Using virtual patient simulation with partial task trainer: A quasi-experimental study.

BACKGROUND: Nursing students need educational approaches that provide sufficient knowledge and practice opportunities to improve their skills. AIM: To analyze the benefits of incorporating partial task trainers into virtual patients, focusing on the effectiveness, performance, self-confidence, satisfaction and knowledge scores among senior nursing students in urinary catheterization for patients with acute urinary retention. DESIGN: A randomized, quasi-experimental design. METHODS: The study was conducted at a nursing faculty between April-May 2023 with 71 senior nursing students: 35 in the virtual patient group (Group I) and 36 in the virtual patient and partial task trainer group (Group II). The data were gathered using: Personal Information Form, Student Satisfaction and Self-confidence in Learning Scale, Simulation Effectiveness Tool, Performance Report and Knowledge Report. RESULTS: The satisfaction and self-confidence scores for Group I were 4.67 (SD 0.49) and 4.38 (SD 0.48), whereas Group II scored 4.88 (SD 0.22) and 4.70 (SD 0.34), respectively. The differences were statistically significant (p<0.05). For the Simulation Effectiveness Tool, Group I scored 31.05 (SD 3.28) for confidence subdimension and 85.05 (SD 7.37) for the total score, whereas Group II scored 32.57 (SD 2.73 and 88.48 (SD 6.60), respectively. These differences were statistically significant (p<0.05). No significant differences were found between the groups in the prebriefing, learning and debriefing subdimensions of the Simulation Effectiveness Tool (p>0.05). Performance and knowledge scores also showed no significant differences (p>0.05). Effect sizes for all statistically significant differences were moderate. CONCLUSIONS: The results show that using virtual patients with partial task trainers increases students' satisfaction and self-confidence and is perceived as effective in developing nursing interventions for patients with acute urinary retention.

Computational modeling of air pollutants for aquatic risk: Prediction of ecological toxicity and exploring structural characteristics.

Assessing the aquatic toxicity originating from air pollutants is essential in sustaining water resources and maintaining the ecosystem's safety. Quantitative structure-activity relationship (QSAR) models provide a computational tool for predicting pollutant toxicity, facilitating the identification/evaluation of the contaminants and identifying responsible structural fragments. One-vs-all (OvA) QSAR is a tailored approach to address multi-class QSAR problems. The study aims to determine five distinct levels of aquatic hazard categories for airborne pollutants using OvA-QSAR modeling containing 254 air contaminants. This QSAR analysis reveals the critical descriptors of air pollutants to target for molecular modification. Various factors, including the selection of relevant mechanistic descriptors, data quality, and outliers, determine the reliability of QSAR models. By employing feature selection and outlier identification approaches, the robustness and accuracy of our QSAR models were significantly increased, leading to more reliable predictions in chemical hazard assessment. The results revealed that models using the Random Forest algorithm performed the best based on the selected descriptors, with internal and external validation accuracy ranging from 71.90% to 97.53% and 76.47%-98.03%, respectively. This study indicated that the aquatic risk of air contaminants might be attributed predominantly to their sp3/sp2 carbon ratio, hydrogen-bond acceptor capability, hydrophilicity/lipophilicity, and van der Waals volumes. These structures can be critical in developing innovative strategies to mitigate or avoid the chemicals' harmful effects. Supporting air quality improvement, this study contributes to the rapid implementation of measures to protect aquatic ecosystems affected by air pollution.

Drug-induced torsadogenicity prediction model: An explainable machine learning-driven quantitative structure-toxicity relationship approach.

