Poor oral health was associated with higher risk of gastric cancer: Evidence from 1431677 participants.

BACKGROUND: In recent years, the association between oral health and the risk of gastric cancer (GC) has gradually attracted increased interest. However, in terms of GC incidence, the association between oral health and GC incidence remains controversial. Periodontitis is reported to increase the risk of GC. However, some studies have shown that periodontitis has no effect on the risk of GC. Therefore, the present study aimed to assess whether there is a relationship between oral health and the risk of GC. AIM: To assess whether there was a relationship between oral health and the risk of GC. METHODS: Five databases were searched to find eligible studies from inception to April 10, 2023. Newcastle-Ottawa Scale score was used to assess the quality of included studies. The quality of cohort studies and case-control studies were evaluated separately in this study. Incidence of GC were described by odds ratio (OR) and 95% confidence interval (CI). Funnel plot was used to represent the publication bias of included studies. We performed the data analysis by StataSE 16. RESULTS: A total of 1431677 patients from twelve included studies were enrolled for data analysis in this study. According to our analysis, we found that the poor oral health was associated with higher risk of GC (OR = 1.15, 95%CI: 1.02-1.29; I2 = 59.47%, P = 0.00 < 0.01). Moreover, after subgroup analysis, the outcomes showed that whether tooth loss (OR = 1.12, 95%CI: 0.94-1.29; I2 = 6.01%, P > 0.01), gingivitis (OR = 1.19, 95%CI: 0.71-1.67; I2 = 0.00%, P > 0.01), dentures (OR = 1.27, 95%CI: 0.63-1.19; I2 = 68.79%, P > 0.01), or tooth brushing (OR = 1.25, 95%CI: 0.78-1.71; I2 = 88.87%, P > 0.01) had no influence on the risk of GC. However, patients with periodontitis (OR = 1.13, 95%CI: 1.04-1.23; I2 = 0.00%, P < 0.01) had a higher risk of GC. CONCLUSION: Patients with poor oral health, especially periodontitis, had a higher risk of GC. Patients should be concerned about their oral health. Improving oral health might reduce the risk of GC.

Management strategies of dental anxiety and uncooperative behaviors in children with Autism spectrum disorder.

BACKGROUND: Children with Autism spectrum disorder (ASD) was frequently experienced dental anxiety and uncooperative behaviors during dental treatment. Oral health care was necessary because of the poor oral hygiene and prevalent dental diseases in this population. AIM: In this systematic review, we evaluated the effectiveness and feasibility for pediatric dentist to manage the dental anxiety in children with ASD. DESIGN: PubMed, Embase, and Cochrane Library were systematically performed on the literature search. The date of eligible publications was from inception to January 2023. After that, the quality of eligible studies was assessed by the Newcastle Ottawa Scale (NOS). Review findings were summarized using the PRISMA Statement for reporting. RESULTS: A total of six studies were systematically evaluated according to the inclusion and exclusion criteria. Five studies were conducted to evaluate ASD Children's anxiety and uncooperative performance in the progressive oral examination, oral disease prophylaxis and fluoride application. The other one study evaluated the success rate of treatment in decayed permanent tooth treatment. In the included studies, four studies indicated that it was extremely necessary to reduce dental anxiety of ASD children to increase the cooperation in sensory-adapted dental environment (SADE). CONCLUSION: It is not always effective and feasible for pediatric dentist to manage the dental anxiety in children with autism during routine oral examination. Meanwhile, it is necessary for ASD children to conduct preoperative psychological assessment, to investigate parents' expectations and cooperation, and to determine whether to start corresponding dental treatment.

[Effects of Bisphosphonates on Beclin1 and LC3â ¡ Induced by High-glucose in Rat Bone Marrow Mesenchymal Stem Cells].

