Search Results (2,181)

Background: Neurodevelopmental disorders (NDDs) are a heterogeneous group of conditions characterized by cognitive, behavioral, and developmental impairments, frequently linked to structural genomic alterations. Copy number variants (CNVs) involving chromosome 16, particularly the short arm 16p, are recognized contributors to neurodevelopmental variability. Despite increasing international evidence, data from Italian clinical cohorts are still limited. Methods: We investigated 1200 patients referred for genetic evaluation due to suspected NDDs, including autism spectrum disorder (ASD), intellectual disability (ID), global developmental delay, and language impairment. All individuals underwent array comparative genomic hybridization (a-CGH) analysis, and identified variants were correlated with detailed clinical, cognitive, and behavioral assessments. The analysis focused on recurrent CNVs at 16p11.2, 16p13.3, and 16p13.11, regions containing dosage-sensitive genes relevant to neurodevelopment. Results: CNVs involving the 16p region were identified in 96 patients (8% of the cohort), encompassing both deletions and duplications. Deletions were mainly associated with developmental delay, language deficits, and ASD-related features, whereas duplications were more frequently linked to behavioral dysregulation, attentional deficits, and variable cognitive impairment. Marked phenotypic variability was observed among individuals carrying similar CNVs, suggesting the contribution of modifying genetic or environmental factors. In a subset of patients, additional CNVs were identified, potentially exacerbating clinical severity, consistent with the two-hit model. Conclusions: This study confirms a strong association between recurrent 16p CNVs and a wide spectrum of neurodevelopmental phenotypes in an Italian clinical cohort. The findings emphasize the diagnostic utility of systematic genomic screening and the importance of an integrated genotype–phenotype approach to improve clinical interpretation, management, and genetic counseling in NDDs. Full article

Fibrosis is a hallmark of the tumor microenvironment in many solid cancers, driving tumor progression, immune evasion, and treatment resistance; however, the molecular and cellular mechanisms underlying fibrogenesis—particularly stromal–immune crosstalk across organs—remain incompletely understood, compounded by organ-specific heterogeneity and a lack of reliable immune-related biomarkers. To address this, we performed an integrative single-cell RNA sequencing (scRNA-seq) analysis of fibrotic tissues from four major organs—liver, lung, heart, and kidney—alongside non-fibrotic controls, applying unsupervised clustering, trajectory inference, cell–cell communication modeling, and gene set variation analysis (GSVA) to map the fibro-immune landscape. Our analysis revealed both conserved and organ-specific features: fibroblasts were the dominant extracellular matrix (ECM)-producing cells in liver and lung, whereas endothelial-derived stromal populations prevailed in heart and kidney. Immune profiling uncovered distinct infiltration patterns—macrophages displayed organ-specific polarization states; T cells were enriched for tissue-resident subsets in lung and mucosal-associated invariant T (MAIT) cells in liver; and B cells exhibited marked heterogeneity, including a pathogenic interferon-responsive subset prominent in pulmonary fibrosis. GSVA further identified divergent signaling programs across organs and lineages, including TGF-β/TNF-α in the heart, NOTCH/mTOR in the kidney, glycolysis/ROS in the lung, and KRAS/interferon pathways in the liver. Cell–cell communication analysis highlighted robust crosstalk between macrophages, T/B cells, and stromal cells mediated by collagen, laminin, and CXCL signaling axes. Together, this cross-organ atlas delineates a highly heterogeneous fibro-immune ecosystem in human fibrotic diseases, revealing shared mechanisms alongside organ-specific regulatory networks, with immediate translational implications for precision anti-fibrotic therapy, immunomodulatory drug repurposing, and the development of context-specific biomarkers for clinical stratification and therapeutic monitoring. Full article

