging hops (DrugBank -> UMLS -> RxNorm/ATC/MeSH). Bigger was not uniformly more complete: on a 698-concept microbiome drug benchmark, Hetionet missed 0 concepts while PrimeKG missed 16. ClinicalBERT-based grouping consolidated 22,205 raw MONDO disease nodes into 17,080 groups but introduced three reproducible failure modes documented in case studies: (i) peer over-merging: for example, all 22 osteogenesis imperfecta subtypes collapsed into a single node despite distinct severity classes; (ii) parent-child collapse: e.g. acute myeloid leukemia merged with myeloid leukemia, erasing the acute/chronic distinction that drives clinical management; and (iii) lexical false positives: neurofibromatosis and schwannomatosis grouped together despite cellular-pathology differences. Discussion. Identifier matching alone is a weak baseline for biomedical KG integration. Cross-ontology bridges and embedding-based consolidation expand coverage but do so at the cost of clinically meaningful resolution, and the resulting failures are systematic rather than random. Reporting only aggregate coverage statistics obscures these losses, which propagate silently into downstream tasks. Conclusion. We provide reusable per-type coverage tables, a taxonomy of three integration failure modes, and concrete recommendations for downstream studies that depend on a unified biomedical KG. We argue that future KG integration work should report per-type coverage and per-cluster confidence rather than aggregate match rates.

Background: While growing enthusiasm for the therapeutic potential of classic psychedelics has led to a rise in non-clinical use, attention to persisting adverse effects has emerged with delay. A subset of individuals reports persisting complications such as hallucinogen persisting perception disorder (HPPD), depersonalization/derealization disorder (DDD), anxiety and depression. Yet few medical services are equipped to address these complications. Aims: This qualitative study examines how societal, medical, and media discourses shape the experiences of individuals with persisting psychedelic-related complications, focusing on help-seeking trajectories. Methods: Thirteen semi-structured interviews with adults experiencing persisting psychedelic-related psychological symptoms (four women, nine men, age 19-49 years; HPPD (n = 10), DDD (n = 6), depression (n = 1), and anxiety (n = 1)) were conducted within a larger study on these complications. Data were analysed using reflexive thematic analysis. Reporting followed the COREQ guidelines. Results: Three interrelated themes emerged: (1) The dissonance between expectation and harm - idealised media and scientific portrayals of psychedelics shaped initial use and complicated recognition of adverse outcomes; (2) Stigma, silence, and self-blame - prohibitionist discourse and internalised shame significantly inhibited help-seeking; and (3) Between systemic absence and self-organised support - participants encountered clinical unpreparedness and epistemic dismissal, which often led them to rely on online peer communities and self-management strategies. Positive clinical encounters, characterised by professional expertise and nonjudgmental engagement, were experienced as helpful. Conclusions: Adequate clinical and conceptual frameworks for persisting psychedelic-related complications are lacking. An interdisciplinary, experience-informed approach integrating realistic risk communication, clinician training, and destigmatisation is required to support affected individuals.

Polycystic ovary syndrome (PCOS) is linked to adverse pregnancy outcomes and increased cardiometabolic risk in offspring, yet the placental mechanisms underlying these risks remain poorly understood. Metformin is prescribed during PCOS pregnancies despite limited mechanistic justification. Using multi-modal molecular analyses of placentas from healthy controls and women with PCOS randomized to placebo or metformin (PregMet trial), restricted to uncomplicated pregnancies, we characterized direct PCOS associated placental alterations independent of confounding complications. PCOS placentas showed transcriptional downregulation across multiple cell types and shifts in cell type proportions. Specifically, syncytiotrophoblasts exhibited reduced expression activity of growth hormone receptor signaling and glycosaminoglycan biosynthesis. Endothelial cells displayed diminished receptor tyrosine kinase pathway activity, including VEGFC, despite increased cell proportion and hypervascularity. Intercellular communication networks were globally suppressed, including reductions in PDGF signaling from Hofbauer cells to fibroblasts. Notably, metformin did not reverse most PCOS-associated molecular alterations and induced transcriptional changes correlated to birth weight and childhood BMI. These findings indicate that PCOS-associated placental features are driven by cell type specific dysregulation of growth factor, angiogenic signaling pathways that are largely unresponsive to metformin. This underscores the need to develop mechanism based, placenta targeted therapeutic alternatives for future pregnancy management.

Teledermatology expands access to dermatologic expertise in rural settings, yet diagnostic uncertainty persists in low-resource primary care. This retrospective study evaluated MedGemma-4B-IT, a compact multimodal vision-language model, as adjunctive clinical decision support for challenging diagnostic cases. We analyzed 77 zero-concordance cases (360 clinical photographs) from a Dermatology Extension for Community Healthcare Outcomes (ECHO) tele-mentoring program (2016-2021). Zero-concordance cases showed no overlap between primary clinician provisional diagnosis and dermatologist-confirmed diagnosis. The model was prompted using dermatologist-style format to generate ranked differential diagnoses. Performance was assessed using strict case-level top-k exact-match accuracy and relaxed matching criteria based on fuzzy string similarity. MedGemma achieved 0.0% strict top-1 accuracy, 1.3% top-3 accuracy, 3.9% top-5 accuracy, and 3.9% top-10 accuracy. Relaxed concept-level matching achieved 28.6% top-1, 63.6% top-5, and 67.5% top-10 accuracy. Image-level accuracy was 44.2% (159/360, 95% CI 39.0-49.5%). The model surfaced the correct diagnosis within differential lists in 45.5% of cases despite no exact top-1 matches, suggesting utility for differential expansion rather than definitive diagnosis. Performance varied across diagnostic categories, with highest accuracy in Other categories (54.5%) and lowest in neoplastic conditions (0.0%). Common errors included confusion between inflammatory and other diagnostic groupings. These findings characterize MedGemma performance on real-world teledermatology cases and inform safe, clinician-in-the-loop integration into teledermatology workflows where specialist oversight remains essential.

