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The widespread use of YouTube has raised concerns about its potential for addiction, particularly in Arabic-speaking populations where social media consumption is prevalent. A culturally tailored tool to assess YouTube addiction is essential for effective research and intervention in these communities. This study aimed to translate and validate the 6-item YouTube Addiction Scale (YAS) into Arabic, ensuring its psychometric robustness for assessing YouTube addiction among Arabic-speaking emerging and young adults. A cross-sectional study was conducted with 1,134 Arabic-speaking emerging and young adults from Bahrain, Saudi Arabia, Jordan, and Tunisia recruited through convenience sampling on social media platforms. The YAS was translated via the forward&#x2012;backward&#x2012;forward technique. The psychometric evaluation included confirmatory factor analysis (CFA), item response theory (IRT), reliability analyses (McDonald's &#x3c9;, Cronbach's &#x3b1;, and composite reliability [CR]), and test-retest reliability. Convergent and divergent validity were assessed through correlations with the Insomnia Severity Index (ISI), Modified Yale Food Addiction Scale (mYFAS), Depression Anxiety Stress Scale (DASS-21), and Bergen Social Media Addiction Scale (BSMAS). The Arabic YAS demonstrated a unidimensional structure with adequate factor loadings (0.55-0.73). The model fit indices were excellent (CFI&#x2009;=&#x2009;0.99, TLI&#x2009;=&#x2009;0.98, RMSEA&#x2009;=&#x2009;0.06, &#x3c7;&#xb2;(9)&#x2009;=&#x2009;40.66, p&#x2009;<&#x2009;0.001), with good internal consistency (&#x3c9;&#x2009;=&#x2009;0.81, &#x3b1;&#x2009;=&#x2009;0.80, CR&#x2009;=&#x2009;0.80) and test-retest reliability (ICC&#x2009;=&#x2009;0.87). IRT analysis confirmed item fit (infit/outfit 0.86-1.17) and person reliability (0.78). Significant correlations with the total score of BSMAS (r&#x2009;=&#x2009;0.66), DASS-21 (r&#x2009;=&#x2009;0.40), mYFAS (r&#x2009;=&#x2009;0.32), and ISI (r&#x2009;=&#x2009;0.26) supported validity. Measurement invariance was confirmed across gender and weekly YouTube use. Scalar invariance was also supported across age groups (18-21 vs. 22-25 years). The Arabic YAS is a psychometrically sound tool for assessing problematic YouTube use among Arabic-speaking emerging and young adults, enabling researchers and clinicians to screen for elevated risk in this high-engagement developmental stage. Further studies should examine age-related differences within and beyond emerging adulthood, as well as longitudinal patterns of use and associated outcomes in this population.

Transcranial direct current stimulation (tDCS) is a promising intervention for treatment-resistant obsessive-compulsive disorder (OCD), yet clinical outcomes remain inconsistent. To investigate the neural mechanisms underlying therapeutic variability, we conducted a patient-specific finite element (FE) modeling study of electric fields (EF) induced by tDCS in OCD patients. Forty-two patients from a double-blind, randomized clinical trial received active tDCS with the cathode over the pre-supplementary motor area and the anode over the right supraorbital region. Clinical response was assessed using the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS), and responders were defined as those achieving &#x2265;35% score reduction. Individual head models were created using SimNIBS, and current density directionality (Jn) and magnitude (Jmagn) were analyzed. Voxel-wise comparisons revealed significantly greater depolarization (Jn>&#x2009;0) in the left anterior prefrontal cortex (BA10) and right frontal eye field (BA 8) associated with a reduction of Y-BOCS. A link between hyperpolarization of right pars orbitalis (BA47) and improvement in symptoms was also found. Notably, no significant findings emerged using EF magnitude (Jmagn), underscoring the relevance of current directionality in treatment response. To our knowledge, this is the first study to associate directional EF modeling with clinical outcomes in OCD. Our findings highlight the importance of considering both EF direction and anatomical variability when optimizing tDCS protocols. This approach may contribute to more personalized and effective neuromodulation strategies for psychiatric disorders.

