Porphyromonas gingivalis promotes the progression of oral squamous cell carcinoma by stimulating the release of neutrophil extracellular traps in the tumor immune microenvironment.

BACKGROUND: The aim of this study was to investigate the impact of Porphyromonas gingivalis (P. gingivalis) on the progression of oral squamous cell carcinoma (OSCC) through neutrophil extracellular traps (NETs) in the tumor immune microenvironment. METHODS: The expression of NETs-related markers was identified through immunohistochemistry, immunofluorescence, and Western blotting in different clinical stages of OSCC samples. The relationship between NETs-related markers and clinicopathological characteristics in 180 samples was analyzed using immunohistochemistry data. Furthermore, the ability to predict the prognosis of OSCC patients was determined by ROC curve analysis and survival analysis. The effect of P. gingivalis on the release of NETs was identified through immunofluorescence and immunohistochemistry, both in vitro and in vivo. CAL27 and SCC25 cell lines were subjected to NETs stimulation to elucidate the influence of NETs on various cellular processes, including cell proliferation, migration, invasion, and metastasis in vitro. Furthermore, the impact of NETs on the growth and metastatic potential of OSCC was assessed using in vivo models involving tumor-bearing mice and tumor metastasis mouse models. RESULTS: Immunochemistry analysis revealed a significant correlation between the NETs-related markers and clinical stage, living status as well as TN stage. P. gingivalis has demonstrated its ability to effectively induce the release of NETs both in vivo and in vitro. NETs have the potential to facilitate cell migration, invasion, and colony formation. Moreover, in vivo experiments have demonstrated that NETs play a pivotal role in promoting tumor metastasis. CONCLUSION: High expression of NETs-related markers demonstrates a strong correlation with the progression of OSCC. Inhibition of the NETs release process stimulated by P. gingivalis and targeted NETs could potentially open up a novel avenue in the field of immunotherapy for patients afflicted with OSCC.

Investigation of an Active Focusing Planar Piezoelectric Ultrasonic Transducer.

Ultrasonic focusing transducers have broad prospects in advanced ultrasonic non-destructive testing fields. However, conventional focusing methods that use acoustic concave lenses can disrupt the acoustic impedance matching condition, thereby adversely affecting the sensitivity of the transducers. In this paper, an active focusing planar ultrasonic transducer is designed and presented to achieve a focusing effect with a higher sensitivity. An electrode pattern consisting of multiple concentric rings is designed, which is inspired by the structure of Fresnel Zone Plates (FZP). The structural parameters are optimized using finite element simulation methods. A prototype of the transducer is manufactured with electrode patterns made of conductive silver paste using silk screen-printing technology. Conventional focusing transducers using an acoustic lens and an FZP baffle are also manufactured, and their focusing performances are comparatively tested. The experimental results show that our novel transducer has a focal length of 16 mm and a center frequency of 1.16 MHz, and that the sensitivity is improved by 23.3% compared with the conventional focusing transducers. This research provides a new approach for the design of focusing transducers.

Association of carotid atherosclerotic plaque and intima-media thickness with the monocyte to high-density lipoprotein cholesterol ratio among low-income residents of rural China: a population-based cross-sectional study.

BACKGROUND: The monocytes to high-density lipoprotein cholesterol ratio (MHR) has been identified as a potential biomarker for cardiovascular and cerebrovascular diseases. In this population-based cross-sectional study, we explored the relationships among carotid artery disease (CAD), including the presence of carotid atherosclerotic plaque (CAP) and carotid artery intima-media thickness (CIMT), the MHR, and related parameter changes. METHODS: This cross-sectional study, Conducted from April to June 2019 in a rural area of Tianjin, involved middle-aged and elderly participants. Based on carotid ultrasound examinations, participants were divided into CAP and non-CAP groups. Logistic regression and Receiver Operating Characteristic (ROC) curve analyses were utilized to assess MHR's predictive value for CAP. Gender-specific analyses were also performed to examine predictive variations. The relationship between CIMT and MHR was evaluated using linear regression. RESULTS: Of the 2109 participants meeting the inclusion criteria, 51.6% were identified with CAP. Multivariate analysis revealed a significant association between MHR and CAP prevalence, (OR, 9.670; 95% CI, 2.359-39.631; P = 0.002), particularly in females (OR, 5.921; 95% CI, 1.823-19.231; P = 0.003), after adjusting for covariates. However, no significant correlation was found between CIMT and MHR when adjusted for other factors. The ROC analysis showed the area under the curve for MHR and CAP to be 0.569 (95% CI: 0.544-0.593; P < 0.001). CONCLUSIONS: These findings suggested that it is crucial to enhance early screening and intervention for CAD, specifically focusing on the prevention and progression of CAP, to address the unique health challenges faced by low-income groups in rural settings. Emphasizing these preventive measures could significantly contribute to improving cardiovascular health outcomes in this vulnerable population.

