The interconnectedness of core mental health features is associated with more severe illness impairment and less effective treatment outcomes. This study aimed to evaluate the network of relationships between obsessive-compulsive symptoms and other psychopathological symptoms in both obsessive-compulsive disorder (OCD) patients and community populations, identifying symptom interconnections. A cross-sectional study was conducted from January 1, 2020, to June 30, 2024. The Chinese versions of the Yale-Brown Obsessive Compulsive Scale (Y-BOCS) and the Symptom Checklist-90 (SCL-90) were used to measure obsessive-compulsive symptoms and other psychopathological symptoms, respectively. Measurement invariance testing was performed using Mplus software (version 8.11). Network structure, centrality, stability, and network comparisons were analyzed using R software (version 4.4.1). The study included 4223 OCD patients and 5253 community participants. In the symptom networks of both groups, SCL3 ("Depression") and SCL4 ("Anxiety") were common core symptoms. SCL10 ("Psychoticism") was a specific core symptom for OCD patients, while SCL2 ("Interpersonal sensitivity") was specific to the community group. Additionally, SCL8 ("Obsessive symptoms") and YBOCS3 ("Distress caused by obsessions") served as bridge symptoms in both groups. The cross-sectional design limited causal inferences; self-report measures were subject to recall bias and other confounding factors; sample representativeness and the range of variables included in the analysis were limited. Depressive and anxiety symptoms emerged as common core symptoms in both OCD patients and community populations. Psychoticism was specifically identified as a core symptom in OCD patients, while obsessive symptoms and obsession-related distress served as bridging symptoms linking OCD with other psychopathological symptoms, highlighting important targets for clinical assessment.
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‒backward‒forward technique. The psychometric evaluation included confirmatory factor analysis (CFA), item response theory (IRT), reliability analyses (McDonald's ω, Cronbach's α, 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 = 0.99, TLI = 0.98, RMSEA = 0.06, χ²(9) = 40.66, p < 0.001), with good internal consistency (ω = 0.81, α = 0.80, CR = 0.80) and test-retest reliability (ICC = 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 = 0.66), DASS-21 (r = 0.40), mYFAS (r = 0.32), and ISI (r = 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 ≥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> 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.
Despite prevalent gender discrimination in medical education, its influence on personal and professional development, foundational competencies in medical training per the Association of American Medical Colleges (AAMC), remains unclear. This retrospective cross-sectional study assesses how experiences of gender discrimination in medical school influence personal and professional identity formation (PPIF) among males and females. Deidentified student-level data were procured from the AAMC data warehouse for 37,610 MD students who matriculated in 2014-2015 and took the Graduation Questionnaire (GQ) between 2016-2020. Gender discrimination frequency was categorized as 'Never', 'Isolated', and 'Recurrent' from GQ responses to questions about denial of opportunities, offensive remarks, and lower evaluations due to gender. Students self-reported their sex as male, female or declined to answer. PPIF was assessed using two separate GQ metrics assessing student agreement on a 5-point Likert scale that their medical school fostered and nurtured their development as a person and a future physician, respectively, and dichotomized. Female students experienced higher rates of isolated (12.6%) and recurrent (20.1%) gender discrimination than males (4.3% isolated, 6.2% recurrent). Females reported slightly lower personal (71.2%) but similar professional development (92.2%) rates compared to males (73.4% personal, 91.2% professional). Both sexes experiencing gender discrimination had lower likelihoods of PPIF than their counterparts without these experiences. If recurrent discrimination occurred, the aRR (95%CI) of professional development was 0.89 (0.87-0.90) for females and 0.78 (0.74-0.81) for males, while for personal development, it was 0.69 (0.67-0.71) for females and 0.61 (0.58-0.66) for males. Compared to females, males showed sharper declines in professional development as discrimination frequency increased from never to isolated (aRR = 0.93, 95% CI [0.92-0.94], p < 0.001) and isolated to recurrent (aRR = 0.95, 95% CI [0.93-0.97], p < 0.001). Gender discrimination negatively influences PPIF for both female and male medical students. Efforts to combat discrimination in medical training and promote holistic student development should be considered. Future work is needed to understand the influence of gender discrimination on the comprehensive development of gender-diverse medical students.
