Human chorionic gonadotropin (hCG) is produced by the placenta and its roles have been studied for over a century, being the first known pregnancy-related protein. Although its main role is to stimulate the production of progesterone by corpus luteal cells, hCG does not represent just one biologically active molecule, but a group of at least five variants, produced by different cells and each with different functions. The hyperglycosylated variant of hCG (H-hCG) plays a key role in trophoblast invasion, placental development and fetal growth. During trophoblast invasion, H-hCG promotes extravillous cytotrophoblast cells to infiltrate the decidua, and also to colonize and remodel the spiral arteries in to low resistance, larger-diameter vessels. As fetal growth is heavily reliant on nutrient availability, impaired trophoblast invasion and remodeling of the uterine arteries, leads to a defective perfusion of the placenta and fetal growth restriction. Understanding the function of H-hCG in the evolution of the placenta might unveil new ways to manage and treat fetal growth restriction.

Transthyretin amyloid cardiomyopathy (ATTR-CM) is an infiltrative disorder characterized by extracellular myocardial deposits of amyloid fibrils, with poor outcome, leading to heart failure and death, with significant treatment expenditure. In the era of a novel therapeutic arsenal of disease-modifying agents that target a myriad of pathophysiological mechanisms, timely and accurate diagnosis of ATTR-CM is crucial. Recent advances in therapeutic strategies shown to be most beneficial in the early stages of the disease have determined a paradigm shift in the screening, diagnostic algorithm, and risk classification of patients with ATTR-CM. The aim of this review is to explore the utility of novel specific non-invasive imaging parameters and biomarkers from screening to diagnosis, prognosis, risk stratification, and monitoring of the response to therapy. We will summarize the knowledge of the most recent advances in diagnostic, prognostic, and treatment tailoring parameters for early recognition, prediction of outcome, and better selection of therapeutic candidates in ATTR-CM. Moreover, we will provide input from different potential pathways involved in the pathophysiology of ATTR-CM, on top of the amyloid deposition, such as inflammation, endothelial dysfunction, reduced nitric oxide bioavailability, oxidative stress, and myocardial fibrosis, and their diagnostic, prognostic, and therapeutic implications.

The number of patients diagnosed with breast cancer and cardiovascular disease is continuously rising. Treatment options for breast cancer have greatly evolved, but radiotherapy (RT) still has a key role in it. Despite many advances in RT techniques, cardiotoxicity is one of the most important side effects. The new cardio-oncology guidelines recommend a baseline evaluation, risk stratification and follow-up of these patients. Cardiotoxicity induced by RT can be represented by almost all forms of cardiovascular disease, with atherosclerosis being the most frequent. An interdisciplinary team should manage these patients, in order to have maximum therapeutic effect and minimum cardiovascular toxicity. This review will summarize the current incidence, risk factors, mechanisms and follow-up of RT-induced cardiovascular toxicity.

Aims: None of the conventional echocardiographic parameters alone predict increased NTproBNP level and symptoms, making diagnosis of heart failure with preserved ejection fraction (HFpEF) very difficult in some cases, in resting condition. We evaluated LA functions by 2D speckle tracking echocardiography (STE) on top of conventional parameters in HFpEF and preHF patients with diastolic dysfunction (DD), in order to establish the added value of the LA deformation parameters in the diagnosis of HFpEF. Methods: We prospectively enrolled 125 patients, 88 with HFpEF (68±9 yrs), and 37 asymptomatic with similar risk factors with DD (preHF) (61±8 yrs). We evaluated them by NTproBNP, conventional DD parameters, and STE. Global longitudinal strain (GS) was added. LA reservoir (R), conduit (C), and pump function (CT) were assessed both by volumetric and STE. 2 reservoir strain (S) derived indices were also measured, stiffness (SI) and distensibility index (DI). Results: LA R and CT functions were significantly reduced in HFpEF compared to preHF group (all p<0.001), whereas conduit was similarly in both groups. SI was increased, whereas DI was reduced in HFpEF group (p<0.001). By adding LA strain analysis, from all echocardiographic parameters, SR_CT<-1.66/s and DI<0.57 (AUC = 0.76, p<0.001) demonstrated the highest accuracy to identify HFpEF diagnosis. However, by multivariate logistic regression, the model that best identifies HFpEF included only SR_CT, GS and sPAP (R² = 0.506, p<0.001). Moreover, SR_CT, DI, and sPAP registered significant correlation with NTproBNP level. Conclusions: By adding LA functional analysis, we might improve the HFpEF diagnosis accuracy, compared to present guidelines. LA pump function is the only one able to differentiates preHF from HFpEF patients at rest. A value of SR_CT < -1.66/s outperformed conventional parameters from the scoring system, reservoir strain, and LA overload indices in HFpEF diagnosis. We suggest that LA function by STE could be incorporated in the current protocol for HFpEF diagnosis at rest as a major functional criterion, in order to improve diagnostic algorithm, and also in the follow-up of patients with risk factors and DD, as a prognostic marker. Future studies are needed to validate our findings.

