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Using an e-Delphi consensus technique to develop the Stressful Adverse Veterinary Events Support (SAVES) Framework
Co-ordinated approaches to supporting veterinary practitioners in relation to adverse events are needed to mitigate associated practitioner stress and to prevent future occurrence. The overarching aim of this study was to develop a framework that could be used profession wide within the veterinary sector to support practitioners in relation adverse events (the Stressful Adverse Veterinary Events Support Framework; SAVES Framework). A three-round e-Delphi consensus approach was taken. The study used an a priori determined panel of 50 stakeholders (50% veterinary practitioners – veterinary surgeons and veterinary nurses registered in the United Kingdom, 28% individuals employed within UK organisations supporting veterinary practitioners and 22% other veterinary care stakeholders). An a priori determined consensus agreement level of ≥75% was set. Forty-six recommendations entering the first round were formulated by reviewing two sources (i) The human and veterinary healthcare literature pertaining to the provision of support for practitioners who are involved in adverse events, and (ii) Transcripts from a study by the authors that explored veterinary practitioners’ experiences of and responses to adverse events. Both sources were used to identify recommendations that may be useful to veterinary practitioners and/or highlighted gaps in the current provision of such support. Recommendations were refined by the primary researcher, through discussion with the core research group and with a wider group of stakeholders via a pretesting and pilot phase before being shared with e-Delphi panellists. Panellists were asked to indicate the degree to which they agreed with each recommendation (Likert Scale; 1 = Very strongly disagree, 7 = Very strongly agree) and were informed that indications of 6 or 7 would be considered agreement and indications of 1 or 2 would be considered disagreement. In rounds one and two, panel members were asked to provide feedback regarding the recommendations, which was (i) Used to modify the recommendations or to formulate additional recommendations, and (ii) Qualitatively analysed to generate themes of factors that may influence implementation of support for veterinary practitioners in relation to adverse events. Twenty-nine of the 46 initial recommendations (63%) reached consensus. Feasibility, a multifaceted and flexible approach and the influence of human thought and behaviour were identified as factors participants thought would influence the implementation of such support. The recommendations provide the first evidence-based guidance for specifically supporting veterinary practitioners in relation to adverse events; future work should focus on assessing their implementation and impact.
Cross-field strength and multi-vendor reliability of MagDensity for MRI-based quantitative breast density analysis
Purpose Breast density (BD) is a significant risk factor for breast cancer, yet current assessment methods lack automation, quantification, and cross-platform consistency. This study aims to evaluate the reliability and cross-platform consistency of MagDensity, a novel magnetic resonance imaging (MRI)-based quantitative BD measure, across different imaging platforms. Methods Ten healthy volunteers participated in this prospective study, undergoing fat-water MRI scans on three scanners: 3T Siemens Prisma, 3T Siemens Biograph mMR, and 1.5T GE Signa. Great effort was made to schedule all scans within a narrow three-hour window on the same day to minimize any potential intra- or inter-day variations, requiring substantial logistical coordination. BD was assessed using the MagDensity technique, which included combining magnitude and phase images, applying a fat-water separation technique, employing an automated whole-breast segmentation algorithm, and quantifying the volumetric water fraction. Agreement between measures across scanners was analyzed using mean differences, two-tailed t-tests, Pearson’s correlation, and Bland-Altman analysis. Results MagDensity measures obtained from the two 3T Siemens scanners demonstrated no statistically significant differences, with high correlation (Pearson’s r > 0.99) and negligible mean differences (< 0.2%). Cross-platform comparison between the 3T Siemens and the 1.5T GE scanners showed larger mean differences (< 4.2%). However, after applying linear calibration, these variations were reduced to within ±0.2%, with strong inter-scanner correlation maintained (Pearson’s r > 0.97). Conclusion MagDensity showed strong intra-vendor consistency and promising cross-platform reliability after leave-one-out calibration. While full standardization remains a long-term goal, these findings provide clear evidence that scanner-related variability can be effectively mitigated through calibration. This technique offers a step further toward more consistent MRI-based BD quantification and may help enable broader clinical implementation.