Background/Objectives: This study introduces a reproducible and interpretable graph-based framework for resting-state functional near-infrared spectroscopy (fNIRS) that enables network-level biomarker discovery for Alzheimer’s disease (AD). Although resting-state fNIRS is well suited for task-free assessment, most existing approaches rely on static channel-wise features or conventional functional connectivity, limiting insight into coordinated network dynamics and reproducibility. Methods: Resting-state prefrontal fNIRS signals were represented as subject-level graphs in which edges captured coordinated fluctuations of nonlinear signal complexity across channels, computed using sliding-window analysis. Graph neural networks (GNNs) were employed as analytical tools to identify disease-stage-related network patterns. Interpretability was assessed using edge-level importance measures, and reproducibility was evaluated through fold-wise stability analysis and consensus network construction. Results: The proposed complexity–fluctuation-based graph representation consistently outperformed conventional amplitude-based functional connectivity. Statistically supported prefrontal network biomarkers distinguishing mild cognitive impairment (MCI) from healthy aging were identified, with statistically significant group differences (p = 0.001). In contrast, network patterns associated with Alzheimer’s disease were more heterogeneous and less consistently expressed. Consensus analysis revealed a subset of prefrontal connections repeatedly selected across cross-validation folds, and attention-based network patterns showed strong spatial correspondence with statistically derived biomarkers. Conclusions: This study establishes a reproducible and interpretable framework for resting-state fNIRS analysis that emphasizes coordinated complexity dynamics rather than classification accuracy. The results indicate that network-level alterations are most consistently expressed at the MCI stage, highlighting its role as a critical transitional state and supporting the potential of the proposed approach for longitudinal monitoring and clinically applicable fNIRS-based assessment of neurodegenerative disease. Full article

The nasal mucosa functions as a highly specialized barrier that integrates epithelial, stromal, neuronal, and immune signals to maintain homeostasis and mount rapid responses to environmental challenges. Among its resident immune populations, innate lymphoid cells—particularly type 2 ILCs (ILC2s)—play a pivotal role in orchestrating type 2 inflammation driven by epithelial-derived alarmins such as IL-25, IL-33, and TSLP. Upon activation, ILC2s release IL-5 and IL-13, promoting eosinophilic inflammation, goblet cell hyperplasia, mucus hypersecretion, and tissue remodeling, all central features of chronic rhinosinusitis with nasal polyps (CRSwNP) and severe allergic rhinitis. Recent advances have revealed substantial ILC plasticity, the presence of nasal-resident ILC progenitors, and the influence of metabolic and neuroimmune cues in shaping ILC activation and persistence. Dupilumab, a monoclonal antibody targeting IL-4Rα, has emerged as a highly effective therapy, providing unique mechanistic insight into the epithelial–ILC axis. By blocking IL-4/IL-13 signaling, dupilumab dampens ILC2 effector functions, reduces IL-5/IL-13 output, restores epithelial barrier integrity, interrupts alarmin-driven amplification loops, and rebalances innate and adaptive immune networks. Clinical and translational studies indicate that baseline ILC2 phenotypes—particularly inflammatory ILC2 subsets—may predict treatment responsiveness, positioning ILC profiling as a promising biomarker strategy. This review synthesizes current knowledge of ILC classification, plasticity, progenitor biology, and epithelial–ILC communication in the nasal mucosa, while integrating emerging evidence on dupilumab-mediated immunomodulation. Collectively, these insights highlight ILCs as central drivers of type 2 inflammation and key targets for precision immunomodulation, offering a framework for personalized treatment approaches in CRSwNP and allergic rhinitis. Full article

Background: Measuring glycated hemoglobin (HbA1c) is essential for assessing long-term glycemic control, yet direct testing remains expensive and underutilized in many large-scale health surveys and resource-constrained settings. This study aims to (i) deliver a highly accurate and interpretable ML model for predicting HbA1c from routinely collected clinical, biochemical, and demographic data, (ii) reduce dependency on extensive laboratory panels by identifying a compact, cost-efficient subset of key predictors, and (iii) establish a transferable, explainable modeling framework applicable across chronic disease biomarkers. Unlike prior HbA1c prediction studies that focus primarily on classification or accuracy-driven models, this work introduces a unified framework for continuous HbA1c regression that jointly integrates cost-oriented feature parsimony, stratified regression validation, and explainability by design. Methods: We aggregated data from the National Health and Nutrition Examination Survey (NHANES) cycles 2007–2020, encompassing 66,148 records and 224 candidate features. We implemented a two-stage feature selection pipeline: Incremental Correlation Selection (ICS) to narrow the variable space, followed by Recursive Feature Elimination with Cross-Validation (RFECV) to isolate the most informative features. Model interpretability was assessed using partial dependence plots and feature importance analysis. Results: The optimal model, LightGBMRegressor with most-frequent imputation, achieved R² = 0.7161, MAE = 0.334, MSE = 0.304, and MAPE = 5.56%, while using only 40 selected features. Interpretability analysis revealed clinically coherent relationships that align with physiological expectations. Discussion: The proposed framework maintains robust predictive performance while substantially reducing the number of required input features, enabling cost-efficient HbA1c estimation together with transparent, physiologically coherent model insights. By consolidating continuous HbA1c prediction, cost-aware feature selection, stratified evaluation, and explainability within a single pipeline are enhanced. Conclusions: This study advances beyond existing approaches and offers a practical blueprint for scalable biomarker estimation in population health and clinical decision-support applications. Its explainable, efficient, and generalizable design positions it as a strong candidate for clinical decision-support and population-health applications. Full article

