Search Results (2,601)

Background: Artificial intelligence (AI) is poised to fundamentally reshape healthcare delivery, offering unprecedented advancements in diagnostics, treatment personalization, and operational efficiency. However, a growing body of international research reveals a critical “optimism–knowledge gap”: healthcare professionals are enthusiastic about AI’s potential but possess limited technical knowledge and practical experience. This gap compromises the safe and effective implementation of AI tools. The Italian healthcare context presents a unique and amplifying challenge, as it is defined by the stringent “human-in-the-loop” oversight mandated by the Garante per la protezione dei dati personali (Italy’s Data Protection Authority). This legal framework makes clinician competence not just a goal, but a prerequisite for regulatory compliance. Objective: This study aimed to provide an exploratory quantitative assessment of AI awareness, practical application, and understanding of its limitations among a sample of clinicians in Italy. It specifically sought to compare the preparedness of hospital-based clinicians and general practitioners (GPs) and to identify the workforce’s perceived educational needs within this unique legal environment. Methods: A descriptive, cross-sectional survey was conducted from February to August 2025. Using a non-probability convenience sampling method via professional networks, the survey yielded 362 total responses. Data were analyzed descriptively and inferentially using Chi-square (${\chi }^2$) tests to compare cohort responses on familiarity, practical exposure, knowledge of limitations, and interest in further training. Results: A universal and high demand for education was found, with 89.9% of all respondents being “Moderately” or “Very” interested in learning more about AI. This optimism coexists with dangerously low practical exposure. The gap was most profound among GPs, 44.1% of whom have “Never” used an AI tool—a rate significantly higher than hospital clinicians (34.9%; ${\chi }^2=3.14$, p = 0.045). Furthermore, 32.6% of GPs admitted that they “understand some benefits but not the limitations.” Conclusions: Italian clinicians mirror the global optimism–knowledge gap. These findings underscore the urgent need for structured, continuous education in AI literacy to address ethical and regulatory imperatives within the Italian healthcare system. Full article

An investigation employing multiple diagnostic techniques was conducted to evaluate the post-fire condition and residual structural safety of a fire-damaged precast concrete industrial building. The evaluation included a detailed visual inspection, mechanical testing of extracted concrete cores, and mineralogical and microstructural analysis through thermo-chemical methods, namely X-ray Diffraction, Scanning Electron Microscopy, and Energy-Dispersive X-ray Spectroscopy, alongside tensile strength tests of reinforcement bars sampled from the affected structure. The building was divided into five sections according to the severity and extent of observed fire damage. Results indicated that the highest in situ temperatures were attained in the most heavily damaged section, whereas the remaining sections experienced progressively lower temperatures, remained below approximately 600 °C. Despite the severe fire exposure in localized areas, all assessed structural elements maintained adequate residual integrity. The reinforcing steel exhibited satisfactory residual mechanical properties, exhibiting yield strengths ranging from 550 to 600 MPa. The integration of visual, mechanical, and microstructural assessments provides a reliable framework for estimating fire temperatures and supporting structural rehabilitation decisions. Full article

Objective: To evaluate the diagnostic and prognostic utility of the hemoglobin–albumin–lymphocyte–platelet (HALP) score and several systemic inflammatory indices derived from routine blood parameters—including the systemic immune-inflammation index (SII), platelet-to-lymphocyte ratio (PLR), pan-immune inflammation value (PIV), and systemic inflammatory response index (SIRI)—for pathogen differentiation and clinical assessment in culture-proven late-onset neonatal sepsis (LOS). Methods: A retrospective analysis was conducted on a cohort of 150 neonates with culture-proven LOS. Systemic inflammatory indices were calculated at baseline (first week of life) and at the time of septic insult. The discriminative power of these indices was assessed via ROC curve analysis, with optimal cut-off points determined by the Youden Index. Risk stratification was performed using Odds Ratio (OR) modeling with 95% Confidence Intervals (CIs) to evaluate the predictive strength of each marker according to its respective threshold. Results: Diagnosis-phase assessments identified SII as the premier discriminator for microbiological etiology (AUC = 0.869; OR = 44.57), outperforming PLR and PIV. Although HALP demonstrated moderate efficacy in distinguishing pathogens, it lacked prognostic value regarding mortality. Conversely, SIRI displayed limited clinical utility, yielding the lowest predictive performance in our cohort. Conclusions: In neonatal sepsis, the HALP score provided additional clinical information when compared with several hematological inflammatory indices. Although HALP was not associated with mortality, prospective multicenter studies are needed to clarify the role of these cost-effective markers in pathogen differentiation and clinical assessment of LOS. Full article