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Women and older adults represent rapidly growing segments of the US veteran population. Understanding sex-specific patterns in psychiatric diagnoses among aging veterans is essential for anticipating future healthcare service needs within the Department of Veterans Affairs (VA). The purpose of this retrospective study was to estimate prevalence and evaluate age-period-cohort trends in overall and broadly defined psychiatric disorders among women and men veterans aged ≥50 years. Using VA Corporate Data Warehouse data, 254,138 women veterans ≥50 years were 1:1 matched to 254,138 men veterans ≥50 years who used VA healthcare between 2000 and 2023. Twelve-month prevalence of overall psychiatric diagnoses and major diagnostic categories, including substance use disorders (SUDs) (alcohol, drug) and mental health disorders (mood, anxiety, and psychotic), were examined by sex across age, period, and cohort axes using descriptive and age-period-cohort interaction analyses. In the overall population, psychiatric diagnoses were identified in 9.0% of women and 10.6% of men veterans. Women had lower SUDs prevalence than men, with SUD rates declining with age in both sexes. Mental health disorders, particularly mood and anxiety disorders, increased during 2015-2023. Psychotic disorders increased with age but declined across calendar periods. Cohort effects were observed for several diagnostic categories, though less pronounced for alcohol use and mood disorders in both sexes and for psychotic disorders among women. In conclusion, psychiatric disorders are increasingly prevalent among more recent cohorts of aging US veterans, with distinct sex-specific age-period-cohort patterns, underscoring the need for tailored strategies for older women and men veterans.
Chagas disease is a neglected tropical disease that disproportionately affects underserved populations and is associated with substantial health and socioeconomic burden. Although age-standardised prevalence and mortality have declined since 1990, approximately 10.5 million people remain infected globally. As infected populations age, Chagas disease is increasingly concentrated among older adults and is transitioning from a predominantly acute infection of younger populations into a chronic cardiomyopathy affecting ageing adults in both endemic and non-endemic regions. Migration has further redistributed the disease burden to Western countries where clinical awareness, routine screening, and timely diagnosis of Chagas disease remain limited. We highlight three priorities to address this evolving burden: reframing Chagas disease from a neglected tropical infection to a neglected chronic cardiomyopathy, expanding responsibility of screening and clinical care beyond endemic countries, and strengthening surveillance and epidemiological data systems. Recognising Chagas disease as a chronic cardiovascular condition will be essential for health systems to address its evolving global burden.
Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by widespread structural brain alterations, yet the specific patterns of brain atrophy and their underlying genetic mechanisms remain incompletely understood. Here, we integrated large-scale neuroimaging meta-analysis with population-scale imaging genetics to systematically characterize the genetic architecture linking gray matter volume (GMV) abnormalities to PD. We first performed a meta-analysis of structural MRI studies comprising 3,212 patients with PD and 2,056 controls, identifying robust patterns of GMV reduction and assessing differences across medication states. Using these meta-analytically defined regions as imaging phenotypes, we extracted GMV measures from the UK Biobank and conducted genome-wide association analysis (GWAS). This analysis identified 12 significant SNPs associated with PD-related GMV atrophy. Furthermore, we performed pleiotropy analysis and identified 22 SNPs jointly associated with PD risk and GMV reduction. Functional enrichment analyses revealed that these shared genes converge on pathways involved in clathrin-mediated endocytosis and synaptic vesicle recycling. Spatiotemporal transcriptomic profiling further characterized the developmental expression patterns of these genes, while molecular docking analyses suggested potential therapeutic targets. Together, these findings provide a comprehensive characterization of the genetic architecture linking brain structural abnormalities to PD, offering new molecular insights into the mechanisms underlying neurodegenerative brain damage and potential avenues for therapeutic intervention.
