Publications by Year: 2026

BACKGROUND: NKX2-1-related disorders result from heterozygous variants in NKX2-1, a gene crucial for brain, lung, and thyroid development. Although movement disorders, hypothyroidism, and neonatal respiratory distress are recognized, the full phenotype and genotype-phenotype relationships remain incompletely defined.

OBJECTIVES: To delineate neurological, respiratory, and endocrine features across ages, characterize movement disorder trajectories - particularly chorea - and explore genotype-phenotype associations with clinical relevance.

METHODS: We conducted a multicenter, cross-sectional study recruiting participants through referral clinicians and European networks. Standardized clinical and genetic data were captured in an electronic database and analyzed with descriptive and inferential statistics.

RESULTS: Sixty-eight individuals (37 female; median age 16 years, range 2-60 years) were included. Motor delay was the commonest presenting feature ( 60%); neonatal respiratory distress syndrome occurred in one-third of cases. The brain-lung-thyroid triad was present in almost half. Chorea affected over 90% and began in early childhood; it was more frequent with single nucleotide variants than with deletions. Deletions are associated with better gross motor function. Frameshift or nonsense variants showed greater respiratory involvement, and variants in the exon-3 homeobox region were associated with age-related reduction of chorea. Neonatal respiratory distress predicted later respiratory symptoms. Greater abnormal involuntary movement severity correlated with poorer manual and gross motor function. Hypotonia and untreated hypothyroidism are associated with more severe chorea. Psychiatric comorbidity occurred in over one-third of cases, mainly attention-deficit/hyperactivity symptoms.

CONCLUSIONS: This largest cohort to date shows early neurological onset, genotype-specific outcomes, and frequent psychiatric comorbidity in NKX2-1-related disorders, refining clinical expectations and supporting genotype-informed diagnosis, counseling, and management. © 2026 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

BACKGROUND: The substantial health burdens and prevalence of cancer-related pain both during and after treatment underscore the need for expanded access to cancer pain specialists and therapeutic pain treatments. Despite growing demand, cancer pain specialists face substantial barriers to providing effective care.

OBJECTIVE: This exploratory study sought to characterize perspectives from a small group of international pain experts to examine patterns of utilization and perceived accessibility of interventional therapies across cancer types and clinical practice settings.

METHODS: An international, anonymous survey of cancer pain experts, identified via rigorous definition criteria, was conducted using the Qualtrics platform. The survey evaluated eight cancer-related pain categories: head and neck cancer, pleural and rib-based lung cancer, pancreatic cancer, pelvic cancer, lumbosacral spine cancer, extremity cancer, chemotherapy-induced peripheral neuropathy, and bone metastases. Respondents were asked to indicate which treatments they currently use for each type of cancer and which they would use if they had access to them.

RESULTS: Cancer pain experts reported limited access to procedures with greater complexity. Some procedures had substantial variability in use and accessibility, specifically SCS, ITDD, permanent PNS, and nucleus tractus cordotomy. Complementary and alternative therapies were desirable but largely unavailable, particularly in academic settings.

CONCLUSION: Despite the growing need for cancer pain management, specialists continue to face substantial barriers to delivering effective care. This exploratory survey of cancer pain experts identified patterns of reported use and access limitations for therapies across cancer types and practice settings. These findings suggest a relationship between procedural complexity and access barriers, with utilization and availability shaped by institutional resources and practice settings.

BACKGROUND: Patient-reported outcomes (PROs) are increasingly used as endpoints in clinical trials. However, the magnitude of observed changes in control arms attributable to placebo effects, as compared with other benefits of trial participation, has not been described.This study seeks to estimate the magnitude of the placebo effect by calculating changes in Kansas City Cardiomyopathy Questionnaire Overall Summary (KCCQ-OS) scores, which quantify the impact of heart failure on patients' symptoms, function, and quality of life, after participants were unblinded to treatment.

METHODS: REDUCE LAP-HF II randomized participants to atrial shunt or sham procedure, with unblinding after 2 years. The KCCQ was collected at baseline, 2, and 3 years after randomization. KCCQ-OS change from baseline to 2 years (placebo effect plus other benefits from trial participation) and the change from 2 to 3 years (placebo effect loss after unblinding) were calculated in sham-treated patients using mean±SD, as were changes from 2 to 3 years in shunt-treated patients (placebo effect benefit).

RESULTS: The analytic cohort included 421 participants (median age 72 years, 65.6% female). Among sham-treated participants (N=182), the mean±SD KCCQ-OS 2-year improvement from baseline was +9.3±22.4 points, with a decrement after unblinding of -1.7±18.2 points from 2 to 3 years. Among shunt-treated patients (N=239), mean±SD KCCQ-OS 2-year improvement was +12.7±22.8 points, with an improvement after unblinding of +1.9±18.2 points. In a hypothetical unblinded trial where placebo effect benefit would be expected in the active intervention arm, and none in the untreated arm, the combined effects would be 3.6 points.

CONCLUSIONS: In a sham-controlled device trial that collected PRO data during blinded allocation to treatment or sham and after unblinding, the estimated mean placebo effect benefit and loss on the KCCQ-OS were small (≤2 points). Finding a modest placebo effect on PROs may increase confidence in their use as clinical trial outcomes.