Drug-induced Torsade de Pointes (TdP), a life-threatening polymorphic ventricular tachyarrhythmia, emerges due to the cardiotoxic effects of pharmaceuticals. The need for precise mechanisms and clinical biomarkers to detect this adverse effect presents substantial challenges in drug safety assessment. In this study, we propose that analyzing the physicochemical properties of pharmaceuticals can provide valuable insights into their potential for torsadogenic cardiotoxicity. Our research centers on estimating TdP risk based on the molecular structure of drugs. We introduce a novel quantitative structure-toxicity relationship (QSTR) prediction model that leverages an in silico approach developed by adopting the 4R rule in laboratory animals. This approach eliminates the need for animal testing, saves time, and reduces cost. Our algorithm has successfully predicted the torsadogenic risks of various pharmaceutical compounds. To develop this model, we employed Support Vector Machine (SVM) and ensemble techniques, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost). We enhanced the model's predictive accuracy through a rigorous two-step feature selection process. Furthermore, we utilized the SHapley Additive exPlanations (SHAP) technique to explain the prediction of torsadogenic risk, particularly within the RF model. This study represents a significant step towards creating a robust QSTR model, which can serve as an early screening tool for assessing the torsadogenic potential of pharmaceutical candidates or existing drugs. By incorporating molecular structure-based insights, we aim to enhance drug safety evaluation and minimize the risks of drug-induced TdP, ultimately benefiting both patients and the pharmaceutical industry.

Nomophobia and Phubbing Levels of Nursing Students: A Multicenter Study.

Today, with the enhancement in the usage of smartphones, the concepts of nomophobia and phubbing have emerged. Nomophobia refers to the fear of being deprived of smartphones/smart devices. Phubbing is the use of a person's smartphone in situations that are not appropriate for the situation, time, and place. Therefore, the study purposed to evaluate nursing students' nomophobia and phubbing scores in Turkey, Portugal, and the United States. The data were collected with the Personal Information Questionnaire, Nomophobia Scale, and Phubbing Scale from N = 446 nursing students. The mean age of the students was 22.04 ± 4.08 years, and 86.5% were women. It was found that the total nomophobia scores of the nursing students were 80.15 ± 21.96, 72.29 ± 28.09, and 99.65 ± 6.11, respectively in Turkey, Portugal, and the United States. When the countries' Nomophobia Scale total scores, "giving up convenience," "not being able to communicate," and "losing connectedness" scores were compared with each other, they were found to be statistically significant ( P < .05). When the countries' Phubbing Scale total scores and all subscale scores were compared with each other were found to be statistically significant ( P < .05). It is seen that nomophobia scores were moderate (60 ≤ NMP-Q nomophobia ≤ 99) and phubbing scores (<40) were below the level indicating addiction in all countries.

Towards safer pesticide management: A quantitative structure-activity relationship based hazard prediction model.

Pesticides are recognized as common environmental contaminants. The potential pesticide hazard to non-target organisms, including various mammal species, is a global concern. The global problem requires a comprehensive risk assessment. To assess the toxic effects of pesticides at the early stage, a toxicological risk analysis is conducted to determine pesticide hazard levels. World Health Organization (WHO) has established five pesticide hazard classes based on lethal dose (LD50) values to perform these assessments. In this paper, we have developed one-vs-all quantitative structure-activity relationship (OvA-QSAR) models using five machine-learning techniques with the selected optimum molecular descriptors. Descriptor selection was conducted based on correlation to evaluate the relevance and significance of individual features in our dataset. Our OvA-QSAR model was built using a dataset obtained from the WHO, covering a wide range of chemical pesticides. These models can predict the hazard category for a pesticide within the five available categories. Notably, our experiments demonstrate the outstanding performance and robustness of the Random Forest (RF) model in addressing the challenge of multi-class classification with the selected descriptors.

Machine Learning-Based Prediction of Drug-Induced Hepatotoxicity: An OvA-QSTR Approach.

Drug-induced hepatotoxicity, also known as drug-induced liver injury (DILI), is among the possible adverse effects of pharmacotherapy. This clinical condition is accepted as one of the factors leading to patient mortality and morbidity. The LiverTox database was built by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) to predict potential liver damage from medications and take appropriate precautions. The database has classified medicines into seven risk categories (A, B, C, D, E, E*, and X) to avoid medicine-induced liver toxicity. The hepatic damage risk decreases from group A to group E. This study did not include the E* and X classes because they contained unverified and unknown data groups. Our study aims to predict potential liver damage of new drug molecules without using experimental animals. We predict which of the LiverTox risk category drugs with unknown liver toxicity potential will fall into using our one-vs-all quantitative structure-toxicity relationship (OvA-QSTR) model. Our dataset, consisting of 678 organic drug molecules from different pharmacological classes, was collected from LiverTox. The OvA-QSTR models implemented by Bayesian Network (BayesNet) performed well based on the selected descriptors, with the precision-recall curve (PRC) areas ranging from 0.718 to 0.869. Our OvA-QSTR models provide a reliable premarketing risk evaluation of pharmaceutical-induced liver damage potential and offer predictions for different risk levels in DILI.

