Insights into the admixture history between modern and archaic humans require accurately inferred introgressed fragments within modern genomes. Here, we introduce two enhancements to hidden Markov models (HMMs) implemented in hmmix. First, we develop a method for sampling hidden state sequences conditional on observed genomic data, enabling robust estimation of admixture summary statistics-such as admixture proportion and fragment length distributions. This represents an improvement compared to relying solely on point estimates as provided by classical decoding methods. Additionally, we integrate the Finite Markov Chain Imbedding (FMCI) framework, allowing exact analytical calculation of these admixture statistics, tailored to large scale human genomes. Second, we implement a novel hybrid decoding method which combines the strengths of Viterbi and Posterior decoding methods, substantially improving the reliability of archaic fragments identified. We validate these improvements on data from the 1000 Genomes Project and demonstrate that our sampling method yields more accurate admixture estimates from single individuals compared to existing approaches requiring extensive population-level datasets. Moreover, we show how hybrid decoding can be instrumental in resolving the inference of local archaic haplotype structure in modern human genomes. These methodological advancements will enhance HMM-based analyses in any field of science and will provide deeper insight into the complex history of genetic interactions between archaic and modern human populations.
Background Small bowel (SB) capsule endoscopy (SBCE) is a key tool for Crohn's disease (CD), but interpretation is time-consuming. Artificial intelligence (AI) could assist, yet automated SB segmentation, a prerequisite for analysis, may be challenging in patients with CD. Aims To evaluate an AI model detecting the pylorus and ileocolonic junction in CD SBCEs. Methods SBCE videos from 60 patients with CD were annotated by three readers. A ResNet-18 convolutional neural network classified frames into stomach, SB, or colon, with Viterbi-based anatomically constrained segmentation, using five-fold cross-validation. Accuracy was assessed by discrepancies relative to expert annotations. Univariate analysis explored associated factors. Expert review was conducted for poorly localized cases. Results The model was trained on 596,980 images, including 426,674 SB images. Median localization error was 2 frames (1 s) for the pylorus and 118.5 frames (~5 min) for the ileocolonic junction. In 26 videos (43.3%), the model localized both junctions with a temporal discrepancy <1 min compared with experts. In 53.3% of cases, AI detection of the ileocolonic junction preceded expert annotation by a median of 46 min, potentially leading to missed terminal ileal lesions. No factors were significantly associated with localization accuracy. Expert review highlighted inadequate cleanliness, capsule stagnation, and back-and-forth movements as main contributors to poor AI performance. Conclusions Our model successfully localizes both SB junctions in nearly half of SBCE videos from patients with CD. It consistently identifies the pylorus but shows modest performance at the ileocolonic junction. Capsule dynamics and bowel cleanliness remain major challenges for automated SB segmentation.
Accelerometer sensors and artificial intelligence (AI) are reshaping automated behavior monitoring in precision livestock management, yet their joint deployment on extensive rangelands is constrained by energy and bandwidth budgets. Low-Power Long-Range Wide-Area Network (LoRaWAN) collars address these constraints by compressing the raw tri-axial signal on the device into a single scalar per reporting interval, the Motion Index (MI). This onboard compression preserves enough signal to separate active behaviors but discards the per-axis and frequency content that fine-grained classification typically relies on. On a dataset of 9222 labeled observations from 24 cows across four breeds, MI distinguishes walking from grazing reliably but fails to separate ruminating from resting; both correspond to a stationary animal and yield near-zero, statistically indistinguishable distributions. Earlier MI-only models reached only about 65% four-class accuracy, and ruminating was commonly merged into resting. We show that much of this loss can be recovered by treating the MI stream as a time series. Session-aware lag features, rolling statistics, and an autoregressive previous-behavior feature lift four-class macro-F1 from 0.647 to 0.94, with per-class F1 of 0.95 for ruminating and 0.92 for resting (and at least 0.92 for every behavior). In autonomous deployment the previous behavior must be predicted rather than observed; for this setting we add a Viterbi sequence-decoding step that combines the classifier's per-step outputs with a learned behavior-transition model, recovering a substantial part of the ruminating signal from the activity stream alone while keeping walking and grazing reliable. The gain is consistent across seven classifiers and four genetically distinct breeds, indicating that it is driven by the features rather than by a specific model.
