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HPV vaccination in primary care: what nurses need to know
Full text linkThe human papillomavirus (HPV) vaccination program is a key component of cancer prevention in the UK, yet uptake remains uneven across regions and social groups. General practice nurses are strategically positioned to improve coverage through vaccination delivery, health promotion and patient education. This article examines the epidemiology of HPV, the spectrum of related diseases and recent policy developments, including the transition to a simplified one-dose schedule for healthy adolescents and increased eligibility among boys and high-risk groups. It also outlines recommended schedules, catch-up strategies and practical guidance for managing immunocompromised patients. Emphasis is placed on addressing vaccine hesitancy, countering misinformation and communicating benefits empathetically and inclusively. By integrating clinical competence with effective communication, nurses can maximise the public health impact of the HPV vaccination programme and reduce the burden of HPV-related disease
The inverse palliative care law in advanced lung disease: a mixed-methods systematic review and meta-analysis
Full text linkBackgroundPeople from socioeconomically deprived backgrounds are at greater risk of developing lung disease and having a higher symptom burden. It remains unclear whether they have equitable access to and experience of palliative care. Therefore, we aimed to synthesise evidence on socioeconomic inequalities in access, receipt of, preference for, and experience of palliative care among people with advanced lung disease.MethodsMixed-methods systematic review, searching four databases (MEDLINE, Embase, PsychINFO, CINAHL) from inception to March 28, 2025. We included studies that reported on socioeconomic position and palliative care in advanced lung disease (lung cancer, mesothelioma, chronic obstructive pulmonary disease, interstitial lung disease). Study quality was assessed using the Mixed Methods Appraisal Tool. Both meta-analysis (using a random effects model with I2 to assess heterogeneity, sensitivity analysis and GRADE of evidence) and narrative synthesis were performed. PROSPERO CRD42024546502.FindingsOf 10,572 records, 54 studies met inclusion criteria (4.2 million participants). Meta-analysis of six studies showed people with lung cancer in the lowest SEP group were 18% less likely to receive palliative care than those in the highest (OR 0.82, 95% CI 0.75–0.90, I2 = 93.9%). GRADE of evidence was assessed as moderate. Qualitative findings identified financial hardship and insurance barriers limited access to pain relief and oxygen. Few studies considered multiple demographic characteristics, but those that did reported worse access among ethnic minorities and rural populations.InterpretationThis review provides novel evidence of how the inverse care law operates in advanced lung disease. People from lower socioeconomic groups are significantly less likely to access palliative care, despite greater need. There is an urgent need for equity-focused research and policy interventions, co-produced with underserved communities that account for intersecting social disadvantages
Voice of Islam Breakfast Show Podcast 24-11-2025. The Dangers of Social Media: Misinformation and the Illusion of Truth
No full textGalloping Bubbles
Full text linkDespite centuries of investigation, bubbles continue to unveil intriguing dynamics relevant to a multitude of practical applications, including industrial, biological, geophysical, and medical settings. Here we introduce bubbles that spontaneously start to ‘gallop’ along horizontal surfaces inside a vertically-vibrated fluid chamber, self-propelled by a resonant interaction between their shape oscillation modes. These active bubbles exhibit distinct trajectory regimes, including rectilinear, orbital, and run-and-tumble motions, which can be tuned dynamically via the external forcing. Through periodic body deformations, galloping bubbles swim leveraging inertial forces rather than vortex shedding, enabling them to maneuver even when viscous traction is not viable. The galloping symmetry breaking provides a robust self-propulsion mechanism, arising in bubbles whether separated from the wall by a liquid film or directly attached to it, and is captured by a minimal oscillator model, highlighting its universality. Through proof-of-concept demonstrations, we showcase the technological potential of the galloping locomotion for applications involving bubble generation and removal, transport and sorting, navigating complex fluid networks, and surface cleaning. The rich dynamics of galloping bubbles suggest exciting opportunities in heat transfer, microfluidic transport, probing and cleaning, bubble-based computing, soft robotics, and active matter
SYNTHETIC MICROSCOPIC PLATELETS IMAGES GENERATION USING WGAN- GP
