Molecular Biology of the Cell

Aim: Cross-sectional summaries of periodontitis based on clinical attachment loss (CAL) are, by definition, conditioned on surviving teeth. Because the most severely affected teeth are more likely to have been lost, these measures may underestimate cumulative disease burden and show an artificial flattening (attenuation) of severity with age. We hypothesised that measures more sensitive to severe attachment loss would show greater attenuation at older ages than measures defined across a broader range of sites. Materials and Methods: Using nationally representative data from adults aged 30+ years in NHANES 2009-2014, we examined age-specific trajectories across multiple continuous measures of periodontal severity and assessed whether divergence between measures followed the pattern predicted under severity-dependent tooth loss. Results: The proportion of observable sites declined from 93% at ages 30-34 to 68% at 80+ years, establishing the structural basis for the divergence observed across severity measures. All severity measures showed nonlinear attenuation with age, with distortion increasing with severity threshold. Higher-threshold measures exhibited the greatest attenuation, while lower-threshold measures showed more stable trajectories. Conclusions: Cross-sectional summaries of periodontitis reflect disease among surviving teeth rather than cumulative damage across teeth originally at risk. Attenuation at older ages is consistent with depletion of the most severely affected teeth rather than biological slowing. Distortion varies by measure, with higher-threshold and mean-based indices most affected, whereas the CAL 3+ mm threshold provides a more stable basis for age comparisons.

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 [≥]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: 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.

Background: Chronic rhinosinusitis (CRS) is a heterogeneous inflammatory airway disease associated with impaired mucociliary clearance and persistent inflammation. While prior work has focused on inflammatory and molecular pathways, the physicochemical properties of mucus itself remain poorly characterized. This study aimed to define compositional and biophysical features of CRS mucus that may contribute to dysfunction. Methods: A prospective cross-sectional study was conducted in 15 adults undergoing endoscopic sinus surgery (11 CRS, 4 controls). Mucus was collected from the middle meatus. Hydration was measured by lyophilization. Ionic composition was quantified using mass spectrometry. Viscoelasticity was assessed via oscillatory shear rheology. Total protein, total carbohydrate, sialic acid (Sia) and fucose (Fuc) content were quantified using enzymatic and chemical assays. Statistical comparisons were performed using nonparametric tests. Results: CRS mucus exhibited significantly higher Ca2+; and Mg2+; concentrations (approximately two-fold; p<0.05) and increased variability in hydration and ion content compared to controls. Rheology showed greater heterogeneity and a non-significant trend toward increased viscoelasticity in CRS. Total protein and carbohydrate content were not significantly different; however, the carbohydrate-to-protein ratio was significantly reduced in CRS (p=0.04). Sia content and Sia-to-carbohydrate ratio were significantly elevated in CRS (p=0.04 and p=0.002), particularly in CRS with nasal polyps. Fuc content did not differ between groups. Conclusions: CRS mucus demonstrates coordinated alterations in ionic composition and glycosylation, characterized by increased cation content, hypersialylation, and reduced carbohydrate-to-protein ratios. These changes may contribute to altered mucus properties and impaired mucociliary clearance, highlighting mucus composition as a potential therapeutic target in CRS.

