Depression, stress, and anxiety versus internet addiction in early and middle adolescent groups: the mediating roles of family and school environments.

BACKGROUND: Family and school environment play a crucial role across the different developmental stages of adolescence. This paper investigates the potential mediating role of family and school environments in the relationship between the three psychosocial predictors of depression, anxiety, stress, and Internet addiction (IA). Specifically, it focuses on the two stages of early and middle adolescence. METHODS: The study involved a survey of 407 adolescents from Qatar, comprising 250 early adolescents and 157 middle adolescents. Inclusion criteria for the study included adolescents between the ages of 10 to 17 years old, residents of Qatar and studying in a Qatar-based school. To assess the constructs of the three psychosocial predictors, IA, family environment, the study utilized the Depression, Stress, and Anxiety Scale (DASS), the Internet Addiction Diagnostic Questionnaire (IADQ), and the Brief Family Relationship Scale, respectively. School environment was measured using questions from the "Health Behavior in School-aged Children: WHO Collaborative Cross-National survey/study (HBSC) 2013-2014. The study applied standard mediation analysis between the DASS components and IA with family and school environment as the mediators. RESULTS: Results from the mediation analysis reveal insights into the relationships between psychosocial predictors and IA. The findings indicate that family and school environments partially mediated the relationship with regards to depression, stress, and anxiety in early adolescents. In middle adolescents, family environment partially mediated the relationship with depression and stress and fully mediating the relationship with anxiety. Meanwhile, school environment only exhibited partial mediation in the relationship with anxiety in middle adolescence. CONCLUSIONS: These results highlight the crucial role parents and schools play in addressing problematic technology usage that develops as a response to depression, anxiety, and stress among adolescents. Moreover, the study reveals nuances in the mediating role of family and school environment in early and middle adolescence. This highlights the evolving nature of these influences across the different stages of development. Notably, this study contributes to the literature by moving beyond the conventional focus on the so-called WEIRD population, and offering valuable insights from a region that is underrepresented in current research.

The influence of adolescents essential and non-essential use of technology and Internet addiction on their physical and mental fatigues.

This study explores the impact of Internet addiction (IA), age, and essential and non-essential technology usage time on the physical and mental fatigue of adolescents. The research surveyed 477 adolescents from Qatar and employed the Internet Addiction Diagnostic Questionnaire (IADQ) and Chalder's Fatigue Scale for data collection. Multiple linear regression and Mann-Whitney U tests were utilized for analysis. The findings indicate that IA, non-essential usage time, and age are positively associated with overall fatigue among adolescents. IA and non-essential usage time are positively linked to physical fatigue, while IA, non-essential usage time, and age are positive predictors of mental fatigue. However, essential usage time is negatively associated with mental fatigue. These results highlight the importance of distinguishing technology usage based on intent and necessity, as well as differentiating between physical and mental fatigue when examining problematic technology usage. This study is among the few conducted in the Middle Eastern context.

Investigating Racial Disparities in Drug Prescriptions for Patients with Endometriosis.

Background: Endometriosis is a chronic disease with a long time to diagnosis and several known comorbidities that requires a range of treatments including of pain management and hormone-based medications. Racial disparities specific to endometriosis treatments are unknown. Objective: We aim to investigate differences in patterns of drug prescriptions specific to endometriosis management in Black and White patients prior to diagnosis and after diagnosis of endometriosis and compare these differences to racial disparities established in the general population. Study Design: We conduct a retrospective cohort study using observational health data from the IBM MarketScan® Multi-state Medicaid dataset. We identify a cohort of endometriosis patients consisting of women between the ages of 15 and 49 with an endometriosis-related surgical procedure and a diagnosis code for endometriosis within 30 days of this procedure. Cohort is further restricted to patients with at least 3 years of continuous observation prior to diagnosis.We identify a non-endometriosis cohort of women between the ages of 15 and 49 with no endometriosis diagnosis and at least 1 year of continuous observation. We compare prevalence of prescriptions across selected drug classes for Black vs. White endometriosis patients. We further examine prevalence differences in the non-endometriosis cohort and prevalence differences pre- and post-diagnosis in the endometriosis cohort. Results: The endometriosis cohort comprised 16,372 endometriosis patients (23.3% Black, 66.0% White). Of the 28 drug classes examined, 17 were prescribed significantly less in Black patients compared to 21 in non-endometriosis cohort (n=3,663,904), and 4 were prescribed significantly more in Black patients compared to 6 in the non-endometriosis cohort. Of the 17 drugs prescribed more often in White patients, 16 have larger disparities pre-diagnosis than post-diagnosis. Conclusions: Our analysis identified significant differences in medication prescriptions between White and Black patients with endometriosis, notably in hormonal treatments, pain management, and treatments for common endometriosis co-morbidities. Racial disparities in drug prescriptions are well established in healthcare, and better understanding these disparities in the specific context of chronic reproductive conditions and chronic pain is important for increasing equity in drug prescription practices.