Machine learning-based approach for efficient prediction of toxicity of chemical gases using feature selection.

Toxic gases can be fatal as they damage many living tissues, especially the nervous and respiratory systems. They can cause permanent damage for many years by harming environmental tissue and living organisms. They can also cause mass deaths when used as chemical weapons. These chemical agents consist of organophosphates, namely ester, amide, or thiol derivatives of phosphorus, phosphonic or phosphinic acids, or can be synthesized independently. In this study, machine learning models were used to predict the toxicity of chemical gases. Toxic and non-toxic gases, consisting of 144 gases, were identified according to the United States Environmental Protection Agency, Occupational Safety and Health Administration, and the Centers for Disease Control and Prevention. Six machine-learning models were used to predict the toxicity of these chemical gases. The performance of the models was verified through internal and external validation. The results showed that the model's internal validation accuracy was 86.96% with the Relief-J48 algorithm. The accuracy value of the model was 89.65% with the Bayes Net algorithm for external validation. Our results reveal that identifying the toxicity of existing and potential chemicals is essential for the early detection of these chemicals in nature.

2D-Quantitative structure-activity relationship modeling for risk assessment of pharmacotherapy applied during pregnancy.

The risk evaluation for pharmacological therapy during pregnancy is critical for maternal and fetal health. The initial risk assessment stage, the risk measurement, begins with pregnancy-labeling categories (A, B, C, D, and X) for pharmaceuticals defined by the US Food and Drug Administration (FDA). Recently, in silico methods have been preferred in toxicology studies to eliminate ethical issues before conducting clinical toxicology studies and animal experiments. Quantitative structure-activity relationship (QSAR) modeling is one of the in silico methodologies. The research focuses on creating a QSAR model that predicts the five FDA pregnancy categories of medications. Our dataset included 868 pharmaceuticals, containing nearly every pharmacological group collected from the FDA. 2D-molecular descriptors were calculated using PaDEL software. Twenty-four QSAR models were developed, and the best four models were discussed. The results of the models were compared according to sensitivity, accuracy, F-score, specificity, receiver operating characteristic (ROC) values, and Matthews correlation coefficient. Considering the statistical results, random forest is the best model for determining the pregnancy risk category of drugs. The accuracy of the model was 76.49% for internal and 93.58% for external validation. According to the kappa statistics, there is an average agreement of 0.583 for internal validation and a perfect agreement of 0.893 for external validation. Because the error rates of the model are very close to 0, the model is highly accurate. Consequently, our novel QSAR model gives guidance on the safe use of pharmaceuticals during pregnancy without requiring animal tests or clinical trials on pregnant women.

Is Virtual Patient Simulation Superior to Human Patient Simulation: A Randomized Controlled Study.

Virtual and human patient simulation methods offer an effective way to increase patient safety, reduce the incidence of errors, and improve clinical decision-making skills. The study was conducted to compare the effects of virtual and human patient simulation methods on performance, simulation-based learning, anxiety, and self-confidence with clinical decision-making scores of nursing students. A quasi-experimental, stratified, randomized controlled study was conducted with third-year nursing students. The students (n = 166) were divided into experimental and control groups. The difference between the pretest-posttest scores of intragroup nursing anxiety and self-confidence with clinical decision-making and total and sub-scale scores of in-group simulation-based learning were statistically significant ( P < .05). Performance scores were found to be statistically significantly high in the virtual patient simulation group ( P < .001). It was determined that virtual patient simulation was superior to other methods in terms of nursing anxiety and self-confidence with clinical decision-making, simulation-based learning, and performance scores.

In silico QSAR modeling to predict the safe use of antibiotics during pregnancy.

