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BACKGROUND: Composite resins are widely used as the material of choice for definitive restorations due to their ability to integrate functional and aesthetic aspects in dental rehabilitation. Recently, one-shade composite resins have been introduced to simplify the clinical workflow. OBJECTIVES: This study aimed to compare the clinical performance of a "chameleon effect" composite resin with that of a multi-shade composite resin in non-carious cervical lesions after 36 months of follow-up. METHODS: This study was a randomized, controlled, double-blind clinical trial with an equivalence design using a split-mouth approach. The sample consisted of 60 patients presenting at least two non-carious cervical lesions, totaling 120 restorations. Restorations were performed using two materials from the same commercial brand: Vittra Unique (one-shade group) and Vittra APS (multi-shade group). The clinical performance of the restorations was longitudinally evaluated according to the FDI criteria. Survival analysis was performed using Kaplan-Meier curves and the log-rank test, while other clinical parameters were compared using the chi-square test (α = 0.05). RESULTS: At baseline, no statistically significant differences were observed between the groups regarding the evaluated biological, functional, and aesthetic parameters (p > 0.05). Both materials demonstrated clinically acceptable performance according to the FDI criteria, with no significant differences between them. CONCLUSIONS: After 36 months, no significant differences were observed between the one-shade and the multi-shade composite resins in the restoration of non-carious cervical lesions. CLINICAL SIGNIFICANCE: This 36-month randomized clinical trial demonstrates that single-shade composite resins achieve structural durability and aesthetic integration equivalent to traditional multi-shade layering when restoring non-carious cervical lesions. This evidence validates a simplified, single-shade restorative workflow, significantly reducing chairside time and operator-dependent variables without compromising the clinical longevity.
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BACKGROUND AND OBJECTIVE: Metastatic hormone-sensitive prostate cancer (mHSPC) exhibits heterogeneous progression patterns, with early progression to metastatic castration-resistant prostate cancer (mCRPC) within 12 months indicating aggressive tumor biology and poor prognosis. Current risk stratification tools (CHAARTED, LATITUDE) offer limited individualized prediction. Machine learning approaches are increasingly applied to predict prostate cancer progression, but most models show modest performance (AUC 0.68-0.72), limited external validation, or require genomic variables unavailable in routine practice. This study aimed to develop and externally validate a novel RINH algorithm for predicting early mCRPC progression (≤ 12 months) using exclusively clinical variables, positioning it as a superior alternative to conventional ML classifiers. METHODS: This multicenter study enrolled 412 patients with de novo mHSPC from seven Spanish academic centers using mixed retrospective-prospective data collection. Twenty clinical variables were recorded, including demographics, PSA, ISUP grade, metastatic localization, CHAARTED/LATITUDE classifications, and treatment modalities. Following RINH-based outlier exclusion (55 patients), 357 patients (29 with early progression, 8.1%) were used to train six ML algorithms: RINH, Logistic Regression, Linear Discriminant, Support Vector Machine, Random Forest, and Subspace Discriminant. A two-tiered validation strategy integrated stratified fivefold cross-validation across all centers and formal external validation using center 1 (n = 121, 19 events) for training and centers 2-7 (n = 207, 10 events) for independent testing. Performance metrics included AUC, sensitivity, specificity, accuracy, and F1-score. KEY FINDINGS AND LIMITATIONS: Artificial intelligence and machine learning (ML) are transforming oncology, promising personalized risk stratification beyond traditional clinical criteria. In metastatic hormone-sensitive prostate cancer (mHSPC), early progression to castration resistance (mCRPC) within 12 months signals aggressive biology and poor prognosis, yet current tools (CHAARTED, LATITUDE) offer limited individualized prediction. Multiple ML models have been proposed with variable success: most achieve modest performance (AUC 0.68-0.72), lack robust external validation, or rely on genomic variables inaccessible in routine practice. We propose a novel approach using the Rivality Index Neighborhood (RINH) algorithm, demonstrating superior predictive capacity in an initial multicenter validation with exclusively clinical variables. This study provides rigorous multicenter external validation, advancing toward implementable precision oncology tools. CONCLUSIONS AND CLINICAL IMPLICATIONS: The RINH algorithm achieves superior predictive performance for early mCRPC progression using exclusively clinical variables, representing a significant advance toward implementable risk stratification. However, low reliability scores in external validation underscore that excellent performance metrics alone do not guarantee stability. Before clinical deployment, validation in substantially larger cohorts with higher progression events is essential. If validated, this model could enable personalized, risk-adapted therapeutic strategies, refining patient selection for treatment intensification or de-escalation.
