Due to the moist environment in the mouth, there are many challenges that arise, such as difficult biofilm removal, short drug retention time, and low tissue repair efficiency, while treating dental caries, periodontal disease, and other oral diseases. As a biomimetic biomaterial, polydopamine (PDA) possesses multifunctional properties, including mussel-inspired adhesion and stimuli-responsive drug release. PDA adhesion properties originate from its surface catechol and amino functional groups, which maintain strong wettability in aqueous environments. With smart responsiveness encompassing photothermal, pH, and enzymatic stimuli, PDA enables controlled drug release under specific conditions. Additionally, PDA exhibits antibacterial, anti-inflammatory, and osteoblast-promoting functions, thus demonstrating significant application potential in the treatment of oral diseases. In hard tissue therapies, specifically for dental caries, PDA promotes enamel remineralization by inducing hydroxyapatite crystal growth and enhances dentin collagen mineralization through Ca2+ chelation while inhibiting cariogenic bacteria. In mandibular defect repair, functionalized PDA coatings on bone implants facilitate mesenchymal stem cell adhesion and differentiation, activate osteogenic signaling pathways, and synergistically promote vascularization to improve bone-implant integration. For soft tissue treatments, specifically for periodontitis, PDA alleviates alveolar bone resorption via antibacterial and anti-inflammatory effects coupled with osteoclast inhibition. In denture stomatitis management, PDA’s strong wet adhesion prolongs drug retention, while its photothermal effect and reactive oxygen generation provide both broad-spectrum antibacterial activity and wound healing promotion. This review summarizes PDA’s synthesis mechanisms and biological functions, with an emphasis on its therapeutic applications in oral diseases, providing innovative strategies for oral healthcare.

This consensus will introduce the characteristics of fillers used in the surgical cavities of domestic nasal surgery patients based on relevant literature and expert opinions. It will also provide recommendations for the selection of cavity fillers for different nasal diseases, with chronic sinusitis as a representative example.
Humans ; Nasal Cavity/surgery* ; Nasal Surgical Procedures ; China ; Consensus ; Sinusitis/surgery* ; Dermal Fillers

[This corrects the article DOI: 10.1016/j.apsb.2022.05.019.].

OBJECTIVES: To evaluate the performance of a multi-constraint representation learning classification model for identifying ovarian cancer with missing laboratory indicators. METHODS: Tabular data with missing laboratory indicators were collected from 393 patients with ovarian cancer and 1951 control patients. The missing ovarian cancer laboratory indicator features were projected to the latent space to obtain a classification model using the representational learning classification model based on discriminative learning and mutual information coupled with feature projection significance score consistency and missing location estimation. The proposed constraint term was ablated experimentally to assess the feasibility and validity of the constraint term by accuracy, area under the ROC curve (AUC), sensitivity, and specificity. Cross-validation methods and accuracy, AUC, sensitivity and specificity were also used to evaluate the discriminative performance of this classification model in comparison with other interpolation methods for processing of the missing data. RESULTS: The results of the ablation experiments showed good compatibility among the constraints, and each constraint had good robustness. The cross-validation experiment showed that for identification of ovarian cancer with missing laboratory indicators, the AUC, accuracy, sensitivity and specificity of the proposed multi-constraints representation-based learning classification model was 0.915, 0.888, 0.774, and 0.910, respectively, and its AUC and sensitivity were superior to those of other interpolation methods. CONCLUSIONS The proposed model has excellent discriminatory ability with better performance than other missing data interpolation methods for identification of ovarian cancer with missing laboratory indicators.
Female ; Humans ; Ovarian Neoplasms/diagnosis* ; Machine Learning ; ROC Curve

This study aimed to comprehensively examine the association of gallstones, cholecystectomy, and cancer risk. Multivariable logistic regressions were performed to estimate the observational associations of gallstones and cholecystectomy with cancer risk, using data from a nationwide cohort involving 239 799 participants. General and gender-specific two-sample Mendelian randomization (MR) analysis was further conducted to assess the causalities of the observed associations. Observationally, a history of gallstones without cholecystectomy was associated with a high risk of stomach cancer (adjusted odds ratio (aOR)=2.54, 95% confidence interval (CI) 1.50-4.28), liver and bile duct cancer (aOR=2.46, 95% CI 1.17-5.16), kidney cancer (aOR=2.04, 95% CI 1.05-3.94), and bladder cancer (aOR=2.23, 95% CI 1.01-5.13) in the general population, as well as cervical cancer (aOR=1.69, 95% CI 1.12-2.56) in women. Moreover, cholecystectomy was associated with high odds of stomach cancer (aOR=2.41, 95% CI 1.29-4.49), colorectal cancer (aOR=1.83, 95% CI 1.18-2.85), and cancer of liver and bile duct (aOR=2.58, 95% CI 1.11-6.02). MR analysis only supported the causal effect of gallstones on stomach, liver and bile duct, kidney, and bladder cancer. This study added evidence to the causal effect of gallstones on stomach, liver and bile duct, kidney, and bladder cancer, highlighting the importance of cancer screening in individuals with gallstones.
Humans ; Mendelian Randomization Analysis ; Gallstones/complications* ; Female ; Male ; Cholecystectomy/statistics & numerical data* ; Middle Aged ; Risk Factors ; Aged ; Adult ; Neoplasms/etiology* ; Stomach Neoplasms/epidemiology*

