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Non-invasive wood moisture sensing for arthropod infestation prevention using a circularly polarized high-isolation antenna system
Abstract This paper presents a novel circularly polarized microstrip antenna system for non-invasive detection of moisture in wood, enabling early identification of arthropod infestation areas before structural damage occurs. The proposed system consists of two microstrip antennas with opposite polarization directions (LHCP/RHCP) for transmission and reception. The circular polarization direction difference between the transmitter and the receiver allows long distance measurement and ensures low coupling between the antennas; thus, high isolation is maintained when no moisture is present. Moisture presence alters this condition, increasing the received power by 12.5 dB. The antenna system is fabricated on a low-cost FR4 substrate with compact dimensions of 50 mm × 50 mm × 1.6 mm and operates at 2 GHz. Comprehensive simulations and experiments demonstrate reliable operation across multiple conditions, including moisture levels (0–100%), temperature (− 20 °C to 90 °C), antenna positioning, wood type (Pine, Douglas fir, and Oak), sample thickness (20–30 mm), and subsurface moisture depth (up to 20 mm). A prototype was then fabricated, and experimental repeated measurements confirmed high repeatability, with a standard deviation $$\:\approx\:$$ 0.7 dB. Calibration analysis yields an average sensitivity of 0.109 dB/%MC. These results indicate that the proposed system is suitable for wood-protective moisture-sensing applications.
Improving the field accuracy of a malaria diagnostic algorithm combining sequential interpretation of rapid diagnostic test detecting PfHRP2 and pLDH in febrile children in a seasonal hyperendemic malaria transmission area in Burkina Faso
Objective To e valuate the field accuracy of a malaria diagnostic algorithm combining sequential interpretation of two-step malaria RDT detecting Pf HRP2 and p LDH with information on previous antimalarial treatments within the past four weeks for the diagnosis of malaria in febrile children under 5 years compared to standard diagnosis using a Pf HRP2 only based RDT. Methods Febrile children aged 6–59 months attending outpatient clinics were randomized to either the control group, which received the standard RDT ( Pf HRP2 only), or the intervention groups (an e-algorithm or a decisional algorithm), which was subjected to the diagnostic algorithm combining an RDT detecting Pf HRP2 and p LDH with information on previous antimalarial treatment. Malaria diagnosis with Pf HRP2-based RDT was reported as positive or negative. The sequential interpretation was reported as (i) positive when the p LDH line appeared, regardless of the Pf HRP2 results, (ii) negative when both lines did not appear and (iii) undetermined when only the Pf HRP2 line appeared, and information on previous antimalarial treatment within the past 4 weeks was used as a decision-support tool to classify active malaria from past infection. Blood samples were also collected for expert microscopy as the gold standard, and for qPCR to further evaluate undeterminate results and potential false-positive RDT outcomes. Results In total 1176 children were included, with 66.7% (784/1176) assigned to the intervention arms and 33.3% (392/1176) to the control arm. In patients assigned to the sequential algorithm, the number of undetermined cases was 12.7% (100/784). Considering microscopy as the gold standard, Pf HRP2-based RDT reported a sensitivity of 96.5% and a of specificity 79.1%, with positive and negative predictive values of 78.3% and 96.7%, respectively. For the sequential algorithm, the sensitivity, specificity, positive and negative predictive values of the conclusive-only results (i.e., Pf HRP2±/ p LDH+ and Pf HRP2-/ p LDH-) were 97.4%, 98.4%, 98.0% and 97.9%. However, when undetermined result were combined with conclusive results, the sensitivity, specificity, positive and negative predictive values were 89.7%, 96.8%, 95.6% and 92.4% respectively. Among recently antimalarial treated participants in sequential algorithm arm, 59.5% (50/84) were qPCR-positive, compared to 68.7% (11/16) qPCR-positivity in those without recent treatment. Conclusions The sequential diagnostic approach improves the diagnosis of malaria in a real world setting, compared to the use of Pf HRP2-(only) based RDT. However, relying only on history of antimalarial treatment in undetermined cases may decrease algorithm’s sensitivity, which could result in missing active or recurrent malaria infections.