Individuals who engage in illicit or nonmedical opioid use may have elevated risk of health and social consequences, including progression to opioid use disorder (OUD). Preventive interventions to reduce this risk are lacking. This trial tested the impact of a primary care-integrated collaborative care approach for reducing risky opioid use, defined as nonmedical use of prescription opioids or any use of illicit opioids. Cluster-randomized controlled trial randomized primary care providers (PCPs) and their patients into the Subthreshold Opioid Use Disorder Prevention (STOP) intervention or enhanced usual care (EUC). Primary care clinics at 5 U.S. sites. PCPs and their patients were recruited January 2021-May 2023. A total of 119 PCP clusters (STOP = 48, EUC = 51) and 202 patients (STOP = 88, EUC = 114) enrolled. Eligible patients were adults (≥18 years) having current risky opioid use, without moderate-severe OUD. Patient participants were majority female (63.4%), white (70.8%) and non-Hispanic (96.5%), with a mean age of 55.7 [standard deviation (SD) = 12.7] years. At baseline, 63.4% of participants had moderate-severe pain (Brief Pain Inventory) and below average physical (79.2%) and mental (62.4%) health (SF-12). The STOP collaborative care intervention consisted of brief advice from the PCP about reducing risky opioid use, meetings with a clinic-embedded nurse care manager over 12 months and remote health coaching (2-6 sessions). Both groups received primary care treatment as usual and overdose risk reduction materials. The primary outcome was total days of risky opioid use, recorded from 6 monthly electronic surveys. A key secondary outcome was moderate-severe OUD at 6 and 12 months. A total of 77 (87.5%) STOP and 107 (93.9%) EUC participants completed the 6-month assessment period. The primary outcome analysis used the Intention-to-Treat sample with multiple imputations of missing data. Mean days of risky opioid use at 180 days were lower in STOP than EUC [12.2 (SD = 27.73) vs. 15.5 (SD = 32.64)]; the difference between groups adjusted for baseline risky opioid use was not statistically significant (rate ratio 0.95, 95% confidence interval = 0.52-1.74). One STOP participant (1.1%) and 13 EUC participants (11.4%) developed moderate-severe OUD at 6 months, and 3 (3.4%) STOP and 6 (5.3%) EUC participants had moderate-severe OUD at 12 months (P < 0.001). This cluster-randomized controlled trial did not find evidence that the STOP intervention for reducing risky opioid use produced greater reductions over 6 months compared with enhanced usual care, though fewer intervention participants progressed to moderate-severe opioid use disorder. Patients had a high burden of pain and comorbidities that may present challenges to reducing opioid use.
BATMAN (balloon-assisted translocation of the mitral anterior leaflet) is an increasingly adopted technique to modify the anterior mitral leaflet (AML) and prevent left ventricular outflow tract (LVOT) obstruction during transcatheter mitral valve replacement (TMVR). The aim of this study was to evaluate the feasibility, efficacy, and safety of the BATMAN technique during transseptal TMVR. This was an international, multicenter cohort study of all consecutive patients at high risk for LVOT obstruction undergoing transseptal BATMAN TMVR for valve-in-valve (ViV), valve-in-ring (ViR) and valve-in-mitral annular calcification (ViMAC) at 22 structural heart disease centers in North America and Europe. The primary efficacy endpoint was the rate of successful TMVR free from LVOT obstruction and procedural death. The primary safety endpoint was the in-hospital composite of death, stroke, or major cardiac structural complications. A total of 83 patients were included: 24 undergoing ViV, 39 ViR, and 19 ViMAC procedures. Technical success was achieved in all but 1 case (98.8%) that was converted to tip-to-base LAMPOON (laceration of the anterior mitral leaflet to prevent outflow obstruction). Pre-emptive mechanical cardiocirculatory support was used in 28.9% of cases. The primary efficacy endpoint was met in 95.1% of cases (100% in ViR, 95.8% in ViV, and 84.2% of ViMAC; P = 0.03). The primary safety endpoint occurred in 7.3% of patients and was higher in the ViMAC group (0% in ViR, 8.3% in ViV, and 21.1% in ViMAC; P = 0.02). There was 1 major cardiac structural complication directly attributed to BATMAN in a ViMAC patient. BATMAN was associated with high technical success and effectiveness in preventing LVOT obstruction and appeared to be safe in ViR and ViV procedures. Adverse events were higher in ViMAC.