Effect of health education intervention on treatment adherence and health status in patients with Chronic obstruction pulmonary disease: A random control trial
Adherence to treatment is critical to effective management of COPD and is key to addressing the growing burden of disease. So, this study conducted to evaluate the effectiveness of a health education intervention on treatment adherence behavior of COPD outpatients and identify the level of health status improvement. A random control trial was conducted in 2022 at two respiratory outpatient clinics in Da Nang City, Vietnam. 90 participants were divided into two group, 45 members of the health education group who received 5-times consulting about disease knowledge, training using inhallers, breathing exercises at clinic, then 12-times teleconsultation; and 45 controls who only joined surveys. Treatment adherence and level of health status improvement were assessed as outcomes. At beginning, there was no significant difference between the intervention and control groups regarding compliance, disease severity, and airway obstruction (p > 0.05). After three months of intervention, adherence to inhaled medication in the intervention group was 3.2 times higher than in the control group, with statistical significance (p = 0.002, OR=3.2; 95% CI: 1.8–5.3). Similarly, adherence to breathing exercises was 2.5 times greater in the intervention group compared to the control group, with a statistically significant result (p = 0.000, OR=2.5; 95% CI: 1.8–3.4). Overall adherence to treatment was 2.2 times higher in the intervention group than in the control group, also showing statistical significance (p = 0.001, OR=2.2; 95% CI: 1.7–3.1). Additionally, the intervention group demonstrated significant improvements in disease severity and airway obstruction (p < 0.001). Education interventions showed effectiveness in increasing treatment adherence and health status of COPD outpatients. Therefore, a broader program should be conducted in the future.
Comparison of systemic immunoinflammatory biomarkers for assessing severe abdominal aortic calcification among US adults aged≥40 years: A cross-sectional analysis from NHANES
Objective Several novel biomarkers, including the systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), aggregate index of systemic inflammation (AISI), platelet-lymphocyte ratio (PLR), neutrophil-lymphocyte ratio (NLR), and monocyte-lymphocyte ratio (MLR), are linked to the systemic immunity inflammation response and the odds and severity of abdominal aortic calcification (AAC). However, still no previous research has systematically compared their association with severe AAC. Methods This study utilized a cross-sectional approach, examining a cohort of 3,047 adults from National Health and Nutrition Examination Survey (NHANES). Weighted logistic regression was utilized to investigate the associations between a range of immunoinflammatory biomarkers and the likelihood of severe AAC. Segmented regression and limited cubic spline models were used in the investigation to characterize the threshold effects and non-linear correlations. Additionally, subgroup and interaction tests, Spearman correlation, least absolute shrinkage, and selection operator regression studies were conducted. Results The 3047 participants included in this study had a mean age of 58.63 years and 51.79% were female. After fully adjusting for all covariates, the ln-SIRI (OR 1.39 [CI 1.10–1.74], P = 0.005), ln-AISI (OR 1.26 [1.03–1.53], P = 0.024), and ln-MLR (OR 1.62 [1.15–2.30], P = 0.006) were significantly correlated with the odds of severe AAC. A non-linear dose-response relationship was observed between ln-SII and severe AAC. Additional subgroup analyses revealed that this relationship was more evident in the diabetic population. Additionally, MLR (AUC = 0.644) predicted the prevalence of severe AAC better than other biomarkers, and the prediction model constructed in conjunction with screened clinical indicators showed good predictive value (AUC = 0.853). Conclusions In this study, we comprehensively evaluated and compared the associations between six biomarkers and severe AAC, and developed a clinical prediction model using the MLR with the best predictive effect. However, cohort studies and model validation are still needed in the future to further confirm their relationship.
Prevalence of vision impairment among patients with diabetes mellitus in sub-Saharan Africa: A systematic review and meta-analysis
Background Diabetes mellitus is a growing global public health challenge, especially in low- and middle-income regions like sub-Saharan Africa, where healthcare resources are often limited. One of its most debilitating complications is vision impairment, which significantly impacts the quality of life, productivity, and independence of individuals. Although several studies have assessed the prevalence of vision impairment among individuals with diabetes in sub-Saharan Africa, their findings have been inconsistent and fragmented across different areas. A comprehensive synthesis of this evidence is crucial for informing healthcare planning, early screening, and management strategies. This study aimed to systematically review and synthesize the existing evidence on the prevalence of vision impairment among patients with diabetes mellitus in the region. Methods A literature exploration was done in an electronic database such as PubMed, Google Scholar, Web of Science, Hinary, and African Journals Online. We included all observational studies reporting the prevalence of vision loss among diabetes mellitus patients in Sub-Saharan Africa. A random-effect meta-analysis model was computed to estimate the pooled prevalence of vision impairment. I2 test and Cochrane Q test statistics were used to assess the heterogeneity of the studies. Subgroup analysis was done based on the countries where the research was conducted. STATA Version 16 statistical software was used for data analysis. Publication bias was examined by funnel plots and Egger’s tests. Results Only 26 studies with 12508 participants, contributing data from 14 countries within Sub-Saharan Africa, met the inclusion criteria for the final analysis. The overall pooled prevalence of visual impairment was found to be 29% (95% CI: 22%−35%). Heterogeneity was observed among included studies (p < 0.001 for high I2 values). Subgroup analysis revealed the source of heterogeneity in the studies carried out in Ethiopia (I2 = 99.2%, p = < 0.001), Nigeria (I2 = 94.59%, p = < 0.001), and Zambia (I2 = 77.34%, p = 0.036). Conclusion The findings of this study indicated that the pooled prevalence of vision impairment among diabetes mellitus patients is relatively higher. It can be concluded that diabetes mellitus patients should be managed properly to prevent visual impairment. Early detection of visual impairment through screening and regular follow-up is recommended to reduce as well as control the burden of visual impairment and its impact on the diabetic population.
