Efficacy of a mouthwash containing ε-poly-L-lysine, funme peptides and domiphen in reducing halitosis and supragingival plaque: a randomized clinical trial.

OBJECTIVE: To evaluate the antibacterial effectiveness of a combination of ε-poly-L-lysine (ε-PL), funme peptide (FP) as well as domiphen against oral pathogens, and assess the efficacy of a BOP® mouthwash supplemented with this combination in reducing halitosis and supragingival plaque in a clinical trial. MATERIALS AND METHODS: The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of the compound against Fusobacterium nucleatum, Porphyromonas gingivalis, Streptococcus mutans, and Aggregatibacter actinomycetemcomitans were determined by the gradient dilution method. Subsequently, the CCK-8 assay was used to detect the toxicity of mouthwash on human gingival fibroblastst, and the effectiveness in reducing halitosis and supragingival plaque of the mouthwash supplemented with the combination was analyzed by a randomized, double-blind, parallel-controlled clinical trial. RESULTS: The combination exhibited significant inhibitory effects on tested oral pathogens with the MIC < 1.56% (v/v) and the MBC < 3.13% (v/v), and the mouthwash containing this combination did not inhibit the viability of human gingival fibroblasts at the test concentrations. The clinical trial showed that the test group displayed notably lower volatile sulfur compounds (VSCs) at 0, 10, 24 h, and 7 d post-mouthwash (P < 0.05), compared with the baseline. After 7 days, the VSC levels of the and control groups were reduced by 50.27% and 32.12%, respectively, and notably cutting severe halitosis by 57.03% in the test group. Additionally, the Plaque Index (PLI) of the test and control group decreased by 54.55% and 8.38%, respectively, and there was a significant difference in PLI between the two groups after 7 days (P < 0.01). CONCLUSIONS: The combination of ε-PL, FP and domiphen demonstrated potent inhibitory and bactericidal effects against the tested oral pathogens, and the newly formulated mouthwash added with the combination exhibited anti-dental plaque and anti-halitosis properties in a clinical trial and was safe. TRIAL REGISTRATION: The randomized controlled clinical trial was registered on Chinese Clinical Trial Registry (No. ChiCTR2300073816, Date: 21/07/2023).

Predicting the potential distribution of 12 threatened medicinal plants on the Qinghai-Tibet Plateau, with a maximum entropy model.

Climate change is a vital driver of biodiversity patterns and species distributions, understanding how organisms respond to climate change will shed light on the conservation of endangered species. In this study, the MaxEnt model was used to predict the potential suitable area of 12 threatened medicinal plants in the QTP (Qinghai-Tibet Plateau) under the current and future (2050s, 2070s) three climate scenarios (RCP2.6, RCP4.5, RCP8.5). The results showed that the climatically suitable habitats for the threatened medicinal plants were primarily found in the eastern, southeast, southern, and some parts of the central regions on the QTP. Moreover, 25% of the threatened medicinal plants would have reduced suitable habitat areas within the next 30-50 years in the different future global warming scenarios. Among these medicinal plants, RT (Rheum tanguticum) would miss the most habitat (98.97%), while the RAN (Rhododendron anthopogonoides) would miss the least habitat (10.15%). Nevertheless, 33.3% of the threatened medicinal plants showed an increase in their future habitat area because of their physiological characteristics which are more adaptable to a wide range of climates. The climatic suitable habitat for 50% of the threatened medicinal plants would migrate to higher altitudes or higher latitudes regions. This study provides a data foundation for the conservation of biodiversity and wild medicinal plants on the QTP.

Metabolic Engineering of High L-Lysine-Producing Escherichia coli for de Novo Production of L-Lysine-Derived Compounds.