Modeling and prediction of 316 L stainless steel relative density in L-PBF process using machine learning
Abstract This study investigates the prediction of relative density in L‑PBF parts using machine learning models trained on a literature-based dataset comprising 287 experimental measurements collected from previously published studies. The input parameters for the proposed models included laser power, scanning speed, and hatch spacing. To model the nonlinear relationship between process variables and relative density, k-nearest neighbours (KNN), adaptive boosting decision trees (AdaBoost-DT), and four multilayer perceptron (MLP) models optimised using Scaled Conjugate Gradient (SCG), Levenberg–Marquardt (LM), Bayesian Regularisation (BR), and Resilient Backpropagation (RB) were developed. The predictions from these six models were subsequently integrated into a single framework using a committee machine intelligence system (CMIS), which provided improved predictive performance. Model accuracy was evaluated using statistical metrics including the coefficient of determination (R²), standard deviation (SD), average percent relative error (APRE), average absolute percent relative error (AAPRE), and root mean square error (RMSE). The results showed that the proposed hybrid modelling framework can effectively capture the complex relationship between L‑PBF process parameters and relative density. However, the heterogeneity of the literature-based dataset and the presence of a limited number of outliers may introduce uncertainty into the predictions.
Retraction: A novel spectral transformation technique based on special functions for improved chest X-ray image classification
Reliability, repeatability, and age associations of the dogSIT (dog Smell Interaction Test) battery in untrained companion dogs (Canis lupus familiaris)
Non-pharmacological interventions for reducing anxiety and depression symptoms among adolescents and young adults: Protocol for a scoping review
Background Anxiety and depression are leading causes of mental health-related disability among adolescents and young adults worldwide. These conditions often begin in adolescence and can persist into adulthood, with long-term consequences for individuals’ well-being, education, and social functioning. Although a growing number of interventions have been developed to address these concerns, they vary widely in format, setting, and target population. Objective This scoping review aims to systematically map the range and characteristics of interventions targeting anxiety and depression among adolescents and young adults aged 10–24. Methods This scoping review will be conducted following the Joanna Briggs Institute (JBI) methodological guidance for scoping reviews and reported in accordance with the PRISMA-ScR checklist. A comprehensive search will be undertaken across several electronic databases (PubMed, PsycINFO, Scopus, CINAHL, Web of Science, and Embase). Eligible studies will include any empirical research focused on interventions aimed at reducing symptoms of anxiety or depression among individuals aged 10–24 years. Two independent reviewers will select and chart the studies, and findings will be summarized using descriptive and thematic analysis. Results The findings will provide an overview of interventions adopted to reduce anxiety and depression among adolescence across diverse settings, identify the most popular or commonly accepted interventions among this cohort and its modalities. The findings will also highlight the need for stakeholders and policymakers to support the design, implementation, and evaluation of targeted mental health strategies for adolescents and young adults. Dissemination Findings will be published in a peer-reviewed journal and shared with stakeholders including researchers, clinicians, and policymakers. The results will inform future research, intervention development, and policy planning aimed at supporting the mental health of adolescents and young adults.
Pro-neuropeptide Y as a circulating biomarker for poor prognosis in prostate cancer
Abstract Prostate cancer (PCa) is common world-wide. Current diagnostics based on testing for circulating levels of prostate specific antigen (PSA) is unspecific, and novel prognostic markers are needed for personalized therapeutic strategies. Pro-neuropeptide Y (pro-NPY) has been reported as a tissue marker for PCa related to poor prognosis. This study explored the prognostic value of circulating pro-NPY in PCa. Plasma samples were obtained from two patient cohorts: (1) men examined due to increased PSA levels in 2003–2011 (n = 796) and (2) patients treated for PCa in 2013–2016 (n = 92). Cohort 2 also provided plasma samples collected ~ 3 months after therapy. For plasma pro-NPY assessment, a sandwich immunoassay was developed. In cohort 1, 315 patients were diagnosed with PCa at the time for blood sampling, 137 were diagnosed during follow-up, and 344 remained disease-free. Plasma pro-NPY provided independent prognostic information from PSA regarding time to metastasis and PCa death. In cohort 2, high plasma pro-NPY levels were confirmed associated with metastatic disease and poor survival. Plasma pro-NPY levels were normalized after androgen-deprivation therapy, suggesting androgen-regulation. In conclusion, high circulating pro-NPY levels are associated with metastasis and poor outcome in PCa. Prospective validation is needed before suggesting pro-NPY for clinical use. The underlying biology and consequences of pro-NPY overexpression remain to be understood.
