Ufuk University

This study examined the effectiveness of TheraBuddy, a web-based interactive visual novel, in enhancing bullying bystander intervention and socioemotional health among K-12 students. Using a four-wave controlled time-series design, 123 students aged 10-16 were randomly assigned to intervention (n = 62) or control (n = 61) groups. Participants completed validated measures at baseline and three-monthly follow-ups. The experimental group showed significantly greater gains in bystander intervention than controls, with effects moderated by bullying role and engagement level; socioemotional health effects were non-significant. Findings highlight the potential of interactive visual novels to promote prosocial action against bullying and inform scalable, school-based prevention strategies.

This study analyzes forest fires in Turkey between 2012 and 2022 using machine learning and artificial neural network methods, considering their timing, causes, burned area size, and spatial distribution. Official and publicly available data from the Turkish Orman Genel M & uuml;d & uuml;rl & uuml;& gbreve;& uuml; were used as the dataset. Decision Tree, Random Forest, Support Vector Machines, Multilayer Perceptron, and Long Short-Term Memory models were applied and comparatively evaluated. The performance of the classification models was evaluated using accuracy and F1 score, while temporal predictions were assessed using the Root Mean Square Error metric. According to the results, the Multilayer Perceptron model showed the highest success in predicting fire causes and burned area size classes (accuracy = 86%, F1 = 0.83). The Random Forest model similarly demonstrated strong performance with 85% accuracy and an F1 score of 0.82. The Long Short-Term Memory model, used for temporal predictions, achieved the lowest error value (Root Mean Square Error = 0.12) in estimating the annual and monthly number of fires and the amount of burned area, successfully capturing the variability, especially in years with excessive fires. Overall, the findings show that the Long Short-Term Memory model is superior in modeling temporal dependencies, while the Random Forest and Multilayer Perceptron models provide reliable results in classification problems. In conclusion, machine learning and artificial neural networks can be effectively used in the development of early warning systems, risk mapping, and decision support mechanisms for forest fires.

Triple-negative breast cancer (TNBC) is a distinct molecular subtype of breast cancer, characterized by high mortality and metastasis. Indoleamine 2,3-dioxygenase (IDO) is a highly expressed enzyme in cancer cells that contributes to immunosuppression. The IDO inhibition has been widely used in cancer immunotherapy. In this study, we evaluated the efficacy of indoximod, an IDO inhibitor, in combination with melatonin on TNBC tumor progression and metastasis in vitro and in vivo. Indoximod reduced the viability of 4T1 cells by inducing apoptosis and G2/M cell cycle arrest. Moreover, the combination of melatonin and TNF-α increased indoximod-mediated cell death. In vivo, coadministration of indoximod and melatonin resulted in enhanced antiproliferative and antimetastatic activities in the TNBC model. Tumor generation-induced neutrophil counts, TNF-α, IL-1β, and IL-10 levels were significantly reduced in mice receiving indoximod and melatonin in combination. Importantly, the accumulation of myeloid-derived suppressor cells (MDSCs), a key driver of tumor progression and metastasis, decreased in the primary tumor, metastatic liver, and lung of mice treated with indoximod and melatonin combination. Our findings revealed that melatonin enhances the immunotherapeutic efficacy of indoximod in TNBC.

BACKGROUND:Demodex mites are believed to contribute to the pathogenesis of both rosacea and demodicosis. While standardized skin surface biopsy (SSSB) remains the gold standard for detecting Demodex density, it is invasive and time-consuming. Ultraviolet (UV) dermoscopy, a noninvasive technique that visualizes fluorescent structures, may offer a rapid alternative for identifying Demodex proliferation. OBJECTIVES:To investigate the relationship between Demodex folliculorum and Demodex brevis counts obtained by SSSB and fluorescent dots detected under UV dermoscopy, and to evaluate the diagnostic value of these findings in rosacea and demodicosis. METHODS:Fifty-four patients with rosacea or demodicosis were evaluated using both UV dermoscopy and SSSB from the same facial region. Orange and bright blue fluorescent dots were recorded dermoscopically, and mite density and species were quantified via SSSB. Correlation analyses and receiver operating characteristic (ROC) curve analysis were performed to assess diagnostic performance. RESULTS:A significant positive correlation was found between total Demodex count and bright blue fluorescent dots (r = 0.343; P = 0.013), but not with orange dots (r = -0.065; P = 0.649). Only D. folliculorum showed significant correlation with blue and total fluorescent dots. ROC analysis identified > 11 bright blue dots as the optimal threshold for predicting Demodex positivity, with a sensitivity of 82.1% and specificity of 71.4% (area under the curve 0.793; P = 0.003). CONCLUSIONS:Bright blue fluorescent dots observed under UV dermoscopy correlate significantly with Demodex density, particularly D. folliculorum. A threshold of > 11 blue dots demonstrated good diagnostic accuracy and may serve as a practical, noninvasive alternative or adjunct to SSSB in clinical settings. Orange fluorescence was not associated with Demodex density and most likely reflects unrelated sebaceous or microbial activity.

Background and Objectives: To evaluate the magnitude, axis and age-related changes in corneal astigmatism in patients before cataract surgery. Materials and Methods: In this retrospective, cross-sectional, and observational study, data from 2152 eyes that underwent phacoemulsification were evaluated. Keratometric values were obtained using the IOL Master 500 device. The frequency, magnitude and axis of corneal astigmatism were determined. The astigmatism axis was categorized as with the rule (WTR), against the rule (ATR), and oblique astigmatism. Quantitative analysis was performed using the power vector method (J0 and J45). The distribution and characteristics of corneal astigmatism data according to age were analyzed. Results: The mean age of the patients was 70.56 ± 8.88 years (range 40–94 years) and 1010 (46.9%) were males. Mean corneal astigmatism, J0 and J45 values were 0.96 ± 0.72, 0.05 ± 0.51, 0.01 ± 0.30 diopters (D), respectively. The most common range of magnitudes was 0.50–0.99 D with 38.8%, followed by <0.50 D (25.3%), 1.00–1.49 D (20.3%), and 1.50–1.99 D (8.7%). The cubic regression curve showed a U-shaped nonlinear relationship between age and corneal astigmatism (p < 0.001). The most common type of astigmatism was WTR with 43.4%, followed by ATR with 37.5% and oblique astigmatism with 19.1%. With the increase in age, the astigmatism axis gradually changed from WTR to ATR. There was a linear trend in the rate of these types of astigmatism across age groups (p < 0.05). Additionally, in patients under 65 years of age, WTR astigmatism was negatively correlated with age, while in patients 65 years of age and older, ATR astigmatism was positively correlated with age (r = −0.217, p < 0.001; r = 0.153, p < 0.001, respectively). Linear regression analyses revealed that the J0 value decreased significantly with age, whereas J45 showed no significant relationship. Specifically, J0 decreased by 0.014 D per year of age (95% confidence interval [CI], 0.011–0.016; p < 0.001). Conclusions: The results obtained in this study may provide information to guide surgeons in the management of astigmatism and the choice of toric intraocular lens in cataract surgery.