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461 papers

Effect of Structured Training on ICU Nurses' Knowledge-Based Competence in Ventilator-Associated Pneumonia Prevention in a Resource-Limited Setting: An Explanatory Sequential Mixed-Methods Study.

Yakubu YH et al. · Jul 1, 2026

Aim To evaluate the effect of a structured training intervention on ICU nurses' knowledge-based competence in VAP prevention in a resource-limited setting and identify personal, environmental and organisational barriers to implementation. Design An explanatory sequential mixed-methods design was employed, integrating a single-group quasi-experimental pre-test-post-test quantitative strand with a qualitative interview strand. Because participant responses were not linked across assessment points, findings represent group-level improvements rather than within-individual change. Methods ICU nurses completed pre-test (n = 57) and post-test (n = 56) assessments using an adapted eight-item VAP prevention knowledge questionnaire. A structured 3-h workshop on VAP prevention bundles, infection control and airway management was delivered between tests; the post-test occurred within 2 weeks. Group differences were analysed using an independent-samples t-test and chi-square tests. Qualitative interview data (n = 8) were analysed thematically to explain and contextualise the quantitative findings. Results Mean knowledge scores increased from 3.63 (SD 1.57) to 5.29 (SD 1.57) out of 8 (t(111) = -5.61, p 2 (2) = 22.43, p Conclusions A brief, context-specific training workshop significantly improved ICU nurses' knowledge-based competence in VAP prevention. However, systemic barriers-equipment shortages, absent protocols, limited supervision and workload pressures-constrain the translation of knowledge gains into consistent bedside practice. Sustainable improvement requires embedding education within broader quality improvement efforts. No patient or public contribution No patient or public contribution.

Medicine

Health, Social and Recidivism Outcomes Among People Who Have Been Incarcerated in New South Wales, Australia: Study Protocol and Cohort Profile for the Prison Outcomes STudy (POST).

Degenhardt L et al. · Jul 1, 2026

Introduction We have been funded to examine post-incarceration health and social outcomes for all people incarcerated in New South Wales, Australia, 2000-2022; assess treatment and services for drug dependence and serious mental illness; and project the impact of expanding intervention coverage. We will use a linked cohort, the Prison Outcomes STudy (POST), which we also describe. Methods The POST cohort was established using linked administrative data for all adults (≥ 18 years) admitted to full-time custody in New South Wales, 2000-2022. Custody records were probabilistically linked to 18 health, justice and mortality datasets. We report baseline sociodemographic and custody characteristics and the frequency of key post-release events. Results 200,486 adults, 15% women (n = 30,698), were incarcerated, with 2,282,367 person-years of follow-up and 11% (257,545 person-years) of follow-up spent in custody. First Nations people comprised 27% of the cohort. Half (48%) of the cohort (n = 95,563) had at least one contact with community mental health services, and 27% (n = 54,100) had received alcohol and other drug treatment. POST will provide population-wide evidence on health and social outcomes after custody, including the effects of treatment for drug dependence and serious mental illness. We will compare across subgroups and the outcomes of post-release service engagement. Mathematical modelling will test the impact of expanding access to care in prison and post-release on outcomes in the community. Discussion and conclusions POST will inform policy and service responses across justice, health and community settings to reduce harms among people who experience incarceration.

Social Sciences

A Multi-Stage Drop-the-Loser Design With Superiority Boundaries.

Greenstreet P et al. · Jul 1, 2026

Multi-arm multi-stage (MAMS) trials have gained popularity, due to their improved efficiency in evaluating multiple treatments. A traditional MAMS trial often decreases the expected sample size of the trial compared with just running a multi-arm approach, but with the drawback of an increase in maximum sample size. For academic led trials, this poses a particular challenge, as funding is typically based on the maximum required sample size. To address this, drop-the-loser designs were introduced, where a fixed number of treatments are dropped at each interim stage, thereby reducing the maximum sample size. In this work, we propose an enhanced multi-stage drop-the-loser design that also allows for early stopping of the entire trial for superiority. This approach aims to retain the benefits of a reduced maximum sample size while also lowering the expected sample size. The proposed design is motivated by a trial in atrial fibrillation. We derive analytical expressions for the type I error rate, power, and expected sample size, and compare the proposed design's performance to alternative methods. We outline the key requirements for implementing the proposed design and discuss the contexts in which it should be considered. For the motivating example, the results show that the proposed design substantially reduces the expected sample size compared to a standard drop-the-loser design, while lowering the maximum sample size relative to running a traditional MAMS trial or multiple separate trials.

