Inhibitory Effect of Nepeta deflersiana on Climax Bacterial Community Isolated from the Oral Plaque of Patients with Periodontal Disease.

BACKGROUND: The red-complex bacteria are one of the most significant complexes found simultaneously in subgingival plaque next to the periodontal pocket. The current antibacterial treatment is not adequate, and multidrug resistance to it is developing. Henceforth, the antibacterial effect of the ethanolic extract of Nepeta deflersiana was put to test against red-complex bacteria in patients with chronic periodontitis. METHODS: Well diffusion and micro broth dilution procedure by Alamar blue were applied to assess the zone of inhibition (ZOI), the minimum inhibitory concentration (MIC), and the minimum bactericidal concentration (MBC). Anti-virulence efficacies of the plant extract that comprise of adherence and formation of biofilms were examined by the process of adherence and biofilm production assay. RESULTS: The crude extract of Nepeta deflersiana exhibited significant inhibitory outcome against periodontopathic bacteria with noteworthy MIC (0.78-3.12 mg/mL), inhibitory zone (12-20 mm), as well as MBC (3.12-12.50 mg/mL). The N. deflersiana extract inhibited bacterial adhesion ranging from 41% to 52%, 53% to 66%, and 60% to 79% at the given MIC × 0.5, MIC × 1, and MIC × 2 in succession. Substantial suppression was also developed in the biofilm production of the investigated periodontopathic strains following exposure to numerous concentrations of N. deflersianan extract for a period of 24 and 48 h. CONCLUSION: These outcomes divulge a new concept that N. deflersiana extract can be utilized to manufacture valuable antibacterial compounds to treat chronic and acute periodontitis. This identifies N. deflersiana as an essential natural source for future drug development.

A hybrid machine learning model combining association rule mining and classification algorithms to predict differentiated thyroid cancer recurrence.

Background: Differentiated thyroid cancer (DTC) is the most prevalent endocrine malignancy with a recurrence rate of about 20%, necessitating better predictive methods for patient management. This study aims to create a relational classification model to predict DTC recurrence by integrating clinical, pathological, and follow-up data. Methods: The balanced dataset comprises 550 DTC samples collected over 15 years, featuring 13 clinicopathological variables. To address the class imbalance in recurrence status, the Synthetic Minority Over-sampling Technique for Nominal and Continuous (SMOTE-NC) was utilized. A hybrid model combining classification algorithms with association rule mining was developed. Two relational classification approaches, regularized class association rules (RCAR) and classification based on association rules (CBAR), were implemented. Binomial logistic regression analyzed independent predictors of recurrence. Model performance was assessed through accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. Results: The RCAR model demonstrated superior performance over the CBAR model, achieving accuracy, sensitivity, and F1 score of 96.7%, 93.1%, and 96.7%, respectively. Association rules highlighted that papillary pathology with an incomplete response strongly predicted recurrence. The combination of incomplete response and lymphadenopathy was also a significant predictor. Conversely, the absence of adenopathy and complete response to treatment were linked to freedom from recurrence. Incomplete structural response was identified as a critical predictor of recurrence risk, even with other low-recurrence conditions. Conclusion: This study introduces a robust and interpretable predictive model that enhances personalized medicine in thyroid cancer care. The model effectively identifies high-risk individuals, allowing for tailored follow-up strategies that could improve patient outcomes and optimize resource allocation in DTC management.

The Impact of Body Resistance Training Exercise on Biomedical Profile at High Altitude: A Randomized Controlled Trial.

BACKGROUND: Obesity causes different diseases, eventually. In our study, the results of resistance exercises were examined on selected biochemical markers in Abha City, Saudi Arabia, which is at the height of 2,270 meters above sea level. METHODS: A randomized controlled research was conducted with 60 participants equally divided into three groups, 20 subjects in each group: group 1 was composed of obese people who received resistance training exercise, group 2 was composed of the obese control group who did not receive resistance training exercise, and group 3 was composed of normal individuals who received resistance exercise training. The resistance exercises were done in the 6th and 12th weeks. Biochemical blood tests were done. RESULTS: Comparing to the control group, glucose decreased very little with insulin also showing little difference. It has been seen that TC, TG, and LDL reduced to a reasonable extent after resistance exercise, while HDL was increased (p ≤ 0.01). Plasma urea and creatinine showed no differences. Interleukin-6 and leptin decreased significantly (p ≤ 0.01), while there was a significant elevation in adiponectin and testosterone (p ≤ 0.01) once comparing group 1 with group 2 and group 3. CONCLUSION: We have seen that resistance exercise helps in reducing lipid profile which will result in a decrease of the cardiac and related risk factors when conducted in obese patients in high-altitude regions. Also, alterations of the levels of interleukin-6, leptin, adiponectin, and testosterone showed that resistance exercise is of benefit and favourable in obese persons in high-altitude regions, which can also pave the way for added development of drugs related to the above parameters.