BACKGROUND: Precision periodontology integrates molecular diagnostics, genomics, and advanced imaging into clinical decision-making. Despite major advances in microbiome characterisation, host genetics, and inflammatory biomarkers, their translation into routine care remains limited. OBJECTIVES: To critically appraise current evidence on microbiome-based profiling, genetic and epigenetic markers, host-response biomarkers, and three-dimensional imaging in periodontology, and to propose a conceptual decision-support framework linking diagnostic outputs to potential therapeutic actions and future implementation research. MATERIALS AND METHODS: A narrative review searching PubMed/MEDLINE, Scopus, Embase, and the Cochrane Library (2010-2025) using terms related to precision periodontology, subgingival microbiome, periodontitis genetics and epigenetics, salivary and GCF biomarkers, aMMP-8, CBCT, risk assessment, and artificial intelligence. Priority was given to meta-analyses, systematic reviews, longitudinal studies, and guideline documents. RESULTS: Microbiological testing has defined but narrow indications; single-SNP genotyping has not demonstrated clinical utility commensurate with cost; aMMP-8 point-of-care testing is among the most extensively investigated host-response tools and may have adjunctive value in selected monitoring and peri-implant scenarios; however, current evidence remains insufficient to support routine diagnostic implementation. CBCT may directly influence surgical decision-making through defect morphology characterisation. AI-based models show promise but lack prospective clinical validation. These conclusions are consistent with the 20th EFP Workshop Consensus Report. CONCLUSIONS: Precision periodontology currently operates in addition to, rather than in replacement of, conventional staging and grading. We propose a conceptual decision-threshold framework for the selective consideration of molecular and advanced imaging tools when their additive contribution may meaningfully inform management. This framework should be regarded as a research-oriented decision-support model rather than a validated clinical algorithm. CLINICAL RELEVANCE: Clinicians are provided with a structured, evidence-based framework that identifies specific clinical scenarios where molecular diagnostics, host-response biomarkers, and three-dimensional imaging may meaningfully modify periodontal treatment decisions, supporting the operationalisation of precision approaches in daily practice.
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NAT2 encodes arylamine N-acetyltransferase 2, a key enzyme in the phase II metabolism of arylamines and arylhydrazines. NAT2 is highly polymorphic, resulting in variable distributions of rapid and poor metabolizers across global populations. Here, we detail the process undertaken by the Clinical Pharmacogenetics Implementation Consortium (CPIC) NAT2 Pharmacogene Curation Expert Panel (PCEP) to assign clinical function to NAT2 star (*) alleles using CPIC's standard terminology. Given the observed impact of NAT2 genetic variability on drug response, CPIC convened the NAT2-PCEP to standardize clinical allele function assignments. The NAT2-PCEP is comprised of multidisciplinary and international members, including researchers, clinicians, and implementers with expertise in pharmacogenomics and NAT2 molecular biology. Extensive in vitro and clinical literature was curated from PubMed and other sources to assess NAT2 genotype-to-phenotype concordance as well as the biochemical function of NAT2 star alleles. The NAT2-PCEP assigned allele clinical function using CPIC's standard terminology (increased, decreased, uncertain, and unknown function) to 59 star alleles cataloged by the Pharmacogene Variation Consortium (PharmVar). Two alleles, NAT2*1 and NAT2*4, were assigned increased function (historically known as rapid), 40 alleles were assigned decreased function (historically known as slow), 10 alleles were assigned uncertain function, and seven alleles were assigned unknown function. Rigorous evidence review and in-depth PCEP discussion were crucial in determining these function assignments. The findings reported here underscore the importance of standardized allele functional terms and diplotype-to-phenotype assignments to further the clinical implementation of NAT2 pharmacogenetic test results.
Adquate methods to assure the quality of data collected at the clinic need to be developed. A full understanding of the limitations of physicians as information processors and reasonable performance expectations for physicians during peak information periods will result in concentrated planning for patient visits and will limit the data that must be collected at the clinic. It is mandatory for each clinical research project that protocol treatment take into account the question of variable provider follow-up versus constant provider follow-up. It is also imperative that all clinical research providers receive special training, testing, and follow-up evaluation. The prime responsibility for the overall conduct of clinical research rests with the principal investigator. A monitoring tool that should be more fully used is the informed patient.