Periodontitis is a chronic non-communicable inflammatory disease in which oxidative stress plays an important role in tissue destruction and alveolar bone loss. Excessive production of reactive oxygen species disrupts redox homeostasis, activates inflammatory signaling pathways, and promotes regulated cell death processes such as pyroptosis and ferroptosis. The Nrf2/Keap1 pathway is a key regulator of antioxidant defense and cellular adaptation to redox imbalance. Impaired Nrf2 signaling has been associated with enhanced oxidative injury, NF-κB and NLRP3 inflammasome activation, osteoclast-driven bone resorption, and reduced regenerative capacity in periodontal tissues. Experimental studies suggest that Nrf2 activation can restore the redox balance and attenuate inflammation and bone destructive responses in a periodontal model. Moreover, therapeutic approaches involving phytochemicals, microbial-derived metabolites, and redox-responsive biomaterials have been reported to influence Nrf2-related signaling in experimental settings. However, the majority of the available evidence is derived from in vitro or animal studies, and the relevance of these findings to clinical periodontitis remains to be established. This review summarizes the current advances linking oxidative stress, redox signaling, cell death pathways, and bone remodeling with Nrf2 dysfunction in periodontitis and outlines the key mechanistic insights while highlighting the existing knowledge gaps.

To address the background-bias problem in computer-aided cytology caused by microscopic slide deterioration, this article proposes a deep learning approach for cell segmentation and background removal without requiring cell annotation. A U-Net-based model was trained to separate cells from the background in an unsupervised manner by leveraging the redundancy of the background and the sparsity of cells in liquid-based cytology (LBC) images. The experimental results demonstrate that the U-Net-based model trained on a small set of cytology images can exclude background features and accurately segment cells. This capability is beneficial for debiasing in the detection and classification of the cells of interest in oral LBC. Slide deterioration can significantly affect deep learning-based cell classification. Our proposed method effectively removes background features at no cost of cell annotation, thereby enabling accurate cytological diagnosis through the deep learning of microscopic slide images.

Background: Older inpatients in acute care hospitals often have impaired swallowing, which adversely affects nutrition and prognosis. However, limited research has ex-amined how dietary form, oral health, and functional independence interact in this population. Methods: We conducted a cross-sectional study of 74 patients (mean age, 87.1 ± 6.0 years) admitted to the Department of Geriatric Medicine, Kanazawa Medical Univer-sity Hospital. Dietary form was determined by swallowing specialists using videoen-doscopic (VE) and videofluoroscopic (VF) evaluations. Oral health was assessed with the Japanese Oral Health Assessment Tool (OHAT-J), and functional independence was measured with the Functional Independence Measure (FIM). Logistic regression, including stepwise selection, identified predictors of dietary form. Receiver operating characteristic (ROC) curve analysis evaluated model discrimination. Results: Higher OHAT scores were linked to a lower likelihood of receiving a regu-lar/soft diet (adjusted OR, 0.69; 95% CI, 0.54–0.89; p = 0.003), while higher FIM scores were linked to a greater likelihood (adjusted OR, 1.04; 95% CI, 1.01–1.07; p = 0.003). Age, serum albumin, and remaining teeth were not predictors. In stepwise regression, only OHAT and FIM remained significant. Model discrimination was strong, with AUC 0.85 for the full model and 0.82 for the simplified model. Conclusions: Poor oral health and low functional independence were associated with restricted dietary forms. Combined use of OHAT-J and FIM offers a practical and reli-able method for predicting dietary form in acute care, supporting integrated oral health and functional assessments for older inpatients.

Oral mucositis is a frequent adverse effect of cancer therapy and is often complicated by pain, oral dryness, and secondary infection. Freeze-dried hyaluronic acid (HA) sheets can protect and moisturize the oral mucosa, but their intrinsic antibacterial activity is limited. We developed freeze-dried HA sheets containing the commercial persimmon tannin product PANCIL^® PS-M and evaluated their release behavior, pH profile, antimicrobial activity, and in vitro cytocompatibility. Persimmon tannin release into phosphate-buffered saline was quantified over 8 h using the Folin–Ciocalteu method. Antimicrobial activity against Streptococcus mitis, Prevotella intermedia, and Candida albicans was assessed by colony counting, and cytocompatibility was evaluated using the human oral cell line T3M-1. Approximately 95% of the incorporated persimmon tannin was released within 8 h, while the surrounding pH remained near neutral to mildly alkaline. The antibacterial HA sheets markedly reduced viable counts of S. mitis and P. intermedia, whereas no inhibitory effect was observed against C. albicans. Neither HA sheet nor antibacterial HA sheet solutions significantly reduced T3M-1 cell viability at concentrations up to 100 µg/mL after 24 h. These findings suggest that persimmon tannin-containing HA sheets may provide sustained antibacterial activity without detectable cytotoxicity under the tested conditions and may serve as a topical adjunct for cancer treatment-related oral mucositis.