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Background Suicide-related behaviours and individual risk factors for suicide differ between ethnicities and demonstrate additional variation based on voluntary and forced migration. People forcibly displaced by violence and conflict, such as those seeking asylum and refugees, are likely to face stressors that can increase suicide risk. Research into evidenced-based suicide prevention strategies among people from asylum-seeking and refugee backgrounds is scarce. However, early, contextually-appropriate, identification and intervention may be a promising way to facilitate support for people in these groups. This research proposes that a contextually-responsive gatekeeper training is an appropriate strategy to increase the identification and support for people from asylum-seeking and refugee backgrounds. Methods The present article relates to the statistical findings of a larger mixed-method study used to validate and refine a contextually-responsive gatekeeper training program. The qualitative results of this research will be published separately. The outcome measures – knowledge about suicide in multicultural contexts, attitudes towards suicide and prevention, and self-efficacy to intervene were measured quantitatively, adopting a similar pre- and post-training procedure used in previous training evaluations. Using Generalised Estimating Equations, statistical comparisons were made between three identical self-report surveys completed by participants across three consecutive time points – pre-training, immediately post-training, and three months following training completion – known in this investigation as time-point zero (T0), time-point one (T1), and time-point two (T2). Lastly, during the T2 follow-up, additional open-ended questions were included to understand which areas of training they feel prepared them effectively and how the program could have better prepared them to intervene. Results A total of 28 participants took part in the study. Quantitative analysis indicated the program’s capacity to exert a significant favourable and lasting influence on knowledge about suicide and self-efficacy to intervene. In addition, follow-up measurements suggest that the content delivered to participants transferred effectively into real-world suicide prevention behaviours. Conclusions Findings suggest that tailored suicide prevention training can have a significant influence on knowledge about suicide in multicultural contexts, self-efficacy to intervene in a crisis, and that course content translates effectively into real-world suicide prevention behaviour. Modifying training practices, based on feedback from contextually-experienced attendees, appears to be a pivotal factor in promoting the support of people from asylum-seeking and refugee backgrounds.

For many disease conditions, tissue samples are colored with multiple dyes and stains to add contrast and location information for specific proteins to accurately identify and diagnose disease. This presents a computational challenge for digital pathology, as whole-slide images (WSIs) need to be properly overlaid (i.e. registered) to identify co-localized features. Traditional image registration methods sometimes fail due to the high variation of cell density and insufficient texture information in WSIs-particularly at high magnifications. In this paper, we proposed a robust image registration strategy to align re-stained WSIs precisely and efficiently. This method is applied to 30 pairs of immunohistochemical (IHC) stains and their hematoxylin and eosin (H&E) counterparts. Our approach advances the existing methods in three key ways. First, we introduce refinements to existing image registration methods. Second, we present an effective weighting strategy using kernel density estimation to mitigate registration errors. Third, we account for the linear relationship across WSI levels to improve accuracy. Our experiments show significant decreases in registration errors when matching IHC and H&E pairs, enabling subcellular-level analysis on stained and re-stained histological images. We also provide a tool to allow users to develop their own registration benchmarking experiments.

Despite the rapid uptake of multigene panel testing (MGPT) for hereditary cancer predisposition, there is limited guidance surrounding indications for testing and genes to include. To inform the clinical approach to hereditary cancer MGPT, we comprehensively evaluated 32 cancer predisposition genes by assessing phenotype-specific pathogenic variant (PV) frequencies, cancer risk associations, and performance of genetic testing criteria in a cohort of 165,000 patients referred for MGPT. We identified extensive genetic heterogeneity surrounding predisposition to cancer types commonly referred for germline testing (breast, ovarian, colorectal, uterine/endometrial, pancreatic, and melanoma). PV frequencies were highest among patients with ovarian cancer (13.8%) and lowest among patients with melanoma (8.1%). Fewer than half of PVs identified in patients meeting testing criteria for only BRCA1/2 or only Lynch syndrome occurred in the respective genes (33.1% and 46.2%). In addition, 5.8% of patients with PVs in BRCA1/2 and 26.9% of patients with PVs in Lynch syndrome genes did not meet respective testing criteria. Opportunities to improve upon identification of patients at risk for hereditary cancer predisposition include revising BRCA1/2 and Lynch syndrome testing criteria to include additional clinically actionable genes with overlapping phenotypes and relaxing testing criteria for associated cancers.

