Browse Articles

Somatic mutation profiles in aged military nuclear test veterans: A comparative whole-genome sequence study

Veterans of the British nuclear testing programme represent an aged group of ex-military personnel who may have been exposed to ionising radiation through their participation at nuclear testing sites. This study aimed to compare the somatic mutational landscape of a cohort of 30 nuclear test veterans with that of an age-matched cohort of 30 control veterans. Variants were identified from publicly available whole-genome sequencing data using a bioinformatics pipeline developed in accordance with the gold standard approaches as defined by the Broad Institute. The resulting set of raw SNV and INDEL variants for each individual veteran were subjected to several filtering steps to reduce the noise arising from common mutations, before the average number and types were compared for each cohort using the Grubbs test. The genomic distribution of these variants was also examined by assessing for any mutation clustering considered characteristic of radiation exposure (SNVs and/or INDELs occurring within 10 bp) using a 10 bp running window and separately, the identification of mutational signatures by fitting SNVs to the COSMIC database Human Cancer v3.4. When comparing the nuclear test veteran and control cohorts, we found no statistically elevated frequency of any variant type or clusters. The dominant SBS signatures in both cohorts were those typically associated with ageing. A qualitative assessment of the functional impact of the most prevalently observed variants in each cohort showed these to also be associated with age. For example, in the control cohort, variants were found in LINC02098-ETS1 and RCL1, genes linked to classic age-related conditions such as hair loss and osteoarthritis. In the nuclear test veteran cohort, we observed multiple variants affecting the CHODL gene in approximately 40% of participants. CHODL encodes chondrolectin, a protein important for maintaining the structural integrity and function of tissues. In conclusion, the absence of significant genetic differences between cohorts, together with the prevalence of age-associated mutations, is consistent with ageing being one of the primary drivers of the observed somatic variation in these veterans, overshadowing any potential environmental, including historical radiation, effects.

Evaluating the utility of AI-generated mammography images in breast cancer analysis

Abstract Generative AI models are increasingly used to address data scarcity and class imbalance in biomedical image analysis, yet their practical value depends on downstream clinical task improvements rather than visual fidelity alone. To gain insight into their downstream utility, we conduct a systematic evaluation of class-conditional AI-generated images and their impact when integrated into a breast cancer classification pipeline. We develop a classifier-free guided denoising diffusion probabilistic model (DDPM) to generate benign and malignant full-image mammograms under varying inference configurations. Model training is based on digital breast tomosynthesis and 2D digital mammograms from the Emory Breast Imaging Dataset. DDPM guidance scales for inference are chosen based on image fidelity and class separability, as evaluated by Fréchet Inception Distance and a pre-trained Oracle classifier, respectively. We also introduce a two-phase training strategy, where models get pretrained on real data augmented with AI-generated data followed by fine-tuning on real data only. Quantitative performance evaluation for guidance strength, AI-generated-to-real data proportion, and training strategy impact, is based on Balanced Accuracy, Sensitivity, and Specificity. Our findings indicate lack of consistent performance gains for AI-generated images used to mitigate data scarcity. Any gains are sensitive to guidance scale, liable to degrade when AI-generated data replaces real beyond a proportion. However, our two-phase training strategy yields consistent improvements, when compared with real-data-only baseline and fixed guidance scales. This suggests that AI-generated data cannot replace real clinical data, but can serve as a useful transfer learning signal when carefully integrated into the training pipelines.

