The tumor immune microenvironment plays a critical role in tumor progression and immunotherapy response, with the abundance and composition of infiltrating immune cells serving as key determinants of therapeutic outcomes. Given the limitations of direct experimental methods, computational deconvolution algorithms are widely applied to infer immune cell infiltration from bulk RNA-seq data. While such estimates have proven valuable for studying tumor–immune interactions, large-scale, systematic analyses across multiple cancer types and treatment contexts remain limited. To address this need, we developed TIMER3 (https://compbio.cn/timer3/), an upgraded web server that substantially extends the functionality and scope of its predecessors. TIMER3 integrates 15 state-of-the-art immune deconvolution algorithms, including both human-specific and mouse-specific methods, to improve the robustness and interpretability of immune cell estimation. It incorporates an expanded collection of public RNA-seq datasets of immunotherapy-related cohorts, enabling large-scale analyses of immune dynamics across pre-treatment and post-treatment conditions. TIMER3 introduces new analytical modules for immunotherapy response, signature-based functional profiling, and interactive visualization of cell composition, gene expression, and survival associations. Collectively, TIMER3 provides a comprehensive, user-friendly platform for dissecting the TIME across diverse datasets, facilitating translational research and precision immuno-oncology.

With recent developments in photosensitizers and light delivery systems, topical 5-aminolevulinic acid-mediated photodynamic therapy (ALA-PDT) has become the fourth alternative therapeutic approach in the management of oral leucoplakia (OLK) due to its minimally invasive nature, efficacy, and low risk of systemic side effects and disfigurement. This report presents step-by-step guidelines for applying topical ALA-PDT in the management of OLK based on both the clinical experience of the authors and a systematic review of the current literature. Studies using protocols with standardized parameters and randomized clinical trials at multiple centres with adequate sample sizes and both interim and long-term follow-ups are needed before universally applicable guidelines can be produced in this field.

Supramolecular hydrogels derived from nucleosides have been gaining significant attention in the biomedical field due to their unique properties and excellent biocompatibility. However, a major challenge in this field is that there is no model for predicting whether nucleoside derivative will form a hydrogel. Here, we successfully develop a machine learning model to predict the hydrogel-forming ability of nucleoside derivatives. The optimal model with a 71% (95% Confidence Interval, 0.69−0.73) accuracy is established based on a dataset of 71 reported nucleoside derivatives. 24 molecules are selected via the optimal model external application and the hydrogel-forming ability is experimentally verified. Among these, two rarely reported cation-independent nucleoside hydrogels are found. Based on their self-assemble mechanisms, the cation-independent hydrogel is found to have potential applications in rapid visual detection of Ag⁺ and cysteine. Here, we show the machine learning model may provide a tool to predict nucleoside derivatives with hydrogel-forming ability.

The association between micronutrient intake and the risk of periodontitis has received much attention in recent years. However, most studies focused on the linear relationship between them. This study aimed to explore the dose–response association between micronutrient intake and periodontitis. A total of 8959 participants who underwent a periodontal examination, and reported their micronutrient intake levels were derived from the US National Health and Nutrition Examination Survey (NHANES, 2009–2014) database. Logistic regression was performed to evaluate associations between micronutrient intake and periodontitis after propensity score matching (PSM), and restricted cubic splines (RCS) analysis was conducted to explore the dose–response associations. Following PSM, 5530 participants were included in the RCS analysis. The risk of periodontitis was reduced with sufficient intake of the following micronutrients: vitamin A, vitamin B1, vitamin B2, and vitamin E. In addition, the risk of periodontitis was increased with excessive intake of the following micronutrients: vitamin B1 (1.8 mg/day, males; 1.3 mg/day, females), vitamin C (90 mg/day, males), and copper (1.1 mg/day, combined). In conclusion, a linear association was found between vitamin A, vitamin B2, vitamin C, and copper and periodontitis—namely, a sufficient intake of vitamin A and vitamin B2 might help reduce the prevalence of periodontitis; by contrast, a high intake of vitamin C and copper increased the risk. In addition, a nonlinear dose–response association was found for the incidence of periodontitis with vitamin B1 and vitamin E. When within reasonable limits, supplemental intake helped reduce the prevalence of periodontitis, while excessive intake did not help significantly and might even increase the risk. However, confounding factors, such as health awareness, should still be considered.