Adolescents' Internet addiction: Does it all begin with their environment?

BACKGROUND: This is one of the few studies that examines adolescent Internet addiction (IA) among Middle Eastern population. The purpose of this study is to determine whether adolescents' family and school environments play a role in their Internet Addiction. METHODS: We conduced a survey that included 479 adolescents in Qatar. The survey collected demographic data, the Internet Addiction Diagnostic Questionnaire (IADQ), the Brief Family Relationship Scale (BFRS) and questions from the WHO Health Behavior in School-aged Children (HBSC) survey that assess school environment, academic performance, teacher support, and peer support of the adolescents. Factorial analysis, multiple regression, and logistic regression were used for statistical analysis. RESULTS: Family environment and school environment were negative and significant predictors of adolescent Internet addiction. The prevalence rate was 29.64%. CONCLUSION: Results imply that interventions and digital parenting programs should not only target adolescents, but also include entities in the developmental environment of adolescents, i.e. their family and school.

The pre-conception maternal exposure to Sofosbuvir affects the mitochondrial biogenesis in prenatal fetal tissues: Experimental study on rats.

BACKGROUND: Hepatitis C virus (HCV) infection is a global public health problem and Egypt has the highest HCV prevalence worldwide. Hence, global efforts target to eliminate HCV by 2030. Sofosbuvir is a nucleotide analogue inhibitor of HCV polymerase essential for viral replication. Animal studies prove that Sofosbuvir metabolites cross the placenta and are excreted in the milk of nursing animals. We aimed to investigate the possible effects of preconception maternal exposure to Sofosbuvir on mitochondrial biogenesis in prenatal fetal liver, skeletal muscle, and placental tissues. METHODS: The study was conducted on 20 female albino rats divided into a control group receiving a placebo and an exposed group receiving 4 mg/kg orally/day for 3 months of Sofosbuvir. At the end of the treatment period, pregnancy was induced in both groups by mating with healthy male rats overnight. At gestational day 17, all pregnant female rats were sacrificed. Each fetus was dissected to obtain the fetal liver, skeletal muscle, and placental tissues. RESULTS: The results of our study indicated that the exposure of young female rats to Sofosbuvir affects pregnancy outcomes. Fetal liver and muscle showed lower mitochondrial DNA-copy number (mtDNA-CN) by about 24% and 29% respectively, peroxisome proliferator-activated receptor-gamma coactivator-1 alpha and its downstream targets; nuclear respiratory factor-1 and mitochondrial transcription factor A. While the placental tissues showed different patterns, particularly elevated in mtDNA-CN by about 43%. CONCLUSIONS: The study provides preliminary evidence of the detrimental effects of Sofosbuvir on the pregnancy outcomes of the exposed females and may impair the placental and fetal organs' development. These effects may be mediated through modulating mitochondrial homeostasis and functions.

The interplay between social media use and problematic internet usage: Four behavioral patterns.