The association of eicosanoids with lung structure and function: Findings from the Multi-Ethnic Study of Atherosclerosis lung study and Framingham Heart Study

Background Eicosanoids are bioactive signaling lipids that have roles in airway remodeling, smooth muscle hypertrophy, emphysema and pulmonary fibrosis via mediation of pro- and anti-inflammatory pathways. Specific eicosanoids have been associated with lung diseases such as asthma and pulmonary fibrosis, yet their association with lung function more broadly is not completely understood. We aimed to investigate the association of eicosanoids and related metabolites with early changes in lung function and structure. Methods We performed comprehensive profiling of over 250 eicosanoids and eicosanoid-related metabolites using directed non-targeted mass spectrometry in the Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study with independent validation in the Framingham Heart Study (FHS). We performed cross-sectional analysis of the associations between metabolites and lung function as assessed by spirometry and quantitative lung measures on computed tomography (CT). Results Among 3384 participants (mean age 63 ± 10 years, 51% women), 51 metabolites were associated with lung function in MESA Lung (22 with % predicted FEV 1 , 18 with % predicted FVC, and 25 with FEV 1 /FVC ratio), with 24 validated among FHS participants. Of these 51 metabolites, 27 were associated with obstructive lung physiology, including linoleic acid derivatives (9-HODE) and other long-chain fatty acids (hydroxyhexadecanoic acid, hydroxyoctadecanoic acid) associated with higher odds. Fourteen metabolites were associated with restrictive physiology, including putative dihydroxy-20:3 and an LTB3 analog associated with lower odds, and omega-3 fatty acids (EPA, stearidonic acid) associated with higher odds. Conclusions Specific eicosanoids and eicosanoid-related metabolites including linoleic acid derivatives and long-chain fatty acids were associated with obstructive, and leukotrienes and omega-3 fatty acids with restrictive lung physiology. These findings highlight bioactive lipids involved in both pro- and anti-inflammatory pathways as potential influencers of lung function and may serve as future therapeutic targets early in lung disease development.

Circulating microRNA profiles associated with tick bite and debilitating symptom complexes attributed to ticks (DSCATT)

Abstract Tick bites in Australia are associated with a poorly understood syndrome known as Debilitating Symptom Complexes Attributed to Ticks (DSCATT), however, the underlying biological mechanisms remain unclear. Here, we investigate host responses to tick bite and DSCATT by profiling circulating host-encoded microRNAs (miRNAs), key regulators of gene expression. Circulating miRNAs were profiled in two cohorts: a longitudinal cohort followed from tick bite for up to 12 months, and a retrospective cohort with DSCATT. Differential expression analysis revealed that tick bite induces widespread changes in circulating miRNAs, with 149 miRNAs showing significant variation over 12 months. Temporal clustering revealed two expression patterns: one oscillating trajectory and a declining then stabilising, with predicted targets enriched for pathways related to immune modulation, tissue remodelling and cellular stress responses. DSCATT patients exhibited 98 differentially expressed miRNAs, including significant overlap with acute tick bite miRNAs, and four miRNAs correlated with symptom severity, including fatigue and dizziness. Machine learning analysis identified a five-miRNA signature that classified acute tick bite with 86% accuracy and receiver operating characteristic area under the curve (ROC AUC) of 0.92. These findings represent, to the best of our knowledge, the first characterisation of host miRNA responses to tick bite and DSCATT, highlighting potential biomarkers and mechanisms underlying chronic symptom development.

Organizational culture, social capital, and emergency capacity in primary healthcare institutions: A cross-sectional structural equation modeling study comparing ordinary and older communities

The emergency capacity of primary healthcare institutions is critical to the effectiveness of grassroots emergency management. This study examines the relationships among organizational culture, social capital, and the emergency capacity of primary healthcare institutions using a structural equation modeling approach. A questionnaire survey was conducted among healthcare professionals, yielding 983 valid responses for analysis. The results indicate that organizational culture, as well as structural, relational, and cognitive dimensions of social capital, are significantly associated with the emergency capacity of primary healthcare institutions within the model. In addition, social capital demonstrates mediating roles in the relationship between organizational culture and emergency capacity. Multi-group structural equation modeling further reveals variations across community types: relational social capital shows stronger associations with emergency capacity in ordinary communities, whereas structural social capital is more prominent in older communities. These findings provide empirical evidence on how organizational culture and social capital are linked to emergency capacity in primary healthcare settings, highlighting the importance of both internal cultural development and external social resources in different phases of emergency management.

