Can you imagine anticipating the struggle between the immune system and a tumor using mathematical tools that understand each patient's uncertainty? Researchers at ESPOL developed an innovative model based on Type-3 Fuzzy Logic that simulates precisely that confrontation. Unlike traditional models, this proposal integrates individual biological variability and delays in the immune response, allowing highly accurate prediction of chaotic patterns and possible tumor relapses. In addition, the study generates clear and interpretable risk maps that facilitate the design of personalized and explainable therapies, aligning with the principles of Explainable Artificial Intelligence (XAI) to support clinical decision-making.

The prognostic nutritional index (PNI), calculated from serum albumin and lymphocyte count, reflects a patient’s immune-nutritional status and has been proposed as a prognostic marker in hepatocellular carcinoma (HCC). However, its role in advanced HCC patients treated with atezolizumab plus bevacizumab (Ate/Bev) remains unclear. In this study, we aimed to evaluate the prognostic value of PNI in patients receiving first-line Ate/Bev therapy.

Researchers at AppliedPhysics.org report early evidence that cells respond selectively to mathematically structured sound, not just acoustic power. In an exploratory Biosystems study, Fibonacci based acoustic signals triggered distinct responses across different cell types, suggesting sound can be tuned to cellular size and mechanics rather than applied as brute force.

The findings point to a potential new direction for cancer research: using low intensity, physics driven acoustic design to target physical differences between cancer and healthy cells. While preliminary and based on model organisms, the work opens the door to a future of more precise, less invasive, mechanically selective therapies.

Five new projects for the development of innovative health technologies have been funded by the European Research Council (ERC) at the Istituto Italiano di Tecnologia (IIT-Italian Institute of Technology) at its sites in Milan, Genoa and Naples. The announcement made today by the European body includes, among the 136 winners across Europe: Annamaria Petrozza and Mario Caironi in Milan, Alessandra Sciutti and Giuseppe Vicidomini in Genoa, and Velia Siciliano in Naples. The three female researchers and the two male researchers at IIT will receive Proof of Concept (PoC) grants of approximately €150,000 each, which will allow them to explore the commercial potential of their research. The fields of application include cancer, dyslexia and diagnostics.

Neurologist Dr. Varun Venkataramani (36) from Heidelberg University Hospital will be awarded the Paul Ehrlich and Ludwig Darmstaedter Early Career Award 2026, the Scientific Council of the Paul Ehrlich Foundation announced today. The prizewinner has fundamentally expanded our understanding of glioblastomas – particularly malignant brain tumors. They arise from glial cells, whose task is to protect and nourish nerve cells. Venkataramani discovered that glioblastomas hijack the nervous system to tap into electrical signals, which they then use to accelerate their deadly growth. A drug to interrupt this electrical activity is already being tested on patients.

Insights into the role of EMT in promoting initiation, plasticity and treatment response in glioblastoma Glioblastoma (GBM) is the most common primary malignant tumor of the central nervous system in humans and the most aggressive form of glioma. Despite therapeutic advances, GBM remains highly invasive, with no definitive cure and a poor overall survival. Epithelial–mesenchymal transition (EMT) results in enhanced migratory, invasive, and stem-like properties. Understanding EMT in glioma may aid in the development of potential strategies to overcome resistance and improve treatment outcomes. 

Objective: Carbohydrate antigen 125 (CA125), which is traditionally used in ovarian cancer diagnostics, is increasingly recognized as a marker of congestion and inflammation in heart failure (HF). This study compared the analytical performance of N-(4-aminobutyl)-N-ethylisoluminol (ABEI)-based CA125 and N-terminal pro-B type natriuretic peptide (NT-proBNP) assays on the Maglumi® X6 analyzer with that of the Roche Cobas e602 system and explored the relationship of CA125 with biomarkers of adverse remodeling in HF with reduced ejection fraction (HFrEF). Methods: Imprecision testing and method comparison were performed on matched serum samples from 108 HFrEF patients. CA125 concentrations were evaluated in relation to the New York Heart Association (NYHA) class, left ventricular ejection fraction (LVEF), galectin-3, and soluble suppression of tumorigenicity 2 (sST2) levels. Prognostic value was assessed by Kaplan-Meier survival analysis using the 35 U/mL threshold. Results: The ABEI based CA125 assay showed low imprecision [coefficient of variation (CV)≤4.5%] and strong agreement with the Cobas e602 assay (R=0.97, slope=1.06, P<0.001). CA125 levels increased progressively with NYHA class (P=0.02), correlated negatively with LVEF (R= 0.38, P<0.001) and positively with galectin-3 (R=0.21, P=0.03) and sST2 (R=0.57, P<0.001). Elevated CA125 levels (≥35 U/mL) were associated with significantly increased cardiovascular mortality (P<0.001). Conclusions: ABEI-based CA125 measurement provides an analytical performance comparable to that of Cobas e602. In HFrEF, CA125 is correlated with clinical severity, fibrosis/inflammation biomarkers, and prognosis. Its integration into multimarker strategies, particularly alongside NT-proBNP and sST2, may enhance risk stratification and therapeutic monitoring, including the response to sodium–glucose cotransporter-2 (SGLT2) inhibitor therapy.

Researchers from the Faculty of Engineering at The University of Hong Kong (HKU) have developed two innovative deep-learning algorithms, ClairS-TO and Clair3-RNA, that significantly advance genetic mutation detection in cancer diagnostics and RNA-based genomic studies.

Hormone receptor–positive breast cancer represents the most common molecular subtype of breast malignancy and is routinely treated with endocrine therapies targeting estrogen signaling. Although these treatments have significantly improved patient survival, the emergence of endocrine resistance remains a major obstacle, particularly in advanced and metastatic disease. Understanding the molecular pathways that bypass classical estrogen receptor inhibition is therefore critical for improving long-term therapeutic outcomes.