Citations

Abstract Glioblastoma (GBM), isocitrate dehydrogenase wild-type (IDHwt) and central nervous system diffuse large B-cell lymphoma (CNS-DLBCL) are aggressive brain tumors with overlapping MRI features, yet distinct treatment approaches. Noninvasive tools are needed to aid in differential diagnosis. Deep learning on T1 postcontrast and T2-weighted MRI sequences were used to differentiate GBM and CNS-DLBCL. A three-stage temporal study design was utilized. Model development was performed on 146 patients with CNS-DLBCL and 146 age-matched, sex-matched, and MRI year–matched patients with GBM diagnosed at Mayo Clinic between 1998 and 2019. Models were tested on independent temporal test cohorts. Initial testing included 240 independent GBM diagnosed at Mayo Clinic between 1998 and 2019. The prospective test cohort included 37 patients with CNS-DLBCL and 256 patients with GBM diagnosed at Mayo Clinic after January 1, 2020, and 36 patients with CNS-DLBCL diagnosed at an external institution. Of the patients diagnosed at Mayo Clinic, 47% had MRIs generated from non-Mayo institutions. Two different model approaches were compared: (i) ensemble approach using area under the receiver operating characteristic curve (AUC) and cross-validation for model selection and (ii) loss approach minimizing cross-entropy loss and cross-validation to evaluate prediction performance. The AUCs on the prospective test cohort were 0.84 [95% confidence interval (CI), 0.78–0.90] and 0.83 (95% CI, 0.77–0.88) for the ensemble and loss approaches, respectively. Stability of ensemble prediction improved with the increasing number of models. Stratified AUC analysis demonstrated consistent performance across sex and age. We utilized a robust temporal study design and applied 2 different analytic approaches to develop a classification model. The findings confirm the feasibility of using MRI-based deep learning models to differentiate GBM from CNS-DLBCL. Significance: GBM, IDHwt and CNS-DLBCL are aggressive brain tumors with overlapping MRI features, yet distinct treatment approaches. Noninvasive tools are needed to aid in differential diagnosis. We developed MRI-based deep learning models to differentiate GBM, IDHwt from CNS-DLBCL using a rigorous three-stage temporal design that included prospective validation. The model AUC on a prospective cohort was 0.84.

ABSTRACT Purpose To validate a custom smartphone application for at‐home visual acuity (VA) measurement in children. Methods A total of 452 children aged 3–17.5 years participated. Certified examiners measured in‐office test–retest VA (logMAR) using gold‐standard Amblyopia Treatment Study HOTV (3‐to‐6‐year‐olds, younger cohort) or electronic Early Treatment of Diabetic Retinopathy Study (7‐to‐17.5‐year‐olds, older cohort) protocols at 3–4.5 m and app‐based VA at 1.5 m. Caregivers measured at‐home app‐based VA at 1.5 m. Results Comparing at‐home app‐based with gold‐standard VA, in eyes 20/40 or better, 95% (143/151) and 93% (91/98) of the younger and older cohorts were within 2 lines, respectively (mean differences: younger = −0.03, older = −0.04; 95% limits‐of‐agreement half‐width (LOA): younger = ±0.26, older = ±0.22). In eyes 20/50 or worse, 66% (42/64) and 75% (76/101) of the younger and older cohorts were within 2 lines, respectively (mean differences: younger = 0.11, older = 0.13, LOA: younger = ±0.50, older = ±0.51). Comparing in‐office app‐based VA with gold‐standard VA, in eyes 20/40 or better, 98% (160/164) and 94% (99/105) of the younger and older cohorts were within 2 lines, respectively (mean differences: younger = −0.03, older = −0.03; LOA: younger = ±0.22; older = ±0.24). In eyes 20/50 or worse, 85% (60/71) and 91% (101/111) of the younger and older cohorts were within 2 lines, respectively (mean differences: younger = 0.04; older = 0.04; LOA: younger = ±0.39; older = ±0.24). For gold‐standard test–retest, in eyes 20/40 or better, 99% (163/164) and 99% (104/105) of the younger and older cohorts had retest within 2 lines, respectively (mean differences: younger = 0.00; older = 0.01; LOA: younger = ±0.17; older = ±0.11). For 20/50 or worse, 92% (66/72) and 100% (111/111) in the younger and older cohorts were within 2 lines, respectively (mean differences: younger = 0.01; older = 0.02; LOA: younger = ±0.35; older = ±0.15). Conclusions Our app demonstrated good concordance with the gold standard at home and in the office for eyes with VA of 20/40 or better. However, concordance decreased considerably for eyes with VA 20/50 or worse, particularly at home.