Cost-effectiveness of bubble continuous positive airway pressure in treating severe pneumonia and hypoxaemia in under-five children in Ethiopia
Background Pneumonia is preventable and treatable, yet it remains the leading infectious cause of illness and death among under-five children. Bubble continuous positive airway pressure (bCPAP) offers a promising option for oxygen therapy combined with appropriate antibiotics and other supportive care. However, the cost-effectiveness of bCPAP in resource-limited settings such as Ethiopia is not documented. We aimed to evaluate the cost-effectiveness of bCPAP in treating severe pneumonia and hypoxaemia in under-five children in Ethiopia. Methods We developed a decision-analytical model (decision tree) to determine the cost-effectiveness of a locally made bCPAP compared with the standard of care (WHO-recommended low-flow oxygen therapy) in general hospitals. Effectiveness was measured as the number of child deaths and disability adjusted life years (DALYs) averted. Cost data were extracted from published literature and local markets. The incremental cost-effectiveness ratio (ICER) was calculated and evaluated against the willingness-to-pay (WTP) thresholds set at multiples (0.34, 1, and 3) of Ethiopia’s GDP per capita. Sensitivity analyses were performed to test the robustness of the results. Results For every 10,000 children with severe pneumonia and hypoxaemia, providing oxygen using locally made bCPAP will save an additional 31 children compared to the standard of care. A locally made bCPAP has an ICER of 139.5 USD per DALY averted. These results were robust in the sensitivity analysis performed, showing a 100% probability of being cost-effective at one times the GDP per capita of Ethiopia. Conclusion A locally made bCPAP is a highly cost-effective intervention for treating severe pneumonia and hypoxaemia in under-five children in Ethiopian general hospitals. These findings provide critical evidence for decision-makers to support and scale-up use of bCPAP in Ethiopia and other similar low and middle income countries.
The role of partner support in the association between water birth and postpartum depression
Retraction: Evaluation of all-for-one tourism development level: Evidence from Xinjiang production and construction corps, China
A semantic-aided abnormal broadcast content analysis method
Additive effect of tDCS and neuromotor recruitment on functional recovery in chronic paraplegia: A randomized controlled trial
Functional recovery in chronic spinal cord injury (SCI) is traditionally viewed as limited, particularly after the spontaneous recovery window has closed. This single-blind randomized controlled trial investigated whether adding anodal Transcranial Direct Current Stimulation (tDCS) to the Neuromotor Recruitment Method (NEUROM)—a protocol combining motor imagery with intensive peripheral sensory stimulation—provides a additive benefit for sensorimotor recovery. Fifty participants with chronic paraplegia (mean time since injury: 15.4 ± 3.2 months; ASIA Impairment Scale A, B, or C) were recruited and randomized into three arms: Reference (standard care, n = 10), NEUROM (n = 20), or NEUROM+tDCS (n = 20). The intervention was administered over 10 days. Outcomes included the Lower Extremity Motor Score (LEMS), Light Touch and Pin Prick sensory scores, and the Assessment of Movement Attempt (AMA). The Reference group showed no significant recovery. Both active groups (NEUROM and NEUROM+tDCS) achieved substantial and statistically identical improvements in sensory function compared to the Reference group ( p < 0.001), suggesting that peripheral recruitment alone is sufficient for afferent restoration. However, a significant dissociation was observed in motor function: the NEUROM+tDCS group demonstrated superior LEMS recovery (Mean change: 18 points) compared to the NEUROM group (14 points; p = 0.01) and reported significantly higher volitional drive ( p < 0.001). These findings indicate a clear dissociation between sensory and motor plasticity in chronic SCI; while peripheral somatosensory recruitment drives afferent sensory restoration, the addition of central stimulation via tDCS is critical for maximizing efferent motor output. This suggests that restoring motor function in chronic paraplegia requires a “top-down” cortical prime to complement “bottom-up” peripheral signaling. Trial Registration: ClinicalTrials.gov NCT04790149 .
