Vision is a warning that directs muscles of the body to respond and gives information about where and when to move. Visual reaction time amongst athletes is mainly concerned with how fast an athlete reacts to a visual stimulus. However, little information is available on reaction time (RT), hand-eye coordination among athletes from different sport branches. In many sports branches; RT and hand-eye coordination is regarded as a prerequisite for success. For this reason, the aim of the current study was to investigate RT differences for eye-hand coordination of athletes from different sports and sedentary people. Study involved 48 athletes, aged 18-25 from different branches [basketball (n=6), arm wrestling (n=4), boxing (n=6), football (n=13), handball (n=4), rugby (n=8), volleyball (n=7)] and 9 sedentary people. Hand-eye coordination tests were conducted by using reaction development and training system FitLight Trainer™ (Fitlight Sports Corp., Canada). Test protocol consisted of 10 series of simple motor reaction task to visual stimuli; each of the 10 series included 22 reactions. Variables not fitting normal distribution were compared by Kruskal Wallis H test. Mean reaction time (MRT) was found to be different among branches (p=0.009). RT for 10 different trials were found to be different for 3rd(p=0.038), 4th(p=0.047), 5th(p=0.022), 6th(p=0.044), 7th(p=0.041), 8th(p=0.011), 9th(p=0.019), 10th(p=0.023) trials. According to results, it can be said that visual reaction times of field players are very specific and insufficient to distinguish. During trainings, it may be advisable to have reaction time development exercises with specific technical/tactical skills related to branches and positions.

Background/Objectives: This study aimed to evaluate salivary leptin, adiponectin, and calprotectin levels and to investigate the associations among these biomarkers in periodontally healthy individuals, as well as in patients with gingivitis and periodontitis. Methods: A total of 165 participants were included: 55 periodontally healthy individuals, 55 with gingivitis, and 55 with periodontitis. Unstimulated saliva was collected via passive drool, and salivary leptin, adiponectin, and calprotectin levels were biochemically quantified using enzyme-linked immunosorbent assay. Results: Salivary leptin levels were significantly lower in the periodontally healthy group than those in the gingivitis and periodontitis groups, whereas adiponectin levels were reduced in the periodontitis group than in the periodontally healthy and gingivitis groups (p < 0.05). Salivary calprotectin levels differed significantly among groups, highest in the periodontitis group, followed by the gingivitis and periodontally healthy groups (p < 0.05). Salivary leptin and calprotectin levels demonstrated significant positive correlations with all clinical periodontal parameters, while adiponectin levels were negatively correlated (p < 0.05). Receiver operating characteristic and logistic regression analyses identified salivary leptin, calprotectin, and adiponectin levels as significant biomarkers for distinguishing periodontal health, gingivitis, and periodontitis (p < 0.05). Conclusions: These findings suggest salivary leptin, calprotectin, and adiponectin may serve as biomarkers and potential risk predictors of periodontal disease.

Background: Early identification of Alzheimer’s disease-related cognitive impairment remains challenging, and existing machine learning (ML) models often suffer from feature instability and limited interpretability. This study developed robust and explainable ML models using cerebrospinal fluid (CSF) biomarkers by systematically comparing sparsity-based (LASSO), importance-based (Boruta), and consensus feature selection strategies. Methods: A publicly available cohort of 333 individuals (91 cognitively impaired, 242 cognitively normal) was analyzed. Data were split into training (70%) and independent test (30%) sets. Multiple classifiers, including Elastic Net-regularized logistic regression (LR), support vector machine (SVM), random forest, XGBoost, and Naive Bayes (NB), were trained using repeated 5-fold cross-validation (10 repetitions; 10 × 5-fold cross-validation) with class weighting. Model performance was evaluated using discrimination, calibration, and clinical utility metrics, and interpretability was assessed using SHAP. Results: All models demonstrated strong discriminative performance on the test set (AUROC 0.861–0.958). LASSO-based models showed high specificity, Boruta-based models achieved higher sensitivity, and consensus-based models provided the most balanced performance. The consensus-LR and -SVM models achieved AUROC values of 0.954 and 0.951, respectively. Beyond discrimination, the consensus-LR model demonstrated good calibration and consistent net clinical benefit in decision curve analysis, analyses that remain relatively underreported in the Alzheimer’s disease machine learning literature. SHAP analyses highlighted biologically plausible contributions from key biomarkers, including tau, Aβ42, NT-proBNP, pancreatic polypeptide, and IL-7. Conclusions: In summary, stable and interpretable ML models for Alzheimer’s disease-related cognitive impairment can be developed using CSF-derived biomarkers obtained through lumbar puncture. The proposed consensus-based feature selection framework improves feature stability and model transparency, facilitating the discrimination between cognitively normal and impaired individuals and providing a foundation for future external validation studies.