Microplastics may pose health risks, particularly for chronic lung diseases. Clarifying the link between circulating microplastics and pulmonary disease is vital for shaping research and interventions. The objective of this study was to evaluate whether microplastics in peripheral blood are associated with COPD or IPF vs. no lung disease. In this pilot prospective case-control study, participants were grouped as control (n = 10), COPD (n = 9), or IPF (n = 10). Relevant comorbidities and exposures were obtained from records and questionnaires. All underwent standardized blood collection (PlasticTox©). Samples were analyzed for total microplastic concentration, stratified by size (<10 µm, 10–30 µm, 30–70 µm). The primary outcome was to show a difference in total microplastic burden between lung disease and controls. Secondary measures were to determine size-specific concentrations and associations with demographic variables and smoking history. Among 29 participants (median age 70 (IQR 64–80); 14 women (48.3%)), COPD/IPF groups had significantly higher total microplastic concentrations vs. controls (median 26.0 vs. 3.5 particles/100 µL; p < 0.01). Particle burdens <10 µm and 10–30 µm were particularly elevated (both p < 0.01). After adjusting for smoking, only the <10 µm fraction remained independently associated with lung disease (adjusted odds ratio 1.94 (95% CI, 1.23–7.04)). In this pilot exploratory study, individuals with COPD or IPF showed greater circulating microplastic levels than controls. These findings should be interpreted as hypothesis-generating, and larger analytically validated studies are needed to clarify directionality, causal mechanisms, contamination control, and the clinical relevance of circulating microplastic burden.

Background: Given the faster onset and shorter duration of action of ultra-rapid insulin lispro-aabc (URLi, Lyumjev™ insulin), compared with insulin lispro (a rapid-acting insulin [RAI]), the present study investigated the safety and glycemic outcomes of MiniMed™ 780G advanced hybrid closed-loop (AHCL) use with URLi in youths and adults with T1D. Methods: This 19-site, single-arm, clinical trial enrolled children and adolescents (aged 7–17 years) and adults (aged 18–80 years) with T1D who underwent a run-in period (∼2 weeks) using open loop or automated basal only with an RAI (aspart or lispro), followed by a 3-month study period using URLi with AHCL (AHCL + URLi). End-of-study glycemic metrics and A1c were compared to predefined thresholds and to data from baseline/run-in. Use of recommended optimal settings (ROS, 100 mg/dL glucose target with an active insulin time [AIT] of 2 h) was also assessed. Results: In an intention-to-treat population ( N = 101 pediatric and N = 110 adult participants), percentage of time in range (%TIR) during the last ∼6 weeks of the study period was significantly greater than the predefined thresholds for both the pediatric and adult participants. A1c was unchanged in the pediatric cohort but significantly reduced in the adult cohort (from 7.4 ± 0.9% to 6.9 ± 0.6% [ P < 0.001]). Compared with run-in, study period %TIR and percentage of time in tight target range (%TITR) were significantly increased (pediatric mean %TIRΔ [17.4%] and %TITRΔ [16.0%], respectively, and adult mean %TIRΔ [10.6%] and %TITRΔ [11.6%], respectively; P < 0.001 for all). Additionally, % time above range for both groups was significantly reduced. Use of ROS allowed more participants to achieve recommended glycemic targets. There were no severe hypoglycemia or diabetic ketoacidosis events. Most caregivers agreed that mealtime versus premeal URLi delivery simplified pediatric meal management. Conclusions: Similar to previous studies investigating automated URLi delivery, URLi use in the MiniMed™ 780G system was safe, and glycemic outcomes were favorable for pediatric and adult participants.

