Faculty Bibliography

BACKGROUND:Road injuries are a leading cause of mortality and morbidity worldwide. Years of international efforts have aimed to strengthen policy engagement, including the 2020 UN General Assembly's proclamation of the Second Decade of Action for Road Safety (2021-30), targeting a 50% reduction in road traffic deaths and serious injuries by 2030. The aim of this study is to provide estimates to monitor progress and identify intervention gaps. METHODS:As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2023, we estimated incidence, mortality, and morbidity of road injuries for 204 countries and territories from 1990 to 2023. Four road injury types and 47 nature-of-injury categories were examined. Morbidity and mortality data from clinical records, vital registration, and police reports were harmonised using meta-analytic techniques to ensure consistency and correct for systematic bias. Incidence was modelled with the meta-regression tool Disease Modelling-Meta-Regression version 2.1 and cause-specific mortality with the Cause of Death Ensemble model, both incorporating location-specific covariates to support interpolation. Years of life lived with disability (YLDs) were estimated from the prevalence and severity of the nature of road injury, and years of life lost (YLLs) from the number of cause-specific deaths multiplied by the standard life expectancy at the age of death. Disability-adjusted life-years (DALYs) were the sum of YLLs and YLDs. All metrics were calculated with 95% uncertainty intervals (UIs). FINDINGS/RESULTS:In 2023, there were 50·9 million (95% UI 46·1-56·1) road injury incident cases, 1·34 million (1·04-1·58) deaths, and 75·3 million (59·8-89·2) DALYs globally. Road injuries were the leading global cause of death among males aged 10-39 years. Between 1990 and 2023, age-standardised incidence decreased by 38·3% (95% UI 36·9-39·7) and mortality decreased by 32·3% (6·1-49·0), but progress varied widely by World Bank income group. Mortality in low-income countries (43·8 [95% UI 31·7-56·0] deaths per 100 000 population) was approximately six times higher than in high-income countries (7·5 [7·1-7·9] deaths per 100 000), despite the high-income countries showing the highest age-standardised incidence rates (858·1 [95% UI 781·9-947·1] cases per 100 000). In the past decade, many countries achieved notable reductions in road injuries, but others, including Ghana and the USA, saw increases. More severe injuries tended to occur in low-income and middle-income countries. INTERPRETATION/CONCLUSIONS:Although global incidence, mortality, and DALY rates from road injuries have declined, progress remains uneven, with pronounced disparities across income groups reflecting systemic inadequacies in infrastructure, vehicle standards, enforcement, and post-crash care. Strengthening emergency response, improving road design, enforcing safety measures, and adapting policies to the evolving demographics remain essential. FUNDING/BACKGROUND:Gates Foundation.

INTRODUCTION/BACKGROUND:Traumatic dental injuries (TDIs) require prompt and accurate guidance, yet little is known about how the prompting influences the quality of artificial intelligence (AI) chatbot responses in these situations. METHODS:Four dental trauma scenarios were developed by expert endodontists. For each scenario, a series of questions was posed to 4 AI chatbots (Claude Sonnet 3.5, Microsoft Copilot, GPT-4, Gemini Pro 2.5) using 2 approaches: unprompted layperson phrasing (n = 10) and endodontist's prompts referencing International Association of Dental Traumatology guidelines (n = 10). Responses were independently evaluated by 2 raters for validity, completeness, and relevance using a 5-point ordinal scale. RESULTS:Scores (n = 1920) of responses to endodontist questions were associated with significantly higher odds of receiving superior ratings across all 3 domains: validity (odds ratio [OR] = 1.82; 95% confidence interval [CI]: 1.35-2.38; P < .001); completeness (OR = 2.50; 95% CI: 1.85-3.33; P < .001) and relevance (OR = 2.94; 95% CI: 2.04-4.17; P < .001). When a threshold-based acceptability (score ≥4) was applied, responses to endodontist queries were more often acceptable in criterion-based as well as overall analyses (P < .05). CONCLUSIONS:The quality of AI chatbot guidance in TDIs is significantly associated with how questions are asked. While clinically structured prompts yield more reliable responses, most patients facing dental trauma are unlikely to formulate questions in this way. This gap highlights an important limitation of current AI chatbot applications in TDIs and underscores the need for caution when relying on these tools without professional input.