Aim: To evaluate the diagnostic value of conventional ultrasound (CUS) and contrast-enhanced ultrasound (CEUS) features in differentiating benign from malignant Bethesda III/IV thyroid nodules, and to identify independent predictors of malignancy. Methods: We retrospectively analyzed 164 surgically confirmed Bethesda III/IV thyroid nodules. CUS and CEUS features were evaluated by two experienced radiologists blinded to pathological outcomes. Univariate analysis compared features between benign and malignant groups. Multivariate logistic regression was used to identify independent predictors. Diagnostic models were constructed based on CUS alone, CEUS alone, and their combination, with performance evaluated using receiver operating characteristic (ROC) curve analysis. The area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each model. Results: The malignancy rate was 48.8% (80/164). Multivariate analysis identified microcalcifications (OR = 4.815, p < 0.001), aspect ratio >1 (OR = 2.499, p = 0.028), and irregular shape (OR = 2.465, p = 0.035) as independent risk factors, while older age (OR = 0.926 per year, p < 0.001) was protective. The CUS model achieved an AUC of 0.815 with high sensitivity (91.3%) and NPV (87.7%). The CEUS model performed poorly (AUC = 0.609). The combined model (AUC = 0.823) showed no significant improvement over CUS alone (p > 0.05). Physician subjective diagnosis based on CEUS TI-RADS yielded an AUC of 0.775. Conclusions: Conventional ultrasound features provide good diagnostic value for Bethesda III/IV nodules, with high sensitivity and NPV suitable for clinical screening. The addition of CEUS offered limited incremental benefit in this specific population, suggesting that the diagnostic value of CEUS for differentiating benign from malignant cytologically indeterminate thyroid nodules (ITNs) may be limited. Full article

Background: MicroRNAs (miRNAs) are stable, small non-coding RNAs involved in asthma-related pathways and are promising diagnostic biomarkers and therapeutic targets in childhood asthma. Objective: To identify miRNAs differentially expressed in preschool wheezing and childhood asthma, evaluate their association with asthma diagnosis and severity-related phenotypes, and explore their potential translational relevance through exploratory bioinformatic analyses. Methods: A systematic search of Medline, Embase, SCOPUS, PubMed, CINAHL, and Web of Science was conducted for English-language articles published up to March 19, 2025. Eligible human studies reported that miRNAs were differentially expressed in children with wheeze or asthma versus healthy controls (p < 0.05, fold change ≥ 1.5). Bioinformatic analysis identified hub genes, constructed protein–protein interaction networks, and predicted drug–gene interactions. Results: Forty-seven studies met the inclusion criteria, yielding 58 differentially expressed miRNAs (31 up, 27 down). Recurrently reported miRNAs included miR-497, let-7e, miR-98, miR-21, miR-126a, miR-196a2, miR-1, miR-146a-5p, miR-210-3p, miR-145-5p, and miR-200c-3p across blood, nasal swabs, BALF, and exhaled breath condensate. miR-26a showed strong diagnostic performance (sensitivity 83%, specificity 93%; p < 0.002, 95% CI 0.831–0.987). Functional enrichment implicated 56 differentially expressed genes in metabolic and immune processes. Ten hub genes (including TNF, IL5, IL13, TLR4) were linked to 339 potential therapeutic agents; the exploratory network analysis highlighted overlap between predicted miRNA-regulated hub genes and existing asthma-relevant drug targets, including approved biologics. Conclusions: Our review findings suggest that several miRNAs are promising candidate biomarkers for childhood asthma phenotyping and severity assessment; however, their diagnostic utility remains exploratory and requires rigorous external validation and standardisation before clinical application. Full article

