Citations

Discoidin Domain Receptor 2 (DDR2) is a collagen-activated tyrosine kinase required for bone and tooth development as well as regeneration of calvarial and long bones. Although DDR2 is expressed in alveolar bone, its functions in this site have not been previously examined. The present study used global and conditional knockout approaches and lineage tracing to examine the functions of DDR2 in alveolar bone formation during tooth socket healing. First molars were extracted from the maxillae of wild-type and globally Ddr2 -deficient ( Ddr2 LacZ/LacZ ) mice, and socket healing was measured after 1, 2, or 4 wk. At all times, Ddr2 inactivation significantly reduced socket healing. These changes were associated with decreased cell proliferation 3 d after extraction without any detectable changes in apoptosis. In addition, collagen fibril orientation, measured using Picrosirius Red staining under polarized light, was disrupted in tooth sockets from Ddr2 -deficient mice. For lineage tracing, 4-wk-old Ddr2 mer-cre-mer ; R26R tdTomato and Gli1 CreERT2 ; R26R tdTomato mice were induced with tamoxifen, first molars were extracted, and socket healing was examined after 1, 3, or 7 d. In both cases, tdTomato+ cells migrated into sockets, where they sequentially co-localized with preosteoblast and osteoblast markers. Thus, both DDR2 + and GLI1 + cells contribute to socket healing. The considerable overlap observed between DDR2 + and GLI1+ cells suggested that DDR2 functioned in GLI1 + cells, which are known to have skeletal progenitor properties. Consistent with localization data, conditional knockout ( Gli1 CreERT2 ; Ddr2 fl/fl ) mice exhibited reductions in socket healing 1 wk postextraction, although the inhibition of socket repair was less than that seen in global Ddr2 knockouts. In summary, this work demonstrates a role for DDR2 in alveolar bone repair, a finding that may have therapeutic implications for the treatment of alveolar bone loss associated with periodontal disease and other disorders.

Purpose Survivors of critical illness often acquire significant physical, cognitive, and psychological burdens following their intensive care unit (ICU) stay, termed “post-intensive care syndrome” (PICS). Screening for PICS is generally not performed outside of research settings largely due to lack of a simple assessment tool and time constraints. Methods This was a prospective cohort study at a single academic medical center in the USA. We included adults (18 years or older) who spent greater than 48 h in any ICU within the medical center. Following transfer out of the ICU and at 3 months, psychological, cognitive, and physical functioning were assessed using the Hospital Anxiety and Depression Scale, Impact of Event Scale-Revised, Montreal Cognitive Assessment-blind, and Barthel Index. We compared these validated questionnaires to a brief, 15-item PICS screening tool (5 questions/domain) that was developed by our multidisciplinary study team and administered to study participants after ICU discharge to general care. The primary objectives of this study were to assess the ability of validated screening tools administered at ICU discharge to predict PICS at 3 months and develop a novel screening tool for identifying patients at risk of PICS. Major Findings 191 participants completed the initial questionnaire, and 109 (56%) completed the 3-month follow-up. PICS tool scores were compared to 3-month follow-up data from validated questionnaires to evaluate predictive performance. Overall, the screening tool weakly-to-moderately correlated with continuous outcomes of the validated measures at 3-month follow-up. When evaluating sensitivity and specificity for individual domains, the screening tool had an area under curve of >0.7 for all three domains, indicating moderate-to-good sensitivity and specificity. Conclusions We developed a brief PICS screening tool that is feasible to use without prior training that modestly predicted the development of PICS following ICU stay.

Introduction: Manual measurement of leg length (LL) and offset can be tedious. This study developed an automated algorithm for measuring LL and offset from pre- and postoperative AP pelvis radiographs in a large cohort of THA patients. Methods: Using a deep learning model trained on 1100 total AP pelvis radiographs, an algorithm was developed to calculate LL and offset. Algorithm measurements were compared with manual measurements by 4 raters on a sample of 100 pre- and postoperative image pairs. Inter- and intra-rater consistency was calculated using the intraclass correlation coefficient (ICC). The algorithm was applied to calculate the pre- and postoperative LL and offset discrepancies and the change in LL and offset bilaterally in a cohort of 15,951 image pairs. Results: ICC values between the algorithm and human raters ranged from 0.83 to 0.88 for offset measurements and 0.92 to 0.97 for LL measurements. Human raters demonstrated good-to-excellent inter-rater ICC and uniformly excellent intra-rater ICC. Entire database measurements demonstrated shorter LLs for arthritic joints versus the contralateral leg preoperatively and reduced LL discrepancy post-arthroplasty. Conclusions: We present a deep learning algorithm for calculating LL and offset using AP pelvis radiographs. This tool can support population-level studies and may assist operative management.

Objective: This post hoc analysis of the CHAMPION-NMOSD trial evaluated the safety and efficacy of ravulizumab in patients with aquaporin-4 antibody-positive (AQP4-Ab+) neuromyelitis optica spectrum disorder (NMOSD) previously exposed or naïve to rituximab (RTX). Methods: Patients received weight-based intravenous ravulizumab with a loading dose followed by maintenance dosing every 8 weeks. Patients were stratified by prior RTX exposure: no RTX exposure (RTX-naïve) vs RTX exposure > 3 months before initiating ravulizumab (RTX-exposed). Key outcomes included treatment-emergent adverse events (TEAEs), serious TEAEs (TESAEs), relapse rates, and vaccination timing from the last RTX dose. Results: Of the 58 patients enrolled, 89.7% were female, with a mean age of 47.4 years, and 21/58 (36.2%) were RTX-exposed. Relapses occurred in 12/21 (57.1%) RTX-exposed patients between their first RTX dose and study entry. The safety profile of ravulizumab was generally similar between RTX-exposed and RTX-naïve groups. Common TEAEs included COVID-19, headache, urinary tract infection, and upper respiratory tract infection. UTIs were more frequent in RTX-exposed individuals. One patient in each group experienced a meningococcal infection. No adjudicated on-trial relapses were reported while on ravulizumab. Conclusions: Following initiation of ravulizumab, RTX-exposed and RTX-naïve patients with AQP4-Ab+ NMOSD achieved sustained disease control and demonstrated a manageable safety profile. Clinical Trial Information: The CHAMPION-NMOSD Trial; ClinicalTrials.gov identifier: NCT04201262 (registered October 06, 2020).