Recent advances in roadside sensing technologies, including camera-based systems, radar, and LiDAR, have enabled high-resolution sampling of vehicle trajectories, overcoming the temporal and spatial limitations of traditional data collection methods. Among these, LiDAR sensing has been widely adopted for traffic monitoring and surrogate safety analysis due to its high spatial accuracy and temporal resolution. However, sensor noise and occlusion in roadside LiDAR frequently introduce tracking point offsets and trajectory discontinuities, reducing the reliability of vehicle counts, traffic state estimation, and conflict analysis. To address these challenges, this study proposes a post-processing method based on time-space analysis to detect and correct occlusion-induced trajectory discontinuities. By exploiting the inherent spatiotemporal consistency of vehicle movements, the proposed approach identifies fragmented trajectories, reconstructs continuous vehicle paths, and recovers realistic traffic patterns. Validated on real-world LiDAR data collected at an urban intersection in Reno, Nevada, across four 30 min traffic periods covering AM and PM peak conditions on weekdays and weekends, the proposed method achieves an average precision of 0.989 and an average F1-score of 0.948, outperforming IMM, GNN-RM, and HMM + Viterbi benchmark methods. Count accuracy improved from 85.5% to 97.4% across all evaluated periods, confirming the method's effectiveness under occlusion conditions.
Diagnosing glaucoma in myopic eyes is challenging due to overlapping structural features, such as optic disc tilt and retinal nerve fiber layer (RNFL) bundle shifts. Lacking standardized criteria for differentiating glaucomatous optic neuropathy (GON) from nonpathologic myopia, current commercial databases often flag healthy myopic eyes as abnormal. To evaluate the diagnostic performance of prespecified optical coherence tomography (OCT) rules for detecting GON in myopic eyes across diverse international populations and devices. This multicenter diagnostic study used a sequential 2-phase design consisting of a modified Delphi process to prespecify diagnostic rules and a cross-sectional diagnostic validation. Participants included adults with nonpathologic moderate or high myopia recruited from an internal validation cohort (Zhongshan Ophthalmic Center, China) and an international external validation cohort (centers in Hong Kong, Taiwan, the US, and India). Eyes with pathologic myopia (staphyloma or myopic maculopathy category ≥2) were excluded. Data were collected from January 2019 through December 2024 and analyzed from December 2024 through June 2025. OCT imaging of the peripapillary RNFL and macular ganglion cell-inner plexiform layer (mGC-IPL), with application of 5 prespecified diagnostic rules to detect GON. Sensitivity and specificity of 5 prespecified OCT rules were evaluated against a clinical reference diagnosis established by masked experts. The rules were as follows: rule A, temporal-superior-inferior-nasal-temporal (TSNIT) curve dip or depression; rule B, inferior peripapillary RNFL (pRNFL) thinning; rule C, inferotemporal mGC-IPL thinning; rule D, rule B or C; and rule E, rule B and C. Participants included 943 adults (1525 eyes) with nonpathologic moderate or high myopia recruited from an internal validation cohort. Of 1525 eligible eyes (mean [SD] participant age, 45.9 [17.2] years; 665 eyes [43.6%] from 402 female participants), 814 (53.4%) had confirmed GON. Rule A demonstrated the highest diagnostic utility. In the internal cohort (n = 841), sensitivity was 0.96 (95% CI, 0.94-0.98) and specificity was 0.95 (95% CI, 0.92-0.97). In the multiethnic external cohort (n = 684 eyes), rule A maintained a sensitivity of 0.93 (95% CI, 0.90-0.95) and specificity of 0.93 (95% CI, 0.90-0.96). Rule D also performed robustly, with an external sensitivity of 0.90 (95% CI, 0.87-0.93) and specificity of 0.93 (95% CI, 0.89-0.97). In this multicenter diagnostic study, morphological assessment of TSNIT curves and combinatorial analysis of inferior pRNFL and inferotemporal mGC-IPL thinning demonstrated high diagnostic accuracy for GON in nonpathologic myopic eyes. These expert-derived rules offer objective, generalizable decision support for reducing diagnostic uncertainty in the myopic population.
