METHOD: The study proposes a novel EEG-based classification approach, focusing on effective connectivity (EC) derived from resting-state EEG signals in combination with support vector machine (SVM) algorithms. EC estimation is performed using the partial directed coherence (PDC) technique. The analysis is conducted on an EEG dataset comprising 35 individuals with AUD and 35 healthy controls (HCs). The methodology evaluates the efficacy of connectivity features in distinguishing between AUD and HC and subsequently develops and assesses an EEG classification technique using EC matrices and SVM.
RESULT: The proposed methodology demonstrated promising performance, achieving a peak accuracy of 94.5% and an area under the curve (AUC) of 0.988, specifically using frequency bands 29, 36, 45, 46, and 52. Additionally, feature reduction techniques applied to the PDC adjacency matrices in the gamma band further improved classification outcomes. The SVM-based classification achieved an accuracy of 96.37 ± 0.45%, showcasing enhanced performance through the utilization of reduced PDC adjacency matrices.
DISCUSSION: These results highlight the potential of the developed algorithm as a robust diagnostic tool for AUD detection, enhancing precision beyond subjective methods. Incorporating EC features derived from EEG signals can inform tailored treatment strategies, contributing to improved management of AUD.
Aim: To produce an accurate model of BCVA changes of postpterygium surgery according to its morphological characteristics by using the machine learning technique. Methodology. A retrospective of the secondary dataset of 93 samples of pterygium patients with different pterygium attributes was used and imported into four different machine learning algorithms in RapidMiner software to predict the improvement of BCVA after pterygium surgery.
Results: The performance of four machine learning techniques were evaluated, and it showed the support vector machine (SVM) model had the highest average accuracy (94.44% ± 5.86%), specificity (100%), and sensitivity (92.14% ± 8.33%).
Conclusion: Machine learning algorithms can produce a highly accurate postsurgery classification model of BCVA changes using pterygium characteristics.
Methods: A cross-sectional survey was conducted among 176 adolescents aged between 13 and 19 years of age with the majority being Malay and Muslim. The Brief Reasons for Living for Adolescents (BRFL-A), Jalowiec Coping Scale and Suicide Ideation Scale were employed.
Results: The results showed that the reasons for living and palliative coping strategy correlated negatively with suicide ideation; although, further analysis using multiple regression revealed that family alliance and optimistic and palliative coping strategies were found to be significant reasons for living that protect adolescents from suicidal thoughts. Also, those adolescents who used emotive and evasive coping strategies had higher suicidal ideation.
Conclusion: Cultural and social values continue to play an important role in protecting adolescents in Malaysia from suicidal behaviour.
MATERIALS AND METHODS: We searched for published English articles in Medline, Web of Science, Scopus, and Google Scholar databases using relevant subject headings without year restriction. We included randomized controlled trials, nonrandomized controlled trials, case-control, cohort, controlled before and after, and uncontrolled before and after studies evaluating the impact of tobacco control policy in the military population. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, three independent reviewers independently screened initially identified articles, reviewed the full text, and extracted the data and any disagreements resolved by consensus after data recheck. Five reviewers used a validated tool to assess the quality of the included studies. The primary outcome was the reduction of any tobacco or nicotine-contained products (TNCPs) use among the troops. The impacts of the tobacco control policy were synthesized and analyzed qualitatively. This study is registered with the International Prospective Register of Systematic Review (CRD42022314117).
RESULTS: Fourteen studies were included in the analysis from 5372 studies screened. Most of the studies were from the USA, and fractions were from Thailand, France, and Taiwan. These studies were methodologically heterogeneous. Most studies employed a total ban policy on TNCP use during basic military training or operational deployment as the primary strategy. Other methods utilized were the brief tobacco intervention, targeted treatment, support, and counseling provided through telephone or mailing systems, the adjunctive behavioral intervention, providing free nicotine gum, the "Pharsai clinic", active and regular smoking restriction, and interventions aimed at intrapersonal, interpersonal, and organizational levels. There is a moderate quality of evidence that the tobacco control policies effectively reduced the prevalence of TNCP use, increased the cessation rate, reduced the intake, and lowered the dependency. The adjunctive interventions provided after the total ban on TNCP use may increase its effectiveness. However, findings from this review need to be carefully considered as the definition of TNCP use status was not universal between studies and lacked a biochemical validation procedure.
CONCLUSIONS: There is reasonable evidence to support that the tobacco control policy employed in the military population has multiple positive impacts in reducing the prevalence of TNCP use, increasing the cessation rates, reducing the intake, and lowering dependency. Other evidence-based strategies need to be fully utilized to materialize the tobacco endgame.
METHODS: A total of 176 adolescents in selected urban areas in the states of Wilayah Persekutuan and Selangor were selected. The Suicide Ideation Scale (SIS) was used to measure the level of severity or tendency of suicidal ideation. The Multidimensional Scale of Perceived Social Support (MSPSS) was used to measure the perceived social support received by the respondent while the Spiritual Wellbeing Scale (SWBS) was used to measure the religious wellbeing (RWB), the existential wellbeing (EWB) and the overall score of spiritual wellbeing (SWB).
RESULTS: The study found that both RWB and EWB showed significant negative correlation with suicidal ideation. Similarly, support from family and friends also showed a negative correlation with suicidal ideation. Further analysis using multiple regressions showed that RWB and SWB, and family support predict suicidal ideation in adolescents.
CONCLUSION: Spiritual wellbeing in combination with family support plays a major role in predicting suicidal ideation. Therefore, intervention for encompassing spirituality and family support may contribute to a more positive outcome in suicidal adolescents.