Citations

Heterotopic ossification (HO), defined as the formation of extraskeletal bone in muscle and soft tissues, is a diverse pathological process caused by either genetic mutations or inciting trauma. Fibrodysplasia ossificans progressiva (FOP) is a genetic form of HO caused by mutations in the bone morphogenetic protein (BMP) type I receptor gene activin A receptor type 1 ( ACVR1 ). These mutations make ACVR1 hypersensitive to BMP and responsive to activin A. Hedgehog (Hh) signaling also contributes to HO development. However, the exact pathophysiology of how skeletogenic cells contribute to endochondral ossification in FOP remains unknown. Here, we showed that the wild-type or FOP-mutant ACVR1 localized in the cilia of stem cells from human exfoliated deciduous teeth with key FOP signaling components, including activin A receptor type 2A/2B, SMAD family member 1/5, and FK506-binding protein 12kD. Cilia suppression by deletion of intraflagellar transport 88 or ADP ribosylation factor like GTPase 3 effectively inhibited pathological BMP and Hh signaling, subdued aberrant chondro-osteogenic differentiation in primary mouse or human FOP cells, and diminished in vivo extraskeletal ossification in Acvr1 Q207D , Sox2-Cre ; Acvr1 R206H/+ FOP mice and in burn tenotomy–treated wild-type mice. Our results provide a rationale for early and localized suppression of cilia in affected tissues after injury as a therapeutic strategy against either genetic or acquired HO.

Abstract Study question What does the collaborative document on adenomyosis terminology, classification, and diagnosis by ESGE, ESHRE, and WES provides? Summary answer This good practice recommendations paper offers guidance on the technical aspects, terminology, classification, and diagnosis of adenomyosis in women of reproductive age. What is known already The existing literature on adenomyosis terminology is heterogeneous and confusing. Various classification systems based on ultrasound and MRI have been proposed, yet none have gained international acceptance or clinical validation. The contradictory data on the diagnostic accuracy of these modalities further complicate adenomyosis diagnosis in reproductive-age women. Study design, size, duration A working group from ESGE, ESHRE, and WES has collaborated to write recommendations concerning the terminology, classification, and diagnosis of adenomyosis. Participants/materials, setting, methods Focusing on terminology, classification, and diagnosis, this good practice paper paves the way for further recommendations on the management and surgical treatment of adenomyosis in reproductive-age women. Main results and the role of chance The document overviews adenomyosis terminology and reviews proposed classification systems based on histology, ultrasound, and MRI, concluding with diagnostic recommendations. Limitations, reason for caution Owing the scarcity of evidence, these recommendations predominantly rely on clinical expertise. Wider implications of the findings The recommendations aim to enhance existing adenomyosis guidelines. Study funding/Competing interests All costs were covered by ESHRE funds, with no external funding for the development process or manuscript production.

Abstract Generative artificial intelligence (AI) holds immense potential for medical applications, but the lack of a comprehensive evaluation framework and methodological deficiencies in existing studies hinder its effective implementation. Standardized assessment guidelines are crucial for ensuring reliable and consistent evaluation of generative AI in healthcare. Our objective is to develop robust, standardized guidelines tailored for evaluating generative AI performance in medical contexts. Through a rigorous literature review utilizing the Web of Sciences, Cochrane Library, PubMed, and Google Scholar, we focused on research testing generative AI capabilities in medicine. Our multidisciplinary team of experts conducted discussion sessions to develop a comprehensive 32‐item checklist. This checklist encompasses critical evaluation aspects of generative AI in medical applications, addressing key dimensions such as question collection, querying methodologies, and assessment techniques. The checklist and its broader assessment framework provide a holistic evaluation of AI systems, delineating a clear pathway from question gathering to result assessment. It guides researchers through potential challenges and pitfalls, enhancing research quality and reporting and aiding the evolution of generative AI in medicine and life sciences. Our framework furnishes a standardized, systematic approach for testing generative AI's applicability in medicine. For a concise checklist, please refer to Table S or visit GenAIMed.org .

