Investigation on the association between serum lipid levels and periodontitis: a bidirectional Mendelian randomization analysis.

OBJECTIVE: Several research has considered the potential correlation between periodontitis and serum lipids. However, serum lipid profiles correlation with periodontitis remains largely unknown. The investigation objective was to examine periodontitis correlation with serum lipid levels using a bidirectional Mendelian randomization (MR) analysis. METHODS: The study employed a bidirectional MR analysis with two samples, utilizing a freely accessible genome-wide association study (GWAS). Furthermore, the primary analysis employed the inverse variance weighted (IVW) method. To determine whether the lipid profiles were associated with periodontitis, a variety of sensitivity analyses (including MR-Egger regression, MR-PRESSO, and weighted median), as well as multivariable MR, were employed. RESULTS: MR analysis performed by IVW did not reveal any relationship between periodontitis and low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides (TG), or total cholesterol (TC). It was also found that LDL, HDL, TG, and TC were not associated to periodontitis. Furthermore, the MR estimations exhibited consistency with other MR sensitivity and multivariate MR (MVMR) analyses. These results show that the correlation between serum lipid levels and periodontitis could not be established. CONCLUSION: The finding indicates a negligible link between periodontitis and serum lipid levels were identified, despite previous observational studies reporting a link between periodontitis and serum lipid levels.

The Provision of Dental Care to COVID-19 Survivors: A Concise Review.

AIMS: It has been reported that there are a certain percentage of COVID-19 patients who recover but suffer from devastating permanent organ damage or failure. Others suffer from long Covid syndrome, with prolonged symptoms that persist more than 12 weeks. However, there is scarcity of literature regarding the provision of dental treatment for these two groups of patients. This manuscript reviews the impact of multi-system involvement on the provision of dental care to these patients. MATERIALS AND METHODS: A search of literature was done in PubMed-Medline and Scopus databases to review the available literature on COVID-19 impacts on pulmonary, cardiovascular, haematologic, renal, gastrointestinal, endocrine, and neurologic systems and respective management in dental clinical settings. RESULTS: The literature search from PubMed-Medline and Scopus databases resulted in 74 salient articles that contributed to the concise review on COVID-19 effects on pulmonary, cardiovascular, haematologic, renal, gastrointestinal, endocrine, and neurologic systems and/or its respective dental management recommendations. CONCLUSIONS: This concise review covers the management of post COVID-19 patients with pulmonary, cardiovascular, haematologic, renal, gastrointestinal, endocrine, or neurologic system complications.

The Predictive Model of Oral Squamous Cell Survival Carcinoma: A Methodology of Validation.

BACKGROUND: Cancer is primarily caused by smoking, alcohol, betel quit, a series of genetic alterations, and epigenetic abnormalities in signaling pathways, which result in a variety of phenotypes that favor the development of OSCC. Oral squamous cell carcinoma (OSCC) is the most common type of oral cancer, accounting for 80-90% of all oral malignant neoplasms. Oral cancer is relatively common, and it is frequently curable when detected and treated early enough. The tumor-node-metastasis (TNM) staging system is used to determine patient prognosis; however, geographical inaccuracies frequently occur, affecting management. OBJECTIVE: To determine the additional relationship between factors discovered by searching for sociodemographic and metastasis factors, as well as treatment outcomes, which could help improve the prediction of the survival rate in cancer patients. Material and Methods. A total of 56 patients were recruited from the ambulatory clinic at the Hospital Universiti Sains Malaysia (USM). In this retrospective study, advanced computational statistical modeling techniques were used to evaluate data descriptions of several variables such as treatment, age, and distant metastasis. The R-Studio software and syntax were used to implement and test the hazard ratio. The statistics for each sample were calculated using a combination model that included methods such as bootstrap and multiple linear regression (MLR). RESULTS: The statistical strategy showed R demonstrates that regression modeling outperforms an R-squared. It demonstrated that when data is partitioned into a training and testing dataset, the hybrid model technique performs better at predicting the outcome. The variable validation was determined using the well-established bootstrap-integrated MLR technique. In this case, three variables are considered: age, treatment, and distant metastases. It is important to note that three things affect the hazard ratio: age (ß 1: -0.006423; p < 2e - 16), treatment (ß 2: -0.355389; p < 2e - 16), and distant metastasis (ß 3: -0.355389; p < 2e - 16). There is a 0.003469102 MSE for the linear model in this scenario. CONCLUSION: In this study, a hybrid approach combining bootstrapping and multiple linear regression will be developed and extensively tested. The R syntax for this methodology was designed to ensure that the researcher completely understood the illustration. In this case, a hybrid model demonstrates how this critical conclusion enables us to better understand the utility and relative contribution of the hybrid method to the outcome. The statistical technique used in this study, R, demonstrates that regression modeling outperforms R-squared values of 0.9014 and 0.00882 for the predicted mean squared error, respectively. The conclusion of the study establishes the superiority of the hybrid model technique used in the study.