The school environment plays a key role in adolescents’ emotional development and well-being, yet little research has compared self-harm and related psychosocial problems across different secondary school types.

Using data from the Growing Up in Ireland (GUI) longitudinal cohort, this study examined differences in the prevalence of self-harm and psychosocial risk factors across different school types: single-sex versus coeducational, fee-paying versus non-fee-paying, disadvantaged versus non-disadvantaged, and schools with different religious ethos. Multilevel regression models distinguished school-level from individual-level effects.

Almost all variance in self-harm and most of the variance in psychosocial problems associated with self-harm occurred at the individual level. Higher self-harm prevalence in single-sex girls’ schools was accounted for by the greater concentration of girls, who had over twice the odds of self-harm compared with boys (OR 2.1, 95% CI 1.71–2.69). No significant differences in self-harm were found by school socio-economic status or religious ethos. Disadvantaged schools showed higher prevalence in seven of nine psychosocial problems, although only internalising problems and truancy/absenteeism remained significantly associated with disadvantaged schools in the fully adjusted models. Adolescents whose parents reported having a religion were less likely to self-harm (OR 0.62, 95% CI 0.50–0.75).

Although schools are important settings for self-harm prevention, findings indicate that interventions should primarily target individuals and high-risk groups. Girls, in particular, may benefit from supports addressing self-harm. Disadvantaged schools, where well-established psychosocial risk factors for self-harm are more common, may benefit from well-being programmes targeting internalising problems and truancy/absenteeism.

Emotion regulation relies on the interplay between prefrontal and limbic brain regions, with prefrontal regions implicated in the top-down modulation of the amygdala. In social anxiety disorder, disruptions in these networks have been reported, but most studies used undirected functional connectivity.

Dynamic causal modelling (DCM) was used to assess effective (i.e. directed) connectivity differences during emotion processing and regulation in individuals with social anxiety disorder compared with healthy controls.

A total of 102 participants (61 with social anxiety disorder, 41 healthy controls) performed a functional magnetic resonance imaging emotion regulation task under two conditions: viewing neutral/negative faces, and downregulating emotions using a self-chosen strategy. DCM was applied to model effective connectivity among the amygdala and key prefrontal regions. Connectivity patterns were characterised in healthy controls, and group comparisons tested how social anxiety disorder differed from this baseline model using parametric empirical Bayes. Leave-one-out cross-validation (LOOCV) evaluated whether connectivity differences predicted diagnostic group, symptom severity and emotion regulation difficulties.

In healthy controls, observation of negative faces was characterised by reciprocal influences between the amygdala and prefrontal cortex (PFC), including increased amygdala-to-ventromedial PFC (vmPFC) connectivity and inhibitory vmPFC-to-amygdala connectivity. During emotion regulation, healthy controls showed negative modulation from the amygdala to all prefrontal regions. Patients with social anxiety disorder did not differ from controls in amygdala–prefrontal connectivity; their alterations were confined to prefrontal circuits, with inhibitory connectivity from the pre-supplementary motor area (preSMA) to dorsolateral PFC during observation and bidirectional excitatory connectivity between the preSMA and vmPFC during regulation. LOOCV indicated that connectivity differences predicted diagnostic group.

The results support the idea that emotion processing and regulation influence connectivity between prefrontal areas and the amygdala in a complex, feedback-driven manner. Our findings suggest that aberrant emotion regulation in social anxiety disorder appears to be more closely linked to differences in intra-prefrontal circuits than deficits in amygdala–prefrontal connectivity.

The health needs of people living with young-onset dementia are largely undocumented. The aim of this study was to describe health system use for community-dwelling people living with new young-onset dementia at a population level.

We conducted a matched retrospective cohort study using routinely collected population-based health administrative data in Ontario, Canada, from January 2006 to March 2021. We matched 92% of people living with young-onset dementia to a comparison group (17,215 of 18,760) without dementia. Rates of health service use over time were estimated using Poisson regression. Long-term care placement and mortality were estimated using Cox regression.

People living with young-onset dementia had higher previous health system use compared to their matched counterparts. This included higher rates of general hospital admissions (rate ratio (RR) = 2.04 95% CI: 1.94, 2.15), emergency department visits (RR = 1.38 95% CI:1.30, 1.46), mental health admissions (RR = 4.03 95% CI:3.33, 4.88) and were more likely to experience hospital discharge delays (RR = 4.72 95% CI: 4.21, 5.29). Dementia specialist visits were 3.80 times higher (95% CI 3.40 to 4.25). The hazard of mortality for people with young-onset dementia was over three times higher compared to those without young-onset dementia (hazard ratio (HR) = 3.43 95% CI: 3.23, 3.65).