Past experiences stored in long-term memory (LTM) provide a valuable resource for making predictions that shape perception and guide goal-directed behavior. Contents from the high-capacity LTM system guide contextual selective attention to enhance sensory and higher-order processing of memory-predicted targets, in a process known as LTM-guided attention. While this essential cognitive function is believed to depend on the hippocampus, evidence is still scarce. In this study, we used a neuropsychological approach to test LTM-guided attention in the context of isolated hippocampal pathology and to explore structure-behavior covariance patterns. We tested healthy individuals (n = 20) and individuals suffering from focal epilepsy, with isolated, unilateral left (n = 20) or right (n = 17) hippocampal sclerosis (HS), in a task probing LTM-guided attention. Behavioral data indicated that individuals with left or right HS retained LTM-guided attention. We also assessed structure-behavior covariance using a multivariate structural neuroimaging approach. Hierarchical clustering analysis revealed that, in healthy individuals, LTM-guided attention performance covaried with atlas-derived subfield measures of the left hippocampal body. The volume of the left hippocampal body also covaried with attentional benefit in individuals with right HS. Interestingly, for individuals with left HS, LTM-guided attention covaried with the volume of the left hippocampus and with part of the right hippocampal volume. Together, these findings suggest that LTM-guided attention can be preserved in unilateral HS, with differences in hippocampal volume-behavior covariance depending on the side of hippocampal pathology.

Understanding the mechanistic impact of fostamatinib, a spleen tyrosine kinase inhibitor, in severe COVID-19 using biomarkers associated with disease severity is crucial for the development of host-directed therapeutics. We analyzed samples from a randomized clinical trial to investigate the impact of fostamatinib on multiple inflammatory biomarkers associated with COVID-19 disease severity. Secondary analyses of biomarkers from a randomized clinical trial. Multicenter randomized clinical trial. A total of 400 adults hospitalized with COVID-19 were enrolled in a phase 3 randomized clinical trial. Absolute neutrophil counts (ANCs) were analyzed across 392 patients and biomarkers were measured in 190 patients with available plasma samples. Adults hospitalized with COVID-19 were randomized to receive either fostamatinib (150&#x2009;mg bid) or placebo. ANCs and 24 biomarkers were assessed at day 0 and over time using a multiplexed Meso Scale Discovery assay (Meso Scale Diagnostics LLC, Rockville, MD). At day 0, participants with World Health Organization ordinal scale 5-7 had elevated ANC counts, compared with ordinal scale 4. In addition, the levels of neutrophil-associated biomarkers, inflammatory cytokines, and mediators of endothelial dysfunction at day 0 were increased in the participants who were ordinal scale 5-7 vs. ordinal scale 4. Randomization to fostamatinib compared with placebo resulted in a decrease in ANC and several neutrophil-associated biomarkers, pro-inflammatory cytokines, and mediators of endothelial dysfunction/tissue damage. This differential finding was also demonstrated in a subgroup of patients (n = 85) with a hypoinflammatory phenotype. Missing plasma samples and neutral phase 3 trial results. Randomization to fostamatinib resulted in lower neutrophil counts and levels of circulating biomarkers in hospitalized patients with COVID-19; however, the observed impact of fostamatinib was modest compared with prior studies.

This is a protocol for a Cochrane Review (intervention). The objectives are as follows: To evaluate the benefits and harms of metformin therapy initiated prior to conception and continued through the first trimester for women with polycystic ovary syndrome, compared with placebo or no metformin treatment, on pregnancy outcomes.

