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Osteomyelitis in complicated bones: the role of FDG PET/CT
No full textOsteomyelitis in infections encompasses heterogeneous group of condition that frequently have high morbidity and comes at a huge cost to healthcare system. Accurate and early diagnosis is important for the proper management of the condition. FDG PET/CT has been found useful in the osteomyelitis of complicated bones, including prosthetic joint infections, fracture related infections and sternal wound infections. The altered anatomy and the replacement of marrow in some cases of metallic implant makes the use of anatomic-based methods less optimal. FDG PET/CT has been found to be useful under these circumstances, however, it also has its own limitation of lack of specificity especially due to inflammation. Recent meta-analysis of the role of FDG PET/CT in complicated osteomyelitis have result in the validation defined the indications for its use. This has led to the publication of best use criteria and recommendations of by joint committees of major nuclear medicine societies.
KEY WORDS: Positron emission tomography computed tomography; Fluorodeoxyglucose F18; Bone fractures; Osteomyelitis; Joint prosthesi
The ongoing impact of the Covid-19 pandemic on adolescents’ (11-18 years) mental health and wellbeing in the UK: a scoping review
Full text linkThis paper reports on the findings of a study into the ongoing impacts of the COVID-19 pandemic on the mental health and wellbeing of young people aged 11–18 years in the UK. The study also explored the key factors contributing to any negative ongoing impacts of the COVID-19 pandemic on young people’s mental health and wellbeing, and their self-care and coping strategies to counter these impacts. The research adopted a scoping review approach using a staged framework – identifying the research question; identifying relevant studies; study selection; charting the data; and collating, analysing and reporting the results. Based on the findings of the study, five priorities for action were proposed to improve young people’s mental health and wellbeing in the post-pandemic era: (1) to identify ways in which young people can be brought to the table in the formulation of policy responses to crises impacting on their lives; (2) to recognise the importance of policy responses targeted at the specific needs of different groups; (3) to prioritise resources to enable connectedness to school and relationships to be nurtured and sustained; (4) to spotlight the need for greater clarity regarding the roles of teachers in supporting young people’s mental health within a system of support and (5) to vigorously support the value of an equal focus on academic achievement and wellbeing in schools, an inclusive, holistic curriculum which balances all learning domains and rethinks the pressures of current assessment and testing regimes and implications for young people’s wellbeing
Prevalence and Reporting Rates of Extraspinal Findings for Lumbar Spine Magnetic Resonance Imaging in a Ghanaian Tertiary Hospital
Full text linkBackground
Extraspinal findings are commonly detected on magnetic resonance imaging of the lumbar spine, but these findings are sometimes omitted from radiological reports. Failing to report these findings could have a clinical impact on the patients. The purpose of this study was to determine the prevalence and reporting rates of extraspinal findings on lumbar spine magnetic resonance imaging.
Methods
Retrospective analysis was done on lumbar spine magnetic resonance images done at the Korle-Bu Teaching Hospital between January 2020 and December 2021. A total of 1267 patients underwent lumbar spine magnetic resonance imaging within the period. The degree of clinical significance of the extraspinal findings was ascertained using the computed tomography colonography reporting and data system classification scheme. The reporting rate was determined by referring to the archived radiological reports. Statistical analysis was done using IBM SPSS Statistics for Windows, Version 25 (Released 2017; IBM Corp., Armonk, New York, United States).
Results
A total of 737 extraspinal findings were detected from 530 patients. The overall reporting rate of extraspinal findings was 62.6% (461/737). The most common extraspinal finding was a simple renal epithelial cyst (n = 333). Clinically significant findings were detected in 107 out of the 530 patients; 36.4% of the clinically significant findings were not reported when compared with the archived reports.
Conclusion
Extraspinal findings on lumbar spine imaging were common in our study population. When radiologists are reporting lumbar spine magnetic resonance imaging, it is crucial to be aware of the risk of missing clinically significant findings
Efficacy of the Best Possible Self intervention for generalised anxiety: exploration of mediators and moderators
Full text linkGeneralised anxiety is increasingly prevalent, yet access to therapeutic interventions remains limited. We present two randomised control trials aimed to investigate the efficacy of the Best Possible Self (BPS) technique as an intervention for reducing anxiety in a non-clinical sample. The BPS was delivered online using survey software, and changes in anxiety were assessed over two weeks. Across both studies, the BPS significantly reduced anxiety, as measured with the Generalised Anxiety Disorder-7 Questionnaire (GAD-7). Evidence was found for the potential mediating role of self-esteem, and analysis of intervention frequency demonstrated that completing two or more sessions of the BPS intervention led to significant reductions in anxiety. Participants who completed only one session reported no significant change in symptoms. Evidence was not found for a moderating role of imagery capacity. These findings suggest that the BPS technique could be an accessible, cost-effective intervention for reducing generalised anxiety
Impact of Tropical Cyclone on Coastal Phytoplankton Blooms and Underlying Mechanisms
Full text linkThis study examines the impact of tropical cyclone (TC) "Wipha" (2019) on phytoplankton chlorophyll-a (Chl-a) dynamics, using observations from two buoy stations (S1 and S2). Results indicate that persistently high turbidity at the inner bay (station S1) restricted underwater light availability, resulting in an insignificant change in mean daily Chl-a concentrations, despite sufficient nutrients. Conversely, at the outer bay (station S2), Chl-a significantly increased after the storm, exhibiting notable delayed correlations with elevated turbidity (r = 0.87, p < 0.01) and aerosol deposition (r = 0.90, p < 0.01). The differential phenomenon at two locations highlights that distinct environmental control the responses of phytoplankton dynamics to the tropical cyclone, primarily related to light availability and nutrient sources.
