Implications of Mineralization Biomarkers in Breast Cancer Outcomes Beyond Calcifications.

The aim of this work was to explore the biomarkers associated with epithelial to mesenchymal transition (EMT) and mineralization processes as new prognostic factors across different breast cancer phenotypes. To this end, 133 breast biopsies, including benign and malignant lesions, with or without microcalcifications, were retrospectively collected. Immunohistochemical analysis was performed to evaluate the expression of vimentin, BMP-2, BMP-4, RANKL, Runx2, OPN, PTX3, and SDF-1, while Kaplan-Meier plots were used to assess their prognostic impact on overall survival in a dataset of 2976 breast cancer patients.

Application of Microsponge Drug Platform to Enhance Methotrexate Administration in Rheumatoid Arthritis Therapy.

Background/objectives: This study aimed to develop a novel nanotechnological slow-release drug delivery platform based on hyaluronic acid Microsponge (MSP) for the subcutaneous administration of methotrexate (MTX) in the treatment of rheumatoid arthritis (RA). RA is a chronic autoimmune disease characterized by joint inflammation and damage, while MTX is a common disease-modifying antirheumatic drug (DMARD), the conventional use of which is limited by adverse effects and the lack of release control.

Methods: MSP were synthesized as freeze-dried powder to increase their stability and allow for a facile reconstitution prior to administration and precise MTX dosing.

Aluminum Concentration Is Associated with Tumor Mutational Burden and the Expression of Immune Response Biomarkers in Colorectal Cancers.

Environmental pollution poses a significant risk to public health, as demonstrated by the bioaccumulation of aluminum (Al) in colorectal cancer (CRC). This study aimed to investigate the potential mutagenic effect of Al bioaccumulation in CRC samples, linking it to the alteration of key mediators of cancer progression, including immune response biomarkers. Aluminum levels in 20 CRC biopsy samples were analyzed using inductively coupled plasma mass spectrometry (ICP-MS).

Machine Learning Predictions of Recovery in Bilingual Poststroke Aphasia: Aligning Insights With Clinical Evidence.

Background: Predicting treated language improvement (TLI) and transfer to the untreated language (cross-language generalization, CLG) after speech-language therapy in bilingual individuals with poststroke aphasia is crucial for personalized treatment planning. This study evaluated machine learning models to predict TLI and CLG and identified the key predictive features (eg, patient severity, demographics, and treatment variables) aligning with clinical evidence.

Methods: Forty-eight Spanish-English bilingual individuals with poststroke aphasia received 20 sessions of semantic feature-based naming treatment in either their first or second language.

Unveiling the molecular profile of a prostate carcinoma: implications for personalized medicine.

Background: Prostate cancer is the most common diagnosed tumor and the fifth cancer related death among men in Europe. Although several genetic alterations such as ERG-TMPRSS2 fusion, MYC amplification, PTEN deletion and mutations in p53 and BRCA2 genes play a key role in the pathogenesis of prostate cancer, specific gene alteration signature that could distinguish indolent from aggressive prostate cancer or may aid in patient stratification for prognosis and/or clinical management of patients with prostate cancer is still missing. Therefore, here, by a multi-omics approach we describe a prostate cancer carrying the fusion of TMPRSS2 with ERG gene and deletion of 16q chromosome arm.