The impact of higher ambient temperature on suicide is well documented in the general population, although it remains unclear in youths despite their particular biosocial vulnerability. In an ecological study, the authors examined this relationship, focusing on seasonal differences. The authors calculated monthly suicide rates in young people (ages 5-24) by county using data from the U.S. Centers for Disease Control and Prevention and the U.S. Census Bureau from 1980 to 2004 in the contiguous United States. Fixed-effects regression was used to estimate relative risk of suicide per 1&#xb0;C change in average monthly temperature overall and by season, accounting for precipitation, region, county, month, and year. Age-stratified analysis (ages 4 to 65+) assessed whether effects were unique to young people. Heterogeneity models examined the impacts of legal sex, income, race, education, geographic division, and rurality. Averaged across seasons, suicide in young people increased 0.75% (95% CI=0.34, 1.16) per 1&#xb0;C increase, comparable to the general population (0.73%, 95% CI=0.53, 0.93). This effect was significant only in summer, and it was substantially larger in summer (2.68% per 1&#xb0;C; 95% CI=1.42, 3.94). Age stratification showed that 15- to 24-year-olds were uniquely vulnerable compared to other age groups (2.97% per 1&#xb0;C; 95% CI=1.30, 4.65). Most geographic regions experienced this association, and no sociodemographic differences were identified. Summer heat is associated with higher suicide rates among late adolescents and young adults, who appear most at risk. This association likely reflects neurobiological and socioenvironmental conditions of young people that amplify heat-related mental health risk. These data highlight the need to study how ambient temperature impacts youth mental health and develop biosocially informed interventions as temperatures rise.

To evaluate the relationship between continuous glucose monitoring (CGM)-measured % time <54 mg/dL (%T < 54) and level 2 hypoglycemic events (L2 events; &#x2265;15 min <54 mg/dL) in individuals with type 1 diabetes (T1D). These analyses examined the associations between CGM-measured %T < 54 and L2 events from eight clinical trials over 3-6 months in participants with T1D. Data from 1532 participants with T1D were analyzed (mean age 37 &#xb1; 21 years; 72% adults): 43% using automated insulin delivery (AID), 43% CGM users not using AID (34% multiple daily injections [MDI]; 66% standard pump), and 14% self-monitoring blood glucose (SMBG) users not using CGM (58% MDI; 42% standard pump). There was a strong correlation between %T < 54 and L2 event rate (r = 0.97), but the relationship differed by the average duration of L2 events. For those with 1% T < 54, the predicted L2 event rate per week was 2.4 events for those with short L2 events (average <30 min), 1.9 events for those with medium duration of L2 events (average 30-60 min), and 1.2 events for those with long L2 events (average >60 min). Those meeting hypoglycemic targets (<1% T < 54) had on average 0.6 L2 events per week, irrespective of technology use. Those not meeting targets (&#x2265;1% T < 54) had on average 2.9 L2 events per week, but this differed based on technology use and observed %T < 54. L2 event frequency and %T < 54 are strongly correlated, but the relationship differs by L2 event duration. Therefore, both frequency and duration of L2 events should be reported together. Time-below-range metrics incorporate both aspects and are core CGM endpoints that summarize overall amount of hypoglycemia exposure.

Electronic health records (EHRs) are valuable sources of data but are susceptible to biases from missing data and sample selection, often due to clinically informative visiting processes and non-probability sampling. This research explores whether genetic data, typically measured on nearly all participants in EHR-linked biobanks, can be used to mitigate these biases. Simulations were performed under conditions of missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR) within random and biased sampling frameworks. We evaluated PRS-informed imputation, PRS-uninformed imputation, and complete case analysis across these scenarios in terms of bias, coverage, and root mean square error (RMSE) of the regression coefficient estimates. A real-world example using data from the Michigan Genomics Initiative (MGI, n&#x2009;=&#x2009;68,063) compared the effectiveness of these methods against national benchmark estimates. PRS-informed imputation generally reduced bias and RMSE and improved coverage, particularly under MAR conditions in random samples. In analyses of biased samples of n&#x2009;=&#x2009;10,000 with MAR exposure-only missingness, weighted, PRS-informed imputation analyses showed substantially lower percent bias (0.6%) and closer to nominal coverage (89.1%) compared to weighted, complete case analyses (9.4%; 74.3%). The MGI estimates showed that PRS-informed approaches aligned more closely with national benchmarks than unweighted complete case analysis. Leveraging genetic data with sample weighting can help reduce bias in outcome-exposure association estimates derived from biobank data. When available, researchers should consider including PRS for imputation and survey methods for sample weighting when estimating outcome-exposure association coefficients in a target population of interest, recognizing that benefits may vary by outcome and data structure.