New Hydrological Insights for the Region:
In contrast to prior studies, the nutrient source leading to increased Chl-a at the outer bay may result from wet deposition of aerosols and re-suspension of suspended matter, rather than direct terrestrial nutrient inputs. Additionally, the prolonged turbidity recovery period (up to 5 days) at the inner bay substantially limited phytoplankton growth, highlighting TC-induced turbidity as a critical factor constraining phytoplankton blooms in eutrophic coastal environments.
Keywords: tropical cyclone; buoy observation; turbidity; lagging correlatio
Emerging technologies and innovative approaches to combat antimicrobial resistance: A narrative review of next-generation therapeutic strategies
Full text linkAntimicrobial resistance (AMR) is one of the most pressing global health challenges, with approximately 700,000 deaths annually directly attributable to resistant bacterial infections. This alarming trend threatens to undermine decades of medical progress. The widespread misuse and overuse of antibiotics have accelerated the emergence of multidrug-resistant (MDR) pathogens, leading to increased morbidity, mortality, and healthcare costs. This review examines the intricate mechanisms underlying the development of AMR and discusses innovative next-generation therapeutic strategies and emerging approaches for combating resistant pathogens. CRISPR-based antimicrobials demonstrated over 90 % in vitro efficacy in selectively eliminating MDR pathogens. Nanotechnology-based solutions, such as those utilizing silver and gold nanoparticles, have demonstrated potent bactericidal activity in preclinical settings; however, toxicity and regulatory concerns persist. Bacteriophage therapy and antimicrobial peptides (AMPs) are advancing through early clinical trials, offering targeted activity and immune-modulating effects. Artificial intelligence (AI)-driven drug discovery has already been clinically integrated, accelerating the design of antibiotics and predicting resistance with high efficiency. Comparative analysis reveals that AI tools possess the highest readiness level, while CRISPR and AMPs are promising but remain in early development stages. These emerging strategies collectively present significant potential to complement or replace conventional antibiotics in addressing AMR. Despite their potential, these technologies face significant implementation challenges, including technical limitations, economic barriers, ethical considerations, and regulatory complexities. This review emphasizes the critical need for multidisciplinary collaboration, sustainable funding models, and global policy frameworks to effectively translate these innovations into clinical practice. The AMR crisis can only be addressed through international collaboration, combining scientific innovation and supportive policy environments
Explainable machine learning models for early Alzheimer's disease detection using multimodal clinical data.
Full text linkAlzheimer's disease (AD) represents a significant global health challenge requiring early and accurate prediction for effective intervention. While machine learning models demonstrate promising capabilities in AD prediction, their black-box nature limits clinical adoption due to a lack of interpretability and transparency. This study aims to develop and evaluate explainable artificial intelligence (XAI) frameworks for AD prediction using comprehensive multimodal patient data, with a focus on enhancing model interpretability through SHAP and LIME techniques. A comprehensive dataset of 2,149 patients aged 60-90 years was obtained from Kaggle, encompassing demographic, medical history, lifestyle, clinical measurements, cognitive assessments, and symptom data. Rigorous preprocessing included MinMax normalisation, Synthetic Minority Over-sampling Technique (SMOTE) for class imbalance, and Backward Elimination Feature Selection reduced 32 features to 26 optimal predictors. Six machine learning models were evaluated: K-Nearest Neighbours (KNN), Support Vector Machine (SVM), Logistic Regression (LR), XGBoost, Stacked Ensemble, and Random Forest (RF). RF's optimal hyperparameters were obtained using Ant colony Optimization Model interpretability was enhanced using SHAP and LIME frameworks for both global and local explanations. The optimised Random Forest with backward elimination feature selection and ant colony optimisation achieved superior performance with 95 % accuracy, 95 % precision, 94 % recall, 94 % F1-score, and 98 % AUC. SHAP analysis identified functional assessment, activities of daily living (ADL), memory complaints, and Mini-Mental State Examination (MMSE) as the most influential predictors. LIME provided complementary local explanations, validating the clinical relevance of identified features. The integration of explainable AI techniques with machine learning models provides clinically meaningful insights for AD prediction, enhancing transparency and fostering trust in AI-driven diagnostic tools whilst maintaining high predictive accuracy. Future work should focus on external validation, clinical workflow integration, and addressing computational requirements for real-world deployment. [Abstract copyright: Copyright © 2025 The Author(s). Published by Elsevier B.V. All rights reserved.
Examining the effect of AI advertising involvement disclosure on advertising value and purchase intentions
No full textPurpose
The integration of artificial intelligence (AI) into advertising has revolutionized how brands create and deliver marketing messages. The involvement of AI in ad creation introduces a critical question: if the AI origin of an advertisement is disclosed, how this transparency affects advertising value and purchase intentions. Grounded on the Persuasion Knowledge Model, this study investigates the effect of AI disclosure on these two key outcomes.
Design/methodology/approach
ChatGPT and Stable Diffusion were employed to generate stimuli. 358 consumers recruited via Prolific were exposed to the stimuli. The data were analyzed using a combination of Hayes’ Process models, MANCOVA and ANCOVA.
Findings
The results reveal that AI disclosure diminishes advertising value and purchase intentions. The relationships are negatively mediated by advertising credibility and positively moderated by consumer attitudes towards AI.
Originality/value
Theoretically, the research contributes to a more comprehensive picture of AI-generated advertising evaluation. Practically, the research offers actionable insights for businesses seeking to balance the advantages of AI with human psychology, ultimately optimizing advertising effectiveness in an increasingly AI-driven marketplace