Tetrahedral framework nucleic acid-based small-molecule inhibitor delivery for ecological prevention of biofilm.

Biofilm formation constitutes the primary cause of various chronic infections, such as wound infections, gastrointestinal inflammation and dental caries. While preliminary achievement of biofilm inhibition is possible, the challenge lies in the difficulty of eliminating the bactericidal effects of current drugs that lead to microbiota imbalance. This study, utilizing in vitro and in vivo models of dental caries, aims to efficiently inhibit biofilm formation without inducing bactericidal effects, even against pathogenic bacteria. The tetrahedral framework nucleic acid (tFNA) was employed as a delivery vector for a small-molecule inhibitor (smI) specifically targeting the activity of glucosyltransferases C (GtfC). It was observed that tFNA loaded smI in a small-groove binding manner, efficiently transferring it into Streptococcus mutans, thereby inhibiting GtfC activity and extracellular polymeric substances formation without compromising bacterial survival. Furthermore, smI-loaded tFNA demonstrated a reduction in the severity of dental caries in vivo without adversely affecting oral microbial diversity and exhibited desirable topical and systemic biosafety. This study emphasizes the concept of 'ecological prevention of biofilm', which is anticipated to advance the optimization of biofilm prevention strategies and the clinical application of DNA nanocarrier-based drug formulations.

Development and validation of machine learning based prediction model for postoperative pain risk after extraction of impacted mandibular third molars.

Background: Predicting postoperative pain risk in patients with impacted mandibular third molar extractions is helpful in guiding clinical decision-making, enhancing perioperative pain management, and improving the patients' medical experience. This study aims to develop a prediction model based on machine learning algorithms to identify patients at high risk of postoperative pain after tooth extraction. Methods: We conducted a prospective cohort study. Outpatients with impacted mandibular third molars were recruited and the outcome was defined as the NRS (Numerical Rating Scale) score of peak postoperative pain within 24 h after the operation ≥7, which is considered a high risk of postoperative pain. We compared the models built using nine different machine learning algorithms and conducted internal and time-series external validations to evaluate the model's predictive performances in terms of the area under the curve (AUC), accuracy, sensitivity, specificity, and F1-value. Results: A total of 185 patients and 202 cases of impacted mandibular third molar data were included in this study. Five modeling variables were screened out using least absolute selection and shrinkage operator regression, including physician qualification, patient self-reported maximum pain sensitivity, OHI-S-CI, BMI, and systolic blood pressure. The overall performance of the random forest model was evaluated. The AUC, sensitivity, and specificity of the prediction model built using the random forest method were 0.879 (0.861-0.891), 0.857, and 0.846, respectively, for the training set and 0.724 (0.673-0.732), 0.667, and 0.600, respectively, for the time series validation set. Conclusions: This study developed a machine learning-based postoperative pain risk prediction model for impacted mandibular third molar extraction, which is promising for providing a theoretical basis for better pain management to reduce postoperative pain after third molar extraction.

First-principles calculation on the lithium storage properties of high-entropy MXene Ti3C2(N0.25O0.25F0.25S0.25)2.

Experimental studies had shown that a variety of surface functional groups exist simultaneously on the surface of Ti3C2Tx MXenes. However, current theoretical calculations on MXenes, used as anode materials for lithium-ion batteries, consider only one surface functional group, which fails to take into account the actual situation. In this study, combining the characteristics of high-entropy materials and two-dimensional MXene material, a model of MXene with multiple surface functional groups was constructed, and its electrochemical performance as an anode material for lithium-ion batteries was further explored. The Ti3C2(N0.25O0.25F0.25S0.25)2 monolayer exhibited metallic properties. Meanwhile, Li atoms could be stably adsorbed on the surface and the diffusion energy barrier of Li on the surface was only 0.17 eV. First-principles calculation showed that Ti3C2(N0.25O0.25F0.25S0.25)2 monolayer had good rate performance and low open-circuit voltage (1 V), corresponding to a lithium storage capacity of 385.38 mA h g-1. The results of our work might inspire further studies on the Li storage performance of high-entropy MXenes experimentally and theoretically.

The effect of root orientation on inferior alveolar nerve injury after extraction of impacted mandibular third molars based on propensity score-matched analysis: a retrospective cohort study.