AbstractBackgroundIntensity Modulated Proton Therapy (IMPT) is a sophisticated radiation treatment allowing for precise dose distributions. However, conventional spot selection strategies in IMPT face challenges, particularly with minimum monitor unit (MU) constraints, affecting planning quality and efficiency.PurposeThis study introduces an innovative Two‐Stage Mixed Integer Linear Programming (MILP) method to optimize spot intensity in IMPT with Lower Bound (LB) constraints. This method seeks to improve treatment planning efficiency and precision, overcoming limitations of existing strategies.MethodsOur approach evaluates prevalent IMPT spot selection strategies, identifying their limitations, especially concerning MU constraints. We integrated LB constraints into a MILP framework, using a novel three‐phase strategy for spot pool selection, to enhance performance over traditional heuristic methods and L1 + L∞ strategies. The method's efficacy was tested in eight study cases, using Dose‐Volume Histograms (DVHs), spot selection efficiency, and computation time analysis for benchmarking against established methods.ResultsThe proposed method showed superior performance in DVH quality, adhering to LB constraints while maintaining high‐quality treatment plans. It outperformed existing techniques in spot selection, reducing unnecessary spots and balancing precision with efficiency. Cases studies confirmed the method's effectiveness in producing clinically feasible plans with enhanced dose distributions and reduced hotspots, especially in cases with elevated LB constraints.ConclusionsOur Two‐Stage MILP strategy signifies a significant advancement in IMPT treatment planning. By incorporating LB constraints directly into the optimization process, it achieves superior plan quality and deliverability compared to current methods. This approach is particularly advantageous in clinical settings requiring minimum spot number and high MU LB constraints, offering the potential for improved patient outcomes through more precise and efficient radiation therapy plans.

PURPOSE Telisotuzumab vedotin (Teliso-V) is a c-Met–directed antibody-drug conjugate with a monomethyl auristatin E cytotoxic payload. The phase II LUMINOSITY trial (ClinicalTrials.gov identifier: NCT03539536 ) aimed to identify the optimal c-Met protein–overexpressing non–small cell lung cancer (NSCLC) population for treatment with Teliso-V (stage I) and expand the selected group for efficacy evaluation (stage II). Stage II enrolled patients with nonsquamous epidermal growth factor receptor ( EGFR )-wildtype NSCLC. METHODS Eligible patients had locally advanced/metastatic c-Met protein–overexpressing NSCLC and ≤2 previous lines of therapy (including ≤1 line of systemic chemotherapy). c-Met protein overexpression in nonsquamous EGFR -wildtype NSCLC was defined as ≥25% tumor cells with 3+ staining (high [≥50% 3+]; intermediate [≥25%–<50%]). Teliso-V was administered at 1.9 mg/kg once every 2 weeks. The primary end point was overall response rate (ORR) by independent central review. RESULTS In total, 172 patients with nonsquamous EGFR -wildtype NSCLC received Teliso-V in stages I and II. ORR was 28.6% (95% CI, 21.7 to 36.2; c-Met high, 34.6% [95% CI, 24.2 to 46.2]; c-Met intermediate, 22.9% [95% CI, 14.4 to 33.4]). The median duration of response was 8.3 months (95% CI, 5.6 to 11.3; c-Met high, 9.0 [95% CI, 4.2 to 13.0]; c-Met intermediate: 7.2 [95% CI, 5.3 to 11.5]). The median overall survival was 14.5 months (95% CI, 9.9 to 16.6; c-Met high, 14.6 [95% CI, 9.2 to 25.6]; c-Met intermediate, 14.2 [95% CI, 9.6 to 16.6]). The median progression-free survival was 5.7 months (95% CI, 4.6 to 6.9; c-Met high, 5.5 [95% CI, 4.1 to 8.3]; c-Met intermediate: 6.0 [95% CI, 4.5 to 8.1]). Most common any-grade treatment-related adverse events (AEs) were peripheral sensory neuropathy (30%), peripheral edema (16%), and fatigue (14%); the most common grade ≥3 AE was peripheral sensory neuropathy (7%). CONCLUSION Teliso-V was associated with durable responses in c-Met protein–overexpressing nonsquamous EGFR -wildtype NSCLC, especially in those with high c-Met. AEs were generally manageable.