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Exercise-induced extracellular vesicles mediate apoptosis in human colon cancer cells in an exercise intensity-dependent manner
No full textRegular exercise reduces the incidence and improves the prognosis of many cancer types, but the underlying mechanisms remain elusive. Evidence suggests that exercise exerts its therapeutic effects through extracellular vesicles (EVs), which are essential for cellular communication. Here, we hypothesized that exercise-induced EVs from serum of healthy individuals would exert anti-tumorigenic effects on human colon cancer HT-29 cells, in an exercise intensity-dependent manner. Ten healthy young active males participated in a randomized crossover trial, completing two workload-matched acute exercise bouts, moderate-intensity continuous exercise (MICE) and high-intensity interval exercise (HIIE), on a cycle ergometer. A control session of rest (PRE) was included. EVs were isolated from serum samples collected during PRE and immediately after each exercise session. EVs were co-incubated with HT-29 colon cancer cells, and the effects on cell viability, migration, and apoptosis were measured. EV treatment reduced cell viability in all groups (PRE, MICE, and HIIE) by 35%, 43% and 47%, respectively, vs. PBS. HIIE-EVs showed a significantly greater reduction in cell viability vs. PRE; therefore, only these groups were used for further analysis. PRE EVs reduced migration by 27%, and HIIE-EVs by 39%. HIIE-EVs increased expression of pro-apoptotic markers: Bax/Bcl-2 ratio by 56% and Caspase 3 by 30% vs. PBS, with no change observed in the PRE group. Further, 16% of cells in PRE and 28% of cells in HIIE were TUNEL-positive, indicating DNA fragmentation. To our knowledge, this is the first human study that illustrates the therapeutic potential of exercise-induced EVs in cancer treatment
Predicting students' academic procrastination tendencies using online learning trajectories
No full textThis study aimed to develop a prediction model to classify students based on their academic procrastination tendencies, which were measured and classified as low and high using a self-report tool developed based on the students' assignment submission behaviours logged in the learning management system's database. The students' temporal learning traces were used to extract the features used in the prediction models. The study participants were 51 students enrolled in the Database Management Systems course, which was conducted online using the Moodle learning management system. The study compared the performance of different machine learning algorithms in predicting students' academic procrastination tendencies, analysed the important features of prediction models, and examined whether there is a difference between the academic performance of low and high academic procrastinators. Logistic regression was found to outperform other classification algorithms and reached 90% accuracy in classifying low and high academic procrastinators. Students' regular and early access to course activities were found to be important features in predicting their academic procrastination tendencies. In terms of academic performance, the findings support the existing literature. Students with low academic procrastination tendencies got significantly higher final grades than those with high academic procrastination tendencies. These findings show that students' academic procrastination tendencies can be predicted with high accuracy using online learning trajectories. Such a model will be important in the development of intervention methods for preventing academic procrastination
Relationship Between Environmental Health and Health Status Indicators: A Canonical Correlation Analysis Approach
No full textAn Experimental Design Approach for Producing Curcumin-Loaded Solid Lipid Nanoparticles
No full textBackground/Objectives: Curcumin has well-established efficacy in a variety of disorders due to its prominent antioxidant, antiaging, anti-inflammatory, chemosensitizing, and anticancer activities. Despite its numerous benefits, curcumin exhibits low bioavailability mainly due to its poor solubility, poor absorption, rapid metabolism, and quick excretion, consequently limiting its clinical applications. In this study, we investigated the most convenient ingredients in SLNs to enhance curcumin's solubility by examining the effects of multiple independent variables simultaneously using an experimental design. Methods: After curcumin's saturation solubility was investigated, SLN formulations were produced. The optimum formulation was determined with the help of experimental design. The SLNs were characterized in terms of the particle size and distribution, zeta potential, shape, entrapment efficiency, drug loading capacity, and drug release. The cell viability and cell internalization were also evaluated. Results: An impressive synergistic effect was achieved with the combination of Brij and Gelucire 48/16, which increased curcumin's solubility in water by 452.5 times. Curcumin-loaded SLNs were successfully produced with a spherical shape and particle size of 389.3 +/- 9.95 nm. The encapsulation efficiency was directly proportionate to the amount of curcumin and the stirring speed. Curcumin in the SLNs entered the cancer cells more easily than curcumin alone. Conclusions: Our results demonstrate that the quantity of surfactant is a significant factor influencing the efficiency of drug loading. Finally, the 3:1 (Brij-Gelucire48/16) ratio markedly enhanced the loading efficiency. The cellular internalization and, consequently, the anticancer efficacy against adenocarcinomic human alveolar basal epithelial cells were improved with SLNs. This could be a promising approach for lipid-based colloidal drug delivery systems
Does intraoperative ultrasonography improve surgical precision/ outcome? A bibliometric/narrative analysis.
No full text[Antibiotic Susceptibility Profile and Biofilm Formation in Sequential Chronic Pseudomonas aeruginosa Isolates from Pediatric Patients with Cystic Fibrosis].
No full textIdentifying Predictors of No-Show Appointments at a University Hospital in Turkey
No full textAim: This study aimed to identify the number of patients not attending their outpatient appointments and the risk factors associated with no-show appointments in a university hospital. Methods: Patients’ no-show appointments were analyzed retrospectively in a university hospital in Çorum, a typical medium-sized province of Turkey, in a one-month period between January 1 and February 1, 2020. Data were obtained from the hospital information systems. Multivariate logistic regression analysis was used to determine factors affecting no-show appointments. Results: The proportion of no-show appointments was 16.2%. Logistic regression analysis showed a significant correlation between patients' sex, age, marital status, outpatient status, and not going to their appointments (p<0.05). Conclusions: The findings of this study will help better understand and manage the problem of no-show appointments. To reduce the negative effect of no-show appointments it is important to develop appropriate interventions.</jats:p