Background: Black and Latino adults in the United States experience a disproportionate burden of cardiometabolic conditions due to interacting behavioral, social, and structural drivers of health. Less is known about the impact of integrating digital health tools into CHW-led interventions to improve cardiometabolic health. This trial evaluates a multilevel community-digital health promotion model delivered by CHWs to improve service utilization, health behaviors and cardiometabolic health among Black and Latino adults. Methods: This community-partnered trial uses a randomized delayed-control group with a phased recruitment design. Four cohorts (N = 664) are enrolled through three community-based organizations (CBOs). Eligible participants are 18 years who self-identify as Black or Latino, and have prediabetes/diabetes, hypertension, or overweight/obesity. Participants are allocated to either (1) a multilevel intervention consisting of CBO and CHW capacity building combined with individualized CHW-led lifestyle coaching and group activities supported by digital tools, or (2) a delayed control group receiving SMS-only cardiometabolic health education. Data collected at baseline, 6, 9, and 18 months include surveys and health metrics. Qualitative data are collected from participants and community partners to assess intervention acceptability, implementation facilitators and barriers, and sustainability. Results: The primary outcome is health service utilization at 6 and 9 months. Secondary outcomes include health behaviors, health metrics, and social determinants of health. Sustainability of health behaviors and health metrics is assessed at 18 months. Conclusions: Findings will provide evidence to inform scalable, sustainable community-digital health models for CHW-supported cardiometabolic health interventions in underserved communities.

Background: Frontotemporal dementia (FTD) lacks widely accessible disease-specific biomarkers. Optical coherence tomography (OCT) and OCT angiography (OCTA) may provide non-invasive measures of retinal changes associated with neurodegeneration. We conducted a systematic review and meta-analysis evaluating retinal biomarkers in FTD compared with Alzheimer disease (AD) and controls. Methods: A systematic search of PubMed and Embase was conducted through April 25, 2026 according to PRISMA guidelines. Studies evaluating OCT/OCTA biomarkers in FTD with comparator groups were included. Inverse weighted random-effects models, publication bias assessments, and meta-regressions were performed. Results: Ten studies involving 139 individuals with FTD, 87 with AD, 29 with mild cognitive impairment, 14 with TDP-43 proteinopathy, 5 with tauopathy, and 255 controls were included in the systematic review; five studies were eligible for meta-analysis. Compared with AD, individuals with FTD demonstrated significantly thinner retinal nerve fiber layer (RNFL) thickness (SMD = -0.61, 95% CI -0.98, -0.24). Compared with controls, individuals with FTD exhibited significantly thinner ganglion cell layer-inner plexiform layer (GCL-IPL) thickness (SMD = -0.55, 95% CI -1.02, -0.08), whereas pooled analyses across multiple retinal biomarkers were non-significant (SMD = -0.19, 95% CI -0.52, 0.14). RNFL thickness correlated negatively with female % in FTD and positively with age in both AD and controls. Conclusions: Individuals with FTD exhibit lower RNFL thickness than AD and lower GCL-IPL thickness than controls, suggesting retinal alterations may reflect neurodegeneration. However, larger longitudinal studies with standardized OCT/OCTA protocols are needed to determine the diagnostic and prognostic utility of retinal biomarkers in FTD

ABSTRACT OBJECTIVE: We performed a systematic review and meta-analysis (SRMA) of post-surgical outcomes, comparing chlorhexidine gluconate (CHG) versus povidone iodine (PI) for vaginal antisepsis of major gynecologic procedures. DATA SOURCES: Ovid Medline, Embase, Scopus, Embase, Cochrane, and Clinicaltrials.gov were searched between 1986 and December 2023, for studies comparing CHG with PI for vaginal antisepsis of major gynecologic operations. STUDY ELIGIBILITY CRITERIA: We included Randomized Controlled Trials (RCTs) and non-RCTs comparing CHG to PI for vaginal antisepsis of major gynecologic operations. The primary outcome was surgical site infections (SSIs) and the secondary outcome was urinary tract infections (UTIs) and vaginal irritation. METHODS: Summary estimates were calculated by fixed effects models when I2 [&le;] 25% and by random effects models when I2 > 25%. Statistical analysis was performed using RevMan 5.4.1. The protocol for this systematic review was registered on PROSPERO (ID CRD42022378101). RESULTS: Nine studies met the inclusion criteria, four of which were randomized controlled trials (RCTs). 9538 patients were included, 4300 (45%) of whom were allocated to CHG and 5238 (55%) to PI. No statistically significant difference in SSI incidence was found for vaginal antisepsis with CHG versus PI in pooled analyses (n= 9538 patients; RR 1.20; 95% CI 0.92-1.57; I2 =0%). In contrast, a significantly higher risk of UTIs was observed for vaginal antisepsis with CHG than with PI (n=6061 patients; RR 1.48 95% CI 1.03-2.14; I2 = 0%). CONCLUSION: In our SRMA, there were no significant differences in SSI risk when either CHG or PI was utilized for antiseptic vaginal preparation. Interestingly, vaginal antisepsis with PI was associated with a lower incidence of post-operative UTIs following major gynecologic surgery. Our findings support current guidelines that form of vaginal antisepsis can be used for SSI prevention. They also suggest that PI may result in fewer postoperative UTIs but further randomized studies are needed to support these findings. Key words: surgical site infection, surgical wound infection, urinary tract infection, urogynecologic surgery, Chlorhexidine, Povidone Iodine, surgical antiseptic,