Tau positron emission tomography (PET) is widely used for the in vivo characterization of disease stage and progression in Alzheimer's disease (AD). With the adoption of multiple tau PET tracers including AV-1451, PI-2620, MK-6240 with different binding behaviors in various large-scale studies, there is a great need of effective harmonization methods to enable the cross-tracer integration of tau PET datasets. While previous methods such as CenTauR were proposed to standardize scalar tau PET measures, they are limited in accounting for the heterogeneity of tau pathology. In this work, we propose Feynman-Kac Reweighted Schrödinger Bridge Matching (FKRSBM), a surface-based framework for cross-tracer tau PET harmonization. FKRSBM learns a direct stochastic transport between tracer domains using Schrödinger Bridge matching, avoiding the Gaussianprior routing used in diffusion-based translation. To promote biologically consistent transport, FKRSBM introduces an endpoint penalty favoring bridge pairings with matched tau-pathology status and implements it through a Feynman-Kac reweighted endpoint proposal. To preserve cortical organization, FKRSBM uses a spherical convolutional network for vertex-level harmonization on cortical surface meshes. In our experiments, we demonstrate our method by harmonizing Tau PET images acquired with the AV-1451 (n=1480) and PI-2620 (n=2458) tracers from two large-scale datasets. Compared to previous methods including ComBat, CycleGAN, Diffusion Model(DF), and unregularized Schrödinger Bridge Model(DSBM), the proposed FKRSBM method outperforms these baselines in subgroup-level alignment, tau-positivity consistency, and diagnostic classification while preserving subject-specific cortical topography of tau pathology. The code is available at: https://github.com/jianweizhang17/FKRSBM.
To evaluate demographic and socioeconomic factors associated with pediatric ophthalmology referrals from the UC San Diego (UCSD) EyeMobile, a school-based mobile vision program. The records of children screened from 2021 to 2025 were reviewed retrospectively. Children who failed a screening received a comprehensive eye examination by an optometrist. Referrals to a pediatric ophthalmologist were made for those with potentially significant ocular pathology and no established eye care provider. Socioeconomic status was assessed using the national Child Opportunity Index (COI), analyzed as continuous scores and quintiles. Referral rates were compared across quintiles using two-proportion z tests, and multivariable logistic regression was used to identify factors associated with referrals. Of 42,166 children screened during the study period, 5,657 (14.1%) received a comprehensive examination, and 217 (0.51%) were referred to an ophthalmologist. Leading reasons for reasons included significant refractive errors (39.2%), strabismus (18.4%), and suspicion for keratoconus (14.3%). Referral rates were 0.69% in Very Low COI areas and 0.34% in Very High COI areas. There was a 7.6% reduced odds of referral per 10-point increase in COI (OR = 0.934, P = 0.033). Compared with White children, Black (OR = 2.30, P = 0.012) and Hispanic children (OR = 2.03, P = 0.011) had significantly higher odds of referral. Lower socioeconomic status and minority race/ethnicity were independently linked to higher odds of pediatric ophthalmology referral, highlighting the disproportionate disease burden in underserved groups. Mobile screening programs may help with timely identification of at-risk children and improve health equity.
During the COVID-19 pandemic, selecting vaccination sites and allocating limited doses required balancing accessibility, disease control, and fairness. We formulate a multi-objective mixed-integer linear programming model that jointly determines the locations of mega-sites and allocates vaccine doses while explicitly incorporating travel inconvenience, disease dynamics, and equitable distribution. The model incorporates commuting patterns from both residential and workplace origins to more accurately capture population mobility, and employs a tractable objective formulation that proxies key public health goals, enabling efficient and equitable mass vaccination planning. Compared with the solution empirically used in Los Angeles County in 2020, we recommend more dispersed mega-site locations that result in a 26% reduction in travel inconvenience and avert an additional 200 infections.
