Citations

Background: Healing of the tendon-bone interface (TBI) after rotator cuff injury is limited, often leading to scar-mediated repair. Decellularized tendon fibrocartilage–bone composite (dTFBC) and bone marrow mesenchymal stem cell sheets (BMSCS) improve repair in preclinical models, but full scaffold encapsulation with mechanical preconditioning remains unexplored. Purpose: To evaluate TBI regeneration using mechanically preconditioned, BMSCS-encapsulated dTFBC scaffolds in a rabbit model. Study Design: Controlled laboratory study. Methods: A total of 48 male New Zealand White rabbits were randomized into 4 groups (n = 12 each): (1) standard repair (control); (2) repair with dTFBC; (3) repair with dTFBC-BMSCS; and (4) repair with mechanically preconditioned dTFBC-BMSCS (mdTFBC-BMSCS). Healing at 12 weeks was assessed via gross observation, histomorphological and immunohistochemical analyses, and biomechanical testing. Results: The mdTFBC-BMSCS group demonstrated a higher ultimate failure load (mean, 203.9 ± 65.6 N) than both the control (118 ± 37.4 N; P = .003) and dTFBC groups (127 ± 44.1 N; P = .008), with no significant difference between the 2 BMSCS-treated groups. Histological analysis revealed enhanced fibrocartilage formation, improved collagen fiber organization, and reduced inflammation infiltration at the TBI in the mdTFBC-BMSCS group. Immunohistochemical analysis showed greater collagen type 2 alpha 1 chain-, interleukin 10-, and arginase 1-positive areas in the mdTFBC-BMSCS group than in the control and dTFBC groups. Conclusion: Mechanically preconditioned, fully BMSCS-encapsulated dTFBC scaffolds promoted TBI regeneration, with enhanced cellular integration, fibrocartilage formation, and favorable biomechanical performance. Clinical Relevance: This biomimetic scaffold strategy may enhance biological healing and reduce the risk of retear after rotator cuff repair.

A comprehensive, machine-interpretable taxonomy of artificial intelligence (AI) performance metrics provides a shared vocabulary and structured foundation to facilitate rigorous, transparent, and responsible evaluation of medical AI systems.

Abstract Background The association of germline pathogenic and likely pathogenic variants (GPVs) in hereditary breast cancer genes with underlying tumor biology and clinical outcomes remains incompletely understood. This study characterized differences in somatic alterations and intrinsic subtypes between sporadic and hereditary breast cancers associated with GPVs in ATM, BRCA1, BRCA2, CHEK2, or PALB2. Methods This retrospective cohort study included women with breast cancer and an ATM, BRCA1, BRCA2, CHEK2, or PALB2 GPV who had undergone tumor sequencing and whole transcriptome RNA expression analysis. Clinicopathological features, intrinsic subtypes, somatic alterations, and survival were compared by GPV status and immunohistochemistry-defined subtype and to sporadic cases. All statistical significance tests were 2-sided. Results The 4988 women with breast cancer in this study included 98 BRCA1, 126 BRCA2, 74 PALB2, 54 ATM, and 83 CHEK2 GPVs. Compared with sporadic cases, hormone receptor–positive/(ERBB2) formerly HER2–negative tumors in BRCA1 GPVs were statistically significantly enriched for basal subtype (45.5% vs 11.4%; P < .001), while CHEK2 carriers had a higher prevalence of luminal A subtype (80.4% vs 60.3%; P = .006). In hormone receptor–positive/(ERBB2)-negative breast cancers, BRCA1 GPVs were enriched for TP53 alterations (84.6% vs 29.8%; q < 0.001), ATM GPVs with FGFR1 alterations (35.4% vs 12.7%; q = 0.04), and BRCA2 GPVs with APC alterations (10.1% vs 1.5%; q = 0.004). Conversely, BRCA2 GPVs were inversely associated with PIK3CA alterations (13.0% vs 34.1%; q = 0.005) and CHEK2 GPVs with TP53 alterations (8.0% vs 29.8%; q = 0.02). Conclusions The GPVs in BRCA1, BRCA2, ATM, CHEK2, and PALB2 are associated with distinct intrinsic breast cancer subtypes and somatic genomic alterations. These findings may enhance precision in risk stratification and guide personalized treatment strategies.

