Search Results (178)

Leakage detection is a key challenge in water distribution systems, yet the reliability of numerical methods is often difficult to validate, as consistent field data before and after application are seldom available. To tackle this limitation, a reliable experimental and numerical platform is under development at Politecnico di Milano to validate advanced monitoring tools and methodologies: the E-NET benchmark network, a scaled (length scale 1:44) model of a widely used numerical benchmark case. An active monitoring strategy for new rupture localization is here applied to the numerical model of the E-NET. The method is further refined by applying a search-space reduction criterion based on sensor adjacency and distance, yielding results consistent with the original methodology while significantly reducing computational time. The combined numerical–experimental benchmark will allow quantitative validation of the methodology on a physical network under real transient conditions and support the assessment of the inherent limitations in current network models and optimization algorithms. These results mark a crucial step towards real network applicability. Full article

Soil hydraulic functioning plays an important role in soil water management under increasingly variable climatic conditions. Total water storage alone, however, does not necessarily reflect the stability of retained water after drainage. This study evaluated the effects of waste paper cellulose and biochar, applied individually and in combination, on soil hydraulic behaviour across contrasting soil types. Water-holding capacity (WHC), maximum capillary water capacity (WMCC), water retention capacity after 24 h drainage (WRCC24), soil texture, and organic matter were determined in 64 soil and soil-related samples. Retention efficiency (RE = WRCC24/WMCC) was used as an indicator of water retention stability. WHC was strongly associated with soil organic matter, whereas RE was primarily related to soil texture and likely reflected differences in pore-system characteristics. Cellulose markedly increased WHC, particularly in soils with initially low hydraulic performance, but changes in WHC were not directly related to changes in RE, indicating partly independent hydraulic responses. Combined cellulose–biochar treatments showed complementary effects: cellulose primarily enhanced total water storage, while biochar improved retention stability. The results demonstrate that total water storage and retention stability may respond differently to soil amendments and should therefore be evaluated together when assessing amendment performance. The findings also highlight the potential of combined cellulose–biochar amendments for improving water retention stability under water-limited conditions. Full article

The chemical complexity of post-consumer plastics represents a major challenge for achieving high-quality recycling. In this study, post-consumer high-density polyethylene (HDPE) packaging materials were analysed using pyrolysis–gas chromatography–mass spectrometry (Py-GC/MS) to investigate relationships between compound origin, degradation pathways, and contaminant profiles. More than one hundred organic compounds were detected and classified into four main groups: product-related inputs, polymer formulation chemistry, polymer degradation processes, and external contamination. Polymer degradation products, particularly radical rearrangement and cyclisation compounds, represented the most diverse group, indicating advanced transformation of the polymer matrix associated with repeated processing. Additive-derived compounds, including phenolic structures and epoxide-containing species, contributed to the pool of non-intentionally added substances (NIAS), while persistent compounds, such as fluoropolymer-derived residues, were detected across most samples. In contrast, product-related inputs showed high variability and a generally lower contribution. Multivariate analysis revealed that samples were not clustered according to product category but rather distributed along gradients defined by degradation, additive transformation, and contamination processes. These findings demonstrate that the chemical composition of recycled HDPE is determined or influenced by multiple independent factors. The results support the need for chemistry-informed recycling strategies. Full article

Background/Objectives: Furcation defects pose a significant challenge in the diagnosis and treatment planning of periodontal diseases. Accurate clinical identification of furcation involvement is essential for improving treatment outcomes. This study aimed to evaluate the accuracy and effectiveness of various artificial intelligence (AI) algorithms in detecting furcation defects (FD) in mandibular molars. Methods: A total of 654 panoramic radiographs were randomly selected from patients who visited the Department of Oral and Maxillofacial Radiology at the Faculty of Dentistry, Ankara University. Each image was labeled as either “healthy” or “FD” and subsequently preprocessed. The performance of different deep learning algorithms in identifying FD was subsequently evaluated. Results: In the classification models employed, the highest scores were calculated as accuracy 97.9%, precision 97.10%, sensitivity 97.08%, and F1 score 97.09% in the Xception model. In the segmentation tests, the highest scores were calculated as accuracy 99.96%, precision 99.26%, sensitivity 97.57%, and F1 score 98.41% in the ENet model. Conclusions: Results of this study indicated that the use of artificial intelligence systems in detecting furcation involvement in mandibular molar teeth in panoramic radiography images is promising. Further studies covering larger data sets, including maxillary molar teeth, will increase the success rates in detecting furcation involvement. Full article