BACKGROUND: Early diagnosis of Alzheimer's disease and related dementias (AD/ADRD) is critical but often constrained by limited access to fluid and imaging biomarkers, particularly in low-resource settings.

OBJECTIVE: To develop and evaluate a predictive model for cognitive decline using survey-based data, with attention to model interpretability and fairness.

METHODS: Using data from the Mexican Health and Aging Study (MHAS), a nationally representative longitudinal survey of adults aged 50 and older (N = 4095), we developed a machine learning model to predict future cognitive scores. The model was trained on survey data from 2003 to 2012, encompassing demographic, lifestyle, and social determinants of health (SDoH) variables. A stacked ensemble approach combined five base models-Random Forest, LightGBM, XGBoost, Lasso, and K-Nearest Neighbors-with a Ridge regression meta-model.

RESULTS: The model achieved a root-mean-square error (RMSE) of 39.25 (95 % CI: 38.12-40.52), representing 10.2 % of the cognitive score range, on a 20 % held-out test set. Features influencing predictions, included education level, age, reading behavior, floor material, mother's education level, social activity frequency, the interaction between the number of living children and age, and overall engagement in activities. Fairness analyses revealed model biases in underrepresented subgroups within the dataset, such as individuals with 7-9 years of education.

DISCUSSION: These findings highlight the potential of using accessible, low-cost SDoH survey data for predicting risk of cognitive decline in aging populations. They also underscore the importance of incorporating fairness metrics into predictive modeling pipelines to ensure equitable performance across diverse groups.

BACKGROUND: Large language models (LLMs) like ChatGPT are increasingly being recognized as credible tools for use across diverse healthcare settings. While artificial intelligence (AI) use has previously been evaluated in emergency medicine, its use in subspecialty care - particularly spine surgery - remains underexplored. This study evaluates the clinical accuracy, management appropriateness, completeness, helpfulness, and overall quality of ChatGPT responses compared to those of board-certified, spine surgeons in response to common emergency department (ED) consultations.

METHODS: A 7-part questionnaire was developed based on common ED spine consultations (eg, Cauda Equina Syndrome, compression fracture in elderly patients, purulent drainage from surgical wound, acute lumbar disc herniation, incomplete spinal cord injury, epidural abscess, and metastatic spine disease). Each case included 3-4 questions pertaining to examination, diagnosis, management, and counseling. Responses from ChatGPT and 7 board-certified spine surgeons were restricted to 3-4 sentences per question. Three emergency medicine physicians rated each de-identified questionnaire response using a 5-point Likert scale. Statistical analysis was conducted using a 2-sample T-test with unequal variance. Inter-rater reliability was assessed using pairwise weighted Cohen's kappa coefficient (κ).

RESULTS: When comparing AI responses versus spine surgeon responses to proposed ED consultations, AI responses were rated to be superior across all 5 metrics of clinical accuracy, management appropriateness, completeness, helpfulness, and overall quality (p<.05). Inter-rater reliability was assessed using the average pairwise weighted Cohen's kappa coefficient which showed substantial agreement (κ=0.76).

CONCLUSIONS: ChatGPT responses to emergency department spine consultations were rated as significantly higher compared to board-certified spine surgeons by emergency medicine providers. Though further improvement and validation is warranted, these findings suggest that ChatGPT can be a useful clinical adjunct for spine-related emergency department consultations.

OBJECTIVE: We aim to evaluate the effectiveness of the novel real-time continuous glucose monitoring (rtCGM) system "Glunovo" in improving glycemic control and patient outcomes in individuals with poorly controlled type 2 diabetes (T2D).

RESEARCH DESIGN AND METHODS: This prospective, open-label, randomized controlled trial included 172 patients with T2D from the Fatebenefratelli-Sacco Hospital in Milan. Participants were randomized into 2 groups: 86 patients received the Glunovo rtCGM system (case group), whereas 86 continued standard self-monitoring blood glucose with glucometers (control group). The primary outcome was the change in hemoglobin A1c levels after 6 months. Secondary outcomes included glucose metrics and patient well-being assessed by the World Health Organization-5 Well-Being Scale.

RESULTS: After 6 months, the Glunovo group showed a significantly higher reduction in hemoglobin A1c levels (Δ = -1.4%) compared to the control group (Δ = -0.6%). Time in range significantly increased in the rtCGM group (Δ = +18.4%). Time above range and glucose management indicator showed a greater reduction in the rtCGM group, with no changes in the time below range. Patient satisfaction increased significantly over the study period with the rtCGM system.

CONCLUSION: The use of the Glunovo rtCGM system significantly improved glycemic control and patient satisfaction compared to self-monitoring blood glucose. These findings suggest that the Glunovo rtCGM is an effective tool for managing poorly controlled T2D.

CLINICAL TRIAL REGISTRATION: NCT07089979.

OBJECTIVES: The state of pediatric disaster readiness of United States emergency departments is largely unknown. Using the questions from the National Pediatric Readiness Project (NPRP) 2021 assessment, we sought to define the domains important for a hospital's disaster response for children, develop a consensus-based pediatric weighted disaster readiness score (WDRS), measure the state of pediatric disaster readiness, and identify gaps in disaster planning.