Spiral ganglion neurons (SGNs) transmit auditory signals from the cochlea to the brain and are divided into two main types: type I and type II, distinguished by their anatomy and connectivity. However, the function of type II SGNs remains poorly understood due to their scarcity and lack of clear physiological markers. In this study, we use two Cre-dependent fluorescent reporter mouse lines of both sexes to enhance the identification and targeting of type II SGNs for whole-cell patch-clamp recordings. We reveal a set of distinguishing biophysical features, most notably, the presence of an inactivating potassium current and weaker voltage-gated sodium currents, that clearly separate type II SGNs from their type I counterparts. Additionally, we uncover greater-than-expected heterogeneity among type II SGNs, including variation in size, excitability, and ion channel expression. These features suggest the existence of distinct subtypes of type II SGNs, with potential differences in function. We find that most type II SGNs are relatively unexcitable and incapable of repetitive firing. Instead, they appear to be better suited to integrating sustained signals, potentially supporting roles in detecting cochlear damage or modulating efferent feedback. Additionally, through computational modeling, we demonstrate that removing the inactivation component of the inactivating potassium current specific to type II SGNs allowed repetitive spiking to similar levels seen in type I SGNs, suggesting a crucial role for the current in stifling type II SGN activity. Together, our findings define biophysical signatures that distinguish SGN types and subtypes, offering new insight into their contributions to normal hearing and cochlear pathology.Significance Statement The sensory neurons of the cochlea are divided into type I and type II spiral ganglion neurons. Type I spiral ganglion neurons convey the main features of sound information. The rarer type II spiral ganglion neurons appear to be putative auditory nociceptors, responding to cochlear damage. By combining genetic tools, electrical activity recordings, and computational models, we demonstrate that type I and type II spiral ganglion neurons have distinctive ion channel profiles and firing properties. Furthermore, we report previously undescribed ion channel diversity within the type II spiral ganglion neuron population, suggesting varied functions. Our results highlight the parallels between type II spiral ganglion neurons and somatosensory nociceptors and provide a framework for selectively targeting distinct auditory neuron populations.
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Unknown patterns within cerebrospinal fluid (CSF) flow complicate the understanding and treatment of neurological disease. Previous research using phase-contrast MRI purported that CSF in the spine exhibited a pattern of flow toward the cranium in wider areas and caudal flow in more narrow areas. By performing the non-invasive CSF tagging technique, TimeSTAMP MRI (4,480 discrete measurements in 17 patients) and visualizing all CSF displacement measurement in an overlaid fashion, stratified by location along the spine, we report bulk caudal flow in the anterior cervicothoracic spine and rostral flow in the posterior cervicothoracic spine. This supports the presence of distinct CSF currents relative to location along the spine.
Background/Objectives: Normal pressure hydrocephalus (NPH) is a treatable cause of gait impairment and fall risk in older adults, yet it remains frequently underdiagnosed. This study aimed to validate an automated measurement of the callosal angle, a recognized imaging marker of NPH, adapted for use on routine head computed tomography (CT). Methods: We performed a retrospective analysis of 198 patients with probable NPH, confirmed by gait improvement following lumbar tap test, and 198 age- and sex-matched controls presenting with headache and negative head CT findings (mean age 74 ± 7 years; 60% male in both groups). Manual callosal angle measurements were independently obtained by trained residents and reviewed by neuroradiologists. Automated and manual measurements were compared using intraclass correlation, and diagnostic performance was assessed across threshold values. Results: Automated callosal angle measurements demonstrated strong agreement with manual measurements (ICC = 0.90). Using an automated callosal angle threshold of <90°, diagnostic accuracy was 84.1%, with sensitivity of 90.4% and specificity of 77.8%. Optimization to a 95° threshold yielded an accuracy of 85.9%, with both sensitivity and specificity of 85.9%. The area under the receiver operating characteristic curve was 0.915 (95% CI, 0.897-0.933). Conclusions: Automated callosal angle assessment on routine head CT provides reliable and scalable detection of NPH, supporting its use as a screening tool to facilitate earlier diagnosis and treatment of a potentially reversible cause of dementia.