Full text linkIntroduction: Data scarcity presents a major challenge in medical imaging. Generative Adversarial Networks (GANs) offer a promising solution by generating synthetic data to augment small datasets. Aim: The primary aim of this study is to evaluate the effectiveness of GAN-based synthetic data generation in enhancing CNN performance for platelet classification. Methods: The initial dataset contained 71 images, categorized into Control, Milrinone, and Zinc-plus-Milrinone classes. Using WGAN-GP, a synthetic dataset of 300images was generated. These synthetic images were incorporated into the training of CNN models, including DenseNet121 and InceptionV3. Model performance was assessed comparing results from the original and augmented datasets. Results: In the non-augmented dataset, DenseNet121 achieved an accuracy of 81%, with 84% precision, 78%recall, and an F1-score of 81%. For InceptionV3, the non-augmented dataset yielded 82%accuracy, 80% precision, 76% recall, and an F1-score of 78%. However, after incorporating GAN-generated synthetic images, DenseNet121 achieved 97% accuracy,97% precision, 95% recall, and a 96% F1-score, while InceptionV3 reached 94% accuracy,93% precision, 90% recall, and 92% F1-score on the GAN-augmented dataset. Discussion/Conclusion: These results highlight the potential of GAN-generated synthetic data to significantly enhance the performance of CNN models in medical image classification, particularly in addressing the limitations of small dataset
Leveraging Quantized Language Models for Automating Behavioral-Based Safety Observations Analysis
No full textThe oil and gas industry faces significant safety challenges, making behavioral-based safety (BBS) a critical approach to mitigating risks by focusing on human behavior. Traditionally, safety observations are analyzed manually, a process prone to errors and inefficiencies, particularly when handling large datasets with unstructured narratives. This research introduces an automated pipeline leveraging open-source pretrained language models (LLMs) to streamline BBS narrative report analysis and categorize at-risk observations. Three 4-bit quantized LLMs (Llama 3.1: 8B, Gemma 2: 9B, Mistral-Nemo: 12B) were evaluated for accuracy and inference speed on 10 sample BBS reports, with Gemma 2 selected for its superior performance. To improve the performance of the auto BBS analyzer, techniques such as in-context learning (ICL) and chain-of-thought (CoT) prompting were employed. The model's results were then manually evaluated on 400 sample BBS reports, yielded accuracies of 98% for task identification, 94.5% for positive observations, 97% for at-risk observations, 95.3% for follow-up actions, and 97% for risk categorization. However, due to the absence of a ground truth dataset, further validation by safety experts is necessary to refine the model’s understanding of industry-specific terms and scenarios. The analysis results were visualized in Power BI dashboards, highlighting frequent terms in positive and at-risk observations, and supporting proactive safety interventions. This study demonstrates the potential of open-source LLMs to enhance safety management in high-risk industries by automating BBS report analysis on corporate servers, ensuring secure handling of proprietary data and offering insights to improve safety outcomes
Experiences of integrating social prescribing link workers into primary care in England — bolting on, fitting in, or belonging: a realist evaluation
Full text linkBackground Following the 2019 NHS Long Term Plan, link workers have been employed across primary care in England to deliver social prescribing. Aim To understand and explain how the link worker role is being implemented in primary care in England. Design and setting This was a realist evaluation undertaken in England, focusing on link workers based in primary care. Method The study used focused ethnographies around seven link workers from different parts of England. As part of this, we interviewed 61 patients and 93 professionals from health care and the voluntary, community, and social enterprise sector. We reinterviewed 41 patients, seven link workers, and a link worker manager 9–12 months after their first interview. Results We developed four concepts from the codes developed during the project on the topic around how link workers are integrated (or not) within primary care: (or not) within primary care: centralising or diffusing power; forging an identity in general practice; demonstrating effect; and building a facilitative infrastructure. These concepts informed the development of a programme theory around a continuum of integration of link workers into primary care — from being ‘bolted on’ to existing provision, without much consideration, to ‘fitting in’, shaping what is delivered to be accommodating, through to ‘belonging’, whereby they are accepted as a legitimate source of support, making a valued contribution to patients’ broader wellbeing. Conclusion Social prescribing was introduced into primary care to promote greater attention to the full range of factors affecting patients’ health and wellbeing, beyond biomedicine. For that to happen, our analysis highlights the need for a whole-system approach to defining, delivering, and maintaining this new part of practice