Skin-lesion recognition from clinical photographs is clinically valuable yet computationally challenging due to large intra-class variation, subtle inter-class boundaries, class imbalance, and heterogeneous acquisition conditions. To address these constraints under realistic compute budgets, we investigate Inception-family convolutional baselines and propose GISLC—a Gated-Inception model that augments a GoogLeNet/Inception-V1 backbone with a lightweight, spatial gating head inspired by ConvLSTM. Unlike static fusion (concatenation/summation) of multi-branch features, the proposed gated head performs per-location, learnable regulation of feature flow across branches, prioritizing diagnostically salient patterns while suppressing redundant activations. Experiments were conducted on the clinical-images subset of the Multimodal Augmented Skin Lesion Dataset (MASLD), an augmented derivative of HAM10000, using stratified train/validation/test splits, clinically motivated augmentation, and class-weighted optimization to mitigate skewed label frequencies. A controlled ablation study evaluates backbone choices and optimization settings and isolates the contribution of gated fusion relative to standard Inception heads. Across runs, the gated fusion strategy improves discriminative performance while remaining parameter-efficient, supporting the view that spatially adaptive regulation can enhance robustness on non-dermatoscopic clinical imagery. We further outline practical steps for calibration analysis and compression-aware deployment in clinical and edge settings. Full article

Fast and reliable fault detection is critical in photovoltaic (PV) systems to improve reliability and energy yield and reduce maintenance costs, ensuring safe and efficient operation under varying operating conditions. Although recent data-driven PV fault detection techniques (FDTs) in literature have demonstrated high diagnostic accuracies, they often suffer from practical limitations, offline operation, lack of fault localization and/or inability to concurrently identify faults. To address these challenges, a unified framework is proposed that simultaneously integrates real-time operation, fault classification and localization, and concurrent-fault identification in a single compact diagnostic approach. This is realized by developing a parallel feedforward neural network (FFNN) architecture whose performance is enhanced by a residual model-based structure, resulting in a more interpretable, scalable, reliable and accurate scheme. In addition, Grey Wolf Optimizer–Support Vector Machine (GWO–SVM) feature selection is incorporated to select the most influential diagnostic features, thus reducing data redundancy, enhancing diagnostic accuracy, and shortening training time. The proposed approach was tested to diagnose five types of PV faults (open circuit, short circuit, partial shading, degradation, and simultaneous faults), as well as classify their intensity and location. Simulation results show that the proposed FFNNs consistently outperform conventional Support Vector Machines (SVMs) in classification accuracy, with accuracies exceeding 98% and 99% for fault classification and localization, respectively, and above 95% for noisy data. Moreover, GWO-SVM proved to offer more stable feature subsets compared to Particle Swarm Optimization–SVM (PSO–SVM) in the considered feature selection structure. Simulation results validated the effectiveness of the proposed unified multiclassification fault diagnosis approach, even under system uncertainties, making it suited for real-world PV systems. Full article