Background Artificial intelligence-enhanced electrocardiography (AI-ECG) enables scalable, low-cost cardiac dysfunction screening, but existing models are annotation-intensive and predominantly adult-derived, leaving paediatric generalizability uncertain. Paediatric cohorts exhibit highly variable cardiac morphology and function compared to adults, which may be useful for learning generalizable AI-ECG models. Methods We pretrained ECG-Fyler on a predominantly paediatric, all-age cohort at Boston Children's Hospital (1992-2023), annotated with a cardiology-specific coding system (Fyler codes), and evaluated it on assessments from echocardiography (echo) and cardiac magnetic resonance (CMR) studies. We validated on an external adult cohort from Columbia University Irving Medical Center. Performance was benchmarked against several AI-ECG foundation models by AUROC across age groups, lesion types, and limited-data scenarios. Findings The pretraining cohort comprised 782,138 ECGs from 255,271 patients (median age: 10.9 years, IQR: [2.8-16.8]). Internal evaluation included 178,495 ECG-echo pairs (median age: 10.9 [3.7-17.0]) and 8,584 ECG-CMR pairs (median age: 20.7 [15.6-29.6]). External validation included 82,543 ECG-echo pairs from adults (median age: 64.0 [52.0-74.0]). ECG-Fyler improved AUROC across biventricular dysfunction and dilation tasks, with the largest gains in low-data settings. In internal validation, ECG-Fyler detected low left ventricular ejection fraction (LVEF [&le;] 40%) from only 100 fine-tuning samples (AUROC: 0.80, 95% CI: [0.78-0.80]), outperforming other models (AUROC < 0.65) and improving with additional fine-tuning (AUROC: 0.94 [0.93-0.94]). Similar improvements were observed for CMR-derived LVEF, RVEF, and ventricular dilation. In external validation on adults, ECG-Fyler exhibited an AUROC of 0.83 (CI: [0.82-0.85]) for LVEF [&le;] 40%. After fine-tuning on less than 10% of external data, LVEF [&le;] 45% performance (AUROC: 0.87 [0.86-0.88]) outperformed a fully trained, site-specific prior model (AUROC: 0.85 [0.84-0.87]). Interpretation Pretraining on richly annotated, paediatric-dominant ECGs yields models that transfer efficiently across institutions and ages, supporting AI-ECG screening and triage when labels or imaging access are limited. Funding National Institutes of Health (R01LM012973); Kostin Innovation Fund, Boston Children's Hospital

The Epic Sepsis Model version 2 (ESMv2) is a prediction model embedded into the electronic medical record used to warn clinicians which hospitalized patients are at risk for sepsis. We conducted a retrospective cohort study of 31,951 hospitalizations of 25,760 patients to compare analyses conducted at the commonly used patient-level (where a maximum prediction prior to the onset of sepsis is used to measure performance) vs novel prediction-level (where each prediction is used to measure performance). Sepsis, defined by the Sepsis 3 criteria occurred during 1,049 hospitalizations (3.3%). Patient-level analyses suggested excellent discrimination AUC 0.86; [IQR 0.85, 0.87], whereas prediction-level analyses demonstrated lower performance AUC 0.62; [IQR 0.57, 0.65]. Low estimates of the positive predictive value (14.5% at the patient level vs 4% at the prediction level) imply a high number of false alerts. Common evaluation approaches may overstate the performance of dynamic prediction models and mislead clinical decision-making.