5-Aminovalerate (5-AVA), 5-hydroxyvalerate (5-HV), and 1,5-pentanediol (1,5-PDO) are l-lysine derivatives with extensive applications in the production of materials such as polyesters, polyurethane, plasticizers, inks, and coatings. However, their large-scale production is limited by the lack of efficient synthetic pathways. Here, we aimed to construct multiple synthetic pathways by screening the key enzymes involved in the synthesis of these compounds in Escherichia coli. The engineered pathway utilizing RaiP demonstrated a superior catalytic efficiency. The LER strain that overexpressed only raiP successfully synthesized 9.70 g/L 5-HV and 8.31 g/L 5-AVA, whereas the strain LERGY that overexpressed raiP, gabT, and yahK accumulated 9.72 g/L 5-HV and 7.95 g/L 5-AVA from 20 g/L glucose. The introduction of exogenous transaminases and dehydrogenases enhanced cell growth and fermentation efficiency with respect to 5-HV synthesis, albeit without significantly impacting the yield. Strain LE05, incorporating only two exogenous enzymes, RaiP and CaR, produced 1.87 g/L 1,5-PDO, 3.85 g/L 5-HV, and 4.78 g/L 5-hydroxyglutaraldehyde from 20 g/L glucose after 6 days. The strain LE02G, fortified with transaminase, dehydrogenase, and NADPH regeneration system, accumulated 7.82 g/L 1,5-PDO, whereas the aldp-knock out LE02G2 synthesized 10.98 g/L 1,5-PDO from 50 g/L glucose in fed-batch fermentation after 6 days, yielding 0.22 g/g glucose (0.37 mol/mol). Introducing the NADPH regeneration pathway and deleting the NADPH-consuming pathways increased the 1,5-PDO yield and decreased the precursor concentration. The proposed pathways and engineering strategies presented in this study can prove instrumental in developing biological routes for the practical production of 5-AVA, 5-HV, and 1,5-PDO.

Gut microbiota landscape and potential biomarker identification in female patients with systemic lupus erythematosus using machine learning.

Objectives: Systemic Lupus Erythematosus (SLE) is a complex autoimmune disease that disproportionately affects women. Early diagnosis and prevention are crucial for women's health, and the gut microbiota has been found to be strongly associated with SLE. This study aimed to identify potential biomarkers for SLE by characterizing the gut microbiota landscape using feature selection and exploring the use of machine learning (ML) algorithms with significantly dysregulated microbiotas (SDMs) for early identification of SLE patients. Additionally, we used the SHapley Additive exPlanations (SHAP) interpretability framework to visualize the impact of SDMs on the risk of developing SLE in females. Methods: Stool samples were collected from 54 SLE patients and 55 Negative Controls (NC) for microbiota analysis using 16S rRNA sequencing. Feature selection was performed using Elastic Net and Boruta on species-level taxonomy. Subsequently, four ML algorithms, namely logistic regression (LR), Adaptive Boosting (AdaBoost), Random Forest (RF), and eXtreme gradient boosting (XGBoost), were used to achieve early identification of SLE with SDMs. Finally, the best-performing algorithm was combined with SHAP to explore how SDMs affect the risk of developing SLE in females. Results: Both alpha and beta diversity were found to be different in SLE group. Following feature selection, 68 and 21 microbiota were retained in Elastic Net and Boruta, respectively, with 16 microbiota overlapping between the two, i.e., SDMs for SLE. The four ML algorithms with SDMs could effectively identify SLE patients, with XGBoost performing the best, achieving Accuracy, Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value, and AUC values of 0.844, 0.750, 0.938, 0.923, 0.790, and 0.930, respectively. The SHAP interpretability framework showed a complex non-linear relationship between the relative abundance of SDMs and the risk of SLE, with Escherichia_fergusonii having the largest SHAP value. Conclusions: This study revealed dysbiosis in the gut microbiota of female SLE patients. ML classifiers combined with SDMs can facilitate early identification of female patients with SLE, particularly XGBoost. The SHAP interpretability framework provides insight into the impact of SDMs on the risk of SLE and may inform future scientific treatment for SLE.