Shear wave velocity prediction using Long Short-Term Memory Network with generative adversarial mechanism
Shear wave velocity (Vs) serves as a crucial petrophysical parameter for subsurface characterization, yet its acquisition remains challenging. While long short-term memory (LSTM) networks have emerged as the predominant solution for Vs prediction by synthesizing contextual relationships among conventional logging curves, existing implementations often overlook characteristic discrepancies between training and prediction datasets, leading to suboptimal performance. This study proposes an enhanced LSTM architecture integrated with a generative adversarial mechanism (LSTM-GAM) to address this limitation. The framework employs a dual-component structure: 1) A primary LSTM backbone that captures contextual dependencies across multi-logging sequences, and 2) An adversarial module where the generator minimizes reconstruction errors while the discriminator identifies essential feature representations common to both training and predictive data. This synergistic architecture not only preserves sequential correlations but also enhances cross-domain adaptability through adversarial feature alignment. We validate the model’s efficacy using logging data from two vertical wells in the South China Sea. Comparative experiments demonstrate the proposed LSTM-GAM achieves superior prediction accuracy with a mean absolute error (MAE) of 59.4 m/s and determination coefficient (R²) of 0.9064, outperforming conventional LSTM network. Further ablation studies reveal consistent performance improvements across varied input configurations, confirming the method’s enhanced generalization capability for Vs prediction tasks. The technical advancement provides an effective data-driven solution for shear wave velocity estimation in complex geological environments.
Sex-specific effects of appetite suppressants on stereotypy in rats
This study investigated the sex-specific effects of commonly prescribed appetite suppressants on body weight and the manifestation of motor side effects, specifically stereotypy. Employing video recordings and DeepLabCut (DLC) for precise behavioral quantification, we analyzed stereotypy, defined as purposeless, repetitive motor behaviors, in male and female rats. Under control (saline) conditions, male rats exhibited a greater propensity for weight gain compared to females. However, in contrast, female rats demonstrated greater and more homogenous weight loss than males following the administration of diethylpropion and tesofensine. Phentermine and mazindol induced comparable weight loss in both sexes, whereas cathine elicited weight reduction exclusively in males. 5-HTP and d-amphetamine administration only prevented weight gain relative to controls. Analysis of motor side effects revealed that drugs primarily targeting dopamine pathways – specifically, phentermine, mazindol, diethylpropion, cathine, and d-amphetamine – induced pronounced stereotypies, particularly head-weaving, in both sexes. Interestingly, tesofensine elicited head-weaving behavior exclusively in female subjects, albeit to a lesser extent than that observed with other dopaminergic agents; conversely, tesofensine was most frequently associated with orolingual dyskinesia. Male subjects treated with these same drugs exhibited an unexpected effect: spontaneous ejaculations, potentially attributable to the combined effects on dopamine and serotonin signaling in brain regions regulating sexual function. Network analysis and Markov transition matrices revealed distinct behavioral profiles associated with head-weaving, which emerged as the dominant attractor state, suggesting potential mechanistic differences among these drugs. Collectively, this study provides a valuable database characterizing the behavioral side effects of appetite suppressants.
