Diagnostics

Background/Objectives: This study aimed to evaluate salivary leptin, adiponectin, and calprotectin levels and to investigate the associations among these biomarkers in periodontally healthy individuals, as well as in patients with gingivitis and periodontitis. Methods: A total of 165 participants were included: 55 periodontally healthy individuals, 55 with gingivitis, and 55 with periodontitis. Unstimulated saliva was collected via passive drool, and salivary leptin, adiponectin, and calprotectin levels were biochemically quantified using enzyme-linked immunosorbent assay. Results: Salivary leptin levels were significantly lower in the periodontally healthy group than those in the gingivitis and periodontitis groups, whereas adiponectin levels were reduced in the periodontitis group than in the periodontally healthy and gingivitis groups (p < 0.05). Salivary calprotectin levels differed significantly among groups, highest in the periodontitis group, followed by the gingivitis and periodontally healthy groups (p < 0.05). Salivary leptin and calprotectin levels demonstrated significant positive correlations with all clinical periodontal parameters, while adiponectin levels were negatively correlated (p < 0.05). Receiver operating characteristic and logistic regression analyses identified salivary leptin, calprotectin, and adiponectin levels as significant biomarkers for distinguishing periodontal health, gingivitis, and periodontitis (p < 0.05). Conclusions: These findings suggest salivary leptin, calprotectin, and adiponectin may serve as biomarkers and potential risk predictors of periodontal disease.

Background: Intellectual disability (ID) is a heterogeneous condition caused by diverse genetic factors, including single-nucleotide variants (SNVs) and copy number variants (CNVs). Whole-exome sequencing (WES) and clinical exome sequencing (CES) have become essential tools for identifying pathogenic variants; however, their relative diagnostic performance in ID has not been fully characterized. Methods: Children diagnosed with ID or related neurodevelopmental disorders underwent WES or CES. Identified variants were classified according to ACMG/AMP and ClinGen guidelines, with segregation analysis performed when parental samples were available. Diagnostic yields were compared across demographic, prenatal, and phenotypic subgroups. A multidimensional semi-quantitative scoring system encompassing 15 clinical domains (e.g., age at onset, neuro-motor function, seizures, MRI findings, vision, and dysmorphic features) was developed. Z-scores were calculated for each parameter, followed by hierarchical cluster analysis (HCA) and correlation modeling to define genotype–phenotype associations and pathway-level clustering. Results: A broad spectrum of pathogenic and likely pathogenic variants across multiple genes and biological pathways was identified in our study. CNV-associated cases frequently exhibited prenatal anomalies or multisystem phenotypes associated with large chromosomal rearrangements. Monogenic variants and their corresponding phenotypic profiles were identified through clinical exome sequencing (CES) and whole-exome sequencing (WES). Phenotypic HCA based on Z-scores revealed three major biological groups of patients with coherent genotype–phenotype relationships: Group 1, severe multisystem neurodevelopmental disorders dominated by transcriptional and RNA-processing genes (POLR1C, TCF4, HNRNPU, NIPBL, ACTG1); Group 2, intermediate epileptic and metabolic forms associated with ion-channel and excitability-related genes (SCN2A, PAH, IQSEC2, GNPAT); and Group 3, milder or focal neurodevelopmental phenotypes involving myelination and signaling-related genes (NKX6-2, PLP1, PGAP3, SMAD6, ATP1A3). Gene distribution significantly differed among these biological categories (χ² = 54.566, df = 34, p = 0.0141), confirming non-random, biologically consistent grouping. Higher Z-scores correlated with earlier onset and greater neurological severity, underscoring the clinical relevance of the multidimensional analytical framework. Conclusions: This study highlights the genetic complexity and clinical heterogeneity of intellectual disability and demonstrates the superior diagnostic resolution of WES and CES. Integrating multidimensional phenotypic profiling with genomic analysis enhances genotype–phenotype integration and enables data-driven phenotype stratification and pathway-based re-analysis. This combined diagnostic and analytical framework offers a more comprehensive approach to diagnosing monogenic ID and provides a foundation for future predictive and functional studies.

Background: Iodinated contrast agents are widely used in computed tomography (CT) imaging; however, they can cause adverse drug reactions (ADRs) ranging from mild hypersensitivity to severe anaphylaxis. While several clinical risk factors have been identified, large–scale studies incorporating environmental variables remain limited. This study aimed to assess the prevalence and predictors of contrast agent-related ADRs over a 10-year period. Methods: We retrospectively analyzed 221,962 adult outpatients who underwent contrast-enhanced CT between January 2014 and December 2023 at a single tertiary center: Patient characteristics, clinical conditions (e.g., hypertension, allergy history), contrast agent types, premedication status, seasonal trends, temperature, and humidity were examined. ADRs were categorized as mild, moderate, or severe based on American College of Radiology (ACR) guidelines. Logistic regression was used to identify independent predictors. Results: The overall prevalence of ADRs was 0.64% (1423 cases). ADRs were more frequent in females, younger patients, and those receiving premedication. Seasonal and environmental patterns were evident: higher ADR rates occurred in summer and autumn, with positive correlations to ambient temperature and humidity. Among contrast agents, Ioversol (1.4%) and Iomeprol (1.2%) showed the highest ADR rates. The prevalence of mild ADRs increased in the post–COVID-19 period, while that of moderate reactions declined. Conclusions: This real–world study identified multiple clinical and environmental factors associated with ADRs to iodinated contrast agents in CT imaging. The findings suggest the importance of individualized risk assessment and the consideration of environmental factors when planning contrast administration.