Journal of Orthopaedic Research

Background: Quadriceps strength and landing mechanics are two modifiable factors associated with anterior cruciate ligament (ACL) injury risk. Collecting detailed biomechanical data is an arduous task. Identifying a relationship using more easily measured variables, such as quadriceps strength, would offer value for athlete counseling and injury prevention programs. Although quadriceps weakness has been associated with altered landing strategies in ACL-reconstructed (ACLR) individuals, this relationship is less clear in healthy athletes. Purpose: To investigate the association between isokinetic quadriceps strength and peak knee flexion angle during a vertical drop jump in healthy adolescent athletes. Study Design: Secondary analysis of previously collected data. Methods: Healthy adolescent athletes had their dominant leg quadriceps strength measured using an isokinetic dynamometer at 60{degrees}/s from 0-90{degrees} of knee flexion. Landing mechanics were assessed during a vertical drop jump using three-dimensional motion capture synchronized with force plates. Pearson correlation was used to evaluate the association between quadriceps strength and peak knee flexion angle during landing, with statistical significance defined as p < .05. Results: There was a weak negative correlation between quadriceps strength and peak knee flexion angle (p = .017, R = -.22 [-.04, -.38]), suggesting that stronger athletes achieved greater knee flexion angles. Discussion: Greater quadriceps strength was associated with increased peak knee flexion angles during landing; however, the weak correlation suggests that strength explains only a small portion of the variability in landing mechanics. These findings deviate slightly from prior literature in healthy populations but are consistent with studies demonstrating that greater quadriceps strength is associated with achieving greater peak knee flexion in ACLR patients. Accordingly, quadriceps strengthening should remain a key component of multifactorial ACL injury prevention programs.

Background: Rheumatoid arthritis is a chronic inflammatory disorder in which exercise is increasingly recognized as an important component of long-term management. Yet, most reviews in this field evaluate the effects of single exercise modalities, while bibliometric studies primarily identify publication trends and research hotspots without showing whether highly visible themes also represent coherent and comparatively mature evidence domains. Methods: We searched the Web of Science Core Collection for publications on exercise interventions in rheumatoid arthritis from 2016 to 2025. CiteSpace (6.4.1) and VOSviewer (1.6.20) were used to analyze publication growth, collaboration networks, keyword co-occurrence, thematic clusters, and burst terms. We then applied structured content coding in Excel 2021 to classify exercise modalities, outcome domains, and mechanistic topics, and integrated these findings into a visual evidence-distribution profile. Results: Publication output increased from 16 studies in 2016 to 37 in 2025. The United States led in productivity, Karolinska Institutet was the most prolific institution, and Kitas, Duda, and Metsios were among the most influential authors. Keyword analyses identified a shift from function- and disease-focused themes toward quality of life, risk factors, and comprehensive management. The integrated analysis revealed an uneven evidence structure: aerobic and resistance training accounted for the most concentrated and recurrently studied exercise-outcome domains, whereas mind-body and water-based interventions formed visible but methodologically heterogeneous clusters. Newer modalities, including blood flow restriction training and high-intensity interval training, showed growing prominence but limited depth of evidence. Conclusion:Exercise research in rheumatoid arthritis has evolved toward broader and more patient-centered management targets, but the field remains imbalanced across intervention types and outcome domains. This study demonstrates the value of combining bibliometric mapping with structured content analysis to distinguish thematic visibility from evidentiary coherence in heterogeneous intervention fields and may offer a transferable analytical framework for research evaluation beyond rheumatoid arthritis. Keywords: Rheumatoid Arthritis; Exercise Intervention; Bibliometrics; Content Analysis; Rehabilitation

Abstract Purpose: MyotonPRO (MTP) and time-harmonic elastography (THE) are increasingly used to assess muscle mechanical properties, yet they operate on fundamentally different physical principles. MTP measures composite MTP stiffness (N/m) through surface oscillations, while THE quantifies intrinsic shear modulus (THE stiffness, kPa) via propagating shear waves. This study aimed at systematically compare MTP and THE measurements in the vastus lateralis muscle across different contraction intensities and examine how the skin layer and subcutaneous fat (SLSF) thickness influence their relationship. Methods: Twenty-six healthy adults (15 males, 11 females; age 25 [SD 4] years) underwent MTP and THE measurements of the vastus lateralis at rest and during isometric contractions at 15% and 30% maximal voluntary contraction (MVC). Effects of contraction intensities on tissue properties were assessed using univariate analyses of variance with repeated measures. Associations between the different outcomes of THE and MTP technologies were explored using Pearson's correlations and partial correlation coefficients separately for each contraction intensity with adjustment of the SLSF thickness of participants. Results: Both technologies detected contraction intensity-dependent stiffening across all outcomes (p < 0.001). THE stiffness increased from 5.3 [1.2] kPa at rest to 15.6 [6.1] kPa at 30% MVC; THE wave attenuation increased from 0.83 [0.19] to 1.42 [0.36] s/m while MTP stiffness increased from 337.3 [49.3] N/m at rest to 529.4 [160.7] N/m at 30% MVC. Correlations between modalities were weak and condition-dependent. THE wave attenuation did not significantly correlate with any MTP outcome across conditions. Conclusion: MTP and THE detect contraction-induced stiffening through fundamentally different physical mechanisms and should not be regarded as interchangeable. Their correlation is modest at rest and breaks down (or reverses) during active contraction, with subcutaneous fat as a key modifying factor. Clinical trial number: Not applicable.