[Mechanism of Gegen Qinlian Decoction in improving glucose metabolism in vitro and in vivo by alleviating hepatic endoplasmic reticulum stress].

This study investigated the mechanism of Gegen Qinlian Decoction(GQD) in improving glucose metabolism in vitro and in vivo by alleviating endoplasmic reticulum stress(ERS). Molecular docking was used to predict the binding affinity between the main effective plasma components of GQD and ERS-related targets. Liver tissue samples were obtained from normal rats, high-fat-induced diabetic rats, rats treated with metformin, and rats treated with GQD. RNA and protein were extracted. qPCR was used to measure the mRNA expression of ERS marker glucose-regulated protein 78(GRP78), and unfolded protein response(UPR) genes inositol requiring enzyme 1(Ire1), activating transcription factor 6(Atf6), Atf4, C/EBP-homologous protein(Chop), and caspase-12. Western blot was used to detect the protein expression of GRP78, IRE1, protein kinase R-like ER kinase(PERK), ATF6, X-box binding protein 1(XBP1), ATF4, CHOP, caspase-12, caspase-9, and caspase-3. The calcium ion content in liver tissues was determined by the colorimetric assay. The ERS-HepG2 cell model was established in vitro by inducing with tunicamycin for 6 hours, and 2.5%, 5%, and 10% GQD-containing serum were administered for 9 hours. The glucose oxidase method was used to measure extracellular glucose levels, flow cytometry to detect cell apoptosis, glycogen staining to measure cellular glycogen content, and immunofluorescence to detect the expression of GRP78. The intracellular calcium ion content was measured by the colorimetric assay. Whereas Western blot was used to detect GRP78 and ERS-induced IRE1, PERK, ATF6, and eukaryotic translation initiation factor 2α(eIF2α) phosphorylation. Additionally, the phosphorylation levels of insulin receptor substrate 1(IRS1), phosphatidylinositol 3-kinase regulatory subunit p85(PI3Kp85), and protein kinase B(Akt), which were involved in the insulin signaling pathway, were also measured. In addition, the phosphorylation levels of c-Jun N-terminal kinases(JNKs), which were involved in both the ERS and insulin signaling pathways, were measured by Western blot. Molecular docking results showed that GRP78, IRE1, PERK, ATF4, and various compounds such as baicalein, berberine, daidzein, jateorhizine, liquiritin, palmatine, puerarin and wogonoside had strong binding affinities, indicating that GQD might interfere with ERS-induced UPR. In vivo results showed that GQD down-regulated the mRNA transcription of Ire1, Atf6, Atf4, Grp78, caspase-12, and Chop in diabetic rats, and down-regulated GRP78, IRE1, PERK, as well as ERS-induced apoptotic factors ATF4 and CHOP, caspase-12, caspase-9, and caspase-3, while up-regulating XBP1 to enhance adaptive UPR. In addition, GQD increased the calcium ion content in liver tissues, which facilitated correct protein folding. In vitro results showed that GQD increased glucose consumption in ERS-induced HepG2 cells without significantly affecting cell viability, increased liver glycogen synthesis, down-regulated ATF6 and p-eIF2α(Ser51), and down-regulated IRE1, PERK, and GRP78, as well as p-IRS1(Ser312) and p-JNKs(Thr183/Tyr185), while up-regulating p-PI3Kp85(Tyr607) and p-Akt(Ser473). These findings suggested that GQD alleviates excessive ERS in the liver, reduces insulin resistance, and improves hepatic glucose metabolism in vivo and in vitro.

Using machine learning techniques to predict the risk of osteoporosis based on nationwide chronic disease data.