Abies georgei var. smithii (Viguie & Gaussen) is a dominant conifer along the southeastern margin of the Qinghai–Tibet Plateau, where heart rot often develops covertly, complicating forest health monitoring and disease management. Fomitopsis subpinicola B.K. Cui, M.L. Han & Shun Liu is an important causal agent of heart rot affecting A. georgei var. smithii in this region, yet rapid, field-deployable molecular diagnostics of this pathogen remain limited. Here, we developed and evaluated two TEF1α-based isothermal platforms for specific detection of F. subpinicola: RAA and LAMP. To reduce potential cross-reactivity, TEF1α sequences from representative taxa within the F. pinicola species complex and closely related non-complex species were aligned for primer/probe design. Candidate RAA primers were screened by gel electrophoresis to select an optimal pair, and two LAMP primer sets were compared by specificity testing to identify the best-performing set. Both assays specifically detected F. subpinicola with no cross-amplification in the tested non-target fungi. Limits of detection were 9.97 copies/μL for fluorescent RAA (25 min), 9.97 × 10² copies/μL for RAA-LFD (15 min), and 9.97 × 10³ copies/μL for LAMP (35 min). In 30 increment core samples from A. georgei var. smithii, all methods consistently detected samples with obvious decay, while fluorescent RAA additionally yielded positives in some apparently asymptomatic samples, indicating promise for early or low-abundance screening. Together, these assays constitute a tiered and application-oriented detection system, enabling flexible selection of diagnostic approaches according to sensitivity requirements, operational conditions, and field surveillance needs for heart rot of A. georgei var. smithii. Full article

Background/Objectives: Autism spectrum disorder (ASD) is a clinically and genetically heterogeneous neurodevelopmental condition, and the diagnostic yield of whole-exome sequencing (WES) varies across settings. This single-center study aimed to determine the molecular diagnostic yield of WES in pediatric ASD and to explore genotype–phenotype overlap using a structured, phenotype-driven reanalysis strategy. Methods: We enrolled 60 children with syndromic and non-syndromic ASD, who underwent detailed clinical and dysmorphology assessment. WES for single-nucleotide and copy-number variant (CNV) detection was performed in an accredited laboratory, followed by clinician-driven reinterpretation, integrating expanded phenotypic data and ACMG/AMP-based variant classification. Genes were considered if they harbored rare, potentially pathogenic variants and were previously reported or curated in established ASD-associated gene resources. Results: The initial external laboratory report identified 5 of 60 patients (8.3%) with a pathogenic (P) or likely pathogenic (LP) variant (positive result), 30 of 60 (50.0%) with a variant of unknown significance (VUS) (inconclusive result), and 25 of 60 (41.7%) with a negative result. Clinician-based variant reinterpretation identified pathogenic or likely pathogenic variants in 9 of 60 patients (15.0%), representing an 80% relative increase in diagnostic yield, as well as 43 VUSs distributed across 34 patients, while 17 patients had no reportable variants (negative result). Overall, reanalysis revealed 11 additional variants of interest (pathogenic, likely pathogenic, or VUS) that had not been reported in the initial assessment. In total, 52 sequence and copy-number variants in 46 genes were detected, most of which were VUSs (83%). Conclusions: In this pediatric ASD cohort, WES with phenotype-driven reinterpretation and CNV assessment yielded a clinically positive result in 15% of patients and uncovered additional candidate variants, highlighting both the value and the current interpretative challenge of comprehensive genomic testing in ASD. Full article

Ochratoxin A (OTA), a prevalent food contaminant, is closely linked to the development of various cancers, including clear cell renal cell carcinoma (ccRCC). However, the potential mechanisms remain to be explored. In this study, we employed network toxicology, machine learning, and molecular docking techniques to systematically investigate the potential molecular mechanisms underlying OTA-associated ccRCC. We normalized transcriptional data from two Gene Expression Omnibus (GEO) datasets and analyzed it using differential expression analysis and weighted gene co-expression network analysis (WGCNA), identifying 3224 ccRCC-associated target genes. These were intersected with 232 predicted OTA target genes, yielding a total of 56 overlapping targets. The results of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicated that these targets were primarily enriched in critical biological processes, including extracellular matrix remodeling, immune microenvironment regulation, signaling pathway transduction, cellular metabolism, and protein homeostasis. Machine learning analysis identified “glmBoost + RF” (a sequential combination of feature selection and classifier) as the optimal model, from which nine key genes were extracted. SHapley Additive exPlanations (SHAP) analysis revealed five core genes (IGFBP3, ITGA5, PYGL, SLC22A8, LTB4R), with IGFBP3 and ITGA5 serving as the principal driver genes of the model. Validation of the model’s diagnostic efficacy and single-cell transcriptome analysis indicated that the core genes exhibited significant differential expression patterns, cell-type-specific expression characteristics, and high independent diagnostic efficacy. Molecular docking analyses predicted stable interactions between OTA and the core target proteins. These findings suggest potential molecular links between OTA exposure and ccRCC, providing a foundation for hypothesis generation and future experimental validation. Full article