Synaptogenesis-related neuron–glioma interactions are increasingly recognized in glioma, yet it remains unclear whether routine H&E morphology can capture these programs and improve prognostic stratification. We integrated H&E whole-slide images, transcriptomes, and clinical data from 434 TCGA gliomas. Deep learning and quantitative pathology yielded an integrated histomorphologic feature set of 2678 features. Synaptogenesis-related activity was quantified using ssGSEA for ninety-eight synaptogenesis-related genes. In the training cohort, Spearman analysis identified 149 correlated histomorphologic features, which were refined to thirty-five by elastic net regularization. Seventeen prognostic candidates were entered into the MIME1 framework, and the most parsimonious model, Enet[0.1], retained fourteen non-zero-coefficient features to define the synaptogenesis-associated histomorphologic signature and construct the pathology-derived risk score (PRS). Multi-omic analyses, Human Protein Atlas validation, and single-nucleus RNA-seq were used to investigate the hub gene and its cellular context. PRS robustly stratified survival in both training and validation cohorts and remained an independent prognostic factor after adjustment for age and 2021 WHO CNS grade. High-risk tumors showed increased stromal and immune scores and enrichment of immune, adhesion, and phagosome-related pathways. EFNB2 emerged as the hub gene and was enriched in glioblastoma, and EFNB2-positive malignant cells displayed prominent communication with neurons, including EFNB2-EPHB1 signaling. Exploratory re-analysis of the myeloid compartment further showed that glioblastoma was enriched for suppressive TAM-like states relative to astrocytoma grade 2, supporting a shift toward a more tumor-associated and potentially immunosuppressive microenvironment. Routine H&E histomorphology can capture synaptogenesis-related molecular programs in glioma. The resulting PRS provides clinically relevant prognostic stratification, while EFNB2-positive malignant cells may represent a candidate hub for neuron–tumor communication within a remodeled tumor ecosystem. Full article

(1) Background: Advances in machine learning (ML)-based survival modeling enable the analysis of high-dimensional biomedical data. However, many approaches rely on the proportional hazards (PH) assumption, which is frequently violated in oncology and can limit the interpretability of hazard ratio-based results. Using Estrogen Receptor (ER) status in the METABRIC breast cancer cohort as a case study, we propose a framework that integrates machine learning survival models with Restricted Mean Survival Time (RMST) to provide a more robust and clinically interpretable approach for survival analysis under non-proportional hazards. (2) Methods: Overall survival was analyzed in 1104 patients. PH violations were confirmed using Schoenfeld residuals and Kaplan–Meier inspection. We compared four models: stratified Cox Elastic Net (Cox E-Net), Random Survival Forest (RSF), Gradient Boosting Survival Analysis (GBSA), and DeepHit. Performance was assessed using Harrell’s C-index, time-dependent IPCW C-index, and Integrated Brier Score (IBS). RMST at 180 months was utilized to quantify absolute survival differences between ER subgroups. To improve the stability of the estimates, 200 bootstrap resamples were performed, and 95% confidence intervals were derived from the bootstrap distribution. (3) ER status demonstrated significant PH violation (p < 0.005) with crossing survival curves. Discrimination (C-index 0.664–0.725) and calibration (IBS 0.149–0.169) were comparable across models, with RSF achieving the highest overall performance. Despite similar accuracy, survival curve structures differed substantially. Cox E-Net and RSF reproduced the observed crossing pattern, whereas GBSA generated smoother trajectories and DeepHit showed marked compression of subgroup separation. In the independent test cohort, the empirical RMST difference at 180 months was 16.6 months (ER-positive: 130.4; ER-negative: 113.8). Model-based RMST differences ranged from 1 month (DeepHit) to 27 months (Cox E-Net), with RSF and GBSA (12.8 and 13.8 months) most closely approximating the empirical benchmark. (4) Conclusions: We propose a novel, model-agnostic ML + RMST framework that addresses non-proportional hazards while providing quantifiable, time-specific clinical benefit. Moreover, models with similar discrimination and calibration produced markedly different survival curve behavior and absolute RMST estimates, demonstrating that accuracy metrics alone are insufficient for clinical interpretation. By linking prognostic modeling with absolute survival quantification, this framework advances survival evaluation beyond relative risk ranking toward individualized, clinically meaningful decision support. Full article