Introduction Denosumab increases bone mineral density and reduces fracture risk in patients with osteoporosis. However, whether BMD response to denosumab differs by age, particularly during longer term treatment, remains unclear. This study investigated the association between baseline age and BMD gain during 3 years of denosumab treatment in patients with osteoporosis. Methods This retrospective study included patients with osteoporosis who were treated with denosumab. DXA-based BMD and bone turnover markers were followed for up to 3 years. Percent BMD gain from baseline, defined as %BMD gain, was evaluated. The longitudinal association between baseline age and %BMD gain was assessed using multivariable linear mixed-effects models for the lumbar spine and total hip. Analyses were performed in the treatment naive cohort and the overall cohort according to prior osteoporosis treatment status. Results A total of 255 patients were included in the analysis, of whom 110 had not received prior osteoporosis treatment. In multivariable linear mixed-effects models, older baseline age was associated with smaller lumbar spine %BMD gain in the treatment naive cohort at both 1 and 3 years. Each 1-year increase in age was associated with a 0.187 percentage-point lower lumbar spine %BMD gain at 1 year and a 0.293 percentage-point lower gain at 3 years (1 year: {beta} = -0.187, p = 0.006, 3 years: {beta} = -0.293, p = 0.031). In contrast, baseline age was not significantly associated with total hip %BMD gain in the treatment naive cohort (1 year: {beta} = -0.011, p = 0.826; 3 years: {beta} = 0.028, p = 0.727). In the overall cohort, baseline age was not significantly associated with %BMD gain at either the lumbar spine or total hip at 1 or 3 years (all p > 0.05). Conclusion Older baseline age was associated with a modestly smaller lumbar spine BMD gain in treatment naive patients, whereas no significant age-related association was observed at the total hip. In the overall cohort, age was not significantly associated with BMD gain at either site. These findings suggest that age may have a limited, site specific influence on BMD response to denosumab, particularly in treatment naive patients, and may support more individualized treatment planning in patients with osteoporosis.

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.

Background. A substantial minority (~20%) of patients fail to achieve meaningful pain reduction following surgery intended to relieve pain. Risk is elevated in patients with nociplastic pain features, but available self-report measures were not designed for pre-surgical screening. We aimed to develop a brief, data- driven screener for poor analgesic response to surgery. Methods. Participants were recruited from tertiary orthopedic and chronic pelvic pain clinics. Total hip arthroplasty participants had Kellgren-Lawrence grades III-IV with hip pain greater than or equal to 1 year; hysterectomy participants had chronic pelvic pain greater than or equal to 6 months. The primary outcome was a 50% reduction in worst pain at six months. Items were selected via elastic net regression with k-fold cross-validation from 68 candidates. Results. Of 428 participants (81% female; mean age 51), 35% failed to achieve a 50% pain reduction. The resulting 11-item screener - the GenerAlized sensory sensitivity for sUrGical rEsponsiveness (GAUGE) - comprises pain across seven body regions and four symptom items measuring interoception (nausea, numbness/tingling) and exteroception (sensitivity to sound, sensitivity to odors). GAUGE outperformed the Central Sensitization Inventory, Fibromyalgia Survey Criteria, and PainDETECT for predicting surgical non-response (RR 1.535, 95% CI 1.342-1.55; AUC 0.738; sensitivity 0.741, specificity 0.635) and for predicting Patient Global Impression of Change. In an independent validation cohort of 54 total knee arthroplasty patients, GAUGE outperformed the Fibromyalgia Survey Criteria in predicting pain severity at six-months. Conclusions. GAUGE is a data-driven, theoretically grounded screener for poor analgesic response to surgery, with potential utility for pre-surgical counseling and clinical trial enrichment.

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: 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

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.