Next generation sequencing (NGS)-based assays continue to redefine the field of genetic testing. Owing to the complexity of the data, bioinformatics has become a necessary component in any laboratory implementing a clinical NGS test. The computational components of an NGS-based work flow can be conceptualized as primary, secondary, and tertiary analytics. Each of these components addresses a necessary step in the transformation of raw data into clinically actionable knowledge. Understanding the basic concepts of these analysis steps is important in assessing and addressing the informatics needs of a molecular diagnostics laboratory. Equally critical is a familiarity with the regulatory requirements addressing the bioinformatics analyses. These and other topics are covered in this review article. Bioinformatics has become an important component in clinical laboratories generating, analyzing, maintaining, and interpreting data from molecular genetics testing. Given the rapid adoption of NGS-based clinical testing, service providers must develop informatics work flows that adhere to the rigor of clinical laboratory standards, yet are flexible to changes as the chemistry and software for analyzing sequencing data mature.

The molecular events and transcriptional plasticity driving brain metastasis in clinically relevant breast tumor subtypes has not been determined. Here we comprehensively dissect genomic, transcriptomic and clinical data in patient-matched longitudinal tumor samples, and unravel distinct transcriptional programs enriched in brain metastasis. We report on subtype specific hub genes and functional processes, central to disease-affected networks in brain metastasis. Importantly, in luminal brain metastases we identify homologous recombination deficiency operative in transcriptomic and genomic data with recurrent breast mutational signatures A, F and K, associated with mismatch repair defects, TP53 mutations and homologous recombination deficiency (HRD) respectively. Utilizing PARP inhibition in patient-derived brain metastatic tumor explants we functionally validate HRD as a key vulnerability. Here, we demonstrate a functionally relevant HRD evident at genomic and transcriptomic levels pointing to genomic instability in breast cancer brain metastasis which is of potential translational significance. The molecular landscape of breast cancer brain metastases (BCBM) is still understudied, especially for different breast cancer subtypes. Here, the authors characterise subtype-specific BCBMs using genomics and transcriptomics and identify homologous recombination deficiency as a key therapeutic vulnerability.

Introduction The aims of this study were to evaluate (1) grasping forces with the application of a tactile feedback system in vivo and (2) the incidence of tissue damage incurred during robotic tissue manipulation. Robotic-assisted minimally invasive surgery has been shown to be beneficial in a variety of surgical specialties, particularly radical prostatectomy. This innovative surgical tool offers advantages over traditional laparoscopic techniques, such as improved wrist-like maneuverability, stereoscopic video displays, and scaling of surgical gestures to increase precision. A widely cited disadvantage associated with robotic systems is the absence of tactile feedback. Methods and procedure Nineteen subjects were categorized into two groups: 5 experts (six or more robotic cases) and 14 novices (five cases or less). The subjects used the da Vinci with integrated tactile feedback to run porcine bowel in the following conditions: ( T 1: deactivated tactile feedback; T 2: activated tactile feedback; and T 3: deactivated tactile feedback). The grasping force, incidence of tissue damage, and the correlation of grasping force and tissue damage were analyzed. Tissue damage was evaluated both grossly and histologically by a pathologist blinded to the sample. Results Tactile feedback resulted in significantly decreased grasping forces for both experts and novices ( P  < 0.001 in both conditions). The overall incidence of tissue damage was significantly decreased in all subjects ( P  < 0.001). A statistically significant correlation was found between grasping forces and incidence of tissue damage ( P  = 0.008). The decreased forces and tissue damage were retained through the third trial when the system was deactivated ( P  > 0.05 in all subjects). Conclusion The in vivo application of integrated tactile feedback in the robotic system demonstrates significantly reduced grasping forces, resulting in significantly less tissue damage. This tactile feedback system may improve surgical outcomes and broaden the use of robotic-assisted minimally invasive surgery.