We observed higher rates of secondary and tertiary health system utilization among people living with new young-onset dementia versus matched comparators. This complex pattern of health system use highlights the need for health system planning to address continuous care for people living with young-onset dementia.

Early identification of patients with carbapenemase-producing Enterobacterales (CPE) colonization is crucial for infection control; however, microbiological testing may delay detection and be costly. Machine learning may enhance predictive analytics for timely identification of at-risk patients.

Four machine learning models: Decision Tree, Random Forest, Gradient Boosting, and XGBoost, were used to predict CPE colonization within 48 hours of admission using microbiological and demographic data. Model performance was assessed through sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). Uniform Manifold Approximation and Projection (UMAP) evaluated topological separability of CPE-positive cases and CPE-negative controls.

From January 1, 2015 to December 31, 2024, 453,372 fecal specimens were submitted for CPE screening, with 194,917 (43.0%) collected within 48 hours of admission, comprising 3,328 CPE-positive cases (1.7%) and 191,589 CPE-negative controls. The Gradient Boosting classifier showed the best performance, achieving an AUROC of 0.598, sensitivity of 54.4%, and specificity of 59.1%. Demographic factors (age ≥ 75 and male sex), history of hospitalization, and known CPE colonization in the past year, and admission specialty (general medicine and general surgery) were consistently included in all models as top predictors. UMAP revealed significant overlap between CPE-positive and CPE-negative patients, indicating challenges in differentiating the risk profiles.

This study highlights the complexities of using machine learning to predict CPE colonization within 48 hours of admission. The low AUROC values suggest that the models may not effectively predict CPE colonization at the patient level, potentially due to inherent rarity of events and overlapping risk profiles.

Surgical site infection (SSI) surveillance can be time consuming and resource intensive. This study investigates the potential of generative artificial intelligence (GenAI) to augment the detection and classification of SSIs.

A case control study of patients with SSI following spine surgery at one US hospital. SSIs were classified into superficial, deep, and organ space. All SSIs were confirmed by infection prevention (IP) experts as they occurred from October, 2023 to September, 2025 and matched 1:1 by year to surgeries deemed non-SSI. A secure GenAI was used to determine if patients had an SSI based on standardized prompts and clinical data. IP nurses used GenAI output to review cases with the ability to ask GenAI questions within the data provided or independently open the medical record. We compared GenAI determinations to initial IP nurses’ determinations.

A total of 555 patients had spine surgeries. All 16 SSIs were matched by year to 16 non-SSI. All SSIs were correctly identified by GenAI (sensitivity 100%, 16/16) and only 1 non-SSI was incorrectly identified as SSI (specificity 93.7%, 15/16). Although GenAI accurately identified all SSI cases, it was discordant with original review at classifying the level of infection in 37.5% (6/16) of cases. Upon final IP physician review, GenAI was correct in 66.7% (4/6) of discordant cases (often determining “organ space infections” rather than “deep”). Median time to complete GenAI assisted SSI reviews was 9 minutes (IQR 7–21).

GenAI is a promising tool to assist in SSI surveillance following spinal surgery that could improve efficiency.

We evaluated airway volume in patients after total laryngectomy.

A total of 53 patients who underwent total laryngectomy were included in this study. Twelve of the patients had supraglottic Ca and 41 had transglottic Ca. Total lung volume, and intrapulmonary and tracheal airway volumes were measured.

After total laryngectomy, tracheal airway volume decreased more in transglottic Ca patients compared with supraglottic Ca patients (p < 0.05). Even though intrapulmonary airway volumes and total lung volumes were not different between transglottic Ca and supraglottic Ca patients (p > 0.05), these volumes decreased post-operatively in both groups (p < 0.05). In older patients, the post-operative decrease in the tracheal airway volume was higher than in younger patients (p < 0.05). Pre-operative radiotherapy was applied in 50.0 per cent of supraglottic Ca patients and 58.5 per cent of transglottic Ca patients (p > 0.05).

Because lower airway volume and total lung volume decrease after total laryngectomy, pulmonary rehabilitation should be administered post-operatively and appropriate follow up is essential to prevent tracheostomal narrowing.

Develop and implement a framework for reviewing hospital-onset bacteremia and fungemia (HOB) events to identify quality improvement opportunities.

