Prevalence and factors associated with digital addiction among students taking university entrance tests: a GIS-based study.

BACKGROUND: The surge in digital media consumption, coupled with the ensuing consequences of digital addiction, has witnessed a rapid increase, particularly after the initiation of the COVID-19 pandemic. Despite some studies exploring specific technological addictions, such as internet or social media addiction, in Bangladesh, there is a noticeable gap in research focusing on digital addiction in a broader context. Thus, this study aims to investigate digital addiction among students taking the university entrance test, examining its prevalence, contributing factors, and geographical distribution using GIS techniques. METHODS: Data from a cross-sectional survey were collected from a total of 2,157 students who were taking the university entrance test at Jahangirnagar University, Bangladesh. A convenience sampling method was applied for data collection using a structured questionnaire. Statistical analyses were performed with SPSS 25 Version and AMOS 23 Version, whereas ArcGIS 10.8 Version was used for the geographical distribution of digital addiction. RESULTS: The prevalence of digital addiction was 33.1% (mean score: 16.05 ± 5.58). Those students who are attempting the test for a second time were more likely to be addicted (42.7% vs. 39.1%), but the difference was not statistically significant. Besides, the potential factors predicted for digital addiction were student status, satisfaction with previous mock tests, average monthly expenditure during the admission test preparation, and depression. No significant difference was found between digital addiction and districts. However, digital addiction was higher in the districts of Manikganj, Rajbari, Shariatpur, and Chittagong Hill Tract areas, including Rangamati, and Bandarban. CONCLUSIONS: The study emphasizes the pressing need for collaborative efforts involving educational policymakers, institutions, and parents to address the growing digital addiction among university-bound students. The recommendations focus on promoting alternative activities, enhancing digital literacy, and imposing restrictions on digital device use, which are crucial steps toward fostering a healthier digital environment and balanced relationship with technology for students.

Assessment of the Prevalence of Infections in Pediatric Patients With Acute Lymphoblastic Leukemia.

Infections cause notable treatment-related morbidity during pediatric acute lymphoblastic leukemia/lymphoma (ALL/LLy) therapy. Infections are the most critical cause of morbidity and mortality in children undergoing treatment for acute lymphoblastic leukemia (ALL). Children with ALL, who are frequently underweight, are at increased risk of community-acquired pathogens, nosocomial multidrug-resistant pathogens, and opportunistic microorganisms. A weakened immune system from ALL itself and chemotherapy's side effects further worsen the prognosis. PubMed and Google Scholar articles were curated in a Google document with shared access. Discussion and development of the paper were achieved over Zoom meetings. This narrative review aims to analyze and summarize various pathogens responsible for infections in children receiving treatment for ALL and their treatment regimen and prophylaxis. The incidence of viral infection is higher in ALL patients, followed by bacterial and fungal infections. Prevention via prophylaxis and timely initiation of treatment is essential for positive outcomes.

Cancer Classification Utilizing Voting Classifier with Ensemble Feature Selection Method and Transcriptomic Data.

Biomarker-based cancer identification and classification tools are widely used in bioinformatics and machine learning fields. However, the high dimensionality of microarray gene expression data poses a challenge for identifying important genes in cancer diagnosis. Many feature selection algorithms optimize cancer diagnosis by selecting optimal features. This article proposes an ensemble rank-based feature selection method (EFSM) and an ensemble weighted average voting classifier (VT) to overcome this challenge. The EFSM uses a ranking method that aggregates features from individual selection methods to efficiently discover the most relevant and useful features. The VT combines support vector machine, k-nearest neighbor, and decision tree algorithms to create an ensemble model. The proposed method was tested on three benchmark datasets and compared to existing built-in ensemble models. The results show that our model achieved higher accuracy, with 100% for leukaemia, 94.74% for colon cancer, and 94.34% for the 11-tumor dataset. This study concludes by identifying a subset of the most important cancer-causing genes and demonstrating their significance compared to the original data. The proposed approach surpasses existing strategies in accuracy and stability, significantly impacting the development of ML-based gene analysis. It detects vital genes with higher precision and stability than other existing methods.

Review of the Current Knowledge of Reactive Attachment Disorder.

Reactive attachment disorder (RAD), classified under Trauma and Stressor Related Disorders in the DSM-5 manual, is a childhood psychiatric illness due to familial or social neglect or due to maltreatment. It is characterized by an inhibited and withdrawn social and emotional behavior toward an adult caregiver, typically before the age of 5. Neurobiological changes in patients with RAD have been shown to be substantially significant with features such as loss of grey matter volume and neurotransmitter deficiencies that not only impact the ability to form healthy attachments but also increase the risk of comorbidities such as depression and anxiety. Different theories, including the current mediation hypothesis and learning theory of attachment, showed childhood maltreatment from caregivers and desensitization toward deficiencies in social development in children from special education teachers to be key components in the development of RAD. Patients with RAD had an increased risk of developing psychiatric comorbidities, including learning disabilities and mood disorders. Institutionalized care and childhood maltreatment have a significant impact on the development of RAD. RAD is an underdiagnosed and underreported condition with significant repercussions that can severely impact the development of a child. By being able to raise awareness and promote further research into refining the diagnostic methodology, treatment protocols, and long-term follow-up, children afflicted with this condition may be able to develop better socio-emotional bonds and reduce the incidence of comorbidities such as depression and attention deficit hyperactivity disorder.