High-dimensional structured datasets are common in fields such as semiconductor manufacturing, healthcare, and finance, where redundant and irrelevant features often increase computational cost and reduce predictive accuracy. Feature selection mitigates these issues by identifying a compact, informative subset of features, enhancing model efficiency, performance, and interpretability. This study proposes Maximal Information Coefficient–Simplified Swarm Optimization (MIC-SSO), a two-stage hybrid feature selection method that combines the MIC as a filter with SSO as a wrapper. In Stage 1, MIC ranks feature relevance and removes low-contribution features; in Stage 2, SSO searches for an optimal subset from the reduced feature space using a fitness function that integrates the Matthews Correlation Coefficient (MCC) and feature reduction rate to balance accuracy and compactness. Experiments on five public datasets compare MIC-SSO with multiple hybrid, heuristic, and literature-reported methods, with results showing superior predictive accuracy and feature compression. The method’s ability to outperform existing approaches in terms of predictive accuracy and feature compression underscores its broader significance, offering a powerful tool for data analysis in fields like healthcare, finance, and semiconductor manufacturing. Statistical tests further confirm significant improvements over competing approaches, demonstrating the method’s effectiveness in integrating the efficiency of filters with the precision of wrappers for high-dimensional tabular data analysis. Full article

Background/Objectives: Acute myeloid leukemia (AML) is a heterogeneous hematologic malignancy with historically poor outcomes, particularly among older adults and patients harboring high-risk molecular features. Advances in genomic profiling have enabled the development of targeted therapies, reshaping treatment algorithms beyond conventional cytarabine-anthracycline induction and hypomethylating agent-based regimens. This review summarizes current evidence and emerging therapeutic strategies across four evolving areas: menin inhibition, FLT3 inhibition, IDH inhibition and treatment approaches for TP53-mutated AML. Methods: We reviewed published clinical trials, preclinical studies, and ongoing clinical trials evaluating targeted therapies in AML. Emphasis was placed on agents with regulatory approval or substantial clinical development, including menin inhibitors, FLT3 inhibitors, IDH inhibitors and novel therapies directed at TP53-mutated disease. Mechanistic data, response rates, survival outcomes, and resistance patterns were analyzed to provide an updated overview of therapeutic progress. Results: Menin inhibitors have demonstrated significant activity in NPM1-mutated and KMT2A-rearranged AML, with agents such as revumenib and ziftomenib producing meaningful remission rates and ongoing studies exploring combination strategies to mitigate resistance. FLT3 inhibitors, including midostaurin, gilteritinib, and quizartinib, have improved survival in FLT3-mutated AML, while emerging evidence supports potential benefit in selected FLT3–wild-type disease based on FLT3-like gene expression signatures. IDH inhibitors, namely ivosidenib and enasidenib, have provided increased efficacy in AML patients carrying these mutations. Questions still remain regarding their efficacy in contrast to venetoclax which has been shown to be particularly effective against this population. In contrast, TP53-mutated AML remains a therapeutic challenge: although hypomethylating-agent/venetoclax-based regimens yield improved initial responses, remissions are generally short-lived and overall survival remains poor. Early-phase therapies, including p53 reactivators and multi-kinase inhibitors, show preclinical promise but lack definitive clinical efficacy to date. Conclusions: Targeted therapies have improved outcomes in molecularly defined subsets of AML, with menin, IDH and FLT3 inhibitors representing major advances. However, TP53-mutated AML continues to carry a dismal prognosis, underscoring the need for more effective therapeutic strategies. Continued biomarker-driven research, novel drug combinations, and mechanistic insights will be essential to further refine AML treatment and improve long-term survival across disease subsets. Full article

Beef freshness is a critical indicator of meat quality and safety, and its rapid, non-destructive detection is of significant importance for ensuring consumer health and enhancing quality control throughout the meat industry chain. This study developed a novel methodology for non-destructive beef freshness assessment using visible and near-infrared (Vis-NIR) spectroscopy combined with machine learning, explainable artificial intelligence (xAI) techniques, and the SHapley Additive exPlanations (SHAP) framework. An improved hybrid heuristic method, particle swarm optimization–genetic algorithm (PSOGA), was used for feature selection, optimizing the wavelength subset for predicting beef quality indicators, including total volatile basic nitrogen (TVB-N) and color parameters (L*, a*, and b*). The eXtreme Gradient Boosting (XGBoost) was employed for regression modeling, and the results showed that PSOGA significantly outperforms traditional methods, with the PSOGA-XGBoost model achieving a satisfactory prediction accuracy (R²p values of 0.9504 for TVB-N, 0.9540 for L*, 0.8939 for a*, and 0.9416 for b*). The SHAP framework identified the key wavelengths as 1236 nm and 1316 nm for TVB-N, 728 nm for L*, 576 nm for a*, and 604 nm for b*, providing valuable insights into the determination of key wavelengths and enhancing the interpretability of the model. The results demonstrated the effectiveness of PSOGA and SHAP, providing a promising analytical method for monitoring beef freshness. Full article