Background: Despite the success of combination antiretroviral therapy (cART), vascular comorbidities, including cerebrovascular disease, are more prominent in people living with HIV (PLWH) compared to people without HIV (PWOH). However, quantitative assessments of cerebrovascular morphometry and their associations with cognitive outcomes in the context of HIV are still limited. In this study, we explore this missing link. Methods: Magnetic Resonance Angiography (MRA) data, blood markers, and neurocognitive assessments were collected from 73 PWOH subjects (male: 57, female: 16; age: 53 {+/-} 16) and 99 PLWH subjects (male: 66, female: 30, age: 53 {+/-} 11). Vessel morphometric features were quantified using intraCranial Artery Feature Extraction (iCafe) to investigate associations between vessel morphometry, markers of monocytes, endothelial cell activation, and cognitive performance. Results: HIV status predicted a lower total number of branches ({beta} = -0.224, p = 0.001, d = -0.517) and shorter total distal length ({beta} = -0.173, p = 0.021, d = -0.370) with a moderate effect size. Total branch number was found to be negatively associated with plasma levels of monocyte markers (sCD14: r = -0.167, p = 0.033; sCD163: r = -0.157, p = 0.045) and positively correlated with white matter cerebral blood flow (r = 0.550; p [&le;] 0.05). HIV status was the strongest predictor of overall cognitive performance in ANCOVA model ({beta} = -0.219, p = 0.006, d = -0.453). Conclusions: Our results suggest that cognitive impairment in PLWH is associated with vessel morphology metrics. Monocyte immune activation may contribute to changes in vessel morphology.

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 Atrial fibrillation (AF) is a leading cause of stroke, cardiovascular morbidity, and mortality. Atrial myopathy, characterized by progressive metabolic, electrical, and structural changes, creates the arrhythmogenic substrate that drives AF. Defining the key drivers of atrial myopathic processes is essential for targeted therapies that can mitigate AF progression. Here we explore how reduced ERBB4 expression contributes to the development of left atrial myopathy. Methods We analyzed the Cleveland Clinic Biobank to compare left atrial ERBB4 levels in patients grouped by AF diagnosis. To investigate the impact of reduced ERBB4 levels on atrial tissue substrate, we created mouse models of cardiac-specific Erbb4 deficiency using Mlc2a (myosin light chain 2a)-Cre. Comprehensive physiological assessments were performed. Transcriptomic analyses of the left atrium were performed in an Erbb4 haploinsufficient mouse model and compared with human atrial datasets. Molecular validation of key dysregulated pathways was performed. Results We found that left atrial ERBB4 levels are reduced in patients with AF. Adult cardiomyocyte-specific Erbb4 heterozygous (Erbb4fl/+;Mlc2a-Cre) mice exhibited prolonged P-wave duration in the absence of ventricular dysfunction. Left atrial transcriptomic analysis in Erbb4 haploinsufficient mice showed upregulation of pathways related to fibrosis, apoptosis, and coagulation, and downregulation of pathways related to fatty acid metabolism and mitochondrial function, mirroring changes observed in pressure overload mouse models. A cross-species transcriptomic comparison revealed significant overlap between ERBB4-correlated gene expression and functional pathways in adult human atria and mice with Erbb4 haploinsufficiency. Validating the transcriptomic data, protein and functional assays demonstrated increased fibrosis, apoptosis, and oxidative stress in the mutant left atrial tissue. Conclusion Left atrial ERBB4 levels are reduced in AF patients. A mouse model of Erbb4 deficiency and human atrial transcriptomic analyses highlight a role for ERBB4 in supporting normal atrial metabolism while protecting against inflammation, apoptosis, and fibrosis.