Osteoporosis is a major public health concern that significantly increases the risk of fractures. The aim of this study was to develop a Machine Learning based predictive model to screen individuals at high risk of osteoporosis based on chronic disease data, thus facilitating early detection and personalized management. A total of 10,000 complete patient records of primary healthcare data in the German Disease Analyzer database (IMS HEALTH) were included, of which 1293 diagnosed with osteoporosis and 8707 without the condition. The demographic characteristics and chronic disease data, including age, gender, lipid disorder, cancer, COPD, hypertension, heart failure, CHD, diabetes, chronic kidney disease, and stroke were collected from electronic health records. Ten different machine learning algorithms were employed to construct the predictive mode. The performance of the model was further validated and the relative importance of features in the model was analyzed. Out of the ten machine learning algorithms, the Stacker model based on Logistic Regression, AdaBoost Classifier, and Gradient Boosting Classifier demonstrated superior performance. The Stacker model demonstrated excellent performance through ten-fold cross-validation on the training set and ROC curve analysis on the test set. The confusion matrix, lift curve and calibration curves indicated that the Stacker model had optimal clinical utility. Further analysis on feature importance highlighted age, gender, lipid metabolism disorders, cancer, and COPD as the top five influential variables. In this study, a predictive model for osteoporosis based on chronic disease data was developed using machine learning. The model shows great potential in early detection and risk stratification of osteoporosis, ultimately facilitating personalized prevention and management strategies.

Construction and validation of a machine learning model for the diagnosis of juvenile idiopathic arthritis based on fecal microbiota.

Purpose: Human gut microbiota has been shown to be significantly associated with various inflammatory diseases. Therefore, this study aimed to develop an excellent auxiliary tool for the diagnosis of juvenile idiopathic arthritis (JIA) based on fecal microbial biomarkers. Method: The fecal metagenomic sequencing data associated with JIA were extracted from NCBI, and the sequencing data were transformed into the relative abundance of microorganisms by professional data cleaning (KneadData, Trimmomatic and Bowtie2) and comparison software (Kraken2 and Bracken). After that, the fecal microbes with high abundance were extracted for subsequent analysis. The extracted fecal microbes were further screened by least absolute shrinkage and selection operator (LASSO) regression, and the selected fecal microbe biomarkers were used for model training. In this study, we constructed six different machine learning (ML) models, and then selected the best model for constructing a JIA diagnostic tool by comparing the performance of the models based on a combined consideration of area under receiver operating characteristic curve (AUC), accuracy, specificity, F1 score, calibration curves and clinical decision curves. In addition, to further explain the model, Permutation Importance analysis and Shapley Additive Explanations (SHAP) were performed to understand the contribution of each biomarker in the prediction process. Result: A total of 231 individuals were included in this study, including 203 JIA patients and Non-JIA individuals. In the analysis of diversity at the genus level, the alpha diversity represented by Shannon value was not significantly different between the two groups, while the belt diversity was slightly different. After selection by LASSO regression, 10 fecal microbe biomarkers were selected for model training. By comparing six different models, the XGB model showed the best performance, which average AUC, accuracy and F1 score were 0.976, 0.914 and 0.952, respectively, thus being used to construct the final JIA diagnosis model. Conclusion: A JIA diagnosis model based on XGB algorithm was constructed with excellent performance, which may assist physicians in early detection of JIA patients and improve the prognosis of JIA patients.

Impact of non-surgical periodontal treatment on dental anxiety: A comparative study on patients with periodontitis.

Background/purpose: Dental anxiety is prevalent and may result in the avoidance of periodontal therapy and maintenance. This study aimed to explore the impact of non-surgical periodontal treatment (NSPT) on dental anxiety among patients with periodontitis. Materials and methods: In this study, 122 patients with periodontitis participated. The Chinese version of the Modified Dental Anxiety Scale (MDAS) gauged baseline dental anxiety during the initial appointment. Patients receiving non-surgical periodontal treatment (NSPT) in subsequent appointments formed the NSPT group, while those with a delayed NSPT of at least two months constituted the delayed group. In the NSPT group, the second termination questionnaire was administered one month post the last NSPT visit, just before the periodontal re-evaluation. In the delayed group, the second questionnaire was completed before the delayed NSPT initiation. Results: Baseline MDAS scores were comparable between the delay and NSPT groups. However, the NSPT group exhibited lower total scores and scores for each of the five MDAS items at termination compared with the delay group. At baseline, MDAS total scores were inversely associated with age and were lower in males. A reduction in MDAS total scores between observation points was correlated with NSPT, sex, and age after adjustment. Regarding MDAS item 4 (teeth scaled/polished), score reduction consistently correlated with NSPT and age. Conclusion: Participation in NSPT may alleviate dental anxiety, and consequently enhance the patients' conceptiveness to undergo periodontal maintenance or surgery.