Background: 47,XYY syndrome is a relatively common sex chromosome aneuploidy that remains largely underdiagnosed. While its somatic phenotype is often mild, growing evidence indicates a substantial burden of neurodevelopmental and psychiatric morbidity. However, the characterization of the neuropsychiatric phenotype across development, particularly during adolescence, and the associated treatment challenges remain incomplete. Objectives: To provide a comprehensive narrative review of the neuropsychiatric phenotype of 47,XYY syndrome and to illustrate clinical complexity and treatment response through a case series of adolescents. Methods: A narrative review of the literature was conducted focusing on genetics, neurodevelopmental and psychiatric features, neuroimaging and neurophysiology findings, clinical course, and management strategies in 47,XYY syndrome. This review is complemented by a case series of adolescents with confirmed 47,XYY karyotype, evaluated for developmental history, psychiatric comorbidity and response to pharmacological and non-pharmacological interventions. Results: The literature consistently describes increased risks of language impairment, executive dysfunction, ADHD, autism spectrum traits, and emotional and behavioral dysregulation in males with 47,XYY syndrome. Psychiatric vulnerability appears to increase during adolescence and adulthood, with elevated rates of mood, psychotic, and substance use disorders. The presented cases illustrate a convergent clinical trajectory marked by early developmental delays, progressive behavioral dysregulation in adolescence and limited or inconsistent response to multiple classes of psychotropic medications, suggesting a pattern of pharmacoresistance in a subset of patients. Conclusions: 47,XYY syndrome is associated with a distinct and heterogeneous neuropsychiatric phenotype that extends beyond early neurodevelopmental disorders. Early diagnosis alone may be insufficient to prevent severe psychiatric outcomes, highlighting the need for long-term monitoring and integrated, multidisciplinary management. Further research is required to identify early predictors of high-risk trajectories and to optimize treatment strategies for this population. Full article

Background: Trisomy 18 (T18; Edwards syndrome) and Trisomy 13 (T13; Patau syndrome) are rare autosomal aneuploidies characterized by severe congenital anomalies, high neonatal mortality, and complex clinical trajectories. Objective: This study aimed to describe the clinical features, management approaches, and outcomes of genetically confirmed patients aged 0–18 years diagnosed with T18 or T13 in a tertiary care center. Methods: This retrospective study reviewed hospital records of genetically confirmed T18 and T13 cases identified through ICD-10 codes (Q91–Q92) between January 2015 and December 2024. Patients aged 0–18 years at diagnosis were included. Demographic, clinical, and interventional data were collected from electronic medical records. Survival analyses were conducted using the Kaplan–Meier method, with comparisons assessed using the log-rank test. Results: Among 29 patients, 23 had T18 and 6 had T13. Cardiovascular involvement was the most frequent anomaly, and overall mortality was high despite intensive care. Median survival was 90 days for T18 and 120 days for T13, with more than 80% surviving the first month but showing a steep decline thereafter. Most deaths were attributed to cardiopulmonary complications or sepsis secondary to prolonged intensive care. Kaplan–Meier analysis revealed marked early mortality in both groups, with no significant survival difference (log-rank p ≈ 0.3). A small subset demonstrated longer-term survival with heterogeneous clinical courses. Conclusions: T18 and T13 are associated with high early mortality driven by complex congenital heart disease, respiratory instability, and infection-related complications. Although the overall prognosis remains poor, a minority of patients achieve extended survival, highlighting variable trajectories. Early multidisciplinary care, individualized decision-making, and strict infection prevention remain essential to optimize outcomes and support families. Full article