Mathematics

Validation of an automated chromogenic in situ hybridization protocol for detection of cytomegalovirus in formalin-fixed, paraffin-embedded renal graft biopsies.

Rangel JV et al. · Jul 1, 2026

Introduction Histopathological diagnosis of human cytomegalovirus (HCMV) infection in formalin-fixed, paraffin-embedded (FFPE) tissues stained with hematoxylin-eosin relies on the identification of characteristic cytopathic changes, including eosinophilic intranuclear and cytoplasmic viral inclusions. Chromogenic in situ hybridization (CISH) enables the localization of specific nucleic acid sequences in histological sections, increasing diagnostic sensitivity. Automated CISH platforms allow standardized and reproducible detection of viral RNA or DNA in FFPE tissues. This study aimed to validate an automated CISH protocol for the detection of HCMV in multiple tissue types, including renal allograft biopsies processed at the Anatomical Pathology Division of UERJ. Methods Two groups of FFPE samples were analyzed. The first group included ten samples from various tissues, including kidney, palate, esophagus, stomach, and colon; nine positive and one negative for HCMV by immunohistochemistry (IHC). The second group included twenty renal allograft biopsies; nineteen without previous diagnosis and one positive by IHC. For CISH, fluorescein-conjugated oligonucleotide probes targeting HCMV RNA expressed during the early replication phase were used, with hybridization and detection performed on an automated platform. Results In Group 1, all samples, including the case previously negative by IHC, were positive for HCMV by CISH. In Group 2, six of the twenty renal biopsies were positive, including the sample already identified as positive by IHC. Conclusions The automated CISH protocol demonstrated high sensitivity and reproducibility for HCMV detection, supporting its validation and use in the diagnosis of renal biopsies and other tissues, as well as its incorporation into the routine workflow of Anatomical Pathology laboratories.

Medicine

Dual-scan conformal cone-beam CT for targeted image-quality improvement using dynamic collimation.

Liu Y et al. · Jul 1, 2026

Background Cone-beam computed tomography (CBCT) is routinely used for image guidance in radiation therapy, but conventional full-field CBCT acquisition may expose anatomically irrelevant regions and generate substantial scatter, which degrades soft-tissue contrast and limits the accuracy of target localization and adaptive radiotherapy workflows. A clinically practical imaging strategy should improve image quality in the region of clinical interest while preserving sufficient full-field anatomical information for patient setup and dose-related assessment. Purpose To improve image quality within clinically relevant regions without increasing the total imaging dose, this study developed a dual-scan conformal cone-beam CT method using dynamic collimation. Methods A dual-scan conformal acquisition strategy using dynamic collimation was designed. Dynamic collimation was defined as angle-dependent shaping of the X-ray field around a preplanned target region; it was implemented with a multi-leaf collimator model in the digital phantom and patient-data simulations and with the movable kV collimator in the physical phantom measurement. Collimator positions were calculated from forward projections of the target region, and a target-region-based intensity-optimization model was used to allocate more photons to projection angles and detector regions contributing to the target region. The dual-scan protocol, consisting of a low-dose full-field-of-view (FOV) scan and a higher-dose conformal scan, was reconstructed using a regularized weighted least-squares algorithm with anisotropic total-variation regularization. The method was evaluated using a FORBILD head phantom, patient planning CT datasets, and an anthropomorphic head phantom measured on a Varian Edge on-board imaging system. Results The proposed method achieved the best overall image quality among the compared methods. In the FORBILD phantom, for target region 1 (VOI1), SSIM increased from 0.702 to 0.855, CNR increased from 0.514 to 1.463, and SNR1/SNR2 increased from 33.8/32.8 to 113.1/114.6 compared with conventional FDK reconstruction. In the prostate cancer case, CNR increased from 0.44 to 1.94, and SNR1/SNR2 increased from 25.6/23.3 to 74.6/82.4. In the measured anthropomorphic head phantom, CNR improved from 1.036 to 2.379, and SNR1/SNR2 improved from 42.8/50.1 to 89.8/98.0. Conclusion The proposed dual-scan conformal CBCT strategy using dynamic collimation improves local image quality in the clinically relevant target region while maintaining full-FOV information through the two-scan protocol. This method may provide a practical route toward patient-specific CBCT guidance for adaptive radiotherapy.