Background: Pituitary neuroendocrine tumors (PitNETs) are a core manifestation of multiple endocrine neoplasia type 1 (MEN1), yet their true prevalence, biological behavior, and optimal management remain debated. Earlier reports suggested increased aggressiveness compared with sporadic PitNETs, while more recent surveillance-based studies indicate a predominantly indolent phenotype. Methods: We conducted a retrospective single-center study including all patients with clinical, familial, or genetic MEN1 referred to the Endocrinology Unit of the University of Naples “Federico II”, ENETS Center of Excellence, between January 2004 and June 2025. Demographic, clinical, radiological, hormonal, and therapeutic data were systematically collected. PitNETs were classified by size and hormonal activity. Results: Among 103 MEN1 patients (61 women), 50 (48.5%) were diagnosed with PitNETs at a mean age of 35.1 years. Microadenomas predominated (60%), and tumors were equally distributed between functioning and non-functioning lesions. Prolactin-secreting PitNETs were the most common functioning subtype (42%), followed by rare GH-, ACTH-, or mixed-secreting PitNETs. Dopamine agonists, mainly cabergoline, were prescribed in 38% of cases, while neurosurgical intervention was required in 14%, exclusively for macroadenomas. During follow-up, recurrence occurred in 8% of patients. No significant sex-related differences were observed in prevalence, tumor size, functional status, treatment approach, or outcomes. Conclusions: In our MEN1 cohort, PitNETs were frequent but largely indolent, with a predominance of microadenomas and limited need for surgery. Our findings support individualized, subtype-driven surveillance strategies, with conservative management for clinically non-functioning microadenomas and closer monitoring of prolactin-secreting PitNETs due to variable medical responsiveness. Full article

Winery wastewater contains recalcitrant pollutants, such as phenolic compounds, which can hinder biological treatment processes. While monitoring these systems is essential to prevent treatment failure, quantifying recalcitrant compounds through conventional methods can be time-consuming and costly due to complex analytical procedures and chemical disposal. In this study, machine learning (ML) was used to predict polyphenol concentration after the biological treatment of winery wastewater using a sequencing batch reactor (SBR). ML models, including ElasticNet (ENet), Multi-Layer Perceptron Regressor (MLPR), and Support Vector Regressor (SVR), were developed and tested using a small, high-dimensional dataset and leave-one-out cross-validation (LOOCV). Feature selection and hyperparameter tuning were applied to improve model performance. After optimization, the SVR model achieved the best performance, with MAE, MAPE, and R² of 0.88 mg/L, 9.3%, and 0.75, respectively. Full article

Routine milk-recording data may provide valuable insights into dairy cow welfare, although their ability to accurately reflect herd-level welfare outcomes remains unclear. This study explored the associations between routinely collected milk biomarkers and farm-level welfare status using a comparative machine learning approach. Using the Welfare Quality^® (WQ^®) protocol, 43 commercial dairy farms were classified as Enhanced, Acceptable, or Not Classified. Farm-level milk variables included somatic cell count (SCC), differential somatic cell count (DSCC), fat-to-protein ratio (FPR), fat, protein, casein, lactose, urea, β-hydroxybutyrate (BHB), acetone, total plate count (TPC), and morning milk yield. Kruskal–Wallis tests revealed significant differences among welfare classes for DSCC, SCC, lactose, and milk yield (False Discovery Rate-adjusted p < 0.05). Six machine learning algorithms were trained using 10-fold stratified cross-validation. The Elastic-Net (ENET) model showed the highest mean performance (Accuracy = 0.72 ± 0.19; Kappa = 0.56 ± 0.31), followed by Random Forest and Multilayer Perceptron (Accuracy = 0.70). Model accuracy exhibited substantial variability across cross-validation folds, reflecting the limited sample size and class imbalance. Across models, the most influential variables were SCC, DSCC, lactose, milk yield, FPR, fat, and urea. Overall, the findings provide preliminary and exploratory evidence that routine milk biomarkers capture welfare-relevant patterns at the herd level, supporting their potential role as complementary indicators within data-driven welfare assessment frameworks. Full article