G protein-coupled receptors (GPCRs) and TRPV (transient receptor potential vanilloid) channels are crucial for signal transduction in physiological processes, including neurotransmission, pain, and itch. Downstream effectors of GPCR signaling can directly stimulate TRPV channels or enhance their sensitivity to stimuli, a process known as TRPV sensitization. Traditionally, GPCRs are activated at the cell surface by extracellular agonists, triggering signaling cascades. Recent evidence suggests GPCRs continue to signal from intracellular organelles. The human Mas-related G-protein coupled receptor X1 (MrGPRX1) is a GPCR expressed in primary sensory neurons involved in nociception and pruritus. Recent studies demonstrated how intracellular GPCR signaling regulates neuronal activity. However, there is no evidence characterizing MrGPRX1 trafficking or intracellular signaling. Herein, we characterized MrGPRX1 signaling within the endosomal network and its role in sensitizing TRPV1 channels to enhance itch signaling. Utilizing subcellular targeted biosensors, we demonstrated MrGPRX1 can traffic and signal from endosomes. Immunofluorescence analysis showed that MrGPRX1 internalizes following BAM8-22 stimulation. BRET assays revealed that MrGPRX1 activation induces Gα

Advances in regenerative dentistry have significantly improved the management of bone deficiencies present at, or resulting from, tooth extraction when implant placement or other prosthetic restoration is planned. The integration of biologic mediators, osteoconductive scaffolds, and resorbable barrier membranes has contributed to more predictable regenerative outcomes in appropriately selected cases. Growth factor-enhanced matrices may provide complementary biologic and structural properties that support site development. Xenografts offer long-term volumetric stability, while allografts demonstrate more active remodeling potential; when combined with biologic stimulation, these materials are intended to enhance angiogenesis, cellular recruitment, and graft incorporation. This article reviews the biologic mechanisms, clinical applications, and handling characteristics of these regenerative materials from clinical, radiographic, and histologic perspectives. When used individually or synergistically they support predictable hard- and soft-tissue regeneration, optimize implant site development, and enhance long-term implant stability and esthetic outcomes.

Systemic inflammation often accompanies obstructive sleep apnea (OSA), along with higher chances of heart-related metabolic issues. Although oral appliances serve as an accepted option instead of continuous positive airway pressure (CPAP), their influence on markers of systemic inflammation lacks clear evidence. The aim of this systematic review and meta-analysis was to assess changes in systemic inflammation markers following oral appliance therapy (OAT) among individuals diagnosed with OSA. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards, a systematic review and meta-analysis was conducted. Databases such as PubMed, Embase, Scopus, Web of Science, and Cochrane Library were examined. Studies assessing inflammatory markers before and after therapy among adults with OSA undergoing OAT formed part of the selection. For pooling results, reliance was made upon a random-effects framework. The present research was registered in PROSPERO with the ID CRD420261340212. From a total of 1,974 records screened, six studies qualified for inclusion in the qualitative analysis; among them, two contributed data for each outcome assessed through meta-analysis. Despite pooling outcomes across trials, no meaningful changes emerged in tumor necrosis factor-alpha levels (p = 0.536), nor in C-reactive protein (p = 0.061), or interleukin-6 concentrations (p = 0.230). Consistency between study findings showed minimal variation (I² = 0%). Despite improved clinical results in OSA, OAT shows minor impact on markers of systemic inflammation. To confirm these findings, broader trials with rigorous and uniform designs are needed.

OBJECTIVES/OBJECTIVE:Loneliness is a well-established longitudinal predictor of depression in later life, yet the mechanisms through which this association unfolds remain unclear. This study examined the direct and indirect effects of loneliness on subsequent depressive symptoms in late life, with particular emphasis on persistent loneliness over time. METHODS:Community-dwelling, older Israeli adults (N = 1216; M age = 81.1 years) completed annual assessments of loneliness and depressive symptoms over three years. Structural equation modelling was used to estimate autoregressive pathways, prospective effects of loneliness on subsequent depressive symptoms, and indirect effects operating through the persistent loneliness. Models were adjusted for age, sex, education, marital status, financial status, self-rated health, living arrangement, religiosity, and Holocaust survivor status. RESULTS:Loneliness and depressive symptoms both demonstrated substantial temporal stability over time. After adjusting for prior depressive symptoms and sociodemographic covariates, loneliness remained a significant prospective predictor of subsequent depressive symptoms. Decomposition of longitudinal effects, however, indicated that indirect pathways, transmitted through persistent loneliness, accounted for a greater proportion of the total longitudinal association than direct prospective effects. In fact, direct prospective effects were not statistically significant after accounting for indirect pathways. Baseline loneliness and depressive symptoms did not predict study attrition, supporting the robustness of the longitudinal findings. CONCLUSIONS:The association between loneliness and subsequent depressive symptoms in later life appears to operate primarily through persistent loneliness rather than through direct prospective effects. These findings suggest that chronic loneliness functions as a longitudinal pathway sustaining depressive symptoms over time. Interventions that mitigate loneliness in late life may also reduce depression among older adults.