Intro: Characteristics of the pulse wave transmitted through the carotid arteries are predictive of cognitive decline and cerebrovascular health in humans. This study aimed to identify risk factor trajectories in childhood, adolescence and early adulthood that are associated with forward compression wave intensity (FCWI) in the common carotid artery in adults aged 28 years. Methods: Systolic blood pressure (SBP), body mass index (BMI) and fasting blood glucose (FBG) measured at multiple time-points when participants were aged between 8-20 years were included in a trajectory analysis. At age 28 years, FCWI was measured in 402 (M=206, F=196) participants who underwent a Duplex ultrasound assessment of the common carotid artery. Statistical analysis assessed differences in FCWI between each trajectory group for males and females separately. Results: In males, four trajectory groups were identified for BMI, three for SBP, and two for FBG. In females, three trajectory groups were identified for BMI, SBP, and FG. In males, having higher BMI (P=0.006), SBP (P=0.021) and FBG (P=0.002) from ages 8-20 years was associated with greater FCWI at age 28 years. In females, no associations were found between FCWI at age 28-years and trajectory groups for BMI (P=0.185), SBP (P=0.289) or FBG (P=0.070). Conclusion: Having high BMI, SBP and FBG throughout childhood, adolescence and early adulthood was associated with higher FCWI in the carotid artery at age 28 years in males, but not females. This may have a direct impact on the etiology of cognitive decline and cerebrovascular disease in later life.

Lead is a toxic metal ubiquitous in our environment. While dramatic reductions in lead sources have paralleled equivalent decreases in lead-poisoning rates, chronic lead exposure remains a critical public health concern. Childhood lead exposure (at its lowest levels) is liked to changes in cognitive development but less is known about lead's effects on children's brain structure, especially as a result of in utero exposure. We measured prenatal and early-postnatal lead exposure in shed deciduous teeth of 448 9- and 10-year-old children (from 20 United States cities) and linked those lead levels to childhood brain structure, cognition/behavior, and neighborhood- and family-level socioeconomic characteristics. Here we show negative associations between tooth-lead levels and the thickness of the brain's cortex, particularly in regions linked to language processing. With increasing tooth-lead levels, children of lower-income (versus higher-income) families showed steeper declines in receptive vocabulary. Caregiver-reported behavioral problems exhibited similar associations. With in utero exposure linked to adverse neurodevelopmental outcomes (well before lead exposure and its risks are evaluated by healthcare professionals), prenatal screening of maternal lead levels/exposure, coupled with recommended strategies to reduce its placental transmission, may help reduce lead's effects on future generations.

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.

Hybrid mechanistic-statistical models offer interpretability and adaptability for short-term seasonal epidemic forecasting, but it remains unclear whether their accuracy depends more on increased biological complexity or on the assimilation of richer data. Using eight retrospective influenza seasons in North Carolina, we evaluate whether training on historical data and assimilating auxiliary emergency department (ED) visit data improves four-week-ahead hospital admission forecasts more than adding biological complexity (multi-subtype structure and cross-season immunity). Hierarchical Bayesian training on historical data improves accuracy by 22.4 % (95 % CI: 16.4-28.1 %), and inclusion of ED visit data yields a further 5.3 % (95 % CI: 3.0-7.6 %) improvement, whereas added biological complexity produces diminishing or null gains. We further observe a substitution effect in which ED visit data partially compensates for omitted biological structure. We deployed a simplified model variant in the 2025-2026 CDC FluSight Challenge and ranked among the top ensemble performers, supporting the robustness of Bayesian hierarchical training in real time. Together, these findings indicate that short-term forecast accuracy is driven more by historical learning and assimilating auxiliary signals than by biological fidelity, with implications for how forecasting systems should balance mechanistic complexity.