KU60019 inhibits ovarian cancer progression by targeting DGAT1/has-miR-1273g-3p axis
Ataxia telangiectasia mutated (ATM) blockage can induce apoptosis in ovarian cancer. However, the molecular mechanisms underlying this process remain poorly understood. In this study, ovarian cancer cells (SKOV3) were treated with an ATM inhibitor (KU60019) for 24 hours, and the fold changes of DGAT1 and hsa-miR-1273g-3p were quantified by real-time quantitative polymerase chain reaction (RT-qPCR). Gene Ontology (GO) and pathway enrichment analyses of DGAT1-associated functions were performed. Hsa-miR-1273g-3p mimics were used to investigate the relationship between DGAT1 and hsa-miR-1273g-3p in ovarian cancer cells under ATM inhibitor treatment, and cell apoptosis rate, viability, and migration were detected. The DGAT1 inhibitor reversed KU60019-induced migration impairment in SKOV3 cells. Finally, Kaplan-Meier analysis showed the correlation between DGAT1 level and survival in ovarian cancer patients. We found that ATM blockage significantly suppressed hsa-miR-1273g-3p level and elevated DGAT1 level in SKOV3 cells. DGAT1 was enriched in cytokine receptor interaction, T cell receptor signaling pathway, and cell apoptosis. Hsa-miR-1273g-3p mimics reversed suppression of DGAT1 and impaired cell viability induced by KU60019. Higher levels of DGAT1 associated with worse survival in ovarian cancer patients. KU60019 induced ovarian cancer cell impairment by enhancing DGAT1 level and suppressing hsa-miR-1273g-3p level. Our results demonstrate the antitumor effect of KU60019 in ovarian cancer depended on miR1273g-3p/DGAT1 axis.
Assessment of a multiplex arbovirus PCR Detection Test in an area endemic for Chikungunya, Zika, and Dengue viruses: An evaluation of kit performance characteristics in line with Clinical Laboratory Improvement Amendments (CLIA) Standards
Introduction Several multiplex and singleplex PCR systems exist for the detection of Dengue(DENV), Zika(ZIKV), and Chikungunya(CHIKV) viruses; however, their performance characteristics in East Africa remain unassessed. In this study, we investigated the TaqMan® Arbovirus Triplex Kit, which allows for the simultaneous identification of CHIKV, DENV, and ZIKV viruses in serum samples and singleplex assays, including the RealStar® Chikungunya RT-PCR Kit 2.0, RealStar® Dengue RT-PCR Kit 3.0, and RealStar® Zika Virus RT-PCR Kit 1.0. Methods We used Clinical Laboratory Improvement validation methods and residual specimens sourced from DENV, CHIKV, and ZIKV outbreaks, from Kenya, Brazil, and the Democratic Republic of Congo(DRC) in March 2024. Quality control was ensured through Quality Control for Medical diagnostics (QCMD) commercial positive panels. Gold standard results were established through consensus from multiple kits, including the TaqMan® Arbovirus Triplex Kit, Illumina viral surveillance panel (Illumina VSP), and Altona singleplex kit. Data were analyzed using R to determine specificity, sensitivity, negative predictive value (NPV), positive predictive value (PPV), and diagnostic odds ratio, with probit analysis employed to evaluate the limit of detection. Results The TaqMan® Arbovirus Kit showed sensitivity exceeding 95% for all three virus targets: 97.98% for DENV, and 100% for both CHIKV and ZIKV. Simillarly, the RealStar® RT-PCR Kits exhibited sensitivities: 93.94% for CHIKV, 90.91% for DENV, and 100% for ZIKV. All kits demonstrated specificity above 99%, with a positive predictive value (PPV) over 99% and a negative predictive value (NPV) exceeding 97%. Probit analysis revealed that the TaqMan® Arbovirus Kit also had a lower detection limit compared to the RealStar kit for all tested viruses. Conclusion The TaqMan® Arbovirus Kit provides multiplex capabilities and performance similar to the RealStar® CHIKV, DENV, and ZIKV RT-PCR Kits. Consequently, these kits can be used interchangeably for detecting DENV, CHIKV, and ZIKV viruses in outbreak situations.
