Bone metastases ultimately result from a complex interaction between cancer cells and bone microenvironment. However, prior to the colonization of the bone, cancer cells must succeed through a series of steps that will allow them to detach from the primary tumor, enter into circulation, recognize and adhere to specific endothelium, and overcome dormancy. We now know that as important as the metastatic cascade, tumor cells prime the secondary organ microenvironment prior to their arrival, reflecting the existence of specific metastasis-initiating cells in the primary tumor and circulating osteotropic factors. The deep comprehension of the molecular mechanisms of bone metastases may allow the future development of specific anti-tumoral therapies, but so far the approved and effective therapies for bone metastatic disease are mostly based in bone-targeted agents, like bisphosphonates, denosumab and, for prostate cancer, radium-223. Bisphosphonates and denosumab have proven to be effective in blocking bone resorption and decreasing morbidity; furthermore, in the adjuvant setting, these agents can decrease bone relapse after breast cancer surgery in postmenopausal women. In this review, we will present and discuss some examples of applied knowledge from the bench to the bed side in the field of bone metastasis.

This systematic review aims to synthesize the current knowledge about the applications and challenges of Artificial Intelligence (AI) technologies in healt

Background/Objectives: Methicillin-resistant Staphylococcus aureus (MRSA) infections remain a significant challenge in healthcare. Conventional and molecular techniques used for MRSA identification are either time-consuming or costly. Alternatively, Matrix-Assisted Laser Desorption/Ionization Time-Of-Flight Mass Spectrometry (MALDI-TOF MS) offers a rapid method for microbial identification and has the potential to detect biomarkers that distinguish methicillin resistance in S. aureus isolates. The aim of this study was to identify methicillin-resistant discriminative biomarkers for S. aureus obtained using MALDI-TOF MS. Methods: A systematic review was conducted by searching databases such as PubMed and Web of Science for studies that focused on MRSA detection with biomarkers by MALDI-TOF MS, including all relevant studies published up to July 2024. The review protocol was registered in PROSPERO registry. Results: A total of 15 studies were selected for analysis. Data were extracted on study location, sample size, MALDI-TOF MS analyzer, sample preparation, methicillin resistance and sensitivity biomarkers, and the use of Artificial Intelligence (AI) models. Notably, PSM-mec and delta toxin were frequently reported as informative biomarkers, detectable at 2414 ± 2 Da and 3006 ± 2 Da, respectively. Additionally, eight studies used AI models to identify specific biomarkers differentiating methicillin-resistant and methicillin-sensitive strains, based on differences in peak intensities or the exclusive presence of certain peaks. Moreover, two studies employed detection of MRSA in low concentrations from biological samples and others employed an optimized matrix solution for improved analysis. Conclusions: Overall, MALDI-TOF MS is not only a powerful tool for the identification of bacterial isolates but also shows strong potential for rapid, cost-effective detection of methicillin resistance in S. aureus through biomarker analysis. Given that it is already implemented in several clinical laboratories, this approach could be adopted without significant additional cost.

Joint models (JM) have emerged as a promising statistical framework to concurrently analyse survival data and multiple longitudinal responses. This is particularly relevant in clinical studies where the goal is to estimate the association between time-to-event data and the biomarkers evolution. In the context of oncological data, JM can indeed provide interesting prognostic markers for the event under study and thus support clinical decisions and treatment choices. However, several problems arise when dealing with this type of data, such as the high-dimensionality of the covariates space, the lack of knowledge about the function structure of the time series and the presence of missing data, facts that may hamper the accurate estimation of the JM. We propose to apply JM for the analysis of bone metastatic patients and infer the association of their survival with several covariates, in particular the N-Telopeptide of Type I Collagen (NTX) dynamics. This biomarker has been identified as a relevant prognostic factor in patients with metastatic cancer, but only using static information in some specific time points. We extended this analysis using the full NTX time series for a larger cohort of patients with bone metastasis, and compared the results obtained by the JM and the extended Cox regression model. Imputation based on fuzzy clustering was used to deal with missing values and several functions for NTX evolution were compared, such as rational, exponential and cubic splines. The JM obtained confirm the association between NTX values and patients’ response, attesting the importance of this time series, and additionally provide a deep understanding of the key survival covariates.