To investigate intravitreal anti-VEGF drug use among Medicare Fee-For-Service (FFS) and Medicare Advantage (MA) beneficiaries. Retrospective, cross-sectional analysis of de-identified healthcare data from the American Academy of Ophthalmology's IRIS® Registry (Intelligent Research in Sight) for patient encounters between January 2017 to December 2022. Medicare beneficiaries 65 years and older with FFS or MA coverage with continuous insurance enrollment for at least12 months. Unique FFS and MA beneficiaries receiving intravitreal anti-VEGF were included. Patients were considered to be treated with a particular drug if more than 50% of their injections for at least 12 months were with that drug. For each anti-VEGF drug, we compared proportions of patients treated (more than 50% of their injections) with that drug between FFS and MA and calculated the difference in use between the two Medicare health care programs. Differences in anti-VEGF drug utilization between FFS and MA. 930,411 beneficiaries underwent 12,942,057 intravitreal injections. In the FFS group, aflibercept 2mg was used most frequently (43.8%; n = 4,271,050), followed by bevacizumab (34.7%; n = 3,382,439), ranibizumab (20.6%; n = 2,008,791), brolucizumab (0.6%; n = 56,728) and faricimab (0.4%; n = 40,729). In the MA group, bevacizumab was used most frequently (45.1%; n = 1,436,151), followed by aflibercept 2mg (37.8%; n = 1,201,311), ranibizumab (6.5%; n = 524,758), brolucizumab (0.4%; n = 13,406), and faricimab (0.2%; n = 6,694). Repackaged bevacizumab (lower cost) was more common in the MA group compared with the FFS group (60.0% vs. 47.0%; difference (diff) = 13.0%, 95% CI:12.8, 13.3%; P<.001). Higher cost drugs were used significantly less often among persons with MA compared with FFS, respectively (aflibercept 2mg (28.9 vs. 36.6%; diff = -7.7%; 95% CI -7.9%, -7.4%; P<.001), ranibizumab (11.9% vs. 16.8%; diff = -4.9%; 95% CI -5.03%, -4.7%; P<.001), faricimab (0.06% vs. 0.24%; diff = -0.18%; 95% CI -0.19%, -0.16%); P<.001, and brolucizumab (0.12% vs. 0.19%; diff = -0.07%; 95% CI -0.09%, -0.05%; P<.001). Beneficiaries enrolled in MA were less likely to receive higher cost, anti-VEGF drugs, raising concerns about reduced beneficiary access to newer more expensive anti-VEGF drugs in MA.
Small renal masses are increasingly detected on imaging, but their accurate classification as benign, indolent, or aggressive remains challenging. Such classification can aid further diagnostic workup, improving patient outcomes and saving costs. Artificial intelligence (AI) models show promise in this area. Here, we evaluate the cost-effectiveness of 3 such models compared with standard of care (SOC). We developed a Markov microsimulation model to simulate clinical and economic outcomes in 10,000 patients aged 40 to 75 years with small renal masses over a 10-year horizon. We compared 4 strategies: SOC, MRI + AI, and 2 CT + AI models (with or without image embeddings). The model reported total costs, quality-adjusted life years gained, and incremental cost-effectiveness ratios, with 100 simulations per scenario. CT + AI Model 1 (without embeddings) had the lowest cost ($9079.65) and highest quality-adjusted life years gained (8.8207), outperforming SOC ($10,601.23, 8.8000), MRI + AI ($10,178.30, 8.8108), and CT + AI Model 2 ($9177.93, 8.8205). CT + AI Model 2 (with embeddings) was dominant in 28% of simulations. The costs of CT + AI Model imaging could increase from $265 to $2358.58, while remaining cost-effective incremental cost-effectiveness ratio ≤ willingness to pay) compared with the MRI + AI Model. CT-based AI models are cost-effective alternatives to SOC and MRI-based AI, offering better outcomes at lower cost. Their value stems from accurate risk stratification, enabling timely intervention and reducing overtreatment. These findings support the clinical and economic utility of AI in renal mass evaluation.