Background: Non-communicable diseases (NCDs) represent a critical public health challenge in Kenya, responsible for over 50% of inpatient admissions and 40% of deaths. While digital health tools and artificial intelligence offer promising ways to improve prevention, diagnosis, and management, little is known about how these tools are perceived and used in practice. There is limited research exploring the views and lived experiences of young people in Kenya, who are a strategic priority for NCD prevention because behavioral risk factors are established in this window, and for Community Health Providers (CHPs) who provide health services within the community. This study aims to address this gap by examining the perspectives of the burden of non-communicable diseases and the potential role of digital health technologies, including artificial intelligence, for preventing and managing these conditions in these specific populations. Methods: A qualitative research design using focus group discussions (FGDs) was employed in Nairobi (urban) and Busia (rural) counties between March and July 2024. Eight FGDs were conducted with 60 participants purposively sampled from three stakeholder groups: community health promoters (CHPs), healthcare workers (HCWs), and youth aged 18-35 years. A semi-structured guide, co-developed with a Community Advisory Board, explored beliefs about NCDs, health-seeking behaviors, lifestyle practices, and attitudes toward digital health and AI. Audio recordings were transcribed verbatim, translated where necessary, and analyzed thematically using grounded theory principles on NVivo software (v12). Results: Six consolidated themes emerged: (1) understanding of NCDs and perceived risk; (2) barriers to NCD prevention and care; (3) the role of CHPs; (4) adoption of AI tools for NCD management; (5) trust, ethics and access concerns; and (6) community-driven recommendations for AI integration. Significant barriers including stigma, economic constraints, and barriers to care were documented alongside enthusiasm for AI tools among youth and CHPs in both urban and rural areas. Conclusion: This study shows that AI tools are being used for NCD prevention and management through spontaneous community adoption. However, it emphasizes the need for culturally relevant, equitable, and community-driven solutions. Effective scaling requires the identification and bridging of digital literacy gaps, the establishment of affordable infrastructure, the protection of data privacy, and the integration of artificial intelligence tools into existing community health frameworks. This process should involve the collaboration of trusted intermediaries, such as CHPs and community leaders, to ensure successful outcomes. Future initiatives should prioritize participatory design, policy frameworks for ethical governance, and targeted capacity building to enhance acceptance and sustainability of digital health innovations in low- and middle-income country settings.

Background: Thalamic low-intensity transcranial focused ultrasound (tFUS) has shown promise for increasing behavioral responsiveness in disorders of consciousness (DOC), but no study has examined whether it can causally modulate the well-validated behavioral, electrophysiological, and metabolic biomarkers of DOC impairment. Methods: Sixteen adult patients (44% Female; Age, M=37.81, SD=15.97) with a chronic DOC (Time Since Injury, M=3.39, SD=1.94 years) secondary to severe brain injury (TBI 44%, non-TBI 56%) underwent a 10-day inpatient, longitudinal, single-arm, open-label protocol. tFUS was delivered in a single session targeting the left central thalamus. Well-known behavioral (CRS-R), electrophysiological (EEG {delta}/{beta} ratio), metabolic (18F-FDG PET), and polysomnographic outcomes were assessed at baseline and after sonication. Results: The maximum CRS-R total score increased significantly following tFUS compared to baseline (M=13.27 vs. M=10.33; t(14)=7.407, p<0.001, d=1.913), as did the global EEG {delta}/{beta} ratio (N=14; W=17, p=0.025, r=0.68), with the degree of frontal slowing positively predicting behavioral gains ({tau}b=0.51, p=0.016). Glucose metabolism decreased bilaterally in thalamus and frontal, temporal, and parietal cortices at both post-tFUS timepoints compared to baseline. Finally, N2 sleep increased by 33% following tFUS (N=11; t(10)=2.386, p=0.038, d=0.72), though this did not survive correction. No severe adverse events were observed. Conclusion: Thalamic tFUS can causally modulate well-validated behavioral, electrophysiological, and metabolic biomarkers of DOC. The convergent inhibitory signature across these measures suggests a thalamocortical reset mechanism, complementing existing excitatory neuromodulation approaches and providing the mechanistic foundation for a large, randomized sham-controlled trial.