METHODS: The 2021 NPRP assessment identified 8 domains of disaster readiness. Using an iterative consensus-based approach, we determined weighting for each of these disaster domains. We calculated a normalized pediatric WDRS for all hospitals that participated in the NPRP assessment (range, 0-100 points).

RESULTS: Of 5150 hospitals surveyed in the 2021 NPRP assessment, 3647 responded, and 3458 (94.8%) of these had complete data. Median WDRS was 52.0 (IQR, 36.0, 75.0). Higher WDRS was associated with the presence of a pediatric emergency care coordinator, a comprehensive quality improvement plan for children, staffing with board-certified emergency physicians, and trauma center designation with adult and pediatric capabilities. The WDRS was highly correlated with the NPRP's day-to-day readiness score (r = 0.88). Common missing elements in disaster plans included the lack of pediatric patients in disaster drills and plans for children with behavioral health conditions and special health care needs.

CONCLUSION: Overall, pediatric disaster readiness in the United States is low with high variability across hospitals. Disaster readiness is linked with day-to-day pediatric readiness of emergency departments to care for children.

BACKGROUND & AIMS: Regulatory T cell (Treg) dysfunction is a hallmark of autoimmune hepatitis (AIH), a serious hepatopathy often progressing to end-stage liver disease despite immunosuppression. Treg impairment in AIH has been linked to defective signaling through the aryl hydrocarbon receptor (AhR), a modulator of adaptive immunity. Herein, we investigated whether increased estrogen receptor alpha (ERα), an AhR non-canonical binding partner, impacts Treg immunity and contributes to the sex bias observed in AIH.

METHODS: Tregs were isolated from the peripheral blood of pre-menopausal, post-menopausal and male patients with AIH, as well as controls. Their function, stability, transcriptome and response to AhR were assessed in the absence or presence of methylpiperidinopyrazole (MPP), an ERα-selective antagonist. Therapeutic effects of MPP were tested in a model of Concanavalin-A-induced liver injury in humanized mice.

RESULTS: ERα was upregulated in Tregs from pre-menopausal females with AIH (1.1 ± 0.3) compared with healthy pre-menopausal females (0.4 ± 0.1; p = 0.038), and in pre-menopausal compared with post-menopausal females with AIH (0.3 ± 0.1; p = 0.04). In pre-menopausal females with AIH, MPP enhanced Treg suppressive capacity (2.5-fold increase; p = 0.001), restored response to AhR activation (2-fold increase; p = 0.03), and reduced functional plasticity, evidenced by decreased IL-17A and RORC expression under pro-inflammatory conditions. MPP also ameliorated Concanavalin-A-induced hepatitis in NOD/scid/gamma mice reconstituted with human CD4+ ERα+ cells, as shown by reduced ALT levels (101 ± 24 vs. 25 ± 8; p = 0.024), diminished hepatic inflammation on histology, and increased intrahepatic CD4+FOXP3+ and CD4+CD39+ T-cell frequencies. Bioinformatic and inhibition analyses identified hypoxia-inducible factor-1α as a key inducer of ERα in AIH Tregs.

CONCLUSIONS: ERα upregulation drives Treg dysfunction in pre-menopausal females with AIH and likely contributes to the sex bias in the disease. ERα blockade stabilizes Tregs and limits inflammation in vivo, representing a promising therapeutic strategy to enhance and sustain immune tolerance in AIH.

IMPACT AND IMPLICATIONS: Regulatory T cell (Treg) dysfunction plays a key role in the breakdown of immune tolerance in autoimmune hepatitis (AIH), a severe hepatopathy that often progresses to end-stage liver disease despite immunosuppression. Here we report that upregulation of estrogen receptor-alpha (ERα) alters Treg function and stability in pre-menopausal females with AIH, and that blockade of ERα using a specific antagonist stabilizes Tregs and curbs inflammation in humanized mice with liver injury. These findings implicate aberrant ERα signaling in the sex bias of AIH and support ERα inhibition as a potential therapeutic strategy to re-establish immune tolerance.

Soft optical sensors hold potential for enhancing minimally invasive procedures like colonoscopy, yet their complex, multi-modal responses pose significant challenges. This work introduces a machine learning (ML) framework for real-time estimation of 3D shape and contact force in a soft robotic sleeve for colonoscopy. To overcome limitations of manual calibration and collect large datasets for ML, we developed an automated platform for collecting data across a range of orientations, curvatures, and contact forces. A cascaded ML architecture was implemented for sequential estimation of contact force and 3D shape, enabling an accuracy with errors of 4.7% for curvature, 2.37% for orientation, and 5.5% for force tracking. We also explored the potential of ML for contact localization by training a model to estimate contact intensity and location across 16 indenters distributed along the sleeve. The force intensity was estimated with an error ranging from 0.06 N to 0.31 N throughout the indenters. Despite the proximity of the contact points, the system achieved high localization performances, with 8 indenters reaching over 80% accuracy, demonstrating promising spatial resolution.