Ca2+ signaling and its regulation are important for endothelial cell (EC) function and signaling. Yet, the spatiotemporal organization of Ca2+ activity and its regulation across a vascular plexus is poorly understood in an in vivo mammalian context. To overcome this gap in knowledge, we developed an intravital imaging approach to resolve Ca2+ activity with single-cell resolution in skin vasculature of adult mice via multiphoton microscopy. Here, we tracked thousands of Ca2+ events in the skin capillary plexus during homeostasis and observed signaling heterogeneity between ECs, with just over half displaying Ca2+ activity at any given time. Longitudinal tracking of the same mice revealed that the same capillary ECs maintain Ca2+ activity over days to weeks. Interestingly, activity dynamics, such as frequency and event duration, are not conserved at a single-cell level but are maintained at an EC population level. Molecularly, conditional deletion of the gap junction protein Connexin 43 (Cx43cKO) in ECs leads to a subset of ECs displaying sustained Ca2+ activity, biasing signaling dynamics of the whole network toward chronically persistent activity over time. Sustained capillary Ca2+ activity results in vascular permeability and flow dysregulation. Last, through pharmacological targeting of known agonists/antagonists, we showed that inhibition of L-type Voltage Gated Ca2+ channels non-cell-autonomously restores Ca2+ activity, blood flow, and barrier function in Cx43cKO mice. Collectively, our work provides insight into the spatial and temporal characteristics, extent, and regulation of Ca2+ activity in skin capillaries of live mice.

Core decompression is the leading surgical treatment for pre-collapse osteonecrosis of the femoral head (ONFH). There are many different instruments that are used during core decompression, ranging from drill bits to expandable reamers, yet there is limited guidance regarding how to select the appropriate instrument for an individual patient's unique anatomy. We sought to examine how lesion characteristics affect instrument performance. To quantitatively compare instruments' efficacy at removing necrotic bone while preserving healthy bone, we developed an in-silico metric called the resection fraction, defined as the volume of necrotic bone resected divided by the total volume of bone (healthy and necrotic) resected. We hypothesize the optimal instrument will vary with lesion size and type (ARCO 2021). CT-based 3D models of 100 femurs with ONFH were created from a retrospective database of patients who underwent 3D guided computer navigated core decompression at our institution. Using these models, core decompression procedures were simulated with six different instruments. 3D volumetric analysis was used to determine the volumes of necrotic and healthy bone resected, and the resection fraction was calculated. Statistical analysis was performed to compare instrument performance across all lesions, as well as by lesion size and type. A total of 100 hips from 75 unique patients with ONFH were modeled and each hip's core decompression procedure was simulated with six different instruments. All three expandable reamers had higher resection fractions compared to straight instruments (drill bits). For small volume lesions, the small expandable reamer had the highest resection fractions. The medium expandable reamer had the highest resection fractions for larger lesions. The large expandable reamer had lower resection fractions across all sizes. Compared to straight instruments, expandable reamers had the highest resection fractions. To maximize resection fraction, surgeons should personalize instrument choice to patient anatomy; small expandable reamers should be used for smaller lesions, while medium-sized reamers should be used for larger lesions. The largest expandable reamer performed worse than either the small or medium reamer for almost all lesions. Further work is needed to validate the in-silico resection fraction metric with clinical outcomes.