OBJECTIVE: To investigate the effects of bisphosphonates on autophagy induced by high-glucose in rat bone marrow mesenchymal stem cells (BMSCs). METHODS: BMSCs were isolated and cultured in vitro, identified by undergoing osteogenic/chondrogenic/adipogenic differentiation, the concentration of bisphosphonates was determined by CCK-8 method. The cells were cultured in normal glucose (5.6 mmol/L D-glucose), high glucose (30 mmol/L D-glucose), and high glucose with bisphosphonates (30 mmol/L D-glucose+10-9 mmol/L bisphosphonates). At 48 h, mRNA expression levels of autophagy related genes Beclin1 and microtubule-associated protein 1 light chain 3 (LC3) were dected by real-time PCR, protein expression levels of Beclin1 and LC3Ⅱ were detected by Western blot, and the autophagy body was observed by transmission electron microscopy (TEM). RESULTS: The results showed that BMSCs had the ability of osteogenenic, chondrogenic and adipogenic differentiation. Compared with the control group and high glucose with bisphosphonates group, the mRNA [CM(155mm]expressions of Beclin1 and LC3 and protein expressions of Beclin1 and LC3Ⅱ in the high glucose group were increased (P<0.01 or P<0.05). TEM showed that the number of autophagy body in high glucose group was higher than that in normal group and high glucose with bisphosphonates group. CONCLUSION: Bisphosphonates may play a role of down-regulating the expression of Beclin1 and LC3Ⅱ induced by high-glucose in BMSCs.

Non-destructive and fast identification of cotton-polyester blend fabrics by the portable near-infrared spectrometer.

A series of 376 cotton-polyester (PET) blend fabrics were studied by a portable near-infrared (NIR) spectrometer. A NIR semi-quantitative-qualitative calibration model was established by Partial Least Squares (PLS) method combined with qualitative identification coefficient. In this process, PLS method in a quantitative analysis was used as a correction method, and the qualitative identification coefficient was set by the content of cotton and polyester in blend fabrics. Cotton-polyester blend fabrics were identified qualitatively by the model and their relative contents were obtained quantitatively, the model can be used for semi-quantitative identification analysis. In the course of establishing the model, the noise and baseline drift of the spectra were eliminated by Savitzky-Golay(S-G) derivative. The influence of waveband selection and different pre-processing method was also studied in the qualitative calibration model. The major absorption bands of 100% cotton samples were in the 1400~1600 nm region, and the one for 100% polyester were around 1600~1800 nm, the absorption intensity was enhancing with the content increasing of cotton or polyester. Therefore, the cotton-polyester's major absorption region was selected as the base waveband, the optimal waveband (1100~2500 nm) was found by expanding the waveband in two directions (the correlation coefficient was 0.6, and wave-point number was 934). The validation samples were predicted by the calibration model, the results showed that the model evaluation parameters was optimum in the 1100~2500 nm region, and the combination of S-G derivative, multiplicative scatter correction (MSC) and mean centering was used as the pre-processing method. RC (relational coefficient of calibration) value was 0.978, RP (relational coefficient of prediction) value was 0.940, SEC (standard error of calibration) value was 1.264, SEP (standard error of prediction) value was 1.590, and the sample's recognition accuracy was up to 93.4%. It showed that the cotton-polyester blend fabrics could be predicted by the semi-quantitative-qualitative calibration model.

The establishment and external validation of NIR qualitative analysis model for waste polyester-cotton blend fabrics.

A series of 354 polyester-cotton blend fabrics were studied by the near-infrared spectra (NIRS) technology, and a NIR qualitative analysis model for different spectral characteristics was established by partial least squares (PLS) method combined with qualitative identification coefficient. There were two types of spectrum for dying polyester-cotton blend fabrics: normal spectrum and slash spectrum. The slash spectrum loses its spectral characteristics, which are effected by the samples' dyes, pigments, matting agents and other chemical additives. It was in low recognition rate when the model was established by the total sample set, so the samples were divided into two types of sets: normal spectrum sample set and slash spectrum sample set, and two NIR qualitative analysis models were established respectively. After the of models were established the model's spectral region, pretreatment methods and factors were optimized based on the validation results, and the robustness and reliability of the model can be improved lately. The results showed that the model recognition rate was improved greatly when they were established respectively, the recognition rate reached up to 99% when the two models were verified by the internal validation. RC (relation coefficient of calibration) values of the normal spectrum model and slash spectrum model were 0.991 and 0.991 respectively, RP (relation coefficient of prediction) values of them were 0.983 and 0.984 respectively, SEC (standard error of calibration) values of them were 0.887 and 0.453 respectively, SEP (standard error of prediction) values of them were 1.131 and 0.573 respectively. A series of 150 bounds samples reached used to verify the normal spectrum model and slash spectrum model and the recognition rate reached up to 91.33% and 88.00% respectively. It showed that the NIR qualitative analysis model can be used for identification in the recycle site for the polyester-cotton blend fabrics.