Abstract Motivation Intratumor heterogeneity arises from ongoing somatic evolution and complicates cancer diagnosis, prognosis, and treatment. Reconstructing evolutionary dynamics typically requires spatiotemporal samples, which are often unavailable in clinical settings. Computational approaches that can infer tumor evolutionary history from single-timepoint bulk sequencing data remain limited. Results We present estimating evolutionary events through single-timepoint sequencing (TEATIME), a novel computational framework that models tumors as mixtures of two competing cell populations: an ancestral clone with baseline fitness and a derived subclone with elevated fitness. Using cross-sectional bulk sequencing data, TEATIME estimates mutation rates, timing of subclone emergence, relative fitness, and number of generations of growth. To quantify intratumor fitness asymmetries, we introduce a novel metric—fitness diversity—which captures the imbalance between competing cell populations and serves as a measure of functional intratumor heterogeneity. Applying TEATIME to 33 tumor types from The Cancer Genome Atlas, we revealed divergent as well as convergent evolutionary patterns. Notably, we found that immune-hot microenvironments constraint subclonal expansion and limit fitness diversity. Moreover, we detected temporal dependencies in mutation acquisition, where early driver mutations in ancestral clones epistatically shape the fitness landscape, predisposing specific subclones to selective advantages. These findings underscore the importance of intratumor competition and tumor-microenvironment interactions in shaping evolutionary trajectories, driving intratumor heterogeneity. Lastly, we demonstrate that TEATIME-derived evolutionary parameters and fitness diversity offer novel prognostic insights across multiple cancer types. Availability and implementation R implementation of TEATIME is available on GitHub (https://github.com/liliulab/TEATIME) and Zenodo (https://zenodo.org/records/17422174).

ABSTRACT Background A decrease in renal blood flow (RBF) precedes estimated glomerular filtration rate (eGFR) decline among patients with autosomal dominant polycystic kidney disease (ADPKD) and inversely associates with disease severity and progression. However, it is unknown whether RBF independently predicts progression to kidney failure in patients with ADPKD. Methods A total of 379 participants with early-stage ADPKD (mean age 36.6 ± 8.5 years; eGFR: 92.9 ± 17.1 ml/min/1.73 m2) who participated in the Halt Progression of Polycystic Kidney Disease Study A (HALT PKD A; NCT00283686) with baseline RBF (phase contrast magnetic resonance imaging) data were included in the primary analysis. Kaplan–Meier survival analysis and multivariate Cox proportional hazard models were used to determine the association of baseline RBF as a continuous and categorical variable with kidney failure risk (during the HALT study and subsequently linked to the US Renal Data System) over a median follow-up period of 13.1 years (interquartile range 7.5–14.5) (primary outcome). All-cause mortality was also considered as a competing risk (Fine and Gray method). The associations of RBF with eGFR slope and the change in height-adjusted total kidney volume (∆htTKV) were evaluated with linear and logistic regression models. Results The kidney failure incidence rate was the highest among those with the lowest RBF tertile {1.90 [95% confidence interval (CI) 1.31–2.78] versus 0.34 [95% CI 0.14–0.83] events/100 person-years in tertile 3}. In the fully adjusted model, the lowest RBF tertile was associated with a higher risk of progression to kidney failure [hazard ratio (HR) 3.19 (95% CI 1.05–9.67] versus the highest tertile]. The association of RBF with kidney failure risk was slightly attenuated in adjusted models. All associations remained similar when considering death as a competing risk [HR 0.82 (95% CI 0.67–1.00) per 100 ml/min/1.73 m2 higher RBF]. Higher RBF was associated with a slower eGFR decline and lower odds of rapid annual ∆htTKV. Conclusions Lower baseline RBF independently associates with a greater risk of progression to kidney failure in early-stage patients with ADPKD.

The hospital setting can present physical, emotional, and spiritual challenges for individuals. It is important to recognize these challenges and empower individuals to manage them. The health care team is in a unique position to listen to the needs of hospitalized individuals. Three vignettes illustrate active listening, compassion, and access to resources. With these tools, health care clinicians can humanize the hospital experience and alleviate the emotional burden patients face in the hospital setting.

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.