Natural antimicrobial products are attracting interest in the treatment of periodontal diseases due to their minimal or nonexistent side effects. Propolis, a complex resin content produced by bees, has drawn particular interest for its antibacterial effects, including inhibitory effects on periodontopathogenic bacterial species. This study compared the effects of propolis, a natural product, and chlorhexidine, the gold standard antimicrobial agent, on MMP-1 and MMP-9 levels in gingival crevicular fluid (GCF). Subgingival irrigations of chlorhexidine and propolis solutions were performed in selected deep periodontal pockets of periodontitis patients along with scaling and root planing. The treatment protocol was administered three times at one-month intervals starting from the initial day of the study. GCF MMP-1 and MMP-9 levels, along with clinical periodontal parameters, were evaluated before and after treatment. The study results showed that a statistically significant decrease in clinical periodontal parameters in both groups (p<0.001). Biochemical analysis revealed a statistically significant decrease in levels of GCF MMP-1 and MMP-9 in both groups (p<0.005). Additionally, the decrease in GCF MMP-1 levels was found to be significantly greater in the propolis group compared to the chlorhexidine group (p<0.001).

Background/Objectives: Orthodontic tooth movement is a biological process involving coordinated bone resorption and formation in response to mechanical stimulation. The aim of this study was to evaluate the temporal changes in C-terminal telopeptide of type I collagen (CTX), procollagen type I N-terminal propeptide (PINP), and vitamin D-binding protein (VDBP) levels in gingival crevicular fluid (GCF) and saliva during fixed orthodontic treatment, as well as to assess the relationships among these biomarkers. Methods: The study included a total of 27 systemically and periodontally healthy individuals comprising 14 males and 13 females. Clinical periodontal parameters were assessed at three time points: before treatment (T0), at 24–48 h (T1), and on day 40 (T2). GCF and saliva samples were collected at the same time points. Levels of CTX, PINP and VDBP in GCF and saliva were quantified using enzyme-linked immunosorbent assay. The data were analyzed using both parametric and non-parametric statistical tests. Temporal changes across the three time points were evaluated using mixed-effects models, differences between GCF and saliva biomarker levels were assessed using paired tests, and correlations were examined using Spearman correlation analysis. Results: GCF and salivary CTX levels demonstrated a significant increase from T0 to T1, while PINP levels exhibited a substantial rise from T1 to T2 (p < 0.001). Levels of VDBP in both GCF and saliva did not demonstrate significant temporal changes (p > 0.05). Higher VDBP levels in both fluids were found to be negatively associated with increases in CTX and positively associated with increases in PINP (p < 0.05). Furthermore, salivary CTX and VDBP levels exhibited a consistent increase compared to those measured in GCF at all time points (p < 0.05). Conclusions: Fixed orthodontic forces elicit sequential resorptive and formative responses in both GCF and saliva. The potential of VDBP to function as a local modulator is indicated, with the capacity to influence the balance between osteoclastic and osteoblastic activity. The evaluation of these biomarkers in non-invasive biological samples may offer a valuable approach for monitoring bone metabolism throughout orthodontic treatment.