This study proposes a simple approach for the recognition of polyamide 6.9 samples differing in impurity amounts and viscosities (modulated during the synthesis), which are parameters plausibly variable in polymers’ manufacturing processes. Infrared spectroscopy (ATR-FTIR) was combined with chemometrics, applying statistical methods to experimental data. Both non-supervised and supervised methods have been used (PCA and PLS-DA), and a predictive model that could assess the polyamide type of unknown samples was created. Chemometric tools led to a satisfying degree of discrimination among samples, and the predictive model resulted in a great classification of unknown samples with an accuracy of 88.89%. Traditional physical-chemical characterizations (such as thermal and mechanical tests) showed their limits in the univocal identification of sample types, and additionally, they resulted in time-consuming procedures and specimen destruction. The spectral modifications have been investigated to understand the main signals that are more likely to affect the discrimination process. The proposed hybrid methodology represents a potential support for quality control activities within the production sector, especially when the spectra of compounds with the same nominal composition show almost identical signals.

Salivary thiocyanate (SCN⁻) has long been recognized for its role in mucosal defense and antioxidant function, yet its behavior during physical activity remains unexplored. This study aimed to investigate salivary thiocyanate as a novel salivary biomarker responsive to exercise. A standard Vis–photometric method (thiocyanatoiron-complex formation) was utilized and validated for the rapid quantification of thiocyanate in saliva. The method was applied to two experimental setups: (i) a controlled treadmill protocol involving incremental running intensities (20%, 60%, and 90% VO₂max-mL/kg/min), and (ii) field sampling of athletes from various sports before and after their typical training sessions, managing a total of 162 athletes. This work is the first to quantitatively investigate thiocyanate as an exercise-induced salivary biomarker, validated through both controlled and real-world settings. Additionally, subgroup analysis across sex and smoking status revealed inter-individual variation in salivary SCN⁻ profiles. Across both setups, during controlled exercise intensity increment and typical training routine, thiocyanate concentrations consistently decreased in response to physical exertion. These results were statistically significant and reflected in both male and female athletes. This is the very first study that determines salivary SCN⁻ during any kind of physical exercise and opens new pathways for non-invasive sampling and for monitoring physiological stress and immune response in athletic populations.

The development and progression of abdominal aortic aneurysms (AAA) are related to complex flow patterns and wall-shear-driven mechanobiological stimuli, yet the quantitative relationship between aneurysmal geometry and hemodynamics remains poorly defined. In this study, we conducted a comprehensive hemodynamic analysis of 74 patient-specific abdominal aortas, representing one of the largest Computational Fluid Dynamics (CFD) cohorts reported to date. A multiscale framework coupling 0D-1D systemic circulation models with 3D stabilized finite-element simulations is used to generate physiologically consistent boundary conditions and high-fidelity flow fields. From each model, we extract Time Averaged Wall Shear Stress (TAWSS), Oscillatory Shear Index (OSI), Relative Residence Time (RRT) and Local Normalized Helicity (LNH) indicators alongside an extended set of geometric descriptors characterizing diameter, curvature and torsion. This study provides a clear and comprehensive view of how aneurysm shape influences blood-flow behavior, supported by one of the largest systematically analyzed CFD datasets of AAAs to date. Our results show that specific geometric features reliably shape shear-stress patterns, suggesting that these geometry-driven flow signatures could serve as valuable biomarkers for patient-specific risk assessment. Together, these insights highlight the potential of incorporating detailed geometric descriptors into future models that aim to predict AAA growth and rupture.

The development of Abdominal Aortic Aneurysms (AAA) is related to complex flow patterns and wall-shear–driven mechanobiological stimuli, yet the quantitative relationship between aneurysmal geometry and hemodynamics remains poorly defined. In this study, we conduct a comprehensive hemodynamic analysis of 74 patient-specific abdominal aortas. A multiscale framework coupling zero-dimensional to one-dimensional (0D–1D) systemic circulation models with three-dimensional (3D) stabilized finite-element simulations is used to generate physiologically consistent boundary conditions and high-fidelity flow fields. From each model, we extract the Time Averaged Wall Shear Stress (TAWSS), Oscillatory Shear Index, Relative Residence Time (RRT), and Local Normalized Helicity indicators alongside an extended set of geometric descriptors characterizing diameter, curvature, and torsion. Our results reveal distinct and statistically significant geometry–hemodynamics relationships across the cohort, separately for the aneurysmal sac and the iliac regions. Large aneurysmal sacs exhibit elevated RRT values, enlarged recirculating zones, and reduced TAWSS. Surprisingly, the iliac arteries emerge as dominant contributors to disturbed hemodynamics, showing stronger geometric–hemodynamic correlations than the infrarenal aorta. These observations highlight previously underappreciated downstream effects of AAA morphology. This study provides a comprehensive view of how aneurysm shape influences blood-flow behavior, supported by one of the largest systematically analyzed Computational Fluid Dynamics datasets of AAAs to date. Our results show that specific geometric features reliably shape shear-stress patterns, suggesting that these geometry-driven flow signatures could serve as valuable biomarkers for patient-specific risk assessment. Together, these insights highlight the potential of incorporating detailed geometric descriptors into future models that aim to predict AAA growth and rupture.