Maternal deaths before and during COVID-19 pandemic: Causes and avoidable factors in a tertiary hospital in South Africa, 2018‒2022
Aim To determine the institutional maternal mortality ratio (iMMR) and avoidable factors (AVFs) before and during the COVID-19 pandemic in a tertiary hospital in South Africa. Methods This was a retrospective cross-sectional study. We reviewed medical records to compare iMMR and associated AVFs two years before (March 2018 – February 2020) and two years during (March 2020 – February 2022) COVID-19 pandemic. Results Fifty-eight maternal deaths were recorded but available data was 57 (35 before and 22 during COVID-19 pandemic). The highest iMMR per 100,000 live births over a 12-month period was 329.1 before and 201.8 during COVID-19 pandemic. The mean ages were 31.0 ± 6.9 and 31 ± 6.2 years, p = 0.822 before and during COVID-19 pandemic, respectively. During COVID-19 pandemic, 40.9% (9/22) were diagnosed with COVID-19. Before and during the pandemic, 71.4% (25/35) and 68.2% (15/22) p = 1.0 were admitted into an intensive care unit (ICU), respectively, with corresponding 74.3% (26/35) and 50% (11/22), p = 0.026 requiring mechanical ventilation. There was a significant difference in the primary causes of death (p = 0.009) for the two periods, with preeclampsia with severe features (22.9%, 8/35) being the leading cause before COVID-19 pandemic compared to COVID-19 (31.8%, 7/22) during the pandemic. AVFs related to healthcare professionals were the most common occurring in 22/35 and 12/22 of the deceased before and during COVID-19 pandemic, respectively. Before COVID-19 pandemic, the most frequent patient-, healthcare professional-, and administrative-related AVFs were failure to book for antenatal care, administration of wrong treatment, and lack of ICU/high care bed spaces, respectively. During COVID-19 pandemic, the most common patient-, healthcare professional- and administrative-related AVFs were delay in seeking medical treatment, lack of critical care skills, and unavailability of ICU/high-care bed spaces, respectively. Conclusion iMMR was lower during than before the COVID-19 pandemic. AVFs related to healthcare professionals were the most common.
Systems thinking with active implementation research (STAIR): Protocol for a school-based randomised control trial for childhood obesity prevention
Introduction This paper presents the protocol for the effectiveness-implementation trial: Systems Thinking with Active Implementation Research (STAIR) for childhood obesity prevention. STAIR’s objective is to protect primary school children in Victoria, Australia from unhealthy weight and test implementation strategies for sustaining healthy school environments. STAIR is a 3-year cluster-randomised controlled trial with repeat-cross-sectional child-level data collection utilising an effectiveness-implementation type-1 hybrid design. Schools will be recruited from non-metropolitan government primary schools in Southwest Victoria, Australia, with consenting schools (target = 28) randomly assigned (1:1) to intervention or control, and students in grades 1–6 (n = 3000–6000) recruited using an opt-out approach. Intervention The intervention, co-designed by each school community, will involve multi-step group model building and action formulation process using community-based systems dynamics and baseline data (i.e., child weight status, HRQoL, health behaviours, and school environment characteristics). Implementation strategies will be guided by an active implementation framework, and coordinated by a leadership team of school representatives, community partners, and researchers. Primary and secondary outcome measures Primary outcomes are to measure (1) change in standardised body mass index (z-BMI) between intervention and control groups after three year and (2) change in acceptance and adoption of STAIR interventions and support. Secondary outcomes include differences in obesity prevalence, health behaviour measures and HRQoL, between intervention and control groups after three years. Implementation outcomes will focus on strategy effectiveness, acceptability, and utilisation. Secondary data will be collected from schools and partner organisations. An economic evaluation will assess the cost-effectiveness of intervention and implementation support. Conclusion STAIR aims to create lasting changes in schools to prevent childhood obesity by supporting healthy environments and providing insights into interventions’ cost-effectiveness and feasibility. The findings will guide policymakers and educational institutions in adopting sustainable health promotion strategies. Trial registration ANZCTR-ACTRN12624000461594p
An integrated IKOA-CNN-BiGRU-Attention framework with SHAP explainability for high-precision debris flow hazard prediction in the Nujiang river basin, China
Debris flows represent a persistent challenge for disaster prediction in mountainous regions due to their highly nonlinear and multivariate triggering mechanisms. This study proposes an explainable deep learning framework, the Improved Kepler Optimization Algorithm-Convolutional Neural Network-Bidirectional Gated Recurrent Unit-Attention (IKOA-CNN-BiGRU-Attention) model, for precise debris flow hazard prediction in the Yunnan section of the Nujiang River Basin, China. The model is developed and validated using data from 159 debris flow-prone gullies, integrating deep convolutional, recurrent, and attention-based architectures, with hyperparameters autonomously optimized by IKOA. Model explainability is enhanced using SHapley Additive exPlanations (SHAP), which quantify the influence of key factors. The IKOA-CNN-BiGRU-Attention framework consistently outperforms 13 benchmark models, achieving a root mean square error of 2.33 × 10−6, mean absolute error of 1.51 × 10−6, and mean absolute percentage error of 0.006%. The model maintains high stability across 50 repeated experiments, strong resilience to 20% input noise, and robust generalizability under five-fold cross-validation. Interpretability analysis identifies potential source energy and maximum 24-hour rainfall as primary determinants and uncovers a dual-threshold physical mechanism underlying debris flow initiation. These findings provide a quantitative basis for adaptive early warning and targeted risk mitigation, and establish a transferable framework for explainable geohazard prediction.