Multi-listing (ML) increases the odds of transplantation compared with single-center listing. However, not all patients have equivalent access to ML. The recent allocation policy change prioritized distance from the donor hospital in an attempt to decrease geographic disparities. This study assessed access to liver transplant (LT) given ML practices before and after the acuity circles policy change. United Network for Organ Sharing data were queried for LTs between January 1, 2016, and November 1, 2024. Patients were grouped by listing at one versus multiple centers and by acuity circle era (ACE). Cohort characteristics and comparisons were described using chi-square and Wilcoxon rank tests. Multivariable logistic regressions and propensity score match (PSM) were performed to assess associations between ML and LT. The final cohort consisted of 105,030 patients; 4084 (3.9%) were multi-listed. Racial/ethnic minority groups, less educated, and publicly insured candidates were less often multi-listed. ML was positively associated with LT in all analyses (overall: OR=1.180, p <0.001; PSM: OR=1.164, p =0.001; pre-ACE: OR=1.127, p =0.015; post-ACE: OR=1.218, p <0.001). Education, race/ethnicity, and insurance type were also associated with LT across all analyses. ML patients were more often transplanted at their primary center with a significant increase in primary center LT post-ACE (53.4% vs. 63.4%, p <0.001). ML and LT had a positive correlation. There was a negative association between minority groups and ML as well as LT. Although acuity circles decreased geographic disparities, they did not change ML accessibility across candidates.

Abstract Background/Aims The aim of this prospective cohort study was to evaluate the extent that kinesiophobia at baseline contributed to pain intensity at follow-up after starting first line treatments in patients with a range of musculoskeletal conditions, including rheumatoid arthritis (RA), fibromyalgia (FM) and knee osteoarthritis (OA). Methods 469 patients (RA n = 156, FM n = 149, OA n = 164) recruited from the Nuffield Orthopaedic Centre, Oxford University Hospitals, participated in this study. Pain was assessed with the pain detect questionnaire, with the item average pain severity over the last 4 weeks used for the primary analysis. The Tampa Scale for Kinesiophobia (TSK) was administered to assess fear of movement. The association between TSK and pain at baseline was investigated by analysing their correlation using Pearson’s Product-Moment Correlation. RA, FM and OA groups were treated with standard care, which consisted of DMARDs, pain rehabilitation therapy, and total knee arthroplasty, respectively. The correlation between baseline TSK and change from baseline in 4-week average pain severity was calculated at 3-, 6- and 12-month follow-up using Pearson’s Product-Moment Correlation. A simple linear regression model was used for each cohort to evaluate the extent TSK contributed to change in pain from baseline. Results At baseline, mean TSK was 38.8±7.0 and mean 4-week average pain severity was 6.6±2.0 (all conditions pooled), with a Pearson’s Product-Moment Correlation of 0.28 [95% CI: 0.18-0.37], representing a small positive correlation (p &lt; 0.01) between baseline TSK and baseline pain. The results were similar for all 3 conditions independently (RA correlation 0.30 [95% CI: 0.14-0.44], FM correlation 0.31 [95% CI: 0.15-0.46], OA correlation 0.25 [95% CI: 0.06-0.41], all ps &lt; 0.01). No correlation was found between TSK and change from baseline in pain severity for the RA or FM cohorts at any follow-up. A weak negative correlation between TSK and change in pain severity was found in the knee OA group at 3 months (correlation: -0.18 [95% CI: -0.39-0.04]) and 12 months (correlation: -0.16 [95% CI: -0.39-0.08]); however, these results were not statistically significant (p = 0.12 and 0.19 respectively). The linear regression analysis showed that TSK was not associated with change in pain from baseline for any condition at any timepoint. Mean pain decreased in the RA and OA groups irrespective of TSK, with no improvement in pain seen in the FM group (RA mean pain at baseline: 6.5±2.1, at 12 months: 4.3±2.6; FM mean pain at baseline: 6.6±1.6, at 12 months: 6.6±1.8; OA mean pain at baseline: 6.5±2.2, at 12 months: 1.9±2.6). Conclusion Whilst kinesiophobia and pain severity were correlated at baseline, kinesiophobia did not negatively influence change in pain with treatment in any of the cohorts studied. Disclosure R.C. Murphy: None. A. Irani: None. E. Kelleher: None. A.J. Wall: None.