IL1R2 identified as a key hub gene regulating vascular endothelial function in Kawasaki disease

Lived experiences of diabetes self-management in North Shoa, Ethiopia: A phenomenological inquiry

Background Despite government efforts, diabetes self-management remains inadequate for many patients due to complex barriers. Understanding the lived experiences and barriers to self-care practices is essential for facilitating behavior change and achieving personal health goals for improved diabetes management. Therefore, this study aimed to explore the lived experiences and barriers faced by individuals living with diabetes in adhering to their self-care practices. Methods This phenomenological study was carried out in the North Shoa Zone from 1 to 30 July 2024. The study consisted of a total of 25 participants, 20 diabetic patients and five healthcare informants from four districts. The participants were selected using maximum variation sampling method, considering factors such as age, sex, marital status, occupation, and type of diabetes. A pretested interview guide was used to gather the data, which were then recorded, transcribed, and analyzed with ATLAS.Ti software. A thematic framework was applied to identify key codes, subthemes, and main themes associated with diabetes self-care and the barriers to its practice. Results Diabetes self-management behaviors were found to be insufficient, mainly due to barriers at the individual, interpersonal, and community levels. Despite high adherence to prescribed medications intake among most patients, many still have inadequate practices in self-blood glucose monitoring, diet, regular physical activity, and foot care. Key barriers included limited knowledge of self-care, socioeconomic constraints, lack of guidance, low motivation, stress, limited social support, cultural influences, and poor access to resources. Conclusions Diabetes self-care was inadequate due to several challenging barriers. To improve these practices, it is crucial to integrate behavioral change interventions, provide mental health support, implement stress management strategies, and foster community partnerships to address barriers comprehensively at all levels.

Synergistic effects of salicylic acid based plant growth regulators on growth, photosynthetic performance, and antioxidant defense of Polianthes tuberosa L. under drought stress

Abstract Drought stress is a major limitation to sustainable floriculture in arid regions, severely affecting plant growth and flower quality. To mitigate these effects, this study evaluated foliar application of salicylic acid (SA; 300 ppm) applied alone and in combination with indole-3-acetic acid (IAA; 200 ppm), ascorbic acid (AA; 300 ppm), and chitosan (CS; 300 ppm) on the growth, photosynthetic performance, and antioxidant defense of Polianthes tuberosa L. under graded drought conditions [90%, 70%, and 40% water-holding capacity (WHC)]. The experiment was arranged in a factorial randomized complete block design with five replications. Drought stress was imposed for 8 weeks until first floret opening, while foliar treatments were applied four times (25, 40, 55, and 70 days after planting) starting at the three-leaf stage (BBCH 13). Results showed that severe drought (40% WHC) stress significantly reduced shoot biomass by ~ 60%, root biomass by 65–70%, and root surface area by ~ 45%, compared with well-watered conditions (90% WHC), indicating strong growth inhibition. A significant drought × PGR interaction confirmed that treatment responses were stress dependent. Among treatments, SA + IAA most effectively improved growth, biomass accumulation, and reproductive traits across drought levels, whereas SA + CS improved plant water status and reduced electrolyte leakage. In contrast, SA + AA better maintained chlorophyll fluorescence parameters and photosynthetic efficiency under drought stress. These responses were supported by increased antioxidant enzyme activity (SOD, CAT, POD), higher phenolic content, and soluble proteins, contributing to improved drought tolerance. Overall, SA-based combinations improved drought tolerance in a trait-specific manner, with SA + IAA promoting growth, SA + AA supporting photosynthetic performance, and SA + CS improving water status and membrane stability under water-limited conditions. These findings suggest that SA-based growth regulator combinations may enhance drought resilience and sustain growth in Polianthes tuberosa under water-limited conditions.