Identifying risks in dementia care: Insights from a qualitative study with clinical pharmacists
Introduction People with dementia experience complex healthcare needs. Clinical pharmacists play a crucial role in optimising medication management and ensuring patient safety within the primary care setting. However, little is known about the specific barriers and challenges they face when delivering dementia care. This study investigates the safety of dementia care in the community, focusing on the experiences of clinical pharmacists. Methods A qualitative study using semi-structured interviews with clinical pharmacists. Data analysis employed codebook thematic analysis, guided by the SEIPS 2.0 framework. Results Thirteen clinical pharmacists were interviewed. Key risks include variations in pharmacist expertise, communication barriers, limited resources, and systemic challenges. Using the SEIPS 2.0 framework, the complex interactions between work systems (people, tools, tasks, organisation and environment) and work processes were identified, which impact safety outcomes for both clinical pharmacists and people with dementia. Conclusions Interviews revealed a spectrum of risks associated with dementia care in primary care. This study highlights the complex interplay of factors influencing the safety of dementia care. A proactive, multifaceted approach addressing training, interprofessional collaboration, and system-level adaptations is crucial to mitigate these risks and enhance patient safety in dementia care.
Ediacaran-Cambrian paleosols of Nevada and California
The Cambrian and Ediacaran sequence of California and Nevada is rife with unconformities, paleovalleys, paleosols, and fluvial facies. This study confirms shallow marine environments for grey stromatolitic dolostone and shale of northern localities (Mt Dunfee and Westgard Pass), but fluvial red sandstones and siltstone of southern localities (Johnnie, Eagle Peak, Emigrant Pass, Donna Loy, and Cadiz) include paleosols as evidence for coastal plain and fluvial environments. Three marine transgressions into the southern localities, were in Ediacaran Johnnie Formation, earliest Cambrian Manykodes pedum zone, and Early Cambrian Olenellus trilobite zone. The southern locations have paleosols with Ediacaran fossils Ernietta, Pteridinium, Swartpuntia, and Hallidaya in growth position, as evidence that these vendobiont fossils were non marine. The paleosols include aridland Gypsids and Calcids, as well as weakly developed soils, with diagnostic LYREE enrichment, and low boron content of paleosols. Northern Ediacaran marine rocks, in contrast, are limestones with Cloudina and Wyattia, and shales with Conotubus and Wutubus. Identical marine and non-marine facies and biotas are also known from Ediacaran and Cambrian rocks of Namibia. Ediacaran marine wormlike fossils (Wormworld) were ecologically distinct and geographically separated from non-marine, sessile, vendobionts (Mattressland).
Recognition of common shortwave protocols and their subcarrier modulations based on multi-scale convolutional GRU
Shortwave communication plays a vital role in disaster relief and remote communications due to its long-range capabilities and resilience to interference. However, challenges such as multipath propagation, frequency-selective fading, and low signal-to-noise ratios (SNR) significantly hinder automatic protocol and modulation recognition. Traditional signal processing approaches often fail under such conditions, whereas deep learning offers new possibilities for robust signal classification. This study proposes a Multi-Scale Convolutional GRU (MSC-GRU) model for the automatic recognition of three representative shortwave communication protocols—CLOVER-2000, 2GALE, and 3GALE—and their twelve subcarrier modulation formats. The model transforms temporal signals into two-dimensional representations, applies parallel convolutional branches with different receptive fields, and captures temporal dependencies through a bidirectional GRU. This hybrid architecture enhances both spatial feature diversity and sequential learning capacity. The dataset includes 45,000 labeled samples from both simulated and USRP-based real-world sources, evaluated using five-fold cross-validation. Results show that the MSC-GRU model achieves 100% recognition accuracy for protocol identification at SNR < -10 dB, and 80% accuracy for subcarrier classification at SNR < -8 dB. Standard deviations across folds are reported to ensure statistical reliability. With an inference time under 10 milliseconds per signal on a standard GPU, the model demonstrates practical feasibility for real-time deployment. These results confirm that MSC-GRU provides a robust and scalable solution for shortwave communication protocol recognition in complex environments.