The use of medicines during pregnancy is a growing public health concern due to the risk of developmental toxicity. Healthcare providers heavily rely on the FDA pregnancy risk categories (A, B, C, D, and X). Antibiotics are among the most prescribed drugs during pregnancy and are often listed under category B or C. However, the risk-benefit assessment may be lacking due to challenges in the clinical toxicology studies on pregnant women, such as ethical concerns. The primary focus of this study is to generate a model that predicts the safe use of antibiotics during pregnancy by using in silico approaches. Thus, a QSAR model was created to assess the FDA pregnancy category (B or C) of antibiotics. The dataset consisted of 97 antibiotics obtained from the FDA. A total of 6420 molecular descriptors were determined via multiple software and various machine learning algorithms were utilized. The performance of the models was measured using internal and external validation. The accuracy (ACC) values of the most successful model were 83.82% for the internal and 94.11% for the external validation. Sensitivity (SE), specificity (SP), MCC, and ROC values were 0.878, 0.778, 0.68, and 0.892 for the internal validation and 0.9, 1, 0.887, and 0.936 for the external validation, respectively. Kappa statistics also indicate that there was a substantial agreement for internal validation with 0.6765 and an almost perfect agreement for external validation with 0.8811. In conclusion, our model can be used as an initial step before pre-clinical and clinical studies to predict the safe use of antibiotics in pregnancy.

Coronavirus and Health Anxiety of Nursing Students in Two Different Countries.

INTRODUCTION: Even under normal circumstances, anxiety is quite common among nursing students. Therefore, this study compared nursing students' health and coronavirus anxiety in two European countries. METHOD: The sample of the descriptive, cross-sectional study consisted of 685 undergraduate students studying at two different nursing schools in Turkey and Portugal. The study data were collected with the Personnel Data Collection Form, Coronavirus Anxiety Scale, and Short Health Anxiety Inventory. RESULTS: While there was no difference between the Coronavirus Anxiety Scale scores of Turkish and Portuguese nursing students (p > .05), a statistically significant difference was found between the Short Health Anxiety Inventory total scores and negative consequences scores (p < .05). DISCUSSION: Against the pandemic that the whole world is experiencing, it is recommended to compare nursing students in a cultural context and take precautions.

Nursing students' perceptions on clinical learning environment and mental health: a multicenter study. / Percepções de estudantes de enfermagem sobre o ambiente de aprendizagem clínica e saúde mental: um estudo multicêntrico.

OBJECTIVE: this study aimed at evaluating the perceptions of Nursing students from public universities in three European Union countries on mental health and clinical learning environments, a topic that has been rarely investigated in the literature. METHOD: data collection took place using a demographic data form, the Clinical Learning Environment, Supervision and Nurse Teacher Scale, and the Mental Health Continuum Short Form. A total of 571 participants from Turkey, Lithuania and Portugal were included in the study. RESULTS: there was a significant difference among the three groups regarding clinical learning environment and mental health status (p<.001). Supervision was the most valued element. The Portuguese students presented the highest mean in the Mental Health Continuum Short Form and Clinical Learning Environment, Supervision and Nurse Teacher Scale scores (p<.001). Age, gender and mental health were effective in the Clinical Learning Environment, Supervision and Nurse Teacher Scale scores. CONCLUSION: the results indicated that the Mental Health Continuum Short Form and Clinical Learning Environment, Supervision and Nurse Teacher Scale scores obtained by the Portuguese Nursing students were higher. It was also revealed that the students' perceptions on the clinical learning environment were affected by age and gender, and that their perceptions on mental health were influenced by the Clinical Learning Environment, Supervision and Nurse Teacher scale scores.

Percepções de estudantes de enfermagem sobre o ambiente de aprendizagem clínica e saúde mental: um estudo multicêntrico / Nursing students' perceptions on clinical learning environment and mental health: a multicenter study / Percepciones de estudiantes de enfermería sobre el Ambiente de Aprendizaje Clínico y la salud mental: estudio multicéntrico