Lichen planus (LP) is a chronic immune-mediated inflammatory disease of unknown etiology affecting the skin and mucous membranes and is frequently associated with comorbid conditions, although data from Swedish populations remain limited. This retrospective population-based case–control study included all registered citizens in Region Jönköping, Sweden, between 2013 and 2022, to examine comorbidities, estimate prevalence and incidence, assess diagnostic validity of ICD-10 coding (L43), and evaluate treatment patterns. Incidence and prevalence were calculated, demographic and treatment characteristics were described, and diagnostic validity was assessed through independent medical record review of 70 randomly selected cases to determine positive predictive value (PPV). Associations between LP and predefined comorbidities were analyzed using binomial logistic regression adjusted for age and sex. Among 361,812 individuals, prevalence was 235.5 and incidence 19.6 per 100,000 inhabitants. The PPV of the LP diagnosis was 78.6%, yielding an adjusted prevalence of 184.9 per 100,000 inhabitants. Over one third of prevalent patients received topical therapy, primarily corticosteroids. LP was significantly associated with thyroid, malignant, metabolic, and autoimmune conditions. LP is relatively uncommon, ICD-10 coding shows acceptable validity, and its association with clinically relevant comorbidities highlights the need for comprehensive patient assessment. Full article

The present work is devoted to the analysis of a time-fractional Gardner equation arising in the modeling of nonlinear plasma waves in media endowed with memory and anomalous transport effects. Building on a physically motivated soliton profile, we construct a finite-time fractional ansatz in which the integer-order time variable is replaced by a fractional reparametrization that encodes the Caputo memory kernel. Within this framework, the governing evolution equation is not treated via a formal infinite expansion but rather via a finite approximation, whose quality is assessed directly via the associated residual. The Caputo fractional derivative is evaluated by a strong finite-difference formula that is second-order accurate in time and preserves the nonlocal convolution structure of the fractional operator. This combination of a finite fractional ansatz and a strong Caputo discretization allows us to compute the residual of the time analytically fractional Gardner equation and to use it as a quantitative diagnostic of accuracy and consistency. Two representative classes of nonlinear structures supported by the Gardner equation are examined in detail: a smooth solitary-wave profile and a cnoidal-wave configuration. For each example, the approximate fractional solution is generated, the corresponding residual is evaluated in space–time, and global and final-time residual norms are determined to quantify the influence of the fractional order on the wave dynamics and on the quality of the approximation. The numerical results show that the proposed residual-controlled approach yields residual magnitudes that remain one to two orders of magnitude smaller than those associated with truncated residual power-series approximations constructed from the same data, while preserving the expected qualitative features of fractional solitary and cnoidal waves in non-Markovian plasma environments. Full article