BACKGROUND: The injury of the inferior alveolar nerve (IAN) is one of the most serious complications of impacted mandibular third molars (IMTMs) extraction. The influence of the root orientation of IMTMs on IAN injury is still controversial. A deeper understanding of the risk factors of IAN injury conduces to better prevention of IAN injury. This study aims to explore whether root orientation is an independent risk factor of IAN injury during IMTMs extraction using the statistical strategy of propensity score matching (PSM). METHODS: This retrospective cohort study included 379 patients with 539 cases of high-risk IMTMs screened by panoramic radiography and cone beam computed tomography. The IAN injury incidence after extraction of different groups of IMTMs was analyzed using the chi-square test or Fisher's exact test. The correlation between third molar root orientation and impaction depth/contact degree with IAN was evaluated by the Lambda coefficient. Based on PSM for balancing confounding factors including age, sex, impaction depth, and contact degree, the effect of root orientation on the incidence of IAN injury was further analyzed using Fisher's exact test. RESULTS: There were significant group differences in IAN injury incidence in impaction depth, root orientation, and contact degree of root-IAC before PSM. Root orientation was correlated with impaction depth and contact degree of root-IAC. After PSM, there were 9 cases with IAN injury and 257 cases without IAN injury. There were significant group differences between the buccal and non-buccal groups after PSM, and the risk of IAN injury was higher when the root was located on the buccal side of IAC (OR = 8.448, RR = 8). CONCLUSIONS: Root orientation is an independent risk factor of IAN injury, and the risk is higher when the root is located on the buccal side of IAC. These findings could help better evaluate the risk of inferior alveolar nerve injury before the extraction of IMTMs.

MXene/graphene oxide heterojunction as a high performance anode material for lithium ion batteries.

MXene/graphene oxide composites with strong interfacial interactions were constructed by ball milling in vacuum. Graphene oxide (GO) acted as a bridge between Ti3C2Tx nanosheets in the composite material, which could buffer the mechanical shear force during the ball milling process, avoid the structural damage of nanosheets and improve the structural stability of the composite material during the lithium process. Partial oxidation of Ti3C2Tx nanosheets is caused by high temperatures during ball milling, which is beneficial to improve the intercalation of lithium ions in the material, reduce the stress and electrostatic repulsion between adjacent layers, and cause the composite to have better lithium storage performance. Under the high current density of 2.5 A g-1, the reversible capacity of the Ti3C2Tx/GO composite material after 2000 cycles was 116.5 mA h g-1, and the capacity retention was as high as 116.6%.

Machine learning-based models for predicting mortality and acute kidney injury in critical pulmonary embolism.

OBJECTIVES: We aimed to use machine learning (ML) algorithms to risk stratify the prognosis of critical pulmonary embolism (PE). MATERIAL AND METHODS: In total, 1229 patients were obtained from MIMIC-IV database. Main outcomes were set as all-cause mortality within 30 days. Logistic regression (LR) and simplified eXtreme gradient boosting (XGBoost) were applied for model constructions. We chose the final models based on their matching degree with data. To simplify the model and increase its usefulness, finally simplified models were built based on the most important 8 variables. Discrimination and calibration were exploited to evaluate the prediction ability. We stratified the risk groups based on risk estimate deciles. RESULTS: The simplified XGB model performed better in model discrimination, which AUC were 0.82 (95% CI: 0.78-0.87) in the validation cohort, compared with the AUC of simplified LR model (0.75 [95% CI: 0.69-0.80]). And XGB performed better than sPESI in the validation cohort. A new risk-classification based on XGB could accurately predict low-risk of mortality, and had high consistency with acknowledged risk scores. CONCLUSIONS: ML models can accurately predict the 30-day mortality of critical PE patients, which could further be used to reduce the burden of ICU stay, decrease the mortality and improve the quality of life for critical PE patients.

The characteristics of lung cancer in Xuanwei County: A review of differentially expressed genes and noncoding RNAs on cell proliferation and migration.

The occurrence of lung cancers is the highest in Xuanwei County, Yunnan province, China, especially among nonsmoking women. Domestic combustion of smoky coal induces serious indoor air pollution and is considered to be the main cause of human lung cancers. The occurrence of lung cancer in Xuanwei County has unique characteristics, such as the high morbidity in nonsmoking women or people with no family history. In the present review, we summarize advances in identification of differentially expressed genes, regulatory lncRNAs and miRNAs in cell proliferation and migration of lung cancers in Xuanwei County. Moreover, several regulatory differentially expressed genes (DEGs) or noncoding RNAs have diagnostic and prognostic significance for lung cancers in Xuanwei County and have the potential to serve as biomarkers.