Hybrid type 2 studies are gaining popularity for their ability to assess both implementation and health outcomes as co-primary endpoints. Often conducted as cluster-randomized trials (CRTs), five design methods can validly power these studies: p-value adjustment methods, combined outcomes approach, single weighted 1-DF test, disjunctive 2-DF test, and conjunctive test. We compared these methods theoretically and numerically. Theoretical comparisons of power equations allowed us to identify when one method had more or less power than another globally. We showed that p-value adjustment methods are always less powerful than both the combined outcomes approach and the single 1-DF test, and identified conditions where the disjunctive 2-DF test is less powerful than the single 1-DF test. To further investigate when power advantages shift, we conducted a large-scale numerical study using our novel crt2power R package, which calculates power or sample size for CRTs with two continuous co-primary endpoints using these methods. Across 45,000 input scenarios, we found specific patterns: when treatment effects are unequal, the disjunctive 2-DF test tends to be most powerful; when treatment effects are equal, the single 1-DF test tends to dominate. Together, these comparisons offer practical guidance for powering hybrid type 2 studies.

Obesity is associated with risk for chronic health problems and increased mortality. Weight stigma, which entails negative attitudes and behaviors directed at people based solely on their body size, is psychologically harmful and contributes to obesity and obesity-related health problems. Yet, weight stigma is pervasive in society and commonly experienced by patients in healthcare settings, which lowers the quality of patients' healthcare experiences. Healthcare providers and trainees report feeling underprepared to treat obesity, which may lead to overreliance on weight-management strategies that are not evidence-based and are susceptible to weight stigma, such as fad diets emphasizing unrealistic dietary restriction. In the current manuscript, we discuss current weight stigma interventions with other populations and the rationale for integrating training in evidence-based approaches to treat obesity with weight stigma interventions, specifically cognitive dissonance-based interventions. Current learning theories emphasize empathic perspective-taking, and current methods to reduce weight stigma use social-cognitive theories to raise awareness of stereotypes. However, current methods are insufficient because they create discomfort but fail to alter internalized bias nor advise providers on how to deliver non-stigmatizing, evidence-based obesity interventions. Cognitive dissonance theory posits that resolving tension between beliefs and behaviors will change future behaviors. Cognitive dissonance-based interventions have effectively reduced personal weight stigma and are promising to be similarly effective in reducing weight stigma among healthcare providers. In this manuscript, we highlight how cognitive dissonance theory can help improve interventions to reduce bias and support providers' efforts treating obesity.

Accurate tumor segmentation is essential for early diagnosis, treatment planning, and prognostic evaluation. Although manual annotation can achieve high accuracy, it is time-consuming and requires substantial expert involvement. While deep learning has significantly advanced medical image analysis, fully automated methods often fail to segment atypical lesions within complex abdominal anatomy, leading to missed lesions and misclassification of normal tissues, which may compromise clinical decision-making. To address these challenges, we incorporated guidance masks into a convolutional neural network (CNN)-based deep learning framework. Using our Star-Rain software, users place interactive clicks on lesion locations, and the system adaptively generates task-specific guidance masks. This approach directs the model's attention to relevant regions, particularly in atypical or anatomically complex cases. Our method is validated on four independent cohorts comprising 1,217 CT scans from 726 patients, encompassing hepatic, renal, and pancreatic tumors. Across these datasets, our approach outperforms state-of-the-art baseline models on independent test sets, achieving Dice scores consistently above 0.7 and reducing the false negative rate (FNR) by 0.006 to 0.346 compared to the best fully automated approaches. In addition, the model's segmentation outputs effectively support downstream prognosis tasks, highlighting its clinical value. These findings underscore the promise of semi-automatic deep learning frameworks that integrate minimal user input for reliable tumor segmentation. The proposed approach offers a practical and robust solution for clinical applications, enhancing segmentation accuracy and decision support while reducing the annotation burden. It is important to accurately determine the edge of tumors (tumor segmentation) prior to providing localized treatment. However, radiologists often find this process slow and labor-intensive. Fully automated computational methods can miss unusual or small lesions in the abdomen. To address this, we developed an interactive AI system combining clinical expertise and a computational approach known as deep learning. Unlike previous tools using simple points or boxes, our pipeline establishes guidance masks that capture the tumor&#x2019;s shape. We showed it worked across four datasets covering liver, kidney, and pancreatic tumors. It was particularly successful at identifying small lesions often missed by automated models. Our approach could provide a high-precision solution for clinical use, improving diagnostic accuracy while significantly reducing the time needed for tumor segmentation by medical experts.

