Pedro Ferreira

Introduction and purpose: Lung metastases are common in sarcoma patients, often necessitating systemic therapy or local therapies. Stereotactic body radiotherapy (SBRT) offers a targeted approach, achieving low local failure (LF) and delaying systemic therapy. This study evaluates the outcomes of SBRT for sarcoma lung metastases, focusing on LF, systemic therapy-free survival (STFS), and overall survival (OS).Methods and materials: Retrospective study of patients with metastatic pulmonary lesions treated with SBRT between September 2015 and December 2022. STFS was defined as the time from the end of the first RT course to the start of any systemic therapy, if no previous systemic therapy was given or if a pause of at least one month was achieved with RT. STFS was depicted as a Kaplan-Meier curve. LF was analyzed using a competing risks framework. The response assessment was evaluated based on pre- and post-treatment chest CT or PET-CT. Toxicities were classified according to the Common Terminology Criteria for Adverse Events (CTCAE), version 5.0.Results: A total of 17 patients and 53 lesions were treated in 31 courses of treatment. Median follow-up was 96 months. Median number of lesions treated per treatment course was 2 (range: 1-4). In 94.1% of the treatment courses, the target was the only active site of active disease. In 79.2% of the lesions treated, the biologically effective dose (BED) was equal to or superior to 100Gy with a median BED of treated lesions of 105.6Gy (range: 67.2-180Gy). No SBRT-related toxicities were reported. LF rates at one-, two-, and three-years were 32.0%, 45.3% and 47.2%, respectively. Median STFS was 26 months. STFS rates at one- and two-years were 62% (CI95% 40-95) and 54% (CI95% 33-89), respectively. Median OS was 34 months. OS rates at one- and two-years were both 71% (CI95% 53.3-85.6).Conclusion: SBRT is a promising strategy for managing lung metastases in sarcoma, offering acceptable local control (LC) rates and significant systemic therapy-free survival without accrued toxicities. Further research should focus on optimizing dose schedules and patient selection to enhance outcomes.

Data security is not just a compliance task. Companies that understand and control their data with tools like DSPM and DLP will move faster, adopt AI safely, and turn visibility into a competitive edge.

A chemical reaction that is typically symmetry-forbidden (norbornadiene (NBD) to quadricyclane (QC)) is catalyzed in a single-molecule junction using a combination of nanoconfinement and electrical c...

This study compares the numerical results obtained with an axisymmetric quasi two-dimensional (Q2D) model with those from two different types of dimensional models for smooth–turbulent transient flows generated by an instantaneous valve closure. The first is a high-accuracy three-dimensional computational fluid dynamics (3D-CFD) model. The second type is the one-dimensional (1D) model, analysing results from five unsteady friction formulations, namely four convolution integral-based (CIB) formulations and one instantaneous acceleration-based (IAB) formulation. The Q2D and 1D models are also compared with experimental data collected under laboratory conditions for a fast but non-instantaneous valve closure. The differences between the 1D, Q2D and 3D-CFD modelling approaches are quantified and discussed, focusing on the ability of each model to reproduce the different features of the transient flow phenomenon and the associated computation–accuracy trade-offs. The 3D-CFD model exhibits a more pronounced front-wave rounding, as it more accurately represents the valve conditions, where its closure induces highly turbulent flow with a strongly heterogeneous velocity profile. The Q2D model provides an accurate estimation of unsteady energy dissipation, when fully developed flow conditions are ensured with the advantage of requiring less computational effort than the 3D-CFD. These conclusions also apply to full convolution-based 1D models, whereas approximate 1D formulations can significantly reduce accuracy and limit their range of applicability. The comparison with experimental data corroborates the excellent results of the Q2D model. The Q2D model demonstrates to be an efficient alternative in terms of accuracy and computational time to 3D-CFD and 1D full convolution models.

Despite challenging weather conditions, high school students from across the region gathered to participate in El Paso Inc.'s Emerging Women of Impact conference, a full-day experience designed to inspire, educate and equip young women with the tools they need to pursue leadership and professional success across a wide range of career paths.

The global crisis concerning multidrug-resistant microorganisms necessitates the development of innovative antimicrobial strategies that avoid conventional antibiotics and overcome the toxicity and environmental persistence associated with traditional metal-based biocides.

Accurately assessing battery health across mixed datasets remains a challenge due to differences in chemistry, format, and usage history. This study presents a reproducible framework for preparing battery cycling data using incremental capacity analysis (ICA), with the aim of supporting machine learning (ML) workflows across both first-life and second-life battery datasets. The methodology includes IC curve generation, feature extraction, encoding and scaling, feature reduction, and unsupervised learning exploration. A two-tiered outlier detection system was introduced during preprocessing to flag edge-case samples. Two clustering algorithms, K-means and HDBSCAN, were applied to the engineered feature space to explore patterns in the IC feature space. K-means revealed broad health-related groupings with overlapping boundaries, while HDBSCAN identified finer clusters and flagged additional ambiguous samples as noise. To support interpretation, PCA and t-SNE were used to visualise the feature space in reduced dimensions. Rather than using clustering as a classification tool, the resulting cluster and noise labels are proposed as structure-aware meta-features for supervised learning. The framework accommodates heterogeneous battery datasets and addresses the challenges of integrating data from mixed sources with varying histories and characteristics. These outputs provide a structured foundation for future supervised classification of battery state of health.

Brazil is on the cusp of a significant transformation with the Central Bank of Brazil's announcement to develop an AI-powered CBDC super app.

Why I Stopped Using MediatR and Built CQRS From Scratch in .NET 10 MediatR is a great...

You've probably heard "Clean Architecture" thrown around in job descriptions, code reviews, and tech...