Motivational interviewing: a framework for healthcare assistants to improve patient-centred care
Full text linkHealthcare assistants are integral to patient care in the UK healthcare system, and often the first point of contact for patients to access care. This article explores the application of motivational interviewing as a transformative approach for healthcare assistants to foster patient engagement and wellbeing. Motivational interviewing is a patient-centred and goal-oriented communication method that addresses ambivalence and resistance, empowering patients to make informed health decisions. By embracing the principles of the approach, which are capability, opportunity and motivation, healthcare assistants can shift from directive methods to a collaborative partnership model
Sequence Outlier Detection and Application of Gated Recurrent Unit Autoencoder Gaussian Mixture Model Based on Various Loss Optimization
Full text linkIn the era of big data, detecting outliers in time series data is crucial, particularly in fields such as finance and engineering. This article proposes a novel sequence outlier detection method based on the gated recurrent unit autoencoder with Gaussian mixture model (GRU-AE-GMM), which combines gated recurrent unit (GRU), autoencoder (AE), Gaussian mixture model (GMM), and optimization algorithms. The GRU captures long-term dependencies within the sequence, while the AE measures sequence abnormality. Meanwhile, the GMM models the relationship between the original and reconstructed sequences, employing the Expectation–Maximization (EM) algorithm for parameter estimation to calculate the likelihood of each hidden variable belonging to each Gaussian mixture component. In this article, we first train the model with mean-squared error loss (MSEL), and then further enhanced by substituting it with quantile loss (QL), composite quantile loss (CQL), and Huber loss (HL), respectively. Next, we validate the effectiveness and robustness of the proposed model through Monte Carlo experiments conducted under different error terms. Finally, the method is applied to Amazon stock data for 2022, demonstrating its significant potential for application in dynamic and unpredictable market environments
Measuring serious violence perpetration: comparison of police-recorded and self-reported data in a UK cohort
Full text linkIntroductionDetermining risk factors and consequences of serious violence requires accurate measures of violence. Self-reported and police-recorded offending are subject to different sources of bias.ObjectivesTo compare risk of self-reported and police-recorded serious violence perpetration in late adolescence and early adulthood using linked UK birth cohort and police data, to examine the association between cohort participation and police-recorded violence, and to use police-records to impute missing self-reported data on violenceMethodsWe included individuals in the Avon Longitudinal Study of Parents and Children (ALSPAC) who had been informed about the study's use of their linked data and had not opted out of linkage to police records (n = 12,662). We used descriptive statistics and logistic regression to address our objectives. Multiple imputation using chained equations was used to impute self-reported violence data to examine the likely impact of missing data on estimates of prevalence.ResultsSelf-reported violence perpetration in the past year ranged from 5.3% (at 25 years) to 12.9% (at 20 years) among males and 3.2% (at 17, 22, 24 and 25 years) to 6.4% (at 18 years) among females. Police-recorded serious violence was lower at all ages, peaking at 17-18 years (1.7% among males, 0.5% among females). Study participation was lower among people who had or went on to have a police record for serious violence; as a result, the prevalence of self-reported violence in the imputed data was higher (compared to observed data) at all ages.ConclusionsOverall, our study demonstrates the difficulties in measuring violence. While we have shown that a key advantage of linkage to police records is it enables outcomes to be measured irrespective of study participation, police data undercounts serious violence. Further, observational studies may also underestimate violence perpetration as individuals with police-recorded serious violence are less likely to participate in research. Therefore, while record linkage allows the advantages of both official police records and self-reported measures to be exploited, it does not negate their limitations