Objective: The study aims to identify typical interplay between the use of social media apps on smartphones and Problematic Internet Usage (PIU). Method: Our study utilizes data from a smartphone app that objectively monitors user usage, including the apps used and the start and finish times of each app session. This study included 334 participants who declared a need to be aware of their smartphone usage and control it. Problematic Internet Usage (PIU) was measured using the Problematic Internet Use Questionnaire-Short Form-6 (PIUQ-SF6). The total PIU score can range from 6 to 30, with a score above 15 indicating that a person is at risk of PIU. Time spent on Social Media (SM) apps of Facebook, WhatsApp, and Instagram, and whether people used each of these apps were studied along with the total PIU score. K-Prototype clustering was utilized for the analysis. Results: Four distinct clusters, typifying the relationship between social media use and PIU, were identified. All the individuals in Cluster 1 (Light SM Use Cluster; Cluster size = 270, 80.84% of total dataset) spent between 0 and 109.01 min on Instagram, between 0 and 69.84 min on Facebook, and between 0 and 86.42 min on WhatsApp and its median PIU score was 17. Those who were in cluster 2 (Highly Visual SM Cluster; Cluster size = 23, 6.89% of total dataset) all used Instagram, and each member spent between 110 and 307.63 min on Instagram daily. The cluster median PIU score and average daily usage of Instagram were respectively 20 and 159.66 min. Those who were in Cluster 3 (Conversational SM Cluster; Cluster size = 19, 5.69% of total dataset) all used WhatsApp, and spent between 76.68 and 225.22 min on WhatsApp daily. The cluster median PIU score and average time spent per day on WhatsApp were 20 and 132.65 min, respectively. Those who were in Cluster 4 (Social Networking Cluster; (Cluster size = 22, 6.59% of total dataset) all used Facebook, and each spent between 73.09 and 272.85 min daily on Facebook. The cluster median PIU score and average time spent per day on Facebook were 18 and 133.61 min respectively. Conclusion: The clusters indicate that those who use a particular social media app spend significantly less time on other social media apps. This indicates that problematic attachment to social media occurs primarily for one of three reasons: visual content and reels, conversations with peers, or surfing network content and news. This finding will help tailor interventions to fit each cluster, for example by strengthening interpersonal skills and resistance to peer pressure in the case of Cluster 3 and increasing impulse control in the case of Cluster 2.

Spatiotemporal analysis of speckle dynamics to track invisible needle in ultrasound sequences using convolutional neural networks: a phantom study.

PURPOSE: Accurate needle placement into the target point is critical for ultrasound interventions like biopsies and epidural injections. However, aligning the needle to the thin plane of the transducer is a challenging issue as it leads to the decay of visibility by the naked eye. Therefore, we have developed a CNN-based framework to track the needle using the spatiotemporal features of the speckle dynamics. METHODS: There are three key techniques to optimize the network for our application. First, we used Gunnar-Farneback (GF) as a traditional motion field estimation technique to augment the model input with the spatiotemporal features extracted from the stack of consecutive frames. We also designed an efficient network based on the state-of-the-art Yolo framework (nYolo). Lastly, the Assisted Excitation (AE) module was added at the neck of the network to handle the imbalance problem. RESULTS: Fourteen freehand ultrasound sequences were collected by inserting an injection needle steeply into the Ultrasound Compatible Lumbar Epidural Simulator and Femoral Vascular Access Ezono test phantoms. We divided the dataset into two sub-categories. In the second category, in which the situation is more challenging and the needle is totally invisible, the angle and tip localization error were 2.43 ± 1.14° and 2.3 ± 1.76 mm using Yolov3+GF+AE and 2.08 ± 1.18° and 2.12 ± 1.43 mm using nYolo+GF+AE. CONCLUSION: The proposed method has the potential to track the needle in a more reliable operation compared to other state-of-the-art methods and can accurately localize it in 2D B-mode US images in real time, allowing it to be used in current ultrasound intervention procedures.

Who uses mHealth apps? Identifying user archetypes of mHealth apps.

Objective: This study aims to explore the user archetypes of health apps based on average usage and psychometrics. Methods: The study utilized a dataset collected through a dedicated smartphone application and contained usage data, i.e. the timestamps of each app session from October 2020 to April 2021. The dataset had 129 participants for mental health apps usage and 224 participants for physical health apps usage. Average daily launches, extraversion, neuroticism, and satisfaction with life were the determinants of the mental health apps clusters, whereas average daily launches, conscientiousness, neuroticism, and satisfaction with life were for physical health apps. Results: Two clusters of mental health apps users were identified using k-prototypes clustering: help-seeking and maintenance users and three clusters of physical health apps users were identified: happy conscious occasional, happy neurotic occasional, and unhappy neurotic frequent users. Conclusion: The findings from this study helped to understand the users of health apps based on the frequency of usage, personality, and satisfaction with life. Further, with these findings, apps can be tailored to optimize user experience and satisfaction which may help to increase user retention. Policymakers may also benefit from these findings since understanding the populations' needs may help to better invest in effective health technology.