Investigation of the effects of sodium butyrate on SH-SY5Y neurons treated with amyloid beta42 and lipopolysaccharide: A computational and experimental study
Bridging the biomass data gap: A literature-based Length-Weight Relationship framework for estimating representative dry weights of freshwater invertebrates in Korean rivers
Representative species weight is a critical ecological index for modeling, vulnerability assessment, and toxicity prediction, yet scientifically validated data for aquatic invertebrates remain limited. To address this gap, we present the first literature-based strategy to estimate and validate representative dry weights of freshwater invertebrate species in Korean rivers using Length-Weight Relationships (LWRs). Species length and dry weight records were compiled from domestic field guides, while dry weight records were compiled from both domestic and global literature. LWR coefficients (a and b) were then calculated at genus, family, and order levels and preprocessed under control conditions. Among the taxonomic levels tested, averaging genus-level coefficients yielded the highest concordance with field-measured dry weights (R 2 = 0.6633, n = 240), outperforming broader taxonomic levels. Furthermore, a logarithmic correlation analysis confirmed that greater numbers of LWR sources improve predictive accuracy, particularly at the genus level. Based on this optimal strategy, representative dry weights were estimated for 563 taxa. This methodology fills a critical data gap by leveraging existing literature to generate reliable species-specific weight indices without additional field measurements. Our approach provides a quantitative foundation for biomass estimation in data-limited freshwater ecosystems and supports improved ecological modeling, conservation planning, and machine learning-based impact prediction.
Juglone induces GSDME axis pyroptosis in osteosarcoma cells via the JNK/P38 MAPK pathway
Predicting elevated transcranial doppler velocity among patients with sickle cell anemia in Uganda: A cross-sectional study
Background Sickle cell anemia (SCA) is an autosomal recessive blood disorder resulting from a specific point mutation in the β-globin gene. Over half a million children are born with sickle cell anemia annually. Transcranial Doppler (TCD) velocity is an accurate predictor of the risk of stroke among children with sickle cell anemia. Unfortunately, TCD screening is not routinely done in developing countries due to limited resources. There is a need to develop a model that predicts elevated TCD velocity, utilizing routinely collected data to guide management of children with sickle cell anemia. Methods We conducted a cross-sectional study from 1 st July 2024–30 th August 2024 among children with SCA attending the Sickle Cell Clinic. We developed a risk-prediction model for elevated TCD (≥ 170 cm/s) using sociodemographic, hematological, and clinical factors. We used the least absolute shrinkage and selection operator (LASSO) penalized regression to select the best subset of predictors of increased TCD velocity. Model performance was assessed by determining the discrimination using the area under the curve and calibration by drawing a calibration plot. Results We enrolled 385 children; the mean age was 10.3 (SD 3.8) years. The prevalence of elevated TCD, defined as ≥170 cm/s was 8.3% (95% CI: 5.8, 11.5; n = 32/385). Using a lambda of 0.008, the final model had 12 predictors. The predictors included neuropathy, red blood cell count, heart rate, age, adherence to hydroxyurea, headache, hematocrit, serum lactate dehydrogenase, gender, malnutrition, blood transfusion, and neutrophils. The model predicted elevated TCD with an AUC of 84.7% (95%CI: 74.7, 90.8). Conclusion We developed and validated a model to predict elevated TCD among children living with SCA in Uganda. Further exploration is needed to assess whether this model predicts stroke.
Synergistic effects of organic fertilizer and copper-calcium nanoparticles on onion growth and storage under arid conditions
Abstract Onion ( Allium cepa L.) production in hyper-arid environments is constrained by poor soil fertility, limited water availability, and post-harvest losses that reduce market viability. The interactive effects of foliar application of copper oxide (CuO) and calcium oxide (CaO) nanoparticles (NPs), combined with sugarcane filter mud cake (FMC), on onion growth and storage quality in calcareous hyper-arid soils remain to be characterized. A two-season field study (2023/24–2024/25) evaluated the effects of FMC (30 t ha⁻¹) combined with foliar CuO NPs (10, 20, and 30 mg L⁻¹) and CaO NPs (50, 100, and 150 mg L⁻¹), applied singly and in combination, on onion cv. Sabeeni in Upper Egypt. Vegetative growth, yield traits, nutrient uptake, and storage performance were quantified. Organic fertilization and nanoparticle treatments interacted significantly: yields of 42–45 t ha⁻¹ (15–21% above control) were achieved with low-to-moderate CuO NPs (10–20 mg L⁻¹) and high CaO NPs (150 mg L⁻¹); high CaO NPs alone maintained yields close to control values (< 3% increase); and excessive CuO NPs (30 mg L⁻¹) with insufficient Ca was associated with phytotoxicity and yield reductions (21.29–28.90 t ha⁻¹). Tissue Cu concentrations ranged from 6.17 to 39.33 mg kg⁻¹ and Ca from 2,556 to 4,750 mg kg⁻¹. These results suggest that combining organic amendment with appropriately balanced nanoparticle nutrition may represent a scalable approach to improving onion yield by 15–21% and extending post-harvest shelf life under hyper-arid conditions.
