Identification of druggable targets from the interactome of the Androgen Receptor and Serum Response Factor pathways in prostate cancer.

Background: The Androgen Receptor (AR) pathway is crucial in driving the progression of prostate cancer (PCa) to an advanced state. Despite the introduction of second-generation AR antagonists, such as enzalutamide, majority of patients develop resistance. Several mechanisms of resistance have been identified, including the constitutive activation of the AR pathway, the emergence of AR spliced variants, and the influence of other signalling pathways.

Using Patient Experience Surveys to Identify Potential Diagnostic Safety Breakdowns: A Mixed Methods Study.

Objectives: One in 20 outpatients in the United States experiences a diagnostic error each year, but there are no validated methods for collecting feedback from patients on diagnostic safety. We examined patient experience surveys to determine whether patients' free text comments indicated diagnostic breakdowns. Our objective was to evaluate associations between patient-perceived diagnostic breakdowns reported in free text comments and patients' responses to structured survey questions.

"What Else Could It Be?" A Scoping Review of Questions for Patients to Ask Throughout the Diagnostic Process.

Background: Diagnostic errors are a global patient safety challenge. Over 75% of diagnostic errors in ambulatory care result from breakdowns in patient-clinician communication. Encouraging patients to speak up and ask questions has been recommended as one strategy to mitigate these failures.

Artificial intelligence in digital histopathology for predicting patient prognosis and treatment efficacy in breast cancer.

Introduction: Histological images contain phenotypic information predictive of patient outcomes. Due to the heavy workload of pathologists, the time-consuming nature of quantitatively assessing histological features, and human eye limitations to recognize spatial patterns, manually extracting prognostic information in routine pathological workflows remains challenging. Digital pathology has facilitated the mining and quantification of these features utilizing whole-slide image (WSI) scanners and artificial intelligence (AI) algorithms.