Immune checkpoint inhibitors (ICIs) are widely used for advanced renal cell carcinoma (aRCC); however, predictive biomarkers of response remain insufficiently defined, and treatment resistance is common. We comprehensively analyzed clinical samples from 51 patients with aRCC treated with anti-PD-1 therapy to identify resistance factors. Immunohistochemical analysis of tissue microarrays showed no significant correlation between baseline tumor microenvironment features and therapeutic response. In contrast, peripheral blood flow cytometry demonstrated persistently elevated monocytic myeloid-derived suppressor cells (M-MDSCs) in non-responders. A next-generation patient-derived xenograft (NG-PDX) model established by co-engrafting tumor tissue and peripheral immune cells from the same patient recapitulated patient-specific tumor-immune interactions, using MHC class I/II-knockout immunodeficient mice. In this model, combined blockade of PD-1 and IL-8, a key chemokine recruiting MDSCs, tended to suppress tumor growth. These findings identify immune features associated with PD-1 resistance in aRCC and support NG-PDX models as a platform for evaluating rational immunotherapeutic combinations.
Myelodysplastic syndromes (MDS) are clinically and biologically diverse disorders, emphasizing the need for personalized treatment approaches. The International Working Group for Prognostication of MDS (IWG_PM) recently introduced a molecular classification, referred to as the MDS taxonomy, that categorizes patients into 16 subgroups based on 21 gene mutations, 6 cytogenetic abnormalities, and loss of heterozygosity (LOH) at TP53 and TET2 loci. This study sought to validate and enhance the clinical relevance of the MDS taxonomy by analyzing a large retrospective cohort (n = 5136) and transcriptomic data from a prospective cohort (n = 477). The taxonomy successfully identified subgroups with distinct clinical characteristics and disease progression patterns. However, incorporating gene interactions from taxonomy subgroups did not improve the prognostic performance of the Molecular International Prognostic Scoring System (IPSS-M). We further assessed whether the taxonomy could guide management in patients receiving disease-modifying therapies. Except for the "TP53-complex" subgroup, taxonomy classifications were not predictive of hypomethylating agent response or transplant outcomes. Nonetheless, they correlated with overall survival, suggesting that while both IPSS-M and the taxonomy capture disease biology, other non-genetic factors may influence treatment response. RNA sequencing confirmed the biological distinctiveness of the taxonomy groups. Transcriptomic profiling of CD34+ bone marrow cells revealed unique, homogeneous gene expression patterns, particularly within the AML-like, biTET2, SF3B1, and TP53-complex subgroups. Further integration of multi-omics data may refine MDS classification, improving clinical decision-making and guiding the development of targeted therapies.
The concerning rise in early onset cancer (EOCs), defined as cancers diagnosed under the age of 50, has called attention to identifying individuals who may benefit from earlier cancer screening. These approaches involve patient-centered discussions to establish a personalized, risk-based screening plan. Important factors to consider when establishing a personalized risk-based cancer screening plan include a comprehensive family and personal cancer history and, if appropriate, genetic testing. Limitations in current cancer screening initiatives, healthcare systems, provider and public awareness, and access issues constrain these efforts. Within this context, we explore the current landscape of cancer screening for younger adults in the United States, describe associated challenges and efforts, and propose strategies for risk-based cancer screening in young adults.
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°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°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°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°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.
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’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.
In 2023 Israeli Arabs (i.e., Arab Palestinian citizens of Israel) constituted 22% of Israel's working-age population. In that same year, Israeli Arabs constituted 25% of Israel's employed physicians of working age - up significantly from 8% in 2010. The objectives of this study were to: 1) assess whether, and to what extent, there is an Arab-Jewish income differential among Israeli physicians; 2) assess the extent to which any such differential can be attributed to Arab-Jewish differences in the demographic, geographic, and/or work-related characteristics of the medical workforce; and 3) explore the policy implications of the key findings. The analysis utilized the Central Bureau of Statistics' 2022 Population and Housing Census, which included data on 7,089 physicians, of whom 1,333 were Arab and 5,756 were Jewish or other (hereafter "Jewish"). The main income variable examined was "Total annual gross income from work". In 2022, the average annual physician income for Arab physicians was 26% less than for Jewish physicians (NIS 358 thousand vs. NIS 483 thousand). Arab physicians were more likely to be under age 40 (71% v. 28%), male (77% v. 52%), residents of the North region (48% v. 7%), in non-supervisory roles (87% v. 75%), and generalists or family practitioners (33% v. 22%). Controlling for age and sex, via regression analysis, reduced the income differential to 1%. This major reduction was due to Arab physicians being markedly younger than Jewish physicians. Further controlling for regional distribution and work characteristics (managerial status, specialty status/type, and months worked) did not change the ethnicity differential, but did markedly reduce the coefficients of the age variables. The 2022 Arab-Jewish income differential among physicians was due predominantly to differences in age composition. The differential could potentially shrink in the decades ahead, as more Arab physicians reach the ages at which physician incomes are at their highest. However, as part of the effect of age on income is mediated by work characteristics, the extent to which the wage gap will shrink will depend in part on the extent to which Arab physicians will secure prestigious residency training slots and managerial positions. Health system leaders can play an important role in promoting such developments. In particular, steps should be taken to increase the representativeness of Arabs in Israeli medical schools.