Inflammatory bone loss is a shared pathological feature of chronic diseases such as periodontitis (PD) and rheumatoid arthritis (RA). Despite affecting distinct tissues, these diseases exhibit a bidirectional association and converge on common immune-mediated mechanisms of bone resorption. To uncover the molecular drivers underlying bone destruction across inflammatory comorbidities, we combined bioinformatic analyses with experimental validation, using PD and RA as clinically relevant models of inflammatory disease comorbidities. Elevated blood lactate levels were observed in murine models of PD and RA and correlated positively with disease severity. Single-cell RNA sequencing data from PD and RA cohorts revealed upregulation of lactate metabolism-related genes in specific monocyte subsets, accompanied by enhanced pro-inflammatory signaling and osteoclastogenic programs. Using multiple machine learning approaches, SAT1, TET2 and HIF1A were identified as core lactate-related genes with strong diagnostic potential for both diseases. In vivo and in vitro experiments further validated that lactate-driven reprogramming of monocytes, marked by activation of core lactate-related genes in circulating monocytes and local macrophages, functionally connects immune activation with exacerbated bone resorption in comorbid PD and RA. Together, these findings define a lactate-driven immunometabolic axis connecting immune responses and bone remodeling and identify SAT1, TET2 and HIF1A as potential biomarkers for inflammation-related bone loss. Full article

Pneumonia is one of the main reasons for child mortality, with chest radiography (CXR) being essential for its diagnosis. However, the low radiation exposure in pediatric analysis complicates the accurate detection of pneumonia, making traditional examination ineffective. Progress in medical imaging with convolutional neural networks (CNN) has considerably improved performance, gaining widespread recognition for its effectiveness. This paper proposes an accurate pneumonia detection method based on different deep CNN architectures that combine optimal feature fusion. Enhanced VGG-19, ResNet-50, and MobileNet-V2 are trained on the most widely used pneumonia dataset, applying appropriate transfer learning and fine-tuning strategies. To create an effective feature input, the Chi-Square technique removes inappropriate features from every enhanced CNN. The resulting subsets are subsequently fused horizontally, to generate more diverse and robust feature representation for binary classification. By combining 1000 best features from VGG-19 and MobileNet-V2 models, the suggested approach records the best accuracy (97.59%), Recall (98.33%), and F1-score (98.19%) on the test set based on the supervised support vector machines (SVM) classifier. The achieved results demonstrated that our approach provides a significant enhancement in performance compared to previous studies using various ensemble fusion techniques while ensuring computational efficiency. We project this fused-feature system to significantly aid timely detection of childhood pneumonia, especially within constrained healthcare systems. Full article

Background: Poultry-associated Enterococcus spp. are widespread commensals but may serve as One Health indicators when virulence-associated determinants and antimicrobial resistance co-occur. We characterized paired phenotypic and genomic profiles to delineate species-stratified virulome and resistome patterns. Methods: Isolates originated from a previously established poultry collection with MIC testing. Genotype–phenotype analyses were restricted to the whole-genome sequenced subset (n = 31). The acquired antimicrobial resistance genes were identified using the Comprehensive Antibiotic Resistance Database (CARD), and virulence-associated determinants were screened using the Virulence Factors Database (VFDB). Results were summarized as isolate-level presence/absence matrices and integrated with MIC-derived susceptible/intermediate/resistant categories. Results: The WGS subset comprised E. faecalis (n = 23) and E. faecium (n = 8) with diverse sequence types. Virulome architecture was strongly species-dependent: E. faecalis carried a broad repertoire of adhesion/biofilm-associated determinants, whereas E. faecium showed a limited set of high-confidence virulence-associated hits. Acquired resistance determinants were common across isolates, and resistome profiles displayed structured co-occurrence. Integrated analyses suggested only a modest overall association between virulence-gene burden and acquired resistome size, largely driven by species-level differences. Genotype–phenotype concordance was class-dependent, with incomplete alignment in several antimicrobial classes, consistent with mechanisms beyond the screened acquired gene set. The acquired resistance determinants detected in the WGS subset predominantly mapped to antimicrobial classes commonly used in food-producing animals (e.g., tetracyclines, macrolides, lincosamides, aminoglycosides, and phenicols), supporting interpretation in the context of production-associated antimicrobial selection rather than implying last-line clinical resistance by default. Conclusions: Poultry-derived enterococci may combine genetic features compatible with persistence/colonization and acquired antimicrobial resistance, with co-occurrence patterns shaped primarily by species/lineage background. These findings support risk-stratified One Health surveillance and targeted functional and mechanism-focused follow-up. This integrated virulome–resistome view highlights species-specific risk signatures in poultry-associated Enterococcus and identifies discordant high-level phenotypes that merit targeted mechanistic follow-up. Full article