Physics and Astronomy

Understanding Multi-Victimization: Identifying Socioecological Supports Among Adolescents.

Smith EK et al. · Jul 1, 2026

Background Transgender and gender-diverse adolescents (TGDA) experience multiple victimization types (i.e., multi-victimization) more often than their cisgender peers. While greater socioecological supports are associated with reduced victimization, their role in protecting TGDA against multi-victimization is underexplored. Methods We conducted a cross-sectional analysis of health risk behavior survey data from students (N = 4207) across 13 high schools in a mid-sized U.S. city. We compared victimization rates and socioecological support levels (i.e., parental monitoring, perceived social support, food and housing security) and associations between socioecological support and victimization, accounting for between-school differences. We included two-way interactions between gender and socioecological supports. Results TGDA reported more frequent multi-victimization and lower socioecological support than cisgender adolescents. Across all groups, greater socioecological support was associated with experiencing fewer victimization types. Food security was more protective for cisgender girls than cisgender boys. Implications for school health policy, practice, and equity Enhancing socioecological support may reduce multi-victimization for all youth, with TGDA having the greatest need. Schools' advocacy and innovative efforts to bolster socioecological support are critical to TGDA wellbeing. Conclusions Modifiable socioecological supports may reduce adolescent multi-victimization. TGDA have lower support rates and higher multi-victimization rates, suggesting the need for tailored interventions.

Psychology

The intergenerational transmission of reflective functioning in adoptive families: A prospective study from pre-adoption to early adolescence.

Fiore S et al. · Jul 1, 2026

Parental reflective functioning is crucial for the development of children's reflective functioning, with cross-sectional evidence supporting this idea. However, there is a lack of long-term, prospective studies in this area and no previous studies have examined parental reflective functioning and child reflective functioning in the context of adoption. Using a structural equation modeling approach, this study addressed this knowledge gap by examining the associations between 96 Belgian adoptive mothers' and fathers' reflective functioning during the transition to adoptive parenthood, their levels of parental reflective functioning during their adopted child's early childhood, and the children's reflective functioning in early adolescence (M age = 12 years, SD = .58, range = 11-13; 17 boys, 11 girls). Findings showed that parents' pre-adoptive reflective functioning was positively associated with parental reflective functioning in early childhood. Parental reflective functioning in early childhood, but not pre-adoptive reflective functioning, predicted child reflective functioning in early adolescence. Maternal reflective functioning mediated the association between pre-adoptive reflective functioning and child reflective functioning. Although the small sample size precludes drawing strong conclusions from this study, this study provides new evidence for the intergenerational transmission of reflective functioning even in the context of adoption. Implications for future research are discussed.

Psychology

Sustained Hypoxia-Inducible Factor 1-Alpha Accumulation Disrupts the Articular Niche to Promote Osteoarthritis Pathogenesis.

Gong W et al. · Jul 1, 2026

The precise role of hypoxia-inducible factor-1α (HIF-1α) in osteoarthritis (OA) pathogenesis remains controversial, often debated between a protective compensatory factor and a disease mediator. Here, we demonstrate that sustained, uncoupled HIF-1α accumulation functions as a potent, compartment-specific pathogenic driver of joint destruction. Using genetically engineered mouse models, we reveal that chondrocyte-specific HIF-1α overexpression (Acan CreERT2 ; Hif1αdPA fl/fl ) triggers spontaneous OA and exacerbates destabilization of the medial meniscus (DMM)-induced post-traumatic joint degeneration. Mechanistically, continuous HIF-1α activation drives pathological angiogenesis that physically dismantles the avascular, hypoxic cartilage niche, forcing a profound metabolic dysregulation that culminates in catastrophic matrix degradation. Conversely, sustained HIF-1α activation within the synovial and superficial cartilage compartments (Prg4- GFPCreERT2; Hif1αdPA fl/fl ) drives a slowly progressive, late-onset spontaneous OA through chronic inflammatory accumulation that actively suppresses Col2a1 expression. Furthermore, this robust inflammatory priming establishes a highly vulnerable microenvironment, whereby DMM surgery significantly accelerates the progression of trauma-induced joint collapse. Finally, transient whole-joint HIF-1α induction via an intra-articular injection of lipid nanoparticles (LNP-mRNA) closely recapitulates these detrimental effects. Collectively, our study reconciles existing controversies by establishing sustained HIF-1α accumulation as a spatiotemporally dynamic, broad disease amplifier across the articular ecosystem, highlighting its targeted inhibition as a promising therapeutic strategy for OA.