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 self-prioritisation effect demonstrates that people have a bias to learn and process self-relevant compared to other-relevant information. This tendency may provide a tool to understand how body image and perception become disrupted in people with such a pre-occupation with self-related perceptions, behaviours, and affect (Body Dysmorphic Disorder (BDD)). Using an associative learning matching paradigm, participants were presented with geometric shapes that were paired with either themself, a named friend, or an unnamed stranger. Participants were presented with different label-shape pairings and indicated 'match' or 'mismatch' to the original associations. In Experiment 1, higher BDDQ scores were significantly associated with improved accuracy in identifying the self-related association compared with the Stranger association, Friend was intermediate. Experiment 2 showed that higher BDDQ scores were significantly associated with improved accuracy in identifying 'You' matches compared to 'Friend' matches as well as an effect found in Reaction Time that was not found in Experiment 1. Across both experiments, higher BDDQ scores were significantly associated with overall faster reaction times. In Experiment 2 we assessed whether the reaction time effects were related to impulsivity and depression for which we found no evidence. We discuss these results in relation to the exaggerated salience that person centred cues may play in BDD and show how this process extends to new learning of self-related cues.
To characterize sleep profiles in individuals with neurogenetic disorders (NGDs) and examine the contribution of key clinical and psychiatric symptoms to these profiles. The parents of 248 individuals (aged 3-45 years) diagnosed with a range of NGDs, including PTEN hamartoma tumor syndrome (n = 111), SYNGAP1-related intellectual disability (n = 46), Malan syndrome (NFIX; n = 22), and other NGDs (n = 69; e.g. pathogenic variants in ADNP, CSNK2A1, GRIN2B, and STXB1) participated in this cross-sectional caregiver-report study. Parents completed the Neurobehavioral Evaluation Tool about their child, a validated online platform that includes sleep subscales, and a demographic and clinical information survey. Latent profile analysis identified five distinct sleep profiles: low sleep symptomatology; early morning somnolence; insomnia symptoms; high sleep symptomatology; and bedtime resistance. Sleep profiles differed on age and intellectual functioning. Additionally, the profiles significantly differed on several behavioral and psychiatric problems, including increased self-injury in the insomnia symptoms profile, and greater depressed affect in the high sleep symptomatology profile. The findings of this study highlight distinct sleep profiles across a range of NGDs, regardless of clinical diagnosis. Moreover, differences on key clinical and psychiatric correlates provide evidence for the role of sleep as a transdiagnostic marker across NGDs, with implications for early, targeted sleep assessment and interventions that may have broader positive mental health impacts.
Vaccine hesitancy has emerged as a major challenge for pediatric immunization programs, shaped by a complex interplay of structural, relational, and informational factors. In Latin America, these dynamics are further influenced by health system fragmentation and social inequities, requiring approaches that extend beyond information-based interventions. This review examines the multidimensional nature of vaccine hesitancy in pediatric populations, with a focus on Latin America. It examines the role of digital environments, clinical communication, and system-level factors, and discusses emerging strategies such as narrative communication and prebunking. The literature was identified through a targeted review of peer-reviewed publications and relevant global health reports. Addressing vaccine hesitancy requires integrated strategies that combine trust-building with anticipatory communication. Approaches such as narrative shielding and prebunking may complement traditional interventions by strengthening how caregivers interpret vaccine-related information. Future efforts should prioritize context-specific implementation and evaluation in real-world settings.