Resumo Objetivo: este estudo teve como objetivo avaliar as percepções dos estudantes de Enfermagem das universidades públicas de três países da União Europeia sobre saúde mental e ambientes de aprendizagem clínica, tema pouco investigado na literatura. Método: a coleta de dados ocorreu por meio de um formulário de dados demográficos, a Escala Ambiente de Aprendizagem Clínica, Supervisão e Professor de Enfermagem e o Mental Health Continuum Short Form. Um total de 571 participantes da Turquia, Lituânia e Portugal foram incluídos no estudo. Resultados: houve uma diferença significativa entre os três grupos em relação ao ambiente de aprendizagem clínica e estado de saúde mental (p <0,001). A supervisão foi o elemento mais valorizado. Os estudantes portugueses apresentaram a média mais elevada nos escores do Mental Health Continuum Short Form e Ambiente de Aprendizagem Clínica, Supervisão e Professor de Enfermagem (p<0,001). Idade, sexo e saúde mental influíram nos escores do Ambiente de Aprendizagem Clínico, Supervisão e Professor de Enfermagem. Conclusão: os resultados indicaram que os escores do Mental Health Continuum Short Form e Ambiente de Aprendizagem Clínica, Supervisão e Professor de Enfermagem obtidos pelos estudantes de Enfermagem portugueses foram mais elevados. Revelou-se também que as percepções dos alunos sobre o ambiente de aprendizagem clínica foram afetadas pela idade e sexo, e que suas percepções sobre saúde mental foram influenciadas pelos escores da escala Ambiente de Aprendizagem Clínica, Supervisão e Professor de Enfermagem.

Abstract Objective: this study aimed at evaluating the perceptions of Nursing students from public universities in three European Union countries on mental health and clinical learning environments, a topic that has been rarely investigated in the literature. Method: data collection took place using a demographic data form, the Clinical Learning Environment, Supervision and Nurse Teacher Scale, and the Mental Health Continuum Short Form. A total of 571 participants from Turkey, Lithuania and Portugal were included in the study. Results: there was a significant difference among the three groups regarding clinical learning environment and mental health status (p<.001). Supervision was the most valued element. The Portuguese students presented the highest mean in the Mental Health Continuum Short Form and Clinical Learning Environment, Supervision and Nurse Teacher Scale scores (p<.001). Age, gender and mental health were effective in the Clinical Learning Environment, Supervision and Nurse Teacher Scale scores. Conclusion: the results indicated that the Mental Health Continuum Short Form and Clinical Learning Environment, Supervision and Nurse Teacher Scale scores obtained by the Portuguese Nursing students were higher. It was also revealed that the students' perceptions on the clinical learning environment were affected by age and gender, and that their perceptions on mental health were influenced by the Clinical Learning Environment, Supervision and Nurse Teacher scale scores.

Resumen Objetivo: este estudio tuvo como objetivo evaluar las percepciones de estudiantes de enfermería de universidades públicas de tres países de la Unión Europea sobre la salud mental y los Ambientes de Aprendizaje Clínico, tema poco estudiado en la literatura. Método: la recolección de datos se realizó mediante un formulario de datos demográficos, la Escala de Evaluación de Ambiente de Aprendizaje Clínico, Supervisión y Profesor de Enfermería y el Mental Health Continuum Short Form. Se incluyeron en el estudio un total de 571 participantes de Turquía, Lituania y Portugal. Resultados: hubo una diferencia significativa entre los tres grupos con respecto al Ambiente de Aprendizaje Clínico y al estado de salud mental (p < 0,001). La supervisión fue el elemento más valorado. Los estudiantes portugueses obtuvieron los puntajes promedio más altos en el Mental Health Continuum Short Form y Ambiente de Aprendizaje Clínico, Supervisión y Profesor de Enfermería (p<0,001). La edad, el sexo y la salud mental influyeron en los puntajes de Ambiente de Aprendizaje Clínico, Supervisión y Profesor de Enfermería. Conclusión: los resultados indicaron que los estudiantes de Enfermería portugueses obtuvieron los puntajes más altos en el Mental Health Continuum Short Form y Ambiente de Aprendizaje Clínico, Supervisión y Profesor de Enfermería. También se reveló que las percepciones de los estudiantes sobre el Ambiente de Aprendizaje Clínico se vieron afectadas por la edad y el género, y que sus percepciones sobre la salud mental fueron influenciadas por los puntajes de la escala de evaluación de Ambiente de Aprendizaje Clínico, Supervisión y Profesor de Enfermería.