The effect of tumor composition on the success of adaptive therapy: The case of metastatic Castrate-Resistant Prostate Cancer.

Prostate-specific antigen (PSA) is the most commonly used serum marker for prostate cancer. It plays a role in cancer detection, treatment monitoring, and more recently, in guiding adaptive therapy protocols, where treatment is alternated based on PSA levels. However, the relationship between PSA levels and tumor volume remains poorly understood.

Beyond REM: A New Approach to the Use of Image Classifiers for the Management of 6G Networks.

The management of cellular networks, particularly within the environment rapidly advancing to 6G, presents considerable challenges due to the highly dynamic radio environment. Traditional tools such as Radio Environment Maps (REMs) have proven inadequate for real-time network changes, underlining the need for more sophisticated solutions. In response to these challenges, this work introduces a novel approach that harnesses the unprecedented power of state-of-the-art image classifiers for network management.

Autocrine activation of MAPK signaling mediates intrinsic tolerance to androgen deprivation in LY6D prostate cancer cells.

The emergence of castration-resistant prostate cancer remains an area of unmet clinical need. We recently identified a subpopulation of normal prostate progenitor cells, characterized by an intrinsic resistance to androgen deprivation and expression of LY6D. We here demonstrate that conditional deletion of PTEN in the murine prostate epithelium causes an expansion of transformed LY6D progenitor cells without impairing stem cell properties.

Active Learning Methodology for Expert-Assisted Anomaly Detection in Mobile Communications.

Due to the great complexity, heterogeneity, and variety of services, anomaly detection is becoming an increasingly important challenge in the operation of new generations of mobile communications. In many cases, the underlying relationships between the multiplicity of parameters and factors that can cause anomalous behavior are only determined by human expert knowledge. On the other hand, although automatic algorithms have a great capacity to process multiple sources of information, they are not always able to correctly signal such abnormalities.