Bidirectional associations between mental health conditions and cognitive impairment in patients with pain conditions of the back, neck, and spine: A population-based study
Background Pain conditions (PCs) of the back, neck, and spine are frequently accompanied by psychiatric and cognitive comorbidities in older adults. However, the directionality and magnitude of the associations between psychiatric disorders and cognitive impairment in this population remain insufficiently characterized. Objective This population-based study examines the bidirectional relationship between Mental Health Conditions (MHCs) and Cognitive Impairment (CI) among patients with back, neck, and spine pain. It assesses whether MHCs—including depression, Bipolar Disorder (BD), Generalized Anxiety Disorder (GAD), Post-Traumatic Stress Disorder (PTSD), Panic Disorder (PaD), Persistent Mood Disorder (PMD), Suicidal Behavior (SB), Schizophrenia (SCZ), and Substance Use Disorder (SUD)—are associated with subsequent incident CI, and conversely, whether prior CI is associated with subsequent incident MHCs. Methods Data were drawn from the TriNetX US Collaborative Network (2016/01/01–2021/12/31), comprising over 119 million patients. Cohorts of patients with PCs were defined using ICD-10 codes. Propensity score matching was applied to balance demographics and comorbidities. Kaplan-Meier survival analysis assessed risks over a three-year follow-up. Results A bidirectional association was observed between MHCs and CI. PC Patients with MHCs had a higher three-year risk for CI, with the largest risk ratios (RR) observed for SCZ (RR: 4.594; 95% Confidence Interval [3.974, 5.312]) and BD (RR: 3.761[3.247, 4.356]). Other MHCs, including depression, PMD, GAD, PTSD, PaD, and SUD, were also associated with higher CI risk. Conversely, patients with pre-existing CI exhibited a higher three-year risk for subsequent MHCs, particularly BD (RR: 4.818 [3.045, 7.624]), SCZ (RR: 3.398 [2.874, 4.017]) and SB (RR: 1.913 [1.340, 2.732]). Conclusion Our findings indicate a bidirectional relationship between MHCs and CI among older adults with documented back, neck, and spine pain. Integrated screening and coordinated multidisciplinary care may help identify psychiatric and cognitive comorbidities earlier and support more comprehensive management of this medically complex population.
CaReS-BiNet: A multi-scale deep learning framework for ECG arrhythmia classification
Abstract Automated electrocardiogram (ECG) arrhythmia classification remains challenging due to morphological complexity, severe class imbalance, and poor model generalization across heterogeneous datasets acquired under varying clinical conditions. To address these challenges, this paper proposes CaReS-BiNet, a Convolutional and Residual Squeeze-and-Excitation Bidirectional-LSTM Network that integrates parallel multi-scale one-dimensional convolutional branches, residual connections, lightweight Squeeze-and-Excitation channel attention, and bidirectional LSTM temporal modelling into a unified end-to-end framework. This design enables joint learning of local morphological features and long-range temporal dependencies directly from ECG heartbeat segments, with Gaussian noise injection employed to improve robustness to class imbalance and signal variability. Evaluated on the MIT-BIH Arrhythmia Database under the AAMI EC57 five-class protocol, CaReS-BiNet achieves an accuracy of 98.74%, precision of 98.70%, recall of 98.73%, and F1-score of 98.71%, outperforming the majority of compared state-of-the-art methods on recall and F1-score. Independent evaluation on the PTB Diagnostic ECG Database yields 99.31% accuracy, 99.27% precision, 99.16% F1-score, and an AUC of 0.999, demonstrating superior performance across all reported metrics against a recently proposed hybrid architecture evaluated on the same dataset. This robustness is further supported by consistent performance on an additional heterogeneous ECG dataset, achieving 98.08% accuracy. Binary ventricular ectopic beat (VEB) and supraventricular ectopic beat (SVEB) detection further confirms clinical reliability, with SVEB precision of 97.99% substantially exceeding existing methods. A systematic ablation study validates the individual contribution of each architectural component across both datasets. These results establish CaReS-BiNet as an effective framework for automated arrhythmia classification across diverse ECG datasets.