Prospective, observational study.

Six hospitals (3 academic and 3 community) associated with two health systems.

Clinical team members involved in the care of patients with eligible HOB events.

The Joint Commission Patient Safety Event Taxonomy was adapted to develop a framework for HOB, eliciting potential contributing factors in four categories: general infection prevention, infection-site-specific, system, and human factors. From 9/2022 to 4/2024, HOB events identified from hospital blood culture databases were selected using convenience sampling to a target enrollment of 75 events, limiting inclusion of events associated with neutropenia or central line infections. Multidisciplinary case reviews were performed, and the framework was refined using feedback from the first 17 reviews. We summarized HOB sources, contributing factors, and improvement opportunities using descriptive analyses.

Seventy-four HOB case reviews were analyzed, evenly distributed between academic and community hospitals, and 42% occurred in an ICU setting. A median of 3 care team members (IQR 2–4) participated in each review. At least one infection-site-specific factor was identified in 48% of case reviews, representing 63% of HOB events with a known source of infection. 50% of cases identified a general infection prevention factor, 35% a system factor, and 34% a human factor. 57% of the cases had an actionable finding.

HOB events have diverse causes and elements of preventability, and multidisciplinary review can generate actionable findings beyond the current guideline-defined measures.

Intolerance of uncertainty (IU) – a dispositional inability to react effectively to uncertain situations – has been increasingly conceptualized as a transdiagnostic risk factor for internalizing problems such as generalized anxiety and depression. However, evidence for its temporal role in the development of these conditions remains limited, particularly in adolescents, a group at heightened risk for psychopathology.

A total of 5,291 adolescents (46.2% boys; M age = 14.40 ± 1.56, range = 10–18 years) completed self-report measures of IU, generalized anxiety and depressive symptoms at baseline, 6 months and 12 months. Linear and logistic regression analyses examined whether baseline IU predicted subsequent symptom severity and elevated (above-cut-off) symptom levels over time.

Higher baseline IU significantly predicted increases in generalized anxiety and depressive symptoms, as well as higher odds of elevated generalized anxiety and depressive symptom levels at both 6- and 12-month follow-ups, even after adjusting for baseline symptom severity or baseline elevated symptom status. Baseline IU also predicted the new-onset and persistence of elevated symptoms across both intervals. Stratified analyses revealed developmental and sex differences: IU’s predictive effects were strongest in early adolescence for girls and in middle-to-late adolescence for boys.

IU emerged as a transdiagnostic longitudinal predictor of generalized anxiety and depressive symptoms in adolescents, supporting its value as an early screening marker of vulnerability. Interventions targeting IU may offer an effective strategy for reducing broad internalizing risk during this critical developmental period.

Arrhythmic mitral valve prolapse is a rare yet potentially fatal syndrome. Recently, high-risk markers for malignant arrhythmias have been identified in adults with mitral valve prolapse, and a risk stratification scheme has been proposed. Little is known about the prevalence of arrhythmic mitral valve prolapse, the characteristics of the syndrome, and risk markers in children and young adults.

A database of a high-volume, tertiary congenital heart defects department was searched to identify all patients with MVP between 2018 and 2025 (8 years). Patients’ characteristics, including arrhythmic risk factors, AMVP diagnosis, and last survival, were noted.

Of 12,955 hospitalised patients, 52 were diagnosed with mitral valve prolapse (0.4%; median age 14y [IQR 6], 35% male). Arrhythmic mitral valve prolapse was diagnosed in 13 patients (25% of the mitral valve prolapse cohort). Phenotypic risk factors were highly prevalent (88.5%), but in the crosstab analysis, only an enlarged left atrium was associated with high-risk arrhythmic events (p = 0.0432). Furthermore, patients with arrhythmic events were significantly older (median 16y) than those without (median 13y; p = 0.019). One patient (2.7%, with concomitant LQTS2) presented with unstable ventricular tachycardia and received an ICD. During a median follow-up of 3 years (IQR 1–4.5), all 52 patients were alive.

One in 250 patients admitted to a tertiary invasive paediatric cardiology department has mitral valve prolapse. Among those with mitral valve prolapse, 1 in 4 presents with arrhythmic mitral valve prolapse. Although risk factors are common, outcomes among young arrhythmic mitral valve prolapse patients appear good. Overlapping with primary electrical disease may flag a worse prognosis. Identification of new risk factors for children is warranted.