Does smoking influence tryptophan metabolism in periodontal inflammation? A cross-sectional study.

OBJECTIVES: The aim of this study was to identify the effects of smoking and periodontal inflammation on tryptophan-kynurenine metabolism as well as the correlation between these findings and clinical periodontal parameters. BACKGROUND: It has been shown that the tryptophan amino acid's primary catabolic pathway, the kynurenine pathway (KP), may serve as a key biomarker for periodontal disease. Although there are studies investigating the effect of smoking on KYN-TRP metabolism, the effect of smoking on periodontal disease through KP has not been revealed so far. METHODS: The salivary and serum samples were gathered from 24 nonsmoker (NS-P) stage III, grade B generalized periodontitis and 22 smoker (S-P) stage III, grade C generalized periodontitis patients, in addition to 24 nonsmoker (NS-C) and 24 smoker (S-C) periodontally healthy control individuals. Saliva and serum IL-6, kynurenine (KYN), and tryptophan (TRP) values, and KYN/TRP ratio were analyzed by liquid chromatography-mass spectrometry. Clinical periodontal measurements were recorded. RESULTS: Salivary TRP values were significantly higher in both periodontitis groups than control groups (p < .05). Salivary KYN values were highest in NS-P group (p < .05). Salivary KYN values did not differ significantly between periodontitis groups (p = .84). Salivary KYN/TRP ratio was significantly lower in NS-P group compared to other groups (p < .001). Serum TRP value is higher in S-P group than other groups; however, significant difference was found in S-C group (p < .05). Serum KYN values were significantly lower in smokers than nonsmokers. Serum KYN/TRP ratio is higher in NS-P group. NS-P group has the highest salivary IL-6 levels, NS-C group has the lowest values (p < .05). CONCLUSIONS: Our results point out that smoking exacerbates inflammation in the periodontium and increases TRP destruction and decreases IDO activity by suppressing KP in serum. As a result, kynurenine and its metabolites may be significant biomarkers in the link between smoking and periodontal disease.

Association of serum ADMA, SDMA and L-NMMA concentrations with disease progression in COVID-19 patients.

Introduction: This study determines and compares the concentrations of arginine and methylated arginine products ((asymmetric dimethylarginine (ADMA), symmetric dimethylarginine (SDMA), n-monomethyl-1-arginine (L-NMMA) and homoarginine (HA)) for assessment of their association with disease severity in serum samples of COVID-19 patients. Materials and methods: Serum arginine and methylated arginine products of 57 mild-moderate and 29 severe (N = 86) COVID-19 patients and 21 controls were determined by tandem mass spectrometry. Moreover, the concentrations of some of the routine clinical laboratory parameters -neutrophil lymphocyte ratio (NLR), C-reactive protein, ferritin, D-dimer, and fibrinogen measured during COVID-19 follow-up were also taken into consideration and compared with the concentrations of arginine and methylated arginine products. Results: Serum ADMA, SDMA and L-NMMA were found to be significantly higher in severe COVID-19 patients, than in both mild-moderate patients and the control group (P < 0.001 for each). In addition, multiple logistic regression analysis indicated L-NMMA (cut-off =120 nmol/L OR = 34, 95% confidence interval (CI) = 3.5-302.0, P= 0.002), CRP (cut-off = 32 mg/L, OR = 37, 95% CI = 4.8-287.0, P < 0.001), and NLR (cut-off = 7, OR = 22, 95% CI = 1.4-335.0, P = 0.020) as independent risk factors for identification of severe patients. Conclusions: The concentration of methylated arginine metabolites are significantly altered in COVID-19 disease. The results of this study indicate a significant correlation between the severity of COVID-19 disease and concentrations of CRP, NLR and L-NMMA.

Sample size, power and effect size revisited: simplified and practical approaches in pre-clinical, clinical and laboratory studies.

Calculating the sample size in scientific studies is one of the critical issues as regards the scientific contribution of the study. The sample size critically affects the hypothesis and the study design, and there is no straightforward way of calculating the effective sample size for reaching an accurate conclusion. Use of a statistically incorrect sample size may lead to inadequate results in both clinical and laboratory studies as well as resulting in time loss, cost, and ethical problems. This review holds two main aims. The first aim is to explain the importance of sample size and its relationship to effect size (ES) and statistical significance. The second aim is to assist researchers planning to perform sample size estimations by suggesting and elucidating available alternative software, guidelines and references that will serve different scientific purposes.