Extracellular vesicles (EVs) are particles released in the extracellular space from all cell types in physiological and pathological conditions and emerge as a new way of cell-cell communication by transferring their biological contents into target cells. The levels and composition of circulating EVs differ from a normal condition to a pathological one, making them real circulating biomarkers. EVs have a very complex contribution in both health and disease, most likely in relationship between diabetes and cardiovascular disease. The involvement of EVs to the development of cardiovascular complications in diabetes remains an open discussion for therapists. Circulating EVs may offer a continuous access path to circulating information on the disease state and a new perspective in finding a correct diagnosis, in estimating a prognosis and also in applying an effective therapy. Besides their role as biomarkers and targets for therapy, EVs can be exploited as biological tools in influencing the different processes affected in diabetic cardiovascular diseases. This chapter will summarize the current knowledge about EVs as biological vectors modulating diabetic cardiovascular diseases, including atherosclerosis, coronary artery disease, and peripheral arterial disease. Finally, we will point out EVs’ considerable value as clinical biomarkers, therapeutic targets, and potential biomedical tools for the discovery of effective therapy in diabetic cardiovascular diseases.

Background: Cardiotoxicity is a major limitation of chemotherapy and radiotherapy for thoracic and systemic cancers, contributing significantly to morbidity and mortality among survivors. Early prediction and prevention are critical to balance oncologic efficacy with cardiovascular safety. Artificial intelligence (AI) offers powerful tools to improve risk stratification, enable earlier detection of subclinical injury, and guide treatment planning in cardio-oncology. Methods: We performed a comprehensive review of the literature on AI applications for cancer therapy-related cardiotoxicity. Evidence was identified from PubMed, Scopus, and Web of Science, focusing on electrocardiography, biomarkers, proteomics, extracellular vesicles, genomics, advanced imaging (echocardiography, cardiac magnetic resonance, computed tomography, nuclear imaging), and radiotherapy dose modeling (dosiomics). Translational insights from animal models and in vitro systems were also included. Methodological quality was appraised with reference to TRIPOD-AI, PROBAST-AI, and CLAIM standards. Results: AI applications span multiple domains. Machine learning models integrating biomarkers, exosomes, and extracellular vesicles show promise for noninvasive early detection. Deep learning enables automated analysis of echocardiographic strain and cardiac MRI mapping, while radiomics and dosiomics approaches combine imaging with cardiac substructure dose maps to predict and prevent late radiation-induced injury. Preclinical studies demonstrate AI-driven advances in small-animal imaging, histopathology quantification, and multi-omics data integration, supporting the discovery of translational biomarkers. Despite encouraging performance, most models remain limited by small cohorts, methodological heterogeneity, and scarce external validation. Conclusions: AI has the potential to transform cardio-oncology by shifting from reactive detection to proactive prevention of cardiotoxicity. Future research should prioritize multimodal integration, harmonized multicenter datasets, prospective validation, and guideline-based clinical trials. As emerging data are incorporated, the field is expanding rapidly—dynamic, complex, and evolving.