Abstract Background NRG/RTOG 1010 evaluated trastuzumab added to trimodality therapy for HER2+ localized esophageal adenocarcinoma (EAC) management. Secondary PRO objectives assessed improvement in the FACT‐Esophageal Cancer Subscale (ECS), version 4, with trastuzumab, and if improved ECS correlated with pathologic complete response (pCR). Methods Patients were randomized to weekly paclitaxel/carboplatin/radiation (chemoradiation, CRT) followed by surgery ± trastuzumab (CRT + Tras). Disease‐free survival (DFS) was the primary end point. The projected PRO sample size of 158 patients, based on an 80% participation rate of the DFS primary endpoint sample size of 197 HER2+ patients, would provide ≥ 89% power to detect ≥25% increase in the proportion of CRT + Tras patients with ECS improvement from baseline to 6–8 weeks post‐CRT; one‐sided α = 0.05, using a χ 2 test. Improvement in ECS and its swallowing index (SI) and eating index (EI) was defined as 5‐, 2‐, and 2‐point increases, respectively, from baseline to 6–8 weeks post‐CRT. Univariate logistic regression was assessed if pCR was associated with improved ECS. Results From 2010 to 2015, 203 HER2+ patients were randomized and 194 were eligible. Of 171 PRO consenting patients, the ECS was completed by 162 (95%) at baseline, 108 (64%) 6–8 weeks, 82 (49%) 1 year, and 55 (33%) at 2 years. The proportion of patients with an improvement in 6–8 weeks ECS was higher on the CRT + Tras arm (46% vs. 38%), although not significantly different ( p = .39). There was no correlation between pCR and ECS scores at 1 year, with 39% and 37% of pCR and non‐pCR patients, respectively, having improved 1‐year ECS scores. Conclusions The addition of trastuzumab to CRT for localized HER2+ EAC did not improve PROs.

Artificial intelligence (AI) and machine learning have emerged as transformative analytic methods capable of identifying patterns and proposing treatment strategies from complex, heterogeneous data generated in patient care settings. However, translating reports of AI tools into clinical decision-making requires careful interpretation and contextualization. Here, we present a structured framework (ABCDEFG framework) for clinicians to critically appraise studies reporting AI tools using a high-profile reinforcement learning analysis of vasopressin initiation in septic shock as a case example. We highlight key methodological strengths including adherence to reporting standards, alignment with clinical outcomes, and external validation. We also identify limitations related to causal inference, reward structure design, assumptions about state representation, and implementation readiness. Our framework emphasizes the importance of rigorous, clinically grounded critique before integrating AI tools into practice, and provides a roadmap for clinicians to assess the reliability and relevance of emerging AI-based decision support.

ABSTRACT Patients with neuromyelitis optica spectrum disorder (NMOSD) may experience increased signs and symptoms of their underlying disease when vaccinated against meningococcal disease before receiving complement component 5 inhibitor therapies. This retrospective analysis indicated an overall low relapse incidence (mean [range], 3.3% [0.7%–10.6%]) of physician‐reported relapses occurring within 4 weeks of meningococcal vaccination in those screened in CHAMPION‐NMOSD (NCT04201262), randomized to eculizumab or placebo in PREVENT (NCT01892345), and from the Japanese postmarketing surveillance of eculizumab in patients with anti‐aquaporin‐4 antibody‐positive NMOSD. Further studies are needed to determine if postvaccination relapses are attributable to vaccination or inherent relapse risk among these patients.

Background Effective patient education is critical for informed decision-making and adherence in kidney transplantation. Generative artificial intelligence (AI), particularly large language models (LLMs), has the potential to enhance patient education in kidney transplantation; however, its factual accuracy and readability remain incompletely characterized. Methods We evaluated the performance of the GPT-5.1 (2025) model in generating plain-language explanations for 100 clinically relevant kidney transplantation terms. Explanations were generated using a standardized prompt (first round) and a revised prompt explicitly requesting an eighth-grade reading level or lower (second round). Accuracy was assessed by expert reviewers using a 5-point Likert scale, while readability was evaluated using the Flesch Reading Ease (higher score indicated easier readability of the text) and Flesch–Kincaid Grade Level (higher score indicated higher education level required to understand the text) score. The study was conducted in November 2025. Results All AI-generated explanations demonstrated high accuracy, with no clinically significant errors. In the first round, the mean Flesch Reading Ease score was 23.6 ± 23.4, indicating very difficult readability, and 46% of explanations required a college-level reading ability (mean Flesch–Kincaid Grade Level 13.4 ± 4.8). Following prompt revision, readability improved substantially. The mean Flesch Reading Ease score increased to 62.4 ± 7.5, corresponding to standard readability, and all explanations were written at a middle school level or below (mean Flesch–Kincaid Grade Level 6.3 ± 1.1). Conclusion GPT-5.1 generated highly accurate explanations of kidney transplantation terms across prompting strategies. Explicit readability-focused prompting substantially improved readability without compromising accuracy, underscoring the importance of prompt design when deploying LLMs for patient-centered education in transplantation.