BackgroundPosttraumatic stress disorder (PTSD) is common yet frequently underdiagnosed, in part due to barriers to systematic screening and the reliance on self-report instruments. Large language models (LLMs) have shown promise in extracting clinically relevant information from unstructured language, but their ability to infer item-level PTSD symptom severity from clinical interviews remains unclear.MethodsUsing the Distress Analysis Interview Corpus-Wizard of Oz (DAIC-WoZ), we analyzed 100 semi-structured clinical interview transcripts paired with item-level PTSD Checklist-Civilian Version (PCL-C) scores. Six LLMs (DeepSeek 3.1, Claude Sonnet 4, LLaMA 4 Scout, GPT-4o, GPT-5, and Gemini 2.5 Flash) used zero-shot prompting to predict all 17 PCL-C items. Performance was assessed for binary symptom endorsement (≥3 vs. < 3), 5-point Likert prediction, and DSM-IV symptom-cluster analyses using accuracy, F1 score, and Matthews correlation coefficient (MCC).ResultsFor binary prediction, Claude 4 achieved the highest mean accuracy (0.705; 95% CI, 0.681-0.728), followed by DeepSeek 3.1(0.699; 95% CI, 0.675-0.724) and Gemini 2.5 (0.698; 95% CI, 0.677-0.718). For Likert prediction, DeepSeek 3.1 performed best (accuracy = 0.438; 95% CI, 0.401-0.475), only modestly above the majority-class baseline (0.399; 95% CI, 0.355-0.443). Performance varied by symptom domain, with re-experiencing and hyperarousal symptoms generally predicted more accurately than avoidance/numbing symptoms. Across models, predicted item-level symptom patterns showed a meaningful alignment with observed PCL-C responses despite reduced accuracy in fine-grained severity estimation.ConclusionZero-shot LLMs' performance was insufficient for clinical application in predicting PTSD symptoms from semi-structured interview transcripts. While models showed some ability to capture overall symptom patterns, performance varied across domains and remained limited for fine-grained severity estimation. Given these constraints and the non-trauma-specific nature of the dataset, findings should be interpreted as preliminary, with only modest differences observed between models.Plain Language Summary TitleCan Artificial Intelligence Identify PTSD Symptoms from Conversations? A Study Using Clinical Interview TranscriptsPlain Language SummaryPost-traumatic stress disorder (PTSD) is a common mental health condition, but it is often missed in clinical settings. Screening usually relies on questionnaires that patients must complete themselves, which may not always happen due to time, stigma, or discomfort discussing trauma. Researchers are exploring whether artificial intelligence (AI) could help identify PTSD symptoms from conversations instead.In this study, we tested several advanced AI systems, known as large language models, to see if they could estimate PTSD symptoms based on written transcripts of clinical interviews. These interviews were not specifically designed to assess trauma, which makes the task more challenging but closer to real-world situations. We compared the AI predictions to participants' own questionnaire responses about their symptoms.We found that the AI models were somewhat able to recognize general patterns of PTSD symptoms, especially more visible ones like sleep problems or distressing dreams. However, they struggled with more internal or less obvious symptoms, such as avoidance or emotional numbness. Overall, their accuracy was moderate and not reliable enough for clinical use, particularly when trying to estimate how severe symptoms were.Importantly, differences between the AI models were small, and none performed well enough to replace existing screening methods. These findings suggest that while AI may have future potential as a supportive tool, it is not yet ready to be used for diagnosing or screening PTSD on its own.Further research using better data, improved methods, and real clinical settings is needed before this approach could be considered for practical use.

Somatic mutations rewire the ubiquitin-proteasome system (UPS) to support tumor growth, but the proteome-wide consequences of cancer-driver alterations on UPS composition remain incompletely understood. Using harmonized proteogenomic data from up to 11 CPTAC cohorts, we performed an integrated pan-cancer analysis of UPS protein dysregulation, prognostic associations, and mutation-driven remodeling. We show that mRNA poorly predicts UPS protein abundance, that a defined set of E3 ligases is recurrently dysregulated across cancers, and that somatic mutations (most strikingly TP53 loss) produce coherent UPS protein-quantitative trait locus (pQTL) signatures. Two case studies (UBR5 and TRIM28) illustrate orthogonal modes of UPS rewiring: a mutation-driven axis in which TP53-mutant tumors elevate UBR5 to support replication stress tolerance, and a lineage-driven axis in which TRIM28 engages tissue-restricted regulatory networks with opposing prognostic effects in glioblastoma versus head and neck cancer. Each axis exposes context-specific therapeutic vulnerabilities, including sensitivity to DNA damage response inhibitors (UBR5-high) and lineage-specific drug responses (TRIM28-high). Together, these analyses define a mechanistic framework for how cancer-driver mutations reshape proteostasis through the UPS and nominate mutation- and lineage-defined dependencies for precision degrader therapy. The harmonized pan-tissue atlas and the UbiDash interactive resource that underpin parts of this analysis are reported in our companion paper [1].