Medicine

Explainable machine learning for patient-specific quality assurance in intensity-modulated radiotherapy based on anatomical structures.

Zhang X et al. · Jul 1, 2026

Background Patient-specific quality assurance (PSQA) plays a pivotal role in intensity-modulated radiotherapy (IMRT) to ensure accurate dose delivery. However, conventional measurement-based PSQA approaches are labor-intensive and provide limited insight into the underlying factors contributing to variations in gamma passing rates (GPRs). Anatomical characteristics of the planning target volume (PTV) and organs at risk (OARs) may contain predictive information relevant to GPR performance, yet their potential has not been fully explored within interpretable machine learning frameworks. Purpose This study aimed to develop an interpretable machine learning (ML) framework for predicting GPRs in IMRT based on anatomical features extracted from the PTV and OARs. Methods A retrospective cohort of 243 clinical chest IMRT plans was analyzed. Radiomic and dosimetric features were extracted for each anatomical structure. Two ML regression models-Random Forest (RF) and eXtreme Gradient Boosting (XGBoost)-were developed to predict GPRs for the PTV and OARs under four gamma criteria (3%/3 mm, 3%/2 mm, 2%/3 mm, and 2%/2 mm). The GPR obtained by comparing the dose distribution reconstructed using the independent Monte Carlo (MC) dose calculation software ArcherQA (Wisdom Technology Company Limited, Hefei, China)-based on linear accelerator delivery log files-with the original planned dose distribution was used as the reference standard, and calculated using global gamma analysis with a 10% dose threshold. Model performance was evaluated using the mean absolute error (MAE), root mean square error (RMSE), and Spearman's rank correlation coefficient. Shapley Additive Explanations (SHAP) were applied to interpret feature contributions in the best-performing model. Results Both models demonstrated robust predictive performance across different anatomical structures and gamma criteria. As the gamma criteria became less stringent, prediction errors decreased accordingly. Prediction accuracy was relatively high for OARs; for example, under the 3%/3 mm criterion, the test-set MAE was 0.06% ± 0.01% for the heart and 0.26% ± 0.04% for the whole lung. In contrast, the prediction error was relatively larger for the PTV, with a test-set MAE of 1.98% ± 0.31% under the same criterion. SHAP analysis revealed that texture-related radiomic features contributed most substantially to model predictions. Moreover, feature importance patterns varied according to organ type and gamma-criterion stringency. Conclusions Multi-omics descriptors derived from anatomical structures can reliably predict GPRs in IMRT. The proposed interpretable ML framework not only achieves accurate prediction but also enhances mechanistic understanding through SHAP-based explanations. These findings provide valuable insights into dose verification variability and offer a practical, transparent tool for IMRT patient-specific quality assurance.

Physics and Astronomy

Knowing How to Ask About Digital Culture in Youth Mental Health Care: A Co-Designed Tool.

Paquin V et al. · Jul 1, 2026

Background With the digital cultures that youth are exposed to and participating in come potential risks and protective factors for their mental health. However, despite clear need there is a lack of guidance to help mental health professionals evaluate the role of social media, artificial intelligence, and other technologies in young people's mental health. Aims To co-design with young people the Digital Culture Interview, an interview tool to support the clinical assessment of digital cultural factors in mental health care. Method We recruited a diverse group of 12 participants aged 16-35 years (mean age 22 years) from outpatient mental health clinics in Montreal, Canada. Using the nominal group technique, they identified topics they found most relevant for exploring in a clinical assessment the experiences and practices involving digital technologies. Based on the topics that received the most votes from participants, we co-developed a list of interview questions and written guidance for their administration. Results Participants identified and ranked 48 themes. Drawing from these, 14 questions were developed for inclusion in the Digital Culture Interview, covering four topics: identity and worldview, negative experiences online, coping, and understanding of mental health. Participants emphasised that exploring digital culture in mental health care requires patients' trust and a baseline of knowledge. If done sensitively, this may enhance the patient-clinician alliance and improve mutual understanding. Conclusions The Digital Culture Interview has the potential to enhance rapport and reveal risk and protective factors that are salient to and actionable in mental health care.

Psychology