Pathological activity within frontal cortical circuits is common in many neuropsychiatric disorders, such as obsessive-compulsive disorder (OCD). We developed an invasive brain mapping protocol in which temporary electrodes are implanted in candidate sites to identify personalized stimulation targets that can acutely relieve OCD symptoms. We found that stimulation within segments of the anterior limb of the internal capsule (ALIC) focally suppressed the structurally and functionally connected region of prefrontal and cingulate cortex. By leveraging the topographic organization of the ALIC, we reversibly inactivated frontal cortical sites with ALIC stimulation to determine which cortical regions are necessary for sustaining OCD symptoms. Stimulation of ventral capsule (VC) near the globus pallidus within the ALIC was associated with suppression of lateral orbitofrontal cortex activity and acute and long-term improvements in OCD symptoms. These results provide a paradigm for leveraging ALIC topography to deliver targeted connectomic neuromodulation to frontal cortex to treat neuropsychiatric disorders.

Background: Periodontitis is defined by cumulative, irreversible tissue destruction, yet population-based measurement typically relies on cross-sectional indicators derived from retained teeth. Destruction that occurred earlier in life, particularly disease severe enough to result in tooth loss, is structurally excluded from these measures, potentially leading to systematic underestimation of lifetime periodontal burden. Objective: To develop and evaluate a measurement framework that estimates lifetime periodontal burden from cross-sectional data by explicitly incorporating informative tooth loss under etiological uncertainty. Methods: Data were drawn from 10,324 adults aged [&ge;]30 years participating in the 20090-2016 National Health and Nutrition Examination Survey (NHANES) who completed full-mouth periodontal examination and glycated hemoglobin (HbA1c) testing. Lifetime periodontal burden was estimated by combining observed clinical attachment loss in retained teeth with probabilistic contributions from missing teeth, using three alternative age-stratified attribution schedules derived from epidemiological studies of periodontal extraction. Performance was compared with conventional measures of periodontal severity and extent using distributional analyses, correlations with HbA1c, discrimination of diabetes status, and relative importance analysis. Age-adjusted models were treated as sensitivity analyses. Results: Estimated lifetime periodontal burden exhibited strong, monotonic age gradients across glycemic categories, in contrast to more attenuated patterns observed for severity and extent. Across attribution schedules, lifetime burden showed stronger correlations with HbA1c ({rho} = 0.30-0.32) than conventional measures. In multivariable models including all indices, lifetime burden retained an independent association with HbA1c, whereas severity and extent contributed little unique information. Discriminative performance for diabetes status was consistently higher for lifetime burden than for conventional measures and remained stable across attribution schedules. Conclusions: Lifetime periodontal burden can be estimated from cross-sectional data by explicitly modelling informative tooth loss rather than restricting measurement to retained teeth. Incorporating historical tissue loss under uncertainty yields a more coherent representation of cumulative periodontal destruction than snapshot-based measures and provides a methodological basis for life-course-oriented periodontal epidemiology.

Background: The specific 3D morphological substrates distinguishing the newly defined massive and torrential functional tricuspid regurgitation (FTR) phenotypes from standard severe disease remain under-characterized. Objectives: This study investigates the 3D geometric changes of the tricuspid valve (TV) apparatus across the spectrum of FTR, specifically focusing on the structural definition of massive and torrential grades. Methods: Three-dimensional (3D) transesophageal echocardiography (TEE) was performed in 322 patients with FTR secondary to left-sided heart disease. Patients were stratified into mild-moderate (n=166), severe (n=82), and massive-torrential (n=74) groups. TV geometry, including annular dimensions, leaflet tethering, and subvalvular apparatus, was quantified using 3D modeling software. Results: Patients with massive-torrential TR were characterized by advanced age, female predominance, and atrial fibrillation (75%). 3D analysis demonstrated that massive-torrential TR represents a distinct phenotype defined by extreme annular circularization (ellipticity index 1.0) and planar flattening (P < 0.001). Furthermore, these patients exhibited a critical leaflet-annulus uncoupling, where compensatory leaflet growth (relative length < 80%) failed to match the massive annular dilation. Consequently, the regurgitant orifice in massive-torrential grades appeared highly complex, frequently manifesting as multiple irregular orifices. Conclusions: Massive and torrential FTR are characterized by a unique geometric profile involving extreme annular circularization, severe leaflet tethering, and leaflet-annulus uncoupling. These morphological insights suggest that conventional repair strategies may be insufficient for these advanced phenotypes, highlighting the necessity for pre-procedural 3D TEE to guide device selection.