The impact of experimental designs & system sloppiness on the personalisation process: A cardiovascular perspective
To employ a reduced-order cardiovascular model as a digital twin for personalised medicine, it is essential to understand how uncertainties in the model’s input parameters affect its outputs. The aim is to identify a set of input parameters that can serve as clinical biomarkers, providing insight into a patient’s physiological state. Given the challenge of finding useful clinical data, careful consideration must be given to the experimental design used to acquire patient-specific input parameters. Model sloppiness—where numerous parameter combinations have minimal impact on model predictions, whilst only a few parameters significantly influence outcomes—is a critical concept in this context. In this paper, we conduct the first quantification of a cardiovascular system’s sloppiness to elucidate the structure of the input parameter space. By utilising Sobol indices and examining various synthetic cardiovascular measures with increasing invasiveness, we uncover how the personalisation process and the cardiovascular system’s sloppiness are contingent upon the chosen experimental design. Our findings reveal that continuous clinical measures induce system sloppiness and increase the number of personalisable biomarkers, whereas discrete clinical measurements produce a non-sloppy system with a reduced number of biomarkers. This study underscores the necessity for careful consideration of available clinical data as differing measurement sets can significantly impact model personalisation.
Adolescent awareness and experience of the pubertal changes: A qualitative study from Rwanda
Introduction The transition from puberty to adolescence involves significant body changes that manifest differently across young children. It is essential that adolescents become aware of these changes so that they can easily navigate puberty’s physical, emotional, and interpersonal changes safely in terms of sexual and reproductive health. In Rwanda, there remains a gap in literature on adolescents’ awareness and experiences of pubertal changes among school going adolescents; particularly among those enrolled as day students. This research was undertaken to explore the awareness and understanding of puberty-related changes among school-aged adolescents, as well as their personal experiences of these changes. The findings aim to enhance the sexual education curriculum in Rwanda, with a particular emphasis on day students in rural regions who may be vulnerable to peer and societal pressures that can potentially expose them risky sexual behaviours. Methods A qualitative study was conducted with 120 school-going adolescent girls and boys. Participants were selected using a purposive sampling approach. Twelve focused group discussions were formed in six secondary schools from rural and urban 12-years basic education in the Eastern province of Rwanda. Deductive thematic analysis was used to analyse data using Dedoose software. Results The analysis yielded two main themes: i) what adolescents know about the changes occurring during puberty, encompassing physical, emotional, and behavioral transformations, as well as the social interactions linked to these changes and their perceptions regarding the factors that influence these changes, and ii) personal experiences pertaining to the changes associated with puberty. Most of the participants were aware of body changes that happen during puberty. While puberty marked an achievement of maturity for most of the participants, the onset of menarche for female adolescents was unique due to the embarrassment it caused them. Conclusion The study found that some young girls experience menarche unprepared and get inadequate support to initially navigate that change. Therefore, caregivers should proactively get educated on pubertal changes so that they provide appropriate support to children to navigate puberty safely.
Text intelligent correction in English translation: A study on integrating models with dependency attention mechanism
Improving translation quality and efficiency is one of the key challenges in the field of Natural Language Processing (NLP). This study proposes an enhanced model based on Bidirectional Encoder Representations from Transformers (BERT), combined with a dependency self-attention mechanism, to automatically detect and correct textual errors in the translation process. The model aims to strengthen the understanding of sentence structure, thereby improving both the accuracy and efficiency of error correction. The research uses the Conference on Natural Language Learning (CoNLL)-2014 dataset as an experimental benchmark, which contains a rich collection of grammatical error samples and is a standard resource in linguistic research. During model training, the Adam optimization algorithm is employed, and the model’s performance is enhanced by introducing a customized dependency self-attention mechanism for parameter optimization. To validate the model’s effectiveness, the performance of the baseline model and the improved model is compared using multiple evaluation metrics, including accuracy, recall, F1 score, edit distance, Bilingual Evaluation Understudy (BLEU) score, and average processing time. The results show that the proposed model significantly outperforms the baseline model in terms of accuracy (improving from 0.78 to 0.85), recall (improving from 0.81 to 0.87), and F1 score (improving from 0.79 to 0.86). The average edit distance decreases from 3.2 to 2.5, the BLEU score increases from 0.65 to 0.72, and the average processing time is reduced from 2.3 seconds to 1.8 seconds. This study provides an innovative approach for intelligent text correction tasks, expands the application scenarios of the BERT model, and offers significant support for the practical implementation of NLP technologies. The findings not only highlight the advantages of the improved model but also offer new ideas and directions for future related research.