Understanding the “how” and “why”: A mixed methods process evaluation for the PRO-HIIT intervention

Introduction Process evaluation completes outcome evaluation by explaining “how” and “why” an intervention is (in)effective. The aim of this study was to conduct a mixed methods process evaluation for the PRO-HIIT intervention. Methods The PRO-HIIT intervention replaced the traditional warm-up period with 6–8 minutes of high-intensity interval training in the physical education and activity lessons, aiming to promote physical fitness, psychological parameters and academic performances among Chinese adolescents. The process evaluation was guided by the Medical Research Council guidance for the evaluation of complex interventions. Three process evaluation domains, including twelve process evaluation measures, were assessed using both quantitative and qualitative methods. Key means for process evaluation included were training logbook recording, intensity monitoring, and semi structured focus groups. Results The PRO-HIIT intervention showed high level of retention rate and dose received. The dose delivered was slightly less than anticipated, with an average 26.5 sessions delivered over twelve intervention weeks. The average heart rate was 146 beats per minute, corresponding to 71% maximum heart rate, with the mean heart rate peak was 175 beats per minute (85% maximum heart rate). The average sessional rating of perceived exertion was 5, ranging from 3 to 8. Overall, participants and physical education teachers showed positive response towards the PRO-HIIT intervention. Session duration and work-to-rest ratio were adapted to balance the intervention satisfaction and effectiveness. Barriers to intervention delivery included competing priorities, severe weather, lack of sleep, repetition of exercises, and rating of perceived exertion administration, whereas facilitators included space efficiency, imparting knowledge, peer coaches, music, and physical education teachers perform the session with participants. No intervention-related injury occurred. Conclusions This process evaluation provided a lens through which to facilitate the interpretation of the effectiveness of the PRO-HIIT intervention. The results provide valuable insights into how a school-based HIIT intervention can be implemented and refined.

Sublethal effects of a commercial imidacloprid formulation on appetitive, defensive, and motor behaviors in Africanized honey bees (Apis mellifera)

Abstract Honey bees ( Apis mellifera ) are essential pollinators in both agricultural and natural ecosystems, yet exposure to neonicotinoid insecticides such as imidacloprid poses a recognized risk to their behavior and physiology. We evaluated the sublethal effects of a commercial formulation of imidacloprid (Confidor 350 CS) on appetitive, defensive, and motor behaviors in Africanized honey bees using three experimental protocols: the proboscis extension reflex (PER), the sting extension reflex (SER), and analyses of individual and group locomotion. Imidacloprid exposure produced dose- and time-dependent reductions in both PER and SER, with significant impairments at intermediate and high concentrations. At the motor level, exposed bees exhibited reduced activity and altered movement dynamics across body segments, with heterogeneous reductions in head and abdominal motion, as well as decreased angular displacement and angular speed during group movement. Together, these results indicate that sublethal imidacloprid exposure disrupts multiple sensorimotor behavioral domains, resulting in broad functional impairment under controlled conditions. These findings add to growing evidence that sublethal neonicotinoid exposure can substantially alter honey bee behavior and highlight the need for careful evaluation of their use in agroecosystems.

Whole-genome sequencing reveals a previously unrecognized measles virus cluster in Burundi

Despite remarkable progress towards measles elimination in Burundi, the country has seen a resurge in cases since 2019. Epidemiological investigations have been performed; however, it remained unclear if all measles cases involved in recent outbreaks were linked or caused by multiple independent events including introductions from other countries. Therefore, the objective of this study is to investigate the genetic diversity and evolution of measles virus (MeV) during the last large MeV outbreak in Burundi in 2024. The study was carried out on oropharyngeal swab samples collected from four neighboring health districts. Amplicon-based MeV sequencing was performed on the MinION Mk1D. Consensus sequences were generated from 18 isolates and phylogenetic and Bayesian evolution analysis including 152 closely related public genomes were performed. Results showed that all 18 newly generated whole-genome sequences belonged to the genotype B3. Phylogenetic analysis revealed a diverse population of MeV circulating in Burundi, with sequences divided into two separate clusters. The first cluster consisted of two sequences and was most closely related to Italian sequences, while the second cluster was more related to local transmission in the Great-Lakes region based on the N450 region. Based on whole-genome sequences, the remaining 16 whole-genome sequences from Burundi clustered with one sequence from the Netherlands. The most recent common ancestor of the sequences in the second cluster was estimated to be around the beginning of 2023 (between the end of 2022 and the end of 2023 using the 95% confidence intervals) by using Bayesian evolutionary analysis. Here, we provide the first batch of MeV whole-genome sequences generated on the African continent of the ongoing MeV outbreak in the Great Lakes region of Africa. Using these whole genome sequences, we demonstrated that measles viruses genotype B3 were already circulating in Burundi since the beginning of 2023 well before the outbreak in 2024 was noted. We also showed that the recurrent measles outbreaks in Burundi are ignited from different sources, showing that measles in Burundi is sustained by multiple introductions and emphasizing the importance of ongoing molecular surveillance for elimination efforts.