Biliary tract cancer (BTC) is a rare group of malignancies that develop from the epithelial lining of the biliary tree and have a poor prognosis. Although chemotherapy is the standard of care for patients with advanced BTC in China, its clinical benefits are moderate. In recent years, the approval of targeted therapies and immunotherapies has provided new avenues for the management of advanced BTC. Nonetheless, the increasing number of personalized medicine approaches has created a challenge for clinicians choosing individualized treatment strategies based on tumor characteristics. In this article, we discuss recent progress in implementing precision medicine approaches for advanced BTC in China and examine genomic profiling studies in Chinese patients with advanced BTC. We also discuss the challenges and opportunities of using precision medicine approaches, as well as the importance of considering population-specific factors and tailoring treatment approaches to improve outcomes for patients with BTC. In addition to providing a comprehensive overview of current and emerging precision medicine approaches for the management of advanced BTC in China, this review article will support clinicians outside of China by serving as a reference regarding the role of patient- and population-specific factors in clinical decision-making for patients with this rare malignancy.
Humans ; Precision Medicine/methods* ; Biliary Tract Neoplasms/genetics* ; China ; Molecular Targeted Therapy ; Immunotherapy/methods*

OBJECTIVE: To construct and validate a prognostic prediction model for children with sepsis using the Phoenix sepsis score (PSS). METHODS: A retrospective case series study was conducted to collect clinical data of children with sepsis admitted to the pediatric intensive care unit (PICU) of the Affiliated Hospital of Guizhou Medical University from January 2022 to April 2024. The data included general information, the worst values of laboratory indicators within the first 24 hours of PICU admission, PSS score, pediatric critical illness score (PCIS), and the survival status of the children within 30 days of admission. The statistically significant indicators in univariate Logistic regression analysis were included in multivariate Logistic regression analysis to screen the risk factors affecting the prognosis of children with sepsis and construct a nomogram model. The receiver operator characteristic curve (ROC curve) was drawn to evaluate the predictive performance of the model. The Bootstrap method was used to perform 1 000 repeated sampling internal verification and draw the calibration curve of the model. RESULTS: A total of 199 children with sepsis were included, of which 32 died and 167 survived 30 days after admission. In the univariate Logistic regression analysis, shock, white blood cell count (WBC), international normalized ratio (INR), lactic acid (Lac), PSS score, and PCIS score were identified as statistically significant predictors. These variables were then included in the multivariate Logistic regression analysis, which demonstrated that shock [odds ratio (OR) = 4.258, 95% confidence interval (95%CI) was 1.049-17.288], WBC (OR = 1.124, 95%CI was 1.052-1.210), and PSS score (OR = 1.977, 95%CI was 1.298-3.012) were independent risk factors for mortality in pediatric patients with sepsis (all P < 0.05). A nomogram model was constructed based on these three risk factors, with the model equation as follows: -4.809+1.449×shock+0.682×PSS score+0.117×WBC. The calibration curve results showed that the model's predictions were highly consistent with the actual observations. The ROC curve showed that when the Youden index of the prediction model was 0.792, the sensitivity and specificity were 90.6% and 88.6%, respectively, and the area under the curve (AUC) was 0.957 (95%CI was 0.930-0.984), which was higher than the AUC of shock, WBC, and PSS score alone (0.808, 0.667, 0.908, respectively). CONCLUSIONS Shock, WBC, and PSS score have demonstrated certain predictive value for mortality in children with sepsis. The nomogram model based on the above indicators has important clinical significance for evaluating the prognosis and guiding treatment of children with sepsis.
Humans ; Sepsis/diagnosis* ; Prognosis ; Retrospective Studies ; Logistic Models ; Intensive Care Units, Pediatric ; Nomograms ; Child ; ROC Curve ; Risk Factors ; Male ; Female ; Child, Preschool ; Infant

This paper aimed to use non-targeted urine metabolomics to reveal the potential biomarkers of toxicity in rats with hepatic injury induced by aqueous extracts of Dictamni Cortex(ADC). Forty-eight SD rats were randomly assigned to a blank group and high-dose, medium-dose, and low-dose ADC groups, with 12 rats in each group(half male and half female), and they were administered orally for four weeks. The hepatic injury in SD rats was assessed by body weight, liver weight/index, biochemical index, L-glutathione(GSH), malondialdehyde(MDA), and pathological alterations. The qPCR was utilized to determine the expression of metabolic enzymes in the liver and inflammatory factors. Differential metabolites were screened using principal component analysis(PCA) and partial least squares-discriminant analysis(PLS-DA), followed by a metabolic pathway analysis. The Mantel test was performed to assess differential metabolites and abnormally expressed biochemical indexes, obtaining potential biomarkers. The high-dose ADC group showed a decrease in body weight and an increase in liver weight and index, resulting in hepatic inflammatory cell infiltration and hepatic steatosis. In addition, this group showed elevated levels of MDA, cytochrome P450(CYP) 3A1, interleukin-1β(IL-1β), and tumor necrosis factor-α(TNF-α), as well as lower levels of alanine transaminase(ALT) and GSH. A total of 76 differential metabolites were screened from the blank and high-dose ADC groups, which were mainly involved in the pentose phosphate pathway, tryptophan metabolism, purine metabolism, pentose and glucuronic acid interconversion, galactose metabolism, glutathione metabolism, and other pathways. The Mantel test identified biomarkers of hepatotoxicity induced by ADC in SD rats, including glycineamideribotide, dIDP, and galactosylglycerol. In summary, ADC induced hepatotoxicity by disrupting glucose metabolism, ferroptosis, purine metabolism, and other pathways in rats, and glycineamideribotide, dIDP, and galactosylglycerol could be employed as the biomarkers of its toxicity.
Animals ; Male ; Rats, Sprague-Dawley ; Rats ; Metabolomics ; Biomarkers/metabolism* ; Liver/metabolism* ; Drugs, Chinese Herbal/adverse effects* ; Female ; Chemical and Drug Induced Liver Injury/metabolism* ; Glutathione/metabolism* ; Humans