Background: The objective assessment of hearing in non-cooperative populations, such as neonates, remains a challenge. While Brainstem Evoked Response Audiometry (BERA) is the gold standard, its sensitivity to motion artifacts necessitates alternatives. Objective: This scoping review maps the current literature on functional near-infrared spectroscopy (fNIRS) as a supplementary method in objective audiometry. Data Synthesis: fNIRS shows potential to detect cortical hemodynamic responses, particularly to complex stimuli like speech, which BERA cannot fully assess. Key advantages include motion tolerance and suitability for pediatric and cochlear implant populations. However, the literature reveals significant heterogeneity in stimulation protocols and data processing. Evidence suggests fNIRS is better suited for assessing higher-level auditory processing rather than replacing BERA for threshold estimation. Conclusions: fNIRS is a promising complementary tool. However, due to the lack of standardized protocols and large-scale validation studies, it is not yet a direct clinical replacement for BERA. Future work must focus on protocol standardization and establishing robust normative data. Full article

Background/Objectives: Cardiac metastases (CM) represent a rare manifestation of neuroendocrine neoplasms (NEN). Detailed clinical characteristics and significance remain understudied. Methods: We retrospectively evaluated 1201 patients with NEN treated at an ENETS Center of Excellence to determine prevalence, clinical features, and outcomes of cardiac metastases. CM were identified in 15 patients (prevalence 1.25%) through multimodal imaging, incorporating somatostatin receptor positron emission tomography/computed tomography (SSTR PET/CT). Metachronous CM occurrence accounted for 93% of cases. Results: The majority of patients showed well-differentiated tumors (G1/G2), with ileum being the most frequent site of origin. Clinical symptoms attributable to CM were observed in 27% of affected patients. Following CM detection, therapeutic management was adjusted in 73% of cases, most frequently by initiating peptide receptor radionuclide therapy (PRRT) n = 8, 53%. Median overall survival (OS) from CM diagnosis was 95 months, with an estimated 5-year survival rate of 77%, with a 5-year OS from NEN diagnosis of 87%. Conclusions: CM in NEN are rare and often clinically silent, with SSTR PET/CT proving essential for detection. While treatment adjustments were frequently observed, particularly with PRRT, OS remained favorable, indicating that the presence of CM in NEN serves as an indicator of metastatic spread rather than a standalone diagnostic determinant of survival. Larger, prospective studies are needed to further validate these findings and to better define the clinical implications of CM in NEN. Full article

Volatile organic compounds (VOCs) emitted from indoor surface coatings can significantly impact indoor air quality and health. This study compared emissions from water-based polyurethane (PUR) and acrylate–polyurethane (ACR–PUR) coatings, identifying 94 VOCs across 16 chemical classes. Time-resolved concentrations were analysed via Principal Component Analysis (PCA), which revealed distinct temporal emission patterns and chemically coherent clusters. Aromatic hydrocarbons, alcohols, esters, and isocyanates dominated the emission profiles, with ACR–PUR releasing markedly higher concentrations of symptom-relevant compounds. Acute exposure was linked to toluene, styrene, phenol, and methyl butyl ketone (MBK), which decreased sharply within 60 days, while compounds such as 1,3-dioxolane, isopropylbenzene, and ethenyl acetate exhibited persistent emissions, suggesting increased chronic risk. Although total VOC levels remained below the German UBA “excellent” threshold (<200 µg/m³), neurotoxic and carcinogenic compounds remained detectable. The combination of PCA-based temporal insights with toxicological profiling and emission transfer dynamics offers a refined framework for indoor air risk assessment. These results underscore the need to complement total VOC indices with symptom-oriented, time-resolved screening protocols to better evaluate SBS risk in indoor environments using water-based coatings. Full article