Schizophrenia is a neurodevelopmental disorder involving clinical and genetic heterogeneity. Multiple recurrent copy number variants (CNVs) increase risk for schizophrenia spectrum disorders (SSDs). However, it is unclear how known risk CNVs and broader genome-wide CNVs influence clinical variability. Furthermore, whether biological annotation of CNV scores can improve power for patient stratification is unknown. This study therefore investigated the relationships between severe SSD-related phenotypes and varied CNV metrics, including CNV burden affecting genes involved in different aspects of neurodevelopment. This study of 617 individuals with SSDs examined associations of two severe phenotypes-childhood-onset psychosis and borderline intellectual functioning (IQ)-with 1) known risk CNVs, 2) genome-wide deletion burden scores, and 3) novel scores capturing deletion burden in 18 previously validated and mutually exclusive gene sets, representing distinct aspects of neurodevelopment. Associations with borderline IQ were assessed for replicability in 233 relatives of SSD patients, 581 control subjects, and 9,930 youths from the Adolescent Brain Cognitive Development (ABCD) Study. Known SSD-risk CNVs (odds ratio=7.07, 95% CI=1.60, 31.32) and neurodevelopmental disorder (NDD)-risk CNVs (odds ratio=4.56, 95% CI=1.48, 14.10) were associated with borderline IQ in SSDs. Furthermore, beyond effects of known NDD-risk CNVs, deletion of genes involved in regulating gene expression during fetal brain development was associated with borderline IQ across SSD cases and noncases (odds ratio=2.57, 95% CI=1.44, 4.60) and in the ABCD cohort (odds ratio=1.33, 95% CI=1.00, 1.76). Exploratory structural MRI-based analyses showed associations between fetal gene regulatory gene deletions and altered gray matter volume (b=0.09, 95% CI=0.004, 0.17) and cortical thickness (b=0.14, 95% CI=0.05, 0.24) across SSD cases and noncases. The study results confirm contributions of known risk CNVs to severe phenotypes in SSDs, implicate disrupted fetal brain development in poor cognition, and demonstrate the utility of a neurodevelopmental framework for identifying mechanisms underlying severe SSD-relevant phenotypes.

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Toxoplasma gondii infection poses a substantial global health burden, yet transmission pathways and population susceptibility in urban informal settlements remain poorly characterised, particularly for women of childbearing age. We analysed archived samples from a cross-sectional serosurvey of 728 children and adolescents aged 4-18 years living in a marginalised urban community in Salvador, Brazil, to characterise exposure patterns and identify demographic, socioeconomic, behavioural, household, and environmental factors associated with seropositivity and to assess spatial heterogeneity in exposure risk. Overall seroprevalence was 49%, increasing with age and higher in males than females; Bayesian serocatalytic models estimated sex-specific forces of infection of 0.078 for males and 0.050 for females, with approximately half of female participants still susceptible upon reaching childbearing age, highlighting the risk of congenital toxoplasmosis. In regression analyses, seropositivity was associated with male sex, lower household income, cat ownership, and residence at lower elevation, greater distance from the main road, and reported contact with sewer water. Notably, most seropositive participants (77.3%) did not live in households with cats. Geostatistical modelling demonstrated fine-scale spatial heterogeneity, with clustered hotspots exceeding 50-60% predicted prevalence. Adjustment for measured covariates attenuated but did not eliminate spatial clustering, indicating residual fine-scale spatial structure consistent with unmeasured environmental processes operating beyond individual households, alongside additional unstructured variation that may reflect household-level or peridomestic differences not captured by the measured covariates. Together, these findings provide evidence consistent with an important role for household and peridomestic environmental exposure pathways in T. gondii transmission in informal settlements, extending beyond households with domestic cats and shaped by social marginalisation and environmental vulnerability.

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