Background/Objectives: Influenza, RSV, and SARS-CoV-2 co-circulate and evolve under immune and therapeutic pressures, complicating decision-making for both vaccine formulation and antiviral use. Fragmented, pathogen-specific sequencing approaches limit cross-virus comparability. Methods: We applied a standardized, multiplexed, amplicon-based next-generation sequencing (NGS) workflow to 34 diagnostic specimens (Ct < 35) positive for influenza A/B, RSV-A/B, or SARS-CoV-2. Sequencing libraries were generated and run on an Illumina MiSeq platform (2 × 250 bp). Although the wet-lab workflow is standardized across pathogens, consensus generation and annotation utilized two different analysis environments: Geneious Prime for influenza and MicrobioChek for RSV and SARS-CoV-2. Quality metrics included genome breadth and depth of coverage. Results: Near-complete genomes (mean coverage ≥98%) were recovered for all samples. Influenza A(H1N1)pdm09 sequences clustered in clade 6B.1A; A(H3N2) clustered in subclade 3C.2a1b.2a.2; and influenza B belonged to the Victoria lineage V1A.3a.2. RSV sequences were assigned to Nextclade clades A.D.5.1, A.D.1.10, A.D.2.1, and A.D.3 (RSV-A) and to B.D.4.1.3 and B.D.E.1 (RSV-B), consistent with the ON1 (RSV-A) and BA (RSV-B) genotypes prevalent in recent seasons. Clinically relevant mutations included changes in the influenza HA site and neuraminidase substitutions, RSV F-protein polymorphisms, and spike protein substitutions associated with recent Omicron sublineages (L455F/S, F456L) in SARS-CoV-2. Conclusions: A unified amplicon–NGS approach yields harmonized genomic data across respiratory viruses, enabling timely detection of antigenic drift and resistance markers while supporting integrated, cross-pathogen surveillance. Full article

Diabetic retinopathy (DR) is the largest cause of permanent vision loss in the working-age population, making automated grading critical for timely therapeutic intervention. While recent deep learning algorithms have improved feature discrimination, modern state-of-the-art systems have two fundamental drawbacks. First, most models rely on standard Convolutional Neural Networks, which struggle to capture long-range relationships and lack semantic reasoning, resulting in visual findings that do not correlate with clinical knowledge. Second, present approaches often consider grading as a nominal classification or a pure ordinal regression task, failing to strike a compromise between high classification accuracy and severity-consistent predictions (Quadratic Weighted Kappa). To address these challenges, we propose Dual-SwinOrd, a novel framework that integrates a hierarchical Vision Transformer with a semantically guided dual-head mechanism. Specifically, we use a Swin Transformer backbone to extract hierarchical features, effectively capturing global retinal structures. To handle diverse lesion scales, we incorporate a Progressive Lesion-aware Kernel Attention (PLKA) module and a Semantic Prior Modulation (SPM) module guided by PubMedCLIP, bridging the gap between visual features and medical linguistic priors. In addition, we propose a Dual-Head learning strategy that decouples the optimization objective into two parallel streams: a Classification Head to maximize diagnostic accuracy and an Ordinal Regression Head (DPE) to enforce rank-consistency. This design effectively mitigates the trade-off between precision and ordinality. Extensive experiments on the APTOS 2019 and DDR datasets demonstrate that Dual-SwinOrd achieves state-of-the-art performance, yielding an Accuracy of 87.98% and a Quadratic Weighted Kappa (QWK) of 0.9370 on the APTOS 2019 dataset, as well as an Accuracy of 86.54% and a QWK of 0.9040 on the DDR dataset. Full article

Background: In this study, we aimed to evaluate and compare the diagnostic performance of peripapillary retinal nerve fiber layer (RNFL) thickness, macular ganglion cell–inner plexiform layer (GCIPL) thickness, and GCIPL asymmetry parameters in differentiating healthy eyes from primary angle-closure glaucoma (PACG), primary open-angle glaucoma (POAG), and secondary open-angle glaucoma (SOAG). Methods: This retrospective study included 204 eyes of 204 patients categorized into four groups: healthy controls (n = 46), PACG (n = 53), POAG (n = 58), and SOAG (n = 47). All participants underwent spectral-domain optical coherence tomography (OCT). Peripapillary RNFL thickness, sectoral and average GCIPL thickness, and GCIPL-derived asymmetry ratios were analyzed. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis. Results: Diagnostic accuracy varied according to glaucoma subtype. In distinguishing POAG from healthy controls, the average RNFL thickness (area under the ROC curve [AUC] = 0.82) demonstrated the highest diagnostic performance, followed by the superotemporal, inferotemporal, and average GCIPL thickness parameters. In contrast, no parameter reached an AUC of ≥0.80 in the PACG or SOAG comparisons. GCIPL asymmetry ratios exhibited limited discriminative ability across most analyses. Subtype differentiation was modest; POAG versus SOAG comparisons yielded AUC values up to 0.66, whereas PACG versus SOAG comparisons demonstrated minimal discrimination (AUC range: 0.47–0.63). Conclusions: Peripapillary RNFL and localized temporal GCIPL thickness measurements provide the highest diagnostic accuracy for identifying POAG. Diagnostic performance is reduced in PACG and SOAG, and the OCT parameters show limited ability to differentiate between glaucoma subtypes. GCIPL asymmetry indices do not enhance diagnostic discrimination beyond direct thickness measurements. Full article