Background/Objectives: Insulinomas are rare insulin-secreting pancreatic neuroendocrine tumours (pNETs). Preoperative tumour localisation can usually be achieved by computed tomography (CT), magnetic resonance imaging, or positron emission tomography (PET)-CT. However, cross-sectional imaging can be negative, defining an insulinoma as occult and thus hampering surgical resection. Methods: All patients who underwent minimally invasive (MI) surgery for an insulinoma at the University Medical Center Hamburg-Eppendorf since 2017 were analysed. Clinicopathological parameters and diagnostic and operative approaches were assessed. A literature search of the MI resection of occult insulinomas was conducted. Results: Of eight patients with MI-resected insulinomas, two (25%) had negative preoperative imaging. Mean tumour size was 17.2 ± 13.3 mm. Patients underwent distal pancreatectomy (DP), enucleation, and pancreatic head resection (PHR) in 62.5% (5/8), 25.0% (2/8), and 12.5% (1/8) of cases, respectively. One patient had a major postoperative complication (Clavien–Dindo ≥ 3a). Twenty-four studies reporting on 140 occult insulinomas were identified. Occult insulinomas were more frequent in females, often located in the distal pancreas and G1-differentiated. Glucagon-Like Peptide-1 Receptor/PET-CT most frequently localised the conventionally non-visible insulinomas (positive in 67/76, 88.2%). Enucleation, DP, PHR and other resections were conducted in 47/94 (50.0%), 40/94 (42.6%), 4/94 (4.3%), and 3 (3.2%) of the reported cases. MI resection was reported in 10 of 19 (52.6%) specified resections. Conclusions: Insulinomas can be undetectable in cross-sectional and functional imaging. Surgical exploration with intraoperative ultrasound should be considered when clinical presentation and biochemical findings are highly suggestive for insulinoma. Minimally invasive and parenchyma sparing resection is feasible even for occult insulinomas and should always be considered. Full article

Background/Objectives: Neuroendocrine tumors (NETs) of the lung account for about 30% of NETs. In localized and locally advanced forms, radical surgical resection is the standard of care. Although considered indolent tumors, they appear to be susceptible to post-surgical recurrence, with rates differing between typical and atypical carcinoid. Although still debated, several clinicopathologic factors are potentially associated with recurrence. The aim of this retrospective/prospective observational study is to evaluate the predictive role of clinicopathological factors and radiomics features in patients with NET of the lung. Methods: From January 2021 to April 2024, 45 consecutive patients who underwent radical (R0) surgery for lung NET at the ENETS Center of Excellence of the Sant’Andrea Hospital were enrolled, all with at least 12 months of postoperative follow-up and availability of preoperative unenhanced chest CT. Clinicopathologic and radiomic factors were considered (107 radiomic features). Of the individual characteristics, the impact on recurrence was assessed by univariate logistic regression. Results: Among the 45 patients included, 4 patients (8.9%) experienced disease recurrence. Among the clinicopathological features, major age at diagnosis (p = 0.020), atypical carcinoid (p = 0.010), presence of functional syndrome (p = 0.002), advanced stage at diagnosis (p = 0.013), necrosis (p = 0.017) higher Ki-67 (p = 0.001), higher mitotic count (p = 0.006), and pathologic lymph node (p = 0.006) were associated with disease recurrence. Three radiomic features were found to predict recurrence: DependenceEntropy (p = 0.049), DependenceNonUniformityNormalized (p = 0.024), and Elongation (p = 0.039). In this preliminary analysis, multivariate analysis was not performed due to the small sample size. Conclusions: This study has shown that radiomics can be a valuable tool in predicting recurrence. Currently, to our knowledge, no other studies on the possible application of radiomics as prognostic factors in patients with lung NET have been published. These encouraging findings warrant further investigations with larger, multicenter cohorts to validate these results and implement them by constructing a predictive model of recurrence. Full article