Background Developing medical software requires navigating complex regulatory, ethical, and operational challenges. A comprehensive framework that supports both technical maturity and clinical safety is essential for effective artificial intelligence and machine learning system deployment. This paper introduces the Clinical Artificial Intelligence Readiness Evaluator Lifecycle and the Clinical Artificial Intelligence Readiness Evaluator Agent—a framework and AI-driven tool designed to streamline technology readiness level assessments in medical software development. Methods We developed the framework using an iterative process grounded in collaborative stakeholder analysis. Key institutional stakeholders—including clinical informatics experts, data engineers, ethicists, and operational leaders—were engaged to identify and prioritize the regulatory, ethical, and technical requirements unique to clinical AI/ML development. This approach, combined with a thorough review of existing methodologies, informed the creation of a lifecycle model that guides technology maturation from initial concept to full deployment. The AI-driven tool was implemented using a retrieval-augmented generation strategy and evaluated through a synthetic use case (the Diabetes Outcome Predictor). Evaluation metrics included the proportion of correctly addressed assessment questions and the overall time required for automated review, with human adjudication validating the tool’s performance. Results The findings indicate that the proposed framework effectively captures the complexities of clinical AI development. In the synthetic use case, the AI-driven tool identified that 32.8% of the assessment questions remained unanswered, while human adjudication confirmed discrepancies in 19.4% of these instances. These outcomes suggest that, when fully refined, the automated assessment process can reduce the need for extensive multi-stakeholder involvement, accelerate project timelines, and enhance resource efficiency. Conclusions The Clinical Artificial Intelligence Readiness Evaluator Lifecycle and Agent offer a robust and methodologically sound approach for evaluating the maturity of medical AI systems. By integrating stakeholder-driven insights with an AI-based assessment process, this framework lays the groundwork for more streamlined, secure, and effective clinical AI development. Future work will focus on optimizing retrieval strategies and expanding validation across diverse clinical applications.

Significance The major portion of hereditary breast cancer still remains unexplained, and many susceptibility loci are yet to be found. Exome sequencing of 24 high-risk familial BRCA1/2 -negative breast cancer patients and further genotyping of a large sample set of breast/ovarian cancer cases and controls was used to discover previously unidentified susceptibility alleles and genes. A significant association of a FANCM nonsense mutation with breast cancer, especially triple-negative breast cancer, identifies FANCM as a breast cancer susceptibility gene. Identification of such risk alleles is expected to improve cancer risk assessment for breast cancer patients and families, and may lead to improvements in the prevention, early diagnosis, and treatment of cancer. Inherited predisposition to breast cancer is known to be caused by loss-of-function mutations in BRCA1 , BRCA2 , PALB2 , CHEK2 , and other genes involved in DNA repair. However, most families severely affected by breast cancer do not harbor mutations in any of these genes. In Finland, founder mutations have been observed in each of these genes, suggesting that the Finnish population may be an excellent resource for the identification of other such genes. To this end, we carried out exome sequencing of constitutional genomic DNA from 24 breast cancer patients from 11 Finnish breast cancer families. From all rare damaging variants, 22 variants in 21 DNA repair genes were genotyped in 3,166 breast cancer patients, 569 ovarian cancer patients, and 2,090 controls, all from the Helsinki or Tampere regions of Finland. In Fanconi anemia complementation gene M ( FANCM ), nonsense mutation c.5101C>T (p.Q1701X) was significantly more frequent among breast cancer patients than among controls [odds ratio (OR) = 1.86, 95% CI = 1.26–2.75; P = 0.0018], with particular enrichment among patients with triple-negative breast cancer (TNBC; OR = 3.56, 95% CI = 1.81–6.98, P = 0.0002). In the Helsinki and Tampere regions, respectively, carrier frequencies of FANCM p.Q1701X were 2.9% and 4.0% of breast cancer patients, 5.6% and 6.6% of TNBC patients, 2.2% of ovarian cancer patients (from Helsinki), and 1.4% and 2.5% of controls. These findings identify FANCM as a breast cancer susceptibility gene, mutations in which confer a particularly strong predisposition for TNBC.