Reaction-diffusion circuits generate self-organized spatial patterns through local activation and long-range inhibition, but synthetic implementations in mammalian cells have been limited by the differential-diffusion requirement. Here, we introduce a novel architecture, juxtacrine activation with paracrine inhibition (JAPI), where the activator propagates through cell-cell contacts rather than diffusion. We demonstrate mathematically and numerically that JAPI accesses the same patterning regimes as classical diffusion-based circuits with one fewer free parameter. We then engineer compact synNotch-based JAPI circuits in mammalian fibroblasts and demonstrate their sufficiency for self-organized patterning through tunable, size-limited signal propagation. Functionalized to spatially control morphogen secretion, these circuits perturb feather bud formation on adjacent embryonic chicken epidermis. Finally, we develop a library-based approach to explore coupled, dual-JAPI circuits with tunable cross-inhibition, enabling programmable interactions between patterns and access to a broad morphospace of spatial states. Together, JAPI provides a compact, modular platform for programming self-organized multicellular patterning.
Advances in neural recording, real-time decoding and bioelectronic stimulation have enabled a new class of systems that re-establish functional communication across disrupted neural pathways. In the context of paralysis, these neural bypass interfaces link upstream neural intent to effector activation downstream of the lesion, effectively circumventing sites of injury within the nervous system to restore volitional movement. In this review, we define neural bypass interfaces as an emerging category of bioelectronic medicine, distinct from conventional brain-computer interfaces and neuromodulation technologies. We first outline key neurophysiological and systems-level considerations underlying bypass design, before tracing their evolution from bench to bedside. We then focus on the clinical translation challenges that govern real-world deployment, specifically signal stability and fidelity, stimulation performance, decoding robustness, closed-loop integration and long-term implant viability. Importantly, this review highlights two emerging directions that may shape the next generation of neural bypasses: the use of the spinal cord itself as a source of neural intent, and the development of bidirectional bypasses integrating sensory feedback to enable more adaptive, physiologically aligned control. Ultimately, neural bypasses may go beyond simply restoring movement to drive biological recovery, redefining neurorestorative therapies for paralysis.
Classifying vehicles as travelling on a high-speed arterial road or a low-speed urban road is important for Intelligent Transportation System (ITS), especially for applications like GNSS-based tolling. However, this task still poses a significant challenge. The traditional algorithms like Geometric, Hidden Markov Models (HMMs), and Kalman Filters fail in urban areas where GPS signals are poor, signal reflection which creates errors that confuse these algorithms. This paper proposes a novel hybrid framework that combines an attention-enhanced Recurrent Neural Network (RNN) which is trained to learn the driving or behavioral patterns like acceleration, turns on a highway vs. a service road. The RNN's output is integrated into a Hidden Markov Model (HMM), which ensures that the final path is valid. The proposed model is evaluated using the GeoLife GPS trajectory dataset. The Bi-LSTM component achieves 99.37% accuracy and 0.9909 highway F1. HMM post-processing with topology-aware spatial emissions reduces physically impossible singleton road-type transitions by 9.4%, improving sequence coherence for real-world ITS deployment. Stratified OSM validation on 250 GPS points confirms 98.4% label agreement with ground-truth road-type annotations, and degraded GPS conditions are simulated via Gaussian and multipath noise to reflect urban-canyon environments.
Glaucoma is a progressive optic nerve degenerative disease that often leads to blindness. Local inflammatory responses in the retina and optic nerve are implicated in the pathology of glaucoma. In addition, microbial populations in other parts of the body have been linked to glaucoma. To explore the relationship between oral health and glaucoma we queried the FinnGen database (Risteys 10.0) to assess whether poor oral health carries an increased risk of subsequently developing primary open angle glaucoma (POAG). In a separate study, we also collected mouthwash samples and administered a questionnaire relating to oral health status to a cohort of participants enrolled in Diagnostic Innovations in Glaucoma Study (DIGS) that included 107 participants with glaucoma and 19 healthy non-glaucomatous controls. 16S sequencing was performed to characterize the number of bacteria species and total bacteria count of the samples. A significant association between having dental conditions affecting the teeth, gingiva, or periodontium and developing glaucoma after 1 year, 1-5 years, 5-15 years and cumulatively was detected in the FinnGen data, a population of 429,209 with at least 153,661 having a dental condition and 10,687 having POAG. Among the cohort of the DIGS ancillary study, the total bacterial count of the glaucoma group was significantly higher compared to that of controls (Mean ± SD: 1.7 ± 1.4 and 0.9 ± 0.6, respectively, p < 0.03, two-sample t-test), while the species richness was significantly lower in glaucoma subjects compared to controls (p < 0.0005, Wilcoxon rank sum test). While the top taxa ordered by total abundance were similar between the two groups, mostly organisms associated with the commensal oral microbiome, there were some taxa linked with periodontal disease that were associated with glaucoma cases. However, the study was underpowered for the differences in top taxa between the glaucoma and non-glaucomatous control groups to achieve statistical significance (< 0.05) after adjusting for multiple comparison testing. A different bacterial abundance profile was detected between cases and controls by stepwise linear discriminant analysis. Inclusion of sleep apnea and the presence of cardiovascular disease as covariates in the analysis models did not significantly affect the results. Answers to the questionnaire about oral health and oral/dental history did not show a statistically significant difference between the two groups. The above findings suggest a potential link between oral health and glaucoma that may warrant further investigation.