Objective: Hispanic/Latinx populations in the U.S. experience higher rates of chronic disease linked to physical inactivity, yet digital health interventions remain largely inaccessible to more than 16 million Hispanic/Latinx adults with limited English proficiency. While large language models (LLMs) offer scalable personalization, their use in non-English behavioral coaching is unexplored. This study introduces MHC-Coach-ES, a Spanish-language LLM fine-tuned on the Transtheoretical Model (TTM) of behavior change. Materials and Methods: We fine-tuned Llama 3-70B-Instruct using a two-stage pipeline. First, the model was adapted to Spanish health and motivational language using a 2.21-million-token corpus. Second, it was instruction-tuned on 3,268 translated human written messages to align the model with the Transtheoretical Model (TTM) of Behavioral Change. We compared MHC-Coach-ES with Llama 3-70B-Instruct and translated human-expert messages using a forced-choice preference survey (N = 77) and blinded expert review (N = 2). Results: Spanish-speaking participants significantly preferred MHC-Coach-ES messages over translated human-expert messages (81% preference, P<0.001). Linguistic analysis showed that MHC-Coach-ES produced more temporally anchored messages than the base model (65% vs. 20%), while maintaining readability. In blinded evaluation, clinical experts rated MHC-Coach-ES higher for alignment with Transtheoretical Model stages than human-expert messages (4.83 vs. 4.38 out of 5). The base model also outperformed translated expert messages across preference and expert ratings. Conclusions: Generative AI can operationalize behavioral science frameworks in Spanish, offering a scalable approach to reducing health disparities. The strong performance of both MHC-Coach-ES and the base model highlights the promise of generative and personalized approaches over translation-based localization for theory-driven behavioral interventions.

Neuroimaging based pain decoding faces two underappreciated challenges: between subject variability that prevents classifiers from generalizing across patients, and within session cross validation designs that inflate reported accuracy by conflating within person and between person variance. Here we address both using portable functional near infrared spectroscopy (fNIRS) during pharmacologically verified local nerve anesthesia. Twentyfive patients with clinically painful teeth underwent 36 channel bilateral fNIRS during percussion before ("Pre") and after ("Post") local nerve anesthesia. In 13 block-success patients, a paired Pre versus Post comparison with healthy tooth control identified three temporal hemodynamic response function (HRF) features (late slope, mean first derivative, and baseline normalized amplitude) whose analgesia interaction effects (d = 0.63 to 0.79) exceeded that of raw general linear model (GLM) amplitude (d = 0.56), with a significant difference-in-differences interaction (p = 0.011). Per-patient calibration with these features yielded leave one subject out (LOSO) AUC = 0.68 to 0.76 for nonlinear classifiers (permutation p = 0.002), with HbO-specific feature selection achieving the best performance (RF AUC = 0.760); a healthy tooth negative control was non-significant. End to end deep learning on raw time series (CNN LSTM AUC = 0.719) was competitive with feature based classifiers, while linear models did not reach significance. Critically, head to head comparison of within-session CV and LOSO on the same data revealed mean inflation of +0.13 AUC across all model types, including deep learning, demonstrating that high within session accuracy alone does not establish subject-independent validity. Exploratory analyses suggested complementary roles for oxyhemoglobin (HbO; within patient analgesia detection) and deoxyhemoglobin (HbR; cross patient information), and that trial to trial response variability may complement amplitude for cross patient pain detection. These results show that per patient calibration with temporal HRF features supports subject independent analgesic-state detection under strict LOSO evaluation, and that within-session validation (standard in the fNIRS pain- decoding literature) can substantially overestimate performance.