Phenotypic profile of multi-drug-resistant Klebsiella spp. isolated from urinary tract and wound infections in Togo

Low-cost UWB CPW microwave tattoo sensor for respiratory monitoring using near-field phase variation

Abstract This paper presents a flexible, ultra-thin, and transparent tattoo-based CPW slot microwave sensor designed for respiratory monitoring with potential applications in epilepsy-related assessment. The sensor is fabricated using gold leaf on transparent PVC, enabling excellent skin conformity and long-term comfort. The proposed microwave sensor achieves an ultra-wide operating bandwidth of 2.4–17 GHz. Mechanical reliability is confirmed through bending and crumpling tests, where the sensor maintains stable impedance characteristics. In addition to respiratory monitoring, the proposed sensor shows potential for epilepsy-related monitoring applications, as epileptic seizures are often accompanied by abnormal respiratory patterns such as apnea, irregular breathing, or sudden changes in breathing rate. Continuous monitoring of respiration can therefore provide an indirect, non-invasive indicator of abnormal respiratory patterns associated with seizure activity. Experimental measurements on two healthy adult volunteers demonstrate clear respiratory-phase detection, with breathing rates of approximately 16 breaths per minute (BPM) for the first participant and 21 BPM for the second participant, which fall within the normal respiratory range for healthy adults under non-pathological conditions. Safety evaluation shows that the SAR values remain well below international exposure limits. The maximum simulated SAR values of 0.946 W/kg (1 g) and 0.258 W/kg (10 g) occur at 6 GHz and a transmitted power of 20 dBm, while significantly lower SAR levels are observed at lower frequencies and power levels. With its wide bandwidth, flexibility, skin transparency, and confirmed electromagnetic safety, the proposed sensor demonstrates strong potential for continuous healthcare monitoring, respiratory monitoring with potential relevance to epilepsy-related assessment, and future integration into unobtrusive wearable systems.

Systematic characteristic evaluation of clay-based cementitious material derived from calcium carbide residue and waste tile powder

Acoustic feature decoupling and pre-trained language model integration for music-to-text cross-modal generation

A federated learning framework integrating knowledge graphs and Node2Vec for multi-source medical image classification

Abstract Medical image classification in federated healthcare environments is challenged by the difficulty of learning robust and generalized representations from heterogeneous, non-IID data distributed across multiple institutions. Conventional federated learning approaches primarily rely on visual features and often fail to capture structural relationships among medical images, leading to reduced classification performance and limited generalization across diverse clinical settings. This study proposes GraphMedFL, a federated learning framework that integrates knowledge graphs and Node2Vec-based structural embeddings to enhance classification performance in multi-source environments. Local knowledge graphs are constructed from image features to capture inter-sample relationships, and Node2Vec embeddings are fused with feature representations to enrich local model training. A similarity-aware adaptive aggregation strategy is introduced to address non-IID data distributions across clients. Experiments conducted on three breast cancer imaging datasets (BreakHis, CBIS-DDSM, and INbreast) demonstrate that GraphMedFL achieves superior performance compared to locally trained models, with an accuracy of 98.3% and strong precision-recall balance. The proposed GraphMedFL achieves an accuracy of 98.3% and consistently outperforms baseline federated learning and local training approaches across BreakHis, CBIS-DDSM, and INbreast datasets. It improves precision, recall, and F1-score by a measurable margin compared to conventional methods, demonstrating enhanced robustness and generalization in heterogeneous medical imaging environments.

