Peri-implant oral malignancy (PIOM) refers to malignant tumors arising around dental implants and is an increasingly reported complication of implant therapy. PIOM may follow distinct pathophysiological mechanisms, including chronic peri-implant inflammation and implant-related factors that contribute to carcinogenesis. This current review aims to explore the potential role of peri-implantitis (PI) as a risk factor for PIOM, discussing the proposed pathogenic mechanisms, histological findings, and clinical implications. A comprehensive literature search was conducted in PubMed, Scopus, and Web of Science databases. Relevant case reports, clinical studies, and reviews on the keywords “PIOM” and “PI” published from 2019 up to 2025 were included and qualitatively analyzed. Clinicopathologic characteristics are summarized as location and morphology, disease progression, histopathology, and degree of differentiation, and pathophysiological hypotheses involve inflammatory and electrochemical pathways, epithelial barrier dysfunction, molecular alterations, microbiome dysbiosis, and immune dysregulation. Current evidence remains limited and primarily anecdotal. Several studies suggest that chronic inflammation, titanium particle exposure, corrosion byproducts, and sustained tissue damage in peri-implant tissues may contribute to oncogenesis. While a direct causal link between PI and PIOM remains unproven, chronic peri-implant inflammation may contribute to malignancy development in predisposed individuals. Clinicians should consider a biopsy when peri-implant lesions exhibit atypical features, promptly.

Background: Acute deterioration of patients in general wards often leads to major adverse events (MAEs), including unplanned intensive care unit transfers, cardiac arrest, or death. Traditional early warning scores (EWSs) have shown limited predictive accuracy, with frequent false positives. We conducted a prospective observational external validation study of an artificial intelligence (AI)-based EWS, the VitalCare - Major Adverse Event Score (VC-MAES), at a tertiary medical center in the Republic of Korea. Methods: Adult patients from general wards, including internal medicine (IM) and obstetrics and gynecology (OBGYN)—the latter were rarely investigated in prior AI-based EWS studies—were included. The VC-MAES predictions were compared with National Early Warning Score (NEWS) and Modified Early Warning Score (MEWS) predictions using the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), and logistic regression for baseline EWS values. False-positives per true positive (FPpTP) were assessed based on the power threshold. Results: Of 6,039 encounters, 217 (3.6%) had MAEs (IM: 9.5%, OBGYN: 0.26%). Six hours prior to MAEs, the VC-MAES achieved an AUROC of 0.918 and an AUPRC of 0.352, including the OBGYN subgroup (AUROC, 0.964; AUPRC, 0.388), outperforming the NEWS (0.797 and 0.124) and MEWS (0.722 and 0.079). The FPpTP was reduced by up to 71%. Baseline VC-MAES was strongly associated with MAEs (P<0.001). Conclusions The VC-MAES significantly outperformed traditional EWSs in predicting adverse events in general ward patients. The robust performance and lower FPpTP suggest that broader adoption of the VC-MAES may improve clinical efficiency and resource allocation in general wards.

Objective: Spontaneous remission may influence the outcome of clinical trials and evaluation of antidepressant efficacy, as it is associated with placebo effects and false remission rates. However, the characteristics of spontaneous remission and its biological mechanisms remain poorly understood. This study aimed to explore the metabolic signatures and underlying biological mechanisms of spontaneous remission using metabolomics. Methods: This study conducted untargeted and targeted metabolomic analyses in a discovery cohort (n = 16) comprising patients with major depressive disorder (MDD) and those who were spontaneously remitted without medication.Findings were validated in an independent cohort (n = 185), comprising drug-treated patients and healthy controls. Results: Acetylcarnitine levels were significantly increased in spontaneous remission compared to depression, whereas glycerophosphocholine levels were decreased in spontaneous remission. Both metabolites showed the highest concentration in the control group, followed by the remission group, and the lowest concentration in the depression group, regardless of medication status. These changes suggest alterations in mitochondrial and membrane lipid metabolism. Conclusion Altered levels in acetylcarnitine and glycerophosphocholine may reflect key pathogenic mechanisms of MDD. These findings offer new insight into spontaneous remission as a distinct clinical subtype of depression and may highlight the potential of these metabolites as biomarkers for treatment monitoring in MDD.