Depression is associated with a higher risk for developing Alzheimer's Disease (AD) [1], but the mechanisms that underlie the complex relationship between depression and AD remain largely elusive. The hippocampus is a region of the brain that is commonly affected by both AD and depression and serves as a focal point for our study. We aim to understand the relationship of 1) depression and antidepressant medications to hippocampal subfield volume during normal aging, and 2) whether AD risk, as measured by amyloid and tau pathology and APOE4 status, impacts these relationships. We studied 2009 ethno-racially diverse cognitively unimpaired older adults aged 50 to 90 years who either had depression (n = 630) or were not depressed (n = 1379). Participants with depression were further stratified by antidepressant medication usage. High-resolution MRI scans were used to calculate hippocampal subfield volumes that included the CA1, the subiculum, and a composite region that included the CA2, CA3, and the dentate gyrus (CA23DG). Having depression was associated with a smaller CA23DG, independent of amyloid and tau pathology in the brain. Within the subgroup of participants with depression, those who used antidepressant medications had smaller CA1 and CA23DG volumes than those who did not use these medications.
Iris and ciliary body melanomas are rare but potentially life-threatening tumors that are difficult to distinguish from benign nevi. Earlier diagnosis improves outcomes, but reliable clinical indicators are limited. Glaucoma has been observed in conjunction with iris or ciliary body melanoma, but its prevalence and clinical significance at a population level are not well established. Our objective was to determine the prevalence of glaucoma in iris and ciliary body melanoma compared with nevus and to assess whether unilateral glaucoma may serve as a diagnostic indicator of melanoma. This retrospective cohort study used the IBM MarketScan Research Database, which contains de-identified longitudinal healthcare claims data from commercially insured patients in the USA. Patients with iris or ciliary body melanoma or nevus were identified by International Classification of Diseases, Tenth Revision (ICD-10) diagnostic codes. Outcomes included the prevalence of glaucoma prior to treatment in melanoma versus nevus, age-stratified prevalence of glaucoma, and rates of glaucoma surgery. Odds ratios (ORs) with 95% CIs were calculated to compare groups. A total of 17,978 patients were included (112 with melanoma, 17,866 with nevus). Glaucoma prevalence prior to treatment was significantly higher in the melanoma cohort than nevus cohort (11.6% vs. 1.3%; OR: 10.1; 95% CI: 5.1-18.4; p < 0.001). This pattern persisted across all age groups, with the greatest relative difference observed in patients aged 20-39 years (10.9% vs. 0.7%; OR: 14.2; 95% CI: 1.5-63.0; p = 0.01). Among patients with glaucoma, surgery was required more frequently in the melanoma cohort (18.9%) than nevus cohort (15.0%). Unilateral glaucoma is significantly more common in eyes with iris and ciliary body melanoma than in those with iris and ciliary body nevus in this claims-based cohort, and melanoma patients are more likely to require surgical management. Unilateral glaucoma may serve as an important early clinical indicator of melanoma, although further clinical correlation is necessary. Incorporating glaucoma status into diagnostic criteria could improve recognition, prompt referral and biopsy, and reduce delays in treatment.