IMPORTANCE/UNASSIGNED:Ischemic heart disease (IHD), the leading cause of death in the US, is predominantly due to modifiable risk factors. Estimates of IHD mortality attributable to risk factors provide evidence for health policy decision-making. OBJECTIVE/UNASSIGNED:To estimate the burden of IHD death attributable to risk factors in the US from 1990-2023. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:The Global Burden of Disease Study 2023 (GBD 2023) used vital records and a broad set of epidemiologic data to estimate IHD death rates, risk factor exposure, and relative risk curves for risk-outcome pairs for 1990-2023 for the general population. Data analysis was performed from October 2024 to December 2025. EXPOSURE/UNASSIGNED:Twelve metabolic, behavioral, and environmental risk factors. MAIN OUTCOMES AND MEASURES/UNASSIGNED:The primary outcomes were IHD death rates per 100 000 persons, counts, and attributable risks from 1990-2023 by age, sex, and US state. Estimates include 95% uncertainty intervals (UI). IHD death rates were estimated using ensemble modeling methods. Risk exposures were estimated using bayesian meta-regression methods. Relative risks were estimated following the Burden of Proof framework. RESULTS/UNASSIGNED:In 2023, there were 473 000 IHD deaths (95% UI, 414 000-510 000) in the US, a decrease of 58.7% (95% UI, 56.8%-61.0%) in age-standardized rate since 1990. Between 2010 and 2023, there was a 19.0% decrease (95% UI, 15.0%-22.9%) for males and a 24.5% decrease (95% UI, 20.3%-29.7%) for females in IHD death rates. High systolic blood pressure (SBP), dietary risks, and high low-density lipoprotein cholesterol (LDL-C) were the leading risk factors for IHD deaths in 2023, accounting for 47.2% (95% UI, 36.4%-57.0%), 38.6% (95% UI, 17.2%-56.8%), and 28.5% (95% UI, 19.3%-39.6%) of IHD deaths, respectively. Increased exposure to several risk factors substantially increased their attributable burden for IHD deaths in 2023, with high fasting plasma glucose (FPG) increasing 38.8% (95% UI, 11.5%-81.1%) and high body mass index (BMI) increasing 54.5% (95% UI, 41.8%-66.3%) since 1990. Smoking and particulate matter pollution had the greatest decrease in attributable IHD mortality since 1990, at 33.3% (95% UI, 23.6%-41.7%) and 74.9% (95% UI, 46.7%-88.8%), respectively. CONCLUSION AND RELEVANCE/UNASSIGNED:Per the results of this systematic analysis of GBD 2023, a total of 88.7% (95% UI, 83.4%-92.5%) of IHD deaths were attributable to modifiable risk factors in the US in 2023, with high SBP, dietary risks, and high LDL-C being the greatest contributors. High BMI and high FPG showed the largest attribution increases, while exposure to other risks did not increase significantly for the population.

Adversities in early life compromise children's nurturing care and impact their development. This study explored the biopsychosocial conditions associated with electroencephalography (EEG)-based brain activity in children living in rural Bangladesh, a setting with high rates of malnutrition and suboptimal psychosocial stimulation. In a substudy of the Benefits and Risks of Iron Supplementation in Children (BRISC) study in Bangladesh, we recorded resting EEG brain activity in a random subsample of 440 children at 11 months and 594 children at 20 months of age. EEG band power measures were derived for delta, theta, alpha, and beta power bands. Validated tools were used to assess maternal depression, household food insecurity, and psychosocial stimulation. Children's anthropometry, venous hemoglobin and ferritin concentrations, and inflammation were also measured. Linear regression analyses were conducted to identify the concurrent and lagged biopsychosocial conditions associated with EEG-based brain activity. Growth, nutritional status, psychosocial stimulation, and household food security were associated with concurrent and lagged brain activity. The direction of association differed by EEG frequency band, but the majority of biopsychosocial conditions were positively associated with activity in the higher frequency bands and negatively with lower frequency bands. Our findings indicate that brain activity in children can be influenced by a range of biopsychosocial adversities. Children's nutrition, quality and quantity of psychosocial stimulation, and food security influence their current and later brain activity in this resource-limited setting in South Asia. TRIAL REGISTRATION: ACTRN12617000660381.