An improved capsule network for glioma segmentation on MRI images: A curriculum learning approach.

Glioma segmentation is an essential step in tumor identification and treatment planning. Glioma segmentation is a challenging task because it appears with blurred and irregular boundaries in a variety of shapes. In this paper, we propose an efficient and novel model for automatic glioma segmentation based on capsule neural networks. We improved the architecture and training of the SegCaps model, the first capsule-based segmentation network. The proposed architecture is improved by introducing dilation blocks in the primary capsule block to get deeper features while avoiding resolution reduction. The prediction layer of the network is also modified using one-dimensional convolution filters, enabling the network to not only maximize tumor existence likelihood but also regularize the capsule orientations within the tumor. Our main contribution, however, is to introduce an enhanced curriculum-based training algorithm into the proposed SegCaps model. We adapt the curriculum learning for the model by suggesting a new pacing mechanism based on a roulette-wheel selection algorithm that enriches randomness in the network and prevents bias. A hybrid dice loss function is also employed, which is better adapted to the introduced curriculum-based training procedure. We evaluated the performance of improved SegCaps on the BraTS2020, a multimodal benchmark dataset for brain tumor segmentation. The experimental results confirmed that the improvements yield a top-performing yet memory-efficient deep capsule architecture. The proposed model outperformed the best-reported accuracies on the BraTS2020, achieving improved dice scores of 85.16% and 81.88% for tumor core and enhancing tumor segmentation, respectively. Using 90%, fewer parameters than the popular U-Net also confirmed the high memory efficiency of our proposed model.

Did Usage of Mental Health Apps Change during COVID-19? A Comparative Study Based on an Objective Recording of Usage Data and Demographics.

This paper aims to objectively compare the use of mental health apps between the pre-COVID-19 and during COVID-19 periods and to study differences amongst the users of these apps based on age and gender. The study utilizes a dataset collected through a smartphone app that objectively records the users' sessions. The dataset was analyzed to identify users of mental health apps (38 users of mental health apps pre-COVID-19 and 81 users during COVID-19) and to calculate the following usage metrics; the daily average use time, the average session time, the average number of launches, and the number of usage days. The mental health apps were classified into two categories: guidance-based and tracking-based apps. The results include the increased number of users of mental health apps during the COVID-19 period as compared to pre-COVID-19. Adults (aged 24 and above), compared to emerging adults (aged 15-24 years), were found to have a higher usage of overall mental health apps and guidance-based mental health apps. Furthermore, during the COVID-19 pandemic, males were found to be more likely to launch overall mental health apps and guidance-based mental health apps compared to females. The findings from this paper suggest that despite the increased usage of mental health apps amongst males and adults, user engagement with mental health apps remained minimal. This suggests the need for these apps to work towards improved user engagement and retention.

Biochemical profile of milk thistle (Silybum Marianum L.) with special reference to silymarin content.

The main objective of current study was to evaluate the antioxidant potential and nutritional composition of milk thistle with special reference to silymarin. For the purpose, different varieties of milk thistle were procured from three different cities of Pakistan. The study was comprised of three different phases. In 1st phase, nutritional composition, that is, moisture, fat, protein, fiber, and nitrogen free extract, was determined according to their respective methods. Moreover, antioxidant potential and quantification of silymarin content were explored in 2nd phase. Furthermore, in last phase, milk thistle seeds tea was developed and evaluated for nutritional and sensorial characteristics. At last, data obtained from each parameter was subjected to appropriate statistical design to determine the level of significance. Results showed significant difference in the nutritional and chemical composition of different milk thistle varieties as well as locations. Moreover, moisture content, ash content, fat content, fiber content, protein content, and NFE varied from 6.27% to 5.01%, 2.37 to 1.25%, 23.19 to 19.74%, 7.4 to 4.39%, 30.09 to 20.74%, and 45.42 to 34.13%, respectively. Furthermore, silymarin content quantified though HPLC ranged from 1669.5 mg/g to 1607.6 mg/g for soxhlet extract whereas, 1,840.6 mg/g to 1765.9 mg/g for microwave-assisted extraction extract. Conclusively, it was depicted from the results that in case of variety, Blue was the best than White whereas, Islamabad was best in case of location.