Prioritizing challenges in AI adoption for the legal domain: A systematic review and expert-driven AHP analysis
This research explores the crucial challenges influencing the adoption of Artificial Intelligence (AI) in the legal domain, a field facing escalating challenges due to rapid technological advancements. We have comprehensively identified, extracted, and evaluated 11 pivotal factors across legal, technical, and socio-ethical dimensions through a systematic review based on the PRISMA guideline. These factors are categorized into three principal groups. Utilizing an analytic hierarchy process (AHP), our innovative approach assesses the relative importance of these challenges based on data meticulously gathered from eight domain experts in law and AI. Our findings pinpoint legal aspects as the paramount category, with liability as the foremost concern among the analyzed factors. These insights offer robust and actionable guidelines for integrating AI into legal practices and underscore this study’s unique contribution to bridging the gap between legal professionals and technology developers. By highlighting the practical applications of our results, this paper facilitates a deeper understanding and proactive engagement with the essential considerations pivotal for the future adoption and evolution of AI within the legal domain.
Assessing the latest advances in bone marrow stem cell therapy for Avascular Necrosis hip: A comprehensive systematic review, meta-analysis, and meta-regression of randomized controlled trial studies
Background Avascular necrosis (AVN) of the hip is a disease characterized by vascular disruptions, where 67% of untreated cases may lead to the collapse of the femoral head. None of the current approaches, such as core decompression (CD), vascularized bone grafting, osteotomy, tissue implantation, and other methods, have been proven fully effective in delaying the progression of osteonecrosis. Recent findings indicate that bone marrow stem cells (BMSCs) have significant potential to regenerate necrotic tissue and prevent the progression of AVN in the hip. This study aims to evaluate the efficacy, side effects, treatment failure rate, most effective treatment stage of AVN hip, and application technique to treat AVN hip. Method We performed this systematic review and meta-analysis from randomized controlled trials (RCTs) and sources published between 2013 and 2023 from six databases. The literature searching method was based on predetermined PICOS, study eligibility criteria, and PRISMA guidelines. The extracted data were then assessed quantitatively using R Studio with Harris hip score (HHS), Visual analog scale (VAS), the collapse of the femoral head, and conversion of total hip arthroplasty (THA) as the outcomes of interest, then qualitatively using RoB Tool, the extracted data were analyzed using R Studio. Result A total of 12,939 records were identified through database searching. After the removal of duplicates and non-randomized studies using automation tools, 4,846 articles were screened. Following title and abstract review, 10 studies met the eligibility criteria and were included in the systematic review and meta-analysis, comprising 593 patients and 779 hips. The included studies originated from China (n = 4), France (n = 1), South Korea (n = 1), Australia (n = 1), Iran (n = 1), India (n = 1), and Spain (n = 1). Risk of bias assessment using RoB 2.0 tool revealed 70% of the RCTs were rated as having a low risk of bias, while 30% were judged to have some concerns. Meta-analysis demonstrated that BMSC therapy significantly reduced the risk of femoral head collapse (OR = 0.15; 95% CI: 0.09–0.25; P < 0.00001; I² = 0%) and conversion to THA (OR = 0.20; 95% CI: 0.13–0.31; P < 0.00001; I² = 83%). Functional outcomes, as measured by the HHS, were significantly improved in the BMSC group (MD = 10.70; 95% CI: 9.70–11.69; P < 0.00001; I² = 51%). Pain reduction, assessed using the VAS, also favored BMSC therapy (MD = −8.04; 95% CI: −8.66 to −7.42; P < 0.00001; I² = 99%). Meta-regression analyses explored the influence of study-level covariates on outcomes. No predictor reached statistical significance for HHS or THA conversion. However, mean age showed a borderline significant association with VAS (coefficient = −0.8029; P = 0.065), suggesting a possible trend of more significant pain reduction in older patients. Conclusion Currently established AVN hip treatments are proven to be less effective. The findings strongly support that giving a BMSC at the early-stage AVN hip could improve the patient’s clinical outcome and have fewer side effects and treatment failure compared to conventional treatment.