Pomegranate (Punica granatum L.) is a major fruit crop in tropical and subtropical regions, but changing climatic conditions—especially rising temperatures and intense solar radiation—are increasing physiological disorders. Sunburn, a key heat- and light-induced disorder, causes peel discoloration and tissue damage. This results in significant yield loss and reduced fruit quality. The objective of this study was to characterize sunburn-induced anatomical changes in the widely grown, highly sensitive Hicaznar cultivar in Türkiye, and to identify the optimal phenological stage for the application of sunburn-preventive practices. For this purpose, pomegranate fruit peels were fixed in FAA (Formalin–Acetic Acid–Alcohol) solution, embedded in paraffin blocks, and sectioned at a thickness of 5–7 µm. The sections were stained using the hematoxylin–eosin method and examined under a light microscope. The images captured with a digital camera wereanalyzed and revealed that sunburn damage in the pomegranate peel first appears in the cuticle layer, followed by disruption and fragmentation of the cutaneous and epidermal layers beneath it, and ultimately leads to damage of the parenchyma cells. Furthermore, Light microscopy showed that before visible discoloration, cells near the epidermis undergo phenolic accumulation, cell-wall thickening, and lignification, which are early indicators of sunburn. These microscopic changes provide early diagnostic features for detecting sunburn damage before external symptoms manifest. The study concluded that anatomical changes begin before the visible symptoms of sunburn appear on the fruit, and the most appropriate timing for applying preventive measures against sunburn has been identified. Light microscopy showed that before visible discoloration, cells near the epidermis undergo phenolic accumulation, cell-wall thickening, and lignification, which are early indicators of sunburn. Full article

Accurately assessing dynamic water resource carrying capacity (WRCC) is essential and challenging, particularly in regions like the Gansu sections of the Yellow River Basin (GSYRB), a core water source protection zone in the arid northwest of China, due to its pressing challenge of balancing water resources for socioeconomic needs and ecological security. This study proposes a novel integrated computational assessment framework named SD-VIKOR to address the complexities arising from nonlinear interactions within the “water resources–socioeconomic–ecological environment” (W–S–E) system. The core of this framework is the tight coupling of a system dynamics (SD) simulation model with a VIKOR multi-criteria evaluation module, where indicator weights are objectively–subjectively determined via an Analytic Hierarchy Process (AHP)–entropy weight method. This integrated SD-VIKOR engine enables dynamic, scenario-based WRCC trajectory simulation. To move beyond simulation and enable mechanistic insight, the framework further incorporates a diagnostic suite: a Geodetector module quantifies dominant drivers and their interactions; an obstacle degree model pinpoints key limiting factors; and a coupling coordination degree model evaluates subsystem synergies. Together, they form a closed-loop “dynamic simulation → multi-criteria assessment → driving mechanism analysis and constraint diagnosis → subsystem coordination analysis” workflow. Applied to the GSYRB from 2012 to 2030 under five development scenarios, the framework demonstrated high efficacy. It successfully captured path-dependent WRCC evolution, revealing that the ecological-priority scenario (B2), which shifts system drivers from economic-scale expansion to resource-efficiency and environmental governance, yielded optimal WRCC and the highest system coordination. In contrast, business-as-usual and single-minded economic expansion scenarios underperformed. Six key obstacle factors were quantitatively identified, linking WRCC constraints to natural endowments, economic patterns, and domestic demand. The results reveal pronounced spatial–temporal heterogeneity in WRCC across the GSYRB, with socioeconomic development, water resource use efficiency, and ecological conditions acting as the primary joint drivers of WRCC evolution. Critically, several key indicators are identified as persistent constraints on regional water sustainability. In contrast to conventional static evaluations, the integrated framework captures the complex dynamics and multi-subsystem interactions governing WRCC, offering a more robust diagnostic of resource–environment systems. These insights provide a transferable analytical basis for designing sustainable water management strategies in arid river basins. Full article