Molecular and cellular mechanisms underlying the evolution of form and function in the amniote jaw.

The amniote jaw complex is a remarkable amalgamation of derivatives from distinct embryonic cell lineages. During development, the cells in these lineages experience concerted movements, migrations, and signaling interactions that take them from their initial origins to their final destinations and imbue their derivatives with aspects of form including their axial orientation, anatomical identity, size, and shape. Perturbations along the way can produce defects and disease, but also generate the variation necessary for jaw evolution and adaptation. We focus on molecular and cellular mechanisms that regulate form in the amniote jaw complex, and that enable structural and functional integration. Special emphasis is placed on the role of cranial neural crest mesenchyme (NCM) during the species-specific patterning of bone, cartilage, tendon, muscle, and other jaw tissues. We also address the effects of biomechanical forces during jaw development and discuss ways in which certain molecular and cellular responses add adaptive and evolutionary plasticity to jaw morphology. Overall, we highlight how variation in molecular and cellular programs can promote the phenomenal diversity and functional morphology achieved during amniote jaw evolution or lead to the range of jaw defects and disease that affect the human condition

Postoperative Angiotensin II Receptor Blocker Use is Associated With Reduced 2-Year Reoperation Rates in Male Patients Undergoing Arthroscopic Rotator Cuff Repair

Background Angiotensin II receptor blockers (ARBs) antagonize the effects of transforming growth factor-b1, a cytokine mediator for fibrosis and fatty infiltration in skeletal muscle. The objective of this study was to determine (1) if postoperative ARB use is associated with reduced rates of secondary surgery 2 years following arthroscopic rotator cuff repair, and (2) whether there are differences in these outcomes within male-, female-, and nonesex-specific cohorts. Methods The TriNet X national database was queried to identify patients between 2015 and 2022 who were prescribed an ARB (losartan, valsartan, or olmesartan) within 3 months of arthroscopic rotator cuff repair. ARB patients were propensity matched 1:1 with a non-ARB control. Analyses were conducted to assess outcomes differences within male-, female-, and nonesex-specific cohorts. Two-year secondary surgery rates (manipulation under anesthesia, revision rotator cuff repair, conversion to reverse or anatomic total shoulder arthroplasty, and non-rotator cuff repair arthroscopic shoulder procedures) were evaluated and compared using odds ratios (OR). Results In total, 2,883 matched ARB and non-ARB rotator cuff repair patients were included, with a mean age of 62.5 years, and 42% female in each cohort. In the nonesex-specific analysis, within 2 years after surgery, the non-ARB cohort had similar rates of revision rotator cuff repair as the control. In the female-specific analysis, 1,228 matched ARB and non-ARB females were included, with an average age of 63.2 years, and there were no statistically significant differences in secondary surgery rates. In the malespecific analysis, 1,562 matched males were included, with an average of 62 years. The male non-ARB cohort reported significantly higher rates of undergoing revision rotator cuff repair (OR 1.33, 95% confidence interval [1.01-1.75], P ¼ .039) and significantly higher rates of undergoing total nonerotator cuff repair arthroscopic shoulder procedures (OR 1.35, 95% [confidence interval] [1.04-1.75], P ¼ .025) compared to the ARB male cohort. Conclusion The findings of this study suggest that ARB use may be associated with lower rates of secondary shoulder surgeries in patients undergoing rotator cuff repair, with the effects predominantly observed in male patients

Euclid preparation. III. Galaxy cluster detection in the wide photometric survey, performance and algorithm selection

Galaxy cluster counts in bins of mass and redshift have been shown to be a competitive probe to test cosmological models. This method requires an efficient blind detection of clusters from surveys with a well-known selection function and robust mass estimates, which is particularly challenging at high redshift. The Euclid wide survey will cover 15 000 deg2 of the sky, avoiding contamination by light from our Galaxy and our solar system in the optical and near-infrared bands, down to magnitude 24 in the H-band. The resulting data will make it possible to detect a large number of galaxy clusters spanning a wide-range of masses up to redshift ∼2 and possibly higher. This paper presents the final results of the Euclid Cluster Finder Challenge (CFC), fourth in a series of similar challenges. The objective of these challenges was to select the cluster detection algorithms that best meet the requirements of the Euclid mission. The final CFC included six independent detection algorithms, based on different techniques, such as photometric redshift tomography, optimal filtering, hierarchical approach, wavelet and friend-of-friends algorithms. These algorithms were blindly applied to a mock galaxy catalog with representative Euclid-like properties. The relative performance of the algorithms was assessed by matching the resulting detections to known clusters in the simulations down to masses of M₂₀₀ ∼ 10^(13.25) M⊙. Several matching procedures were tested, thus making it possible to estimate the associated systematic effects on completeness to 80% completeness for a mean purity of 80% down to masses of 10¹⁴ M⊙ and up to redshift z = 2. Based on these results, two algorithms were selected to be implemented in the Euclid pipeline, the Adaptive Matched Identifier of Clustered Objects (AMICO) code, based on matched filtering, and the PZWav code, based on an adaptive wavelet approach

Biologic Treatments for Sports Injuries II Think Tank-Current Concepts, Future Research, and Barriers to Advancement, Part 2:Rotator Cuff

Rotator cuff tears are common and result in considerable morbidity. Tears within the tendon substance or at its insertion into the humeral head represent a considerable clinical challenge because of the hostile local environment that precludes healing. Tears often progress without intervention, and current surgical treatments are inadequate. Although surgical implants, instrumentation, and techniques have improved, healing rates have not improved, and a high failure rate remains for large and massive rotator cuff tears. The use of biologic adjuvants that contribute to a regenerative microenvironment have great potential for improving healing rates and function after surgery. This article presents a review of current and emerging biologic approaches to augment rotator cuff tendon and muscle regeneration focusing on the scientific rationale, preclinical, and clinical evidence for efficacy, areas for future research, and current barriers to advancement and implementation

Swarm learning with weak supervision enables automatic breast cancer detection in magnetic resonance imaging

Background: Over the next 5 years, new breast cancer screening guidelines recommending magnetic resonance imaging (MRI) for certain patients will significantly increase the volume of imaging data to be analyzed. While this increase poses challenges for radiologists, artificial intelligence (AI) offers potential solutions to manage this workload. However, the development of AI models is often hindered by manual annotation requirements and strict data-sharing regulations between institutions. Methods: In this study, we present an integrated pipeline combining weakly supervised learning—reducing the need for detailed annotations—with local AI model training via swarm learning (SL), which circumvents centralized data sharing. We utilized three datasets comprising 1372 female bilateral breast MRI exams from institutions in three countries: the United States (US), Switzerland, and the United Kingdom (UK) to train models. These models were then validated on two external datasets consisting of 649 bilateral breast MRI exams from Germany and Greece. Results: Upon systematically benchmarking various weakly supervised two-dimensional (2D) and three-dimensional (3D) deep learning (DL) methods, we find that the 3D-ResNet-101 demonstrates superior performance. By implementing a real-world SL setup across three international centers, we observe that these collaboratively trained models outperform those trained locally. Even with a smaller dataset, we demonstrate the practical feasibility of deploying SL internationally with on-site data processing, addressing challenges such as data privacy and annotation variability. Conclusions: Combining weakly supervised learning with SL enhances inter-institutional collaboration, improving the utility of distributed datasets for medical AI training without requiring detailed annotations or centralized data sharing