A nurse-led peer support intervention to enhance decision-making for fecal microbiota transplantation in recurrent UTI: a pilot study

Abstract To examine the preliminary effects and feasibility of peer support on decision-making regarding fecal microbiota transplantation (FMT) among patients with recurrent urinary tract infections (rUTIs). This was a prospective, two-arm pilot study conducted from September 2023 to April 2024 in the urology outpatient departments of two tertiary hospitals in China using convenience sampling. Patients were assigned to either a peer support group or a control group. The intervention consisted of weekly WeChat-based interactions between trained peer supporters and patients for four weeks. Decision-making was assessed using validated tools including the Decisional Conflict Scale, Preparation for Decision Making Scale, Decision Self-Efficacy Scale, Choice Predisposition Scale, and Decisional Satisfaction Scale, all derived from the Ottawa Decision Support Framework. Anxiety and depression were measured using the Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS). A total of 24 patients were assigned to a peer support group ( n  = 12) and a control group ( n  = 12). Compared with the control group, the peer support group showed higher FMT-related knowledge, stronger choice predisposition toward FMT, higher decision-making self-efficacy, and lower decisional conflict after the intervention. No significant between-group differences were observed in preparation for decision making, decisional satisfaction, anxiety, or depression. In this small exploratory pilot study, nurse-led peer support showed preliminary potential to improve selected decision-related outcomes among women with rUTIs considering FMT. Rather than confirming efficacy, this study supports the feasibility of a structured nurse-led peer support protocol and provides a basis for future larger-scale research. Given the small sample size, convenience sampling, and single cultural context, these findings should be interpreted cautiously and require confirmation in larger studies. Future studies should also assess whether these preliminary improvements are sustained and translate into actual treatment decisions and long-term decisional satisfaction.

An XR-based multisensory feedback system for real-time sprint technique optimization in track athletes

Reply to Borst et al.: Acupuncture and the placebo effect

Prediction of the effect of biochar on soil CEC improvement based on machine learning

Abstract Biochar is an environmentally friendly soil amendment and is widely used for improving soil properties. Especially the Cation Exchange Capacity (CEC) of soil, which is the main criterion for assessing soil nutrients. Therefore, this study proposes a method for predicting the cation exchange capacity of soil, which is of great significance for the precise application of biochar and improving soil amendment efficiency. This study collects and organizes experimental data from published literature on biochar-amended soils to construct a dataset that includes biochar properties (feedstock type, pyrolysis temperature, specific surface area, cation exchange capacity) and soil properties. The dataset is divided into seven groups based on the properties of biochar to investigate the impact of biochar properties on the model’s prediction results. Using four machine learning algorithms—Light Gradient Boosting Machine (LightGBM), Deep Neural Network (DNN), Categorical gradient Boosting (CatBoost), and Random Forest (RF)—a predictive model for soil CEC after biochar was established. The results show that the CatBoost model performed best, with a coefficient of determination (R 2 ) of 0.963, a Mean Absolute Error (MAE) of 1.346, and a Root Mean Square Error (RMSE) of 2.238, indicating that it is effective in predicting soil CEC after the addition of biochar. Shapley Additive Explanations (SHAP) analysis and Partial Dependence Plot (PDP) results indicate that the pyrolysis temperature of biochar promotes the predicted values of soil CEC, while biochar with high CEC reduces the predicted values of soil CEC. The reason for this counterintuitive result may be that biochar with a high CEC competes for cations in the soil solution. Choosing biochar produced at high pyrolysis temperatures and with a specific surface area (SSA) below 50 m 2 /g can achieve a good improvement effect within the studied conditions. This study develops a promising model for predicting soil CEC, which can better optimize